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An intelligent algorithm for determination and optimization of productivity factors in upstream oil projects
Accepted Manuscript An intelligent algorithm for determination and optimization of productivity factors in upstream oil projects A. Azadeh, M. Pourebrahim Ahvazi, S. Motevali Haghighi PII:
S0920-4105(18)30291-2
DOI:
10.1016/j.petrol.2018.03.099
Reference:
PETROL 4840
To appear in:
Journal of Petroleum Science and Engineering
Received Date: 5 April 2017 Revised Date:
17 January 2018
Accepted Date: 27 March 2018
Please cite this article as: Azadeh, A., Pourebrahim Ahvazi, M., Motevali Haghighi, S., An intelligent algorithm for determination and optimization of productivity factors in upstream oil projects, Journal of Petroleum Science and Engineering (2018), doi: 10.1016/j.petrol.2018.03.099. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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An Intelligent Algorithm for Determination and Optimization of Productivity Factors in Upstream Oil Projects 1
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A. Azadeha , M. Pourebrahim Ahvazi2a, S. Motevali Haghighia&b a
Abstract
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School of Industrial and Systems Engineering, Center of Excellence for Intelligent-Based Experimental Mechanic and Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran b Department of Industrial Engineering, Esfarayen University of Technology, Esfarayen, 9661998195, Iran
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This paper presents a unique intelligent algorithm to identify and optimize factors affecting the productivity of upstream oil projects. Oil industry is the most important economic sector in the world and upstream projects play an important role in this industry. The proposed algorithm is composed of analysis of variance (ANOVA), data envelopment analysis (DEA), artificial neural
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network (ANN), adaptive network based fuzzy inference system (ANFIS), conventional regression (CR), and fuzzy regression (FR). In addition, each stage of the algorithm is equipped with verification and validation mechanism. First, a standard questionnaire containing
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productivity factors is designed and completed by experts of actual upstream oil projects. Then, DEA is used to identify the weights of all influential factors. The relationships between the
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influential factors and each of the criteria of productivity are then evaluated by ANN, ANFIS, CR and FR through relative error. Finally, ANFIS is selected as the preferred methods for optimization of the actual case of this study according to mean absolute error (MAE). Health, safety and environment (HSE), economic and management are determined as the most influential factors through sensitivity analysis. This is the first study that presents an intelligent algorithm 1
He Passed away on June 27, 2017 Corresponding author: Marziye Pourebrahim Ahvazi E-mail address: [email protected]
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for determination and optimization of productivity factors in an actual upstream oil projects. It can be used as a continuous improvement solution for upstream oil projects. Keywords: Upstream Oil Projects; Productivity; Artificial Neural Network (ANN); Adaptive
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Network Based Fuzzy Inference Systems (ANFIS); Analysis of Variance (ANOVA); Data Envelopment Analysis (DEA); Stochastic DEA 1. Introduction
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Energy is one of the most significant factors in developing of economy and society (Azadeh et al., 2015). Oil industry is one of the most influential industries in the world which provides 60
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percent of energy needs (Berends, 2007). After Saudi Arabia, Iraq, UAE and Kuwait, Iran ranks fifth in terms of oil reserves in the world and contain 9.2 percent of total oil revisers of the world (Hessari, 2005). It is a major supplier of oil in the world during different centuries and has a significant impact on the global energy (Rabbani et al., 2014). Oil and gas industry are the main
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provider of energy in Iran. It is one of the most important suppliers of revenues, the main source of public funding (Kiani and Pourfakhraei, 2010), and also has a major share of GDP in Iran. Iran's economy is highly dependent on oil exports (Hessari, 2005).
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Cost overruns and delays of schedule are some inevitable issues that these projects face with them (Jergeas, 2009) and resulted in decreasing productivity. By considering the importance of
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oil industry in the world, especially in Iran, low level of productivity in this industry, makes adverse consequences on the economic activities of the country. To improve the productivity, it is necessary to identify the positive or negative factors affecting the productivity and try to decrease the effect of negative factors and enhance the positive ones (Ghoddousi and Hosseini, 2012). The aims of this study are to determine the oil productivity measures and evaluating the factors that affect the upstream oil project productivity. Determining the most significant factors by considering the relation between the factors and the productivity measures is the final purpose 2
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of this study. In this regards, artificial neural networks, conventional regressions and fuzzy regression methods are applied to find the relation between the influential factors and the criteria of productivity.
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1.1. Upstream Oil Industry in Iran
Iran is a regional power in southwest Asia and occupies and has an important position in the global economy due to having the oil industry, petrochemical industry and natural gas. Iran has
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great potential in oil production and produces about 4 million barrels per day and is capable of producing more than 6 million barrels per day (Rabbani et al., 2014). The process flow of oil
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industry includes discovering the fields of oil, performing the operation and development of oil, transferring the oil from the wells to refineries, oil refining, transporting products to the place of use, marketing and supplying oil products to consumers. Oil and gas projects are divided into three broad categories that include upstream, midstream and downstream projects. The process
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of each category are shown in summary in Fig1. The oil industry is generally divided into three parts: upstream, downstream and midstream. The midstream industry is usually categorized in downstream section. Upstream industry includes the exploration of oil fields, drilling oil wells,
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development of oil fields and oil production. The projects of the oil industry categorized as the mega project. Considerable interrelationship and dependences, the inherent complexity, costly
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and risky are some of the characteristics of these kinds of projects (Barati and Sepasgozar, 2015). As mentioned before, the focus of this research is on upstream projects in Iran. National Iranian Oil Company includes several subsidiaries which about 14 main companies in upstream oil industry.
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Fig1. The selected oil industry
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1.2. Motivation and Significance
Previous studies do not consider full factors productivity analysis of upstream oil projects. Most recent studies in this area use qualitative approaches. This study is the first one which
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comprehensively identifies and optimizes factors affecting upstream oil project productivity. In addition, this is the first research which determines productivity of upstream oil project by a
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combined qualitative and quantitative algorithm. It considers six distinct and useful criteria in exploration, development and production of oil industry sectors. Determining the degree of influence of each group of factors by considering the level of productivity is another motivation of this study.
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This study is organized as follows: Section 2 reviews the related articles. In Section 3, the methodology of this study is presented. Section 4 describes the numerical results and discuss
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them. Finally, in Section 5 the study is concluded. 2. Literature Review
Identification the influential factors of the productivity of projects and determination the
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most significant of them are the challenging issues for researchers in recent years. There are many researches which evaluated influential factors of productivity in various industries in order
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to enhance productivity in that field. Most of the studies are done in the field of construction projects. For example, Alinaitwe et al (2007) evaluated 36 factors impacting the construction productivity of workers and incompetent supervisors, lack of skills of the workers, rework, lack of tools/equipment and poor construction method were identified as the most influential factors
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which resulted in poor productivity. Enshasi et al (2007) identified 45 factors of building projects in Gaza strip classified them in 10 groups and determined the importance of each group as follows: materials/tools, supervision, leadership, quality, time, manpower, project, external
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factors, motivation and safety. Dai et al (2009) tried to identify 83 factors impacting labor productivity by the viewpoints of 1996 craft workers in United States and 10 latent factors of
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these 83 factors were identified by applying principal factor analyses. The aim of the study presented by Soekiman et al (2011) was to identify factors affecting the project completion in Indonesia and its results could be useful in order to promote efficiency. In this study 113 factors through 15 groups were identified. The results showed that supervision, materials, execution plan and design are the most important group factors affecting labor productivity. Jarkas and Bitar (2011) identified 45 factors affecting labor productivity in construction projects in Kuwait. They grouped these factors into 4 categories which as follows: management, technological, labor and 5
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external group factors. They ranked the factors by using the relative importance index. The clarification technical specifications, the variation or change orders during execution, the level of coordination among design disciplines, lack of labor supervision and the proportion
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of work subcontracted were identified as the most influential factors. Ghoddousi and Hosseini (2012) identified 31 factors that had a negative impact on productivity construction projects in Iran. The factors classified into 7 groups, including materials and construction tools, technology
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and manufacturing techniques, management and planning, supervision, rework, weather and site conditions. The group of material and tool was identified as the most influential factors. Kazaz
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and Acıkara (2015) compared the factors affecting the labor productivity from the view point of project managers and craft workers by applying relative importance index (RII) method. They classified the factors in 4 groups as follows: organizational, economic, Socio-psychological and physical group factors. The project managers ranked the four group factors as follow:
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organizational, economic, physical and Socio-psychological group factors. Organizational, economic, Socio-psychological and physical group factors were respectively identified as the important group factors from craft workers’ perspective. Haghighatian and Ezati (2015)
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evaluated the effect of 21 factors on human resource productivity by calculating correlation coefficient and applying regression model. Training human resources, managerial support for
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successful accomplishment of affairs and having job security are identified as the factors which had the most relationship with productivity. In the field of oil industry, Managi et al (2006) evaluated the effect of technology changes on exploration, development and production of oil in United State and estimated total factor productivity in this area. Jergeas (2009) introduced 10 areas for improving construction productivity in oil and gas industry which are as follows: labor management, conditions and 6
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relations, project planning, management of construction and support, engineering management, effective supervision and leadership, communication, contractual strategy and contractor selection, constructability in engineering design, government influence, modularization and
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prefabrication. Taiwo (2010) evaluated the effect of work environment on labor productivity in oil and gas industry in Nigeria. In this regard, four hypotheses were tested to analysis the bad work environment, improvement in work environment, conducive work environment and
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employee productivity problems which may lead to increase or decrease productivity. Trade liberalization, currency stabilization, deregulation, privatization and nature features of oil
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reservoirs such as pressure of wells and chemicals used to separate oil and gas from water, are some effective factors on oil productivity that pointed out by Bridgman et al (2011). Chanmeka et al (2012) evaluated the 11 key factors affecting the performance and the productivity of oil and gas industry. Some of these factors are as follows: total cost of project, project duration,
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percent contingency budget, percent modularization, percent offsite work hours, percent construction indirect / direct work hours, work force predictability, percent scaffolding / direct work hours. LIANG et al (2013) employed three methods, including information amount theory,
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gray correlative method and orthogonal experimental design to evaluate the impact of some physical factors (permeability, porosity, formation pressure, in-situ oil viscosity, horizontal
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section length, fracture length, fracture width and fracturing stage number) on well’s productivity in the Bakken tight oil reservoir. The results of the three methods showed that the fracturing stage number, fracture length, horizontal section length, and permeability factors are the main influential factors on productivity of the Bakken tight oil reservoir. Hui et al (2013) Evaluated productivity and influential factors in south Azadegan oil field. They analyzed the effect of physical properties and fluid parameters on productivity which were oil pressure, choke size,
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interlayer distribution, longitudinal conductivity, permeability, natural aquifer size. They presented a model in order to evaluate the productivity. Barati and Sepasgozar (2015) identified 63 effective factors on the productivity of oil and gas industries. These factors were categorized
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in five groups as follows: project, external, organizational, labor and management factors. The results showed that the management factors are the most important ones.
There are different models in order to identify the relations between influential factors of
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productivity and the productivity measurement and predict the productivity such as regression
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methods and artificial neural networks which are used to predict productivity and determine the relation between factors and productivity in several studies (Tam et al., 2002; Lu and Yeung, 2003). Neural networks are able to identify similarities between a new model of inputs and outputs in a new pattern. They are capable of deal with the incomplete information. This method is very efficient especially when the relationship between inputs and outputs is not clear (Heravi
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and Eslamdoost, 2015). Oral and Oral (2010) used self-organizing maps (a type of artificial neural network) to predict construction labor productivity. They considered the size of crew, crew experience, age of the crew, the system of payment, working hours in one week, working
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hours in one day and distance between the accommodations of the crew members from the site as
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the factors affected the productivity. Concrete pouring, reinforcement and formwork activities were considered as the criteria of productivity measurement. Moselhi and Khan (2012) evaluated 9 influential factors on productivity through 3 groups in construction industry which were weather, workforce and project groups. They ranked them by applying three method which were fuzzy subtractive clustering, neural network and regression method. Finally, temperature (weather group), work type (project group) and floor level (project group) are identified as the most important factors. Nasirzadeh and Nojedehi (2013) used system dynamic approach to
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determine the inter-relations between the effective factors and also the relationship between the effective factors and the labor productivity. The aim of the research of Heravi and Eslamdoost (2015) was to identify the factors affecting the labor productivity. They developed a model to
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measure and predict the productivity by using artificial neural networks. Five factors which are labor competence, poor decision making, motivation of labor, proper site layout and suitable site planning have been identified as the most influential factors.
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As presented in this section, although there are many studies that concentrated on
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identifying influential factors of productivity of projects, in the field of oil and gas industries, there is no study which comprehensively presented the factors affecting on productivity of projects. Most of this study focused on the construction oil project and the other studies have minor points not comprehensive on project productivity in upstream oil industry. In addition,
upstream oil industry. 3. Methodology
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there is no study which evaluates the level of productivity qualitatively in different sector of
The proposed algorithm of this research is presented in Fig 2. This consists of 25 steps
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which are as follow:
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Step 1: Identify factors affecting on oil project productivity. Step 2: Identify oil project productivity criteria. Step 3: Design the questionnaire by considering productivity criteria and influential factors. Step 4: Is the validity of questionnaire content confirmed? If yes go to Step 5. Otherwise go to Step 3. Step 5: Data collection and analysis through experts of upstream oil projects. 9
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Step 6: Validity of questionnaire can be checked by using ANOVA or Kruskal-Wallis experiments (depending on normality of data) in order to test the equality of means of two sample of data for each question. If the null hypothesis is not rejected, the validity of
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questionnaire can be proven.
Step 7: The reliability of questionnaire is measured by using Cronbach alpha. The value of Cronbach alpha is from 0 to 1 and this test can be performed to show reliability of factors
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achieved by questionnaire (Santos, 1999). This reliability is computed by Equation (1) (DeVellis,
N
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2016).
σ2
∑ Xi N (1 − i =1 2 ) α= N −1 σY
(1)
2 Which Y= X1+X2+….+XN , and σ Xi and σ Y2 show the variance of the each component
literature (Nunally, 1978).
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(Xi) and total variance of Y. 0.7 has introduced as acceptable value for Cronbach alpha in
data.
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If validity and reliability are proven go to next step. Otherwise go to Step 5 and collect more
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Step 8: Remove the factors with lower effect By using the one sample t-test, influential factors will be determined. This test is used to investigate if mean values exceed certain limits? (Denni-Fiberesimam and Rani, 2011). The null hypothesis is considered as follows:
: =
: <
.
If the null hypothesis is not
rejected, the related factor will be influential and applied in the next calculations; otherwise, the factor is determined as the less influential factors and removed from the next calculation. 11
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Step 9: Determine the weight of each factor by data envelopment analysis (DEA) DEA is applied to determine weight of each influential productivity factor. Four types of
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DEA models are applied which are CCR and BCC input-oriented and output-oriented models. Forty five influential factors are considered as inputs and six productivity criteria of oil projects are considered as the outputs of DEA. Perturbation analysis is carried out to identify the best DEA model. The model with least sensitive to the noise is selected as the best model and is
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applied to calculate the primary weights.
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In order to calculate the weighs of each factor, the best DEA model is performed with 51 indexes which includes 45 influential factors as inputs and 6 productivity criteria of oil projects as outputs. Then, DEA model is run 45 times by eliminating each of the inputs one by one. The difference between average efficiency of each removing input and the main model is calculated
weights of each factor.
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and the result is divided by sum of these differences. The obtained values are considered as the
Step 10: Data conversion between zero and one.
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Step 11: Calculate the weighted average of each group of influential factors.
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Identify the relation between the influential factors of productivity and the productivity criteria of oil projects (Steps 12 to 20) Through the steps 12 to 20, by applying intelligent methods, conventional regression and fuzzy regression, the relation between the influential factors of productivity and productivity criteria of
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oil projects is evaluated. The method with the lowest mean absolute error (MAE)3 is selected as the best ones to predict the level of productivity. These steps are as follows:
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Step 12: Determine the input and output variables of the model. Step 13: Determine the train and test data in order to use by artificial neural network ANN-MLP, ANN-RBF and ANFIS, CRs and FRs. The train and test data are selected randomly for each of
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the applied method. Applying intelligent methods (Steps 14 to 16)
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Step 14: Define different architectures for ANN-MLP, train the model for each architecture by train data, calculate the MAE by test data and select the best architecture with lowest MAE (MLP*).
Step 15: Repeat Step 14 for ANN-RBF to select the best architecture with lowest MAE (RBF*).
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Step 16: Repeat Step 15 for ANFIS to select the best architecture with lowest MAE (ANFIS*). Step 17: Run linear, quadratic and exponential regressions to evaluate the relations between inputs and outputs variables. The MAE values are calculated for the test data of each output.
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Step 18: Run fuzzy regressions (Tanaka and improved Tanaka models) to identify the relation
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between the inputs and outputs.
Step 19: Select the best method from the previous steps according to the minimum MAE for each output.
Step 20: If the MAE of the best method is less than 0.2, then go to the next phase. Otherwise, distribute questionnaires to more operators. With respect to questionnaires, a maximum error of 0.20 is chosen to allow the significant variations between respondents due to skills, education 3
=1
∑
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and experience. This is a reasonable and logical estimate. It is worth to mention, the steps 14 to 20 are repeated for each output. After selecting the best method to evaluate the relation between the influential factors of
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productivity and productivity criteria of oil projects, steps 21 and 22 are applied in order to measure the productivity score of the system. These steps are repeated for each outputs. Step 21: Run the model by applying the best method for each output.
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Step 22: Calculate the productivity scores (level of productivity) and rank the respondents. For this purpose, first the difference between the actual output (!
#
=!
(#)
& ∗)
and the output of the best
is calculated for all respondents as follows (Equation 2), (Azadeh et al., 2007):
− !%
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method (!%
(#) )
& ∗ (2)
In order to obtain the impact of the largest positive error, the frontier function is shifted from the optimal model as follows (Equation 3), (Azadeh et al., 2007): =
#
!%
& ∗
(3)
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* #
This feature does not contain the largest value of error, but calculates by considering the
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respondent scale (Constant Returns to Scale (CRS)). In this way, find the largest indicates the respondent with the highest level of productivity. Suppose that the largest
* #
* #
which belong
* ,
=
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to respondent k, then (Equation 4), (Azadeh et al., 2007): -. ( #* ) (4)
Therefore, the value of the shift is different for each respondent and is measured as follows (Equation 5), (Azadeh et al., 2007): 0ℎ# =
∗ !(% !(% &
* ,
& ∗ )#
∗ ),
(5)
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In this algorithm, the impact of the respondent’s scale is considered and the unit which applied for the correction, is selected by considering its scale (Azadeh et al., 2007; Costa and Mahrkellos, 1997; Delgado, 2005). The level of productivity for each respondent is calculated by
2007): 3# =
!(%
!#
& )#
+ 0ℎ#
(6)
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the Equation 5. The range of productivity score is between 0 to 1 (Equation 6), (Azadeh et al.,
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This score (3# ) is calculated for each output according to its appropriate model (the model with
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minimum MAE). Total score of productivity is obtained by calculating the mean of 3# of six outputs.
Step 23: Validate the productivity results by applying DEA and SDEA methods. In order to
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validate the results of the previous step under deterministic and stochastic conditions, DEA and SDEA methods are applied. In this way, four types of DEA method are used which are CCR and BCC input and output oriented models. In addition, SDEA method in different levels of risk is
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applied.
Step24: The results of ranking in step 21 is compared to the results of ranking by DEA and
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SDEA methods. The high correlation between total score of productivity and DEA and SDEA results show the validity.
Step25: Determine the weight of each group of the factors by considering the productivity score. The weight of each group factor is determined in this phase. To this purpose, each group factor is removed one by one and the F score (level of productivity) is calculated for each removal. The
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difference between average F score of main model (the model with all factors) and the removed factor is calculated. The result is divided by total differences.
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3.1. Productivity and Influential Factors 3.1.1. Influential Factors
In this study, identifying factors affecting productivity is done by referring to previous studies and using expert opinions. As mentioned before, because of insufficient studies in the
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field of assessing the influential factors of oil project productivity in oil upstream industry, in
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addition to these studies, we use the studies in other fields to identify primary factors. In this way, at first, we used several papers in order to gather different factors. Then, some factors are added or omitted by the viewpoints of oil experts. Finally, 45 factors are identified and classified into 9 groups which as follows: human factors, management factors, project factors, health, safety, environment (HSE) factors, regulatory and contractual factors, factors related to material
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and equipment, economical factors, political and governmental factors and natural factors. Grouping the factors is done according to the previous researches and modified by expert
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opinions. Identified factors and their relative group are shown in Table 1. Table 1. Identification the factors Group Number Factors definition name
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Reference Liberda et al.(2003);Enshassi et al. (2007);Barati and Sepasgozar (2015)
2. Job satisfaction in the workforce
Enshassi et al. (2007);Kazaz and Ulubeyli (2007);Barati and Sepasgozar (2015)
3.Education level of the workforce
Barati and Sepasgozar (2015)
4
4. Motivating Employees
Jergeas (2009)
5
5. Job security
Nasir (2013)
6
6. Friendly atmosphere between staff 1. Labor participation in the decisionmaking process
Enshassi et al. (2007)
3
7
man agem ent
2
human
1. Experienced Workforce
1
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Kazaz وUlubeyli (2007)
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Table 1. Identification the factors Group Number Factors definition name
Reference
2. Overtime hours
Liberda et al. (2003); Enshassi et al. (2007); Jergeas (2009)
9
3. Avoid any discrimination
Nasir (2013)
10
4. Salary payment system
Liberda et al. (2003)
11
5. Weakness and delay in the inspection program
Liberda et al.(2003);Enshassi et al. (2007); Soekiman et al., (2011);Attar et al. (2012)
12
6. Properly monitor labor practices
Liberda et al.(2003); Enshassi et al. (2007);Attar et al. (2012);Barati and Sepasgozar (2015)
13
7. Workforce training
Liberda et al. (2003); Enshassi et al. (2007); Jergeas (2009); Attar et al. (2012); Barati and Sepasgozar (2015);
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8. Risk assessment in each phase of the project 9. Failure to follow the schedule
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10. Empower project managers 11. Disputes between the actors of the project
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Jergeas (2009);Barati and Sepasgozar (2015) Enshassi et al. (2007); Attar et al. (2012) Jergeas (2009)
Jergeas (2009)
2. Complexity of oil projects
Liberda et al.(2003); Barati and Sepasgozar (2015); Barati and Sepasgozar (2015)
20
3. Difficult access to the place of oil projects
Liberda et al.(2003); Jergeas (2009); Barati and Sepasgozar (2015)
21
Liberda et al. (2003); Enshassi et al. 1. Cleanliness and amenities on the camp (2007);Jergeas (2009); Barati and Sepasgozar and site (2015)
24 25 26 27 28
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2. Environmental factors (inadequate lighting, poor ventilation, noise, etc.) 3. Occupational safety inspector at the project site 4. Safety analysis activities, identify risks and anticipated regulatory actions
Enshassi et al. (2007)
5. Waste Management
expert opinion
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23
HSE
22
material and equipment
19
1. The size of the projects project
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1. Lack of materials and equipment 2. Lack of access to spare parts 3. obstacles in the way of transfer of technology related to the oil industry
17
Liberda et al.,(2003); Enshassi et al. (2007)
Nasir (2013)
Enshassi et al. (2007); Osmundsen et al. (2010); Attar et al. (2012); Soekiman et al. (2011); Attar et al. (2012) expert opinion
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Table 1. Identification the factors Group Number Factors definition name
Reference
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5. Delay in reaching Equipment
35
1. Inflation and downturn
41 42 43
44 45
expert opinion
1. Iran's relations with targeted countries 2. Internal political changes
Barati and Sepasgozar (2015)
3. Oil price
3. Sanction 4. Create a competitive atmosphere and ample opportunities for active private contractors 5. Slowness in issuing permits and approvals
expert opinion
6. Providing loans for contractors to develop infrastructure
expert opinion
1. Weather conditions in oil areas
Liberda et al. (2003); Enshassi et al.(2007);Jergeas (2009); Barati and Sepasgozar (2015)
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40
Natural
39
Political and governmental
38
Barati and Sepasgozar (2015)
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Nasir (2013); Ghandi and Lin (2012).
Bridgman et al. (2011), Barati and Sepasgozar (2015) Barros and Managi (2009); Bridgman et al. (2011); Khan (2017) Barati and Sepasgozar (2015)
2. Stability of exchange rate
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36
Liberda et al. (2003)
Barati and Sepasgozar (2015)
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33
economical factors
32
regulatory and contractual
34
1. Conventional strategies that are used in upstream oil projects 2. Conventional methods in projects upstream 3. Contractual claims of the contract correspondents 4. The rules on foreign investment
31
Enshassi et al. (2007); Soekiman et al. (2011);Attar et al. (2012) Attar et al. (2012); Barati and Sepasgozar (2015)
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4. The distance between the storage location and the site
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2. Natural factors related to the oil field
expert opinion
Nasir (2013)
Bridgman et al. (2011)
3.1.2. Identification oil project productivity criteria According to Asian Productivity Organization (APO), productivity is the combination of efficiency and effectiveness. Drucker (1963) expressed the difference between efficiency and effectiveness. Doing things right and doing the right things are the definitions of efficiency and 18
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effectiveness respectively. In the other words, productivity is to achieve efficiency and effectiveness at the same time. In addition, in the field of the productivity of projects, there are many measurements in literature. Project duration is considered as productivity measurement by
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Roll and Rosenblatt (1983). Williams (1980) suggested that using the resources consumed in the best way, will improve productivity. In addition, the indicators such as the completion time of the project, delays and exceeding the schedule are some measures of the project productivity.
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Thomas et al (1990) expressed that productivity can be considered as performance index and calculated by dividing the estimated unit rate to actual unit rate. Nasir (2013) defined the
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productivity factor as follows: dividing estimated productivity to actual productivity. Estimated productivity is calculated by dividing the estimated work hours to estimated quantity of outputs and actual productivity is measured by dividing actual work hours to actual quantities. In addition, Heravi and Eslamdoost (2015) considered the productivity index as the rate of actual
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work hours to estimate work hours which is also pointed out by Yi and Chan (2013). In this study, six qualitative criteria are defined as productivity measurements in order to evaluate the productivity level qualitatively. Table 2 shows these criteria.
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Table 2. Productivity criteria definition Number Criteria definition 1 Economizing the resources
Symbol Cri1
The difference between the use of resources from the amount of planned resources
Cri2
3
The difference between the actual project duration and the scheduled project duration
Cri3
4
The difference between the actual progress of the project with advances predicted for it
Cri4
5 6
The correct definition of project activities The proper conduct of project activities
Cri5 Cri6
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3.2. Data Envelopment Analysis The concept of evaluating the level of efficiency among a group of decision making units
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(DMUs) is indicated by data envelopment analysis (DEA). This is a nonparametric frontier method that the efficiency of each DMU is calculated in comparison with the highest yield. Determining the efficiency by using the nonparametric method was done by Farrel (1957) for the first time. He fitted a production function on a set of inputs and output of a group of DMUs and
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resulted in a piecewise linear function. Based on previous studies, Charnes, Cooper and Rhodes
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(1978) presented a new method to calculate the efficiency of decision-making namely CCR model.Then, Banker, Charnes and Cooper presented BCC model in 1984 (Banker et al., 1984). In general, there are two strategies to improve the performance of non-efficient units and get them to efficient frontier (Charnes and Cooper, 1984). The first strategy is to reduce inputs without reducing output by the time the DMUs get to the efficient frontier namely input-oriented
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and the second strategy is to increase the outputs until getting the DMUs on the efficient frontier without using more inputs. This strategy is known as output-oriented. In addition, it is necessity to consider the changes of inputs to outputs which namely ratio of returns to scale. If by
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increasing the inputs, the ratio of outputs to inputs remains constant, it is constant returns to scale
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(CRS), and if the ratio of outputs to inputs changes, it is variable returns-to-scale (VRS). CCR and BCC methods cover the CRS and VRS respectively. The BCC and CCR input and outputoriented models that are applied in this study, introduced by Charnes et al. (1978) and Banker et al. (1984), are presented in models 7 to 10 in Table 3. It should be noted that we have 9 inputs and 6 categories outputs as shown in models 7 to 10.
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Table3. DEA models
Output-oriented 6 7 = 8 #:
9
. =. 8 > ? @ − 8 :
J
8> ? :
#: @
# .#@
#
9
L6 7 = 8
≤ 0 CDE F = 1, … ,88
#:
≥ 0 # .#
(9)
. =. 8 > ? @ − 8 :
9
8
# .#@
> , J
J
. =. 8 N ? :
@
#
≥ 0
describe the input and output of jth DMU and > ,
:
≤ 1 CDE F = 1, … ,88
J
8 N = 1 :
N ≥ 0
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≤ 0 CDE F = 1, … ,88
= 1 CDE F = 1, … ,88
N# ≥ 0
where .#@ and ?
# .#@
#:
-.7 = 8 N ? (10)
8 N# = 1 #:
9
#:
. =. 8 N# .#@ ≥ 1 CDE F = 1, … ,88 #:
9
J
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BCC
:
= 1 CDE F = 1, … ,88
> ,
9
-.7 = 8 > ? (8)
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CCR
(7)
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J
# .#
J
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9
Input-oriented
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Model
#
are the weights of output
and input respectively and r, i and j represent the number of outputs, inputs and DMUs. 3.3. Stochastic Data Envelopment Analysis Stochastic data envelopment analysis (SDEA) model is applied for evaluation of different DMU in stochastic environment to help decision makers for accurate decisions. In this model, 21
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the inputs (or outputs) can be deterministic. It means that it is unable to control the input (or outputs) because they are affected by external factors which are out of controls. In this study, the SDEA model which is introduced by Sueyoshi (2000), is used in order to validate the results of
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level of productivity by considering stochastic outputs. This model (model 11) is presented in the following equations. J
=. 8 9
#:
8 #:
# .#,
=1
# PQ@ .#@ R −
J
8 > S? @ + T ,@ 3 :
P1 − U@ RV ≥ 0
#
≥0
where T
,@
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CDE F = 1,2, … ,88 , > ,
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9
:
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-. 8 > ?O , (11)
is standard deviation of ?
,
and ?O
,
is the mean of ? , . βj is the expected
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efficiency level of the jth DMU (Sueyoshi, 2000) which is considered equal to 1 in this study. In this model, αj is a parameter to show the degree of risk in environment considered as a risk criterion, this parameter shows degree of risk in environment and the higher value of αj illustrates
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the higher risk of environment. The description of .#@ , ?
method.
@
> and
#
are the same as DEA
3.4. Artificial Neural Network The artificial neural networks are such tools for processing the information that are inspired by biological nervous systems. They processes the information such as the brain and are able to
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learn and generalize from experience (Zhang et al., 1998). ANNs consist of simple processing units with weighted connections to make different structures that are capable of learning the relationships between sets of variables. They are useful for nonlinear processes with unknown
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functional form (Azadeh et al., 2011). Pattern recognition, data mining, classification, forecasting and process modeling are such applications of ANNs (Azadeh et al., 2010).
In the way of solving an ANN problem, three steps should be passed which are training,
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generalization and implementation. The learning process in order to recognize the current pattern of input data is called training process. Back propagation learning algorithm is one the most
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popular learning technique which introduce by Werbos (1974). In this algorithm the gradient steepest decent method is used to minimize the error function. In order to fit the connecting weight, the obtained output in the output layer is compared with the desired output (Kuo et al., 2010). Each ANN network uses a set of training rules that define training method in order to
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evaluate the network capability in extracting feasible solution when the network faces the unknown inputs which are not trained to network (Azadeh et al., 2010). For a successful implementation, it is necessary to determine the proper configuration and parameters which is an
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iterative experimentation process. Although there are several possible solutions, in order to obtain the best results, the precise experimentation must be performed. There are many ANN
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models, however in this research, three types of ANN are applied which as follows: multilayer perceptron (MLP), radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS).
MLP consists of three types of layers of nodes. The first type of layers, namely input layer, receives external information of input data and distributes them to hidden layer. Hidden layer is the second type of layers which describes the relations between the input layer and output layer.
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In this layer, after receiving the inputs from all of the neurons of input layer, the values are added through applied weights and converted to an output value by an activation function. Then the output moves to all of the neurons in the next layer, preparing a feed forward path to the output
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layer. Output layer is the third type of layers of nodes and demonstrates the output nodes of variables (Kuo et al., 2010; Zhang et al., 1998). The more hidden layers lead to more complex networks. In this research, definition of different architectures of MLP neural network is based
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on different training function, two transfer function and the number of neurons in hidden layers. One of the most powerful neural network used in the estimation function is RBF neural
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network which uses radial basic function as the activation function. Free of “local minimum trap and overtraining” and short training time are some of the advantages of RBF (Lau et al., 2010). In these networks, unlike the common practice in other networks, the entire input space will not be responded similarly. Herein, first the center of input space is calculated and then the inputs
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that are close enough to the center are responded. As a result, these networks respond to inputs locally. RBF networks typically have three layers: an input layer, a hidden layer with a radial basic activation function and a linear output layer. The structure of RBF network is similar to
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MLP network but it has just one hidden layer (Principe et al., 2000). Definition of different structures of RBF network is according to two parameters which include the spread of radian
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basis function and the maximum number of neurons. Adaptive network based fuzzy inference system (ANFIS) is a kind of fuzzy neural network. The combination of artificial neural networks and fuzzy logic is resulted in neuro-fuzzy. The adaptive network is a network including of nodes and connections which through the nodes are linked and actually it is a superset of all kinds of feed-forward neural networks (Jang, 1993). According to study of Werbos (1974) the gradient descent is the basic learning rule of adaptive
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network. Solving slowly and tendency to trap in local minima are the disadvantages of this method. Jang (1993) introduced a hybrid learning rule in order to accelerate the learning process which combines the gradient method and the least squares estimate to identify parameters. By
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using feed-forward network, ANFIS finds the proper fuzzy decision rules and by applying the given dataset of inputs and output, it is able to create a fuzzy inference system that its membership function is fitted by back-propagation algorithm with a least squares method
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(Azadeh et al., 2013). In this study, in order to obtain the network with the best quality of training and evaluation, the networks with different parameters will be evaluated. In this regards,
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different architectures will be defined according to three types of fuzzy inference systems, two optimization methods and some of the inference options. 3.5. Fuzzy Regression
The aim of applying regression analysis is to find the relation between the variations of a
(Equation 12):
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depended variable (Y) with the explanatory variables (X) and can be estimated as follows
C(.) = - . + ⋯ + - . + T ( 12)
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where f is a crisp linear function and - , -X , … , - are real values. If there is uncertainty relation
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between inputs and outputs, fuzzy set are used. In fuzzy linear regression analysis, relationship is determined by a fuzzy function (Charfeddine et al., 2004). The fuzzy regression models are divided into two classifications. The first classification is the model with the fuzzy relationship between the variables, in other words, the coefficients of regression model are considered as fuzzy values and the observations are crisp in this model classification. The second type of fuzzy regression models is the model with the fuzzy observation and crisp coefficients.
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The general form of fuzzy regression model is as follows (Equation 13), (Charfeddine et al., 2004):
YZ = [ + [ . + ⋯ + [ . (13)
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Where YZ is a fuzzy output, [# 6 = 1,2, … , is indicated fuzzy coefficient, and \ is the non-
fuzzy input vector.
A triangular membership function can be attributed to the coefficient. [ # is a symmetric fuzzy
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number indicated by (]# , ^# ) where ]@ and ^@ are its center and width respectively. The
membership function of Y is as follows (Charfeddine et al., 2004): ) d|c| ( ) ` _ ? = 1 6C \ = 0 , ? ≠ 0 f (14) 0 6C \ = 0 , ? = 0 |_ bc|
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-. (0,1 −
The aim of fuzzy regression with non-fuzzy observation is to determine the [# coefficient so
that, first, the membership degree for the fuzzy output for all y is at least as large as h (
_ (?)
≥
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ℎ). Then the fuzzy coefficients would be determined in a manner that minimizes the fuzziness of
the fuzzy output which is achieved by minimizing the spread of fuzzy coefficient (^@ ) (Shapiro
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and Koissi, 2008).
In this study, two kinds of fuzzy regression models which belong to the first classification
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of fuzzy regression methods (fuzzy ccoefficients) are applied. The first model which is introduced by Tanaka et al (1982) is as follows (model 15): %
6 ^ + 8 8 ^# .#@ (15) @: #:
] + ∑#: ]# .#@ − (1 − ℎ)(^ + ∑#: ^# .#@ ) ≤ ?@
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] + 8 ]# .#@ + (1 − ℎ) g^ + 8 ^# .#@ h ≥ ?@ #:
#:
Tanaka and Watada, (1988) improved the above model by adding e parameters in the constraints
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which presented the spread of jth collected data.
Here, the actual data points fall within the interval ?@ ± (1 − ℎ)j@ . The constraints of this
(Equations 16 and 17), (Tanaka and Watada, 1988):
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improved model are as follows (the objective function is the same as the above model)
(16)
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% ] + ∑% #: ]# .#@ − (1 − ℎ)P^ + ∑#: ^# .#@ R ≤ ?@ − (1 − ℎ)j@
] + 8 ]# .#@ + (1 − ℎ) g^ + 8 ^# .#@ h ≥ ?@ + (1 − ℎ)j@ (17) #:
#:
4. Results and Discussion
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The numerical results of the proposed algorithm are presented in this section. In this regards, each step of the proposed algorithm is applied in order to analyze the level of productivity of oil projects and determine the importance of each factor. In this algorithm the factors impacting the
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productivity of oil projects are considered comprehensively and the importance of them is determined by considering the level of productivity.
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4.1. Data Collection
A standard questionnaire is designed by using the extracted factors from literature and interviewing with the experts of upstream oil industry in Iran. It is tried to design a comprehensive questionnaire to cover most of the influential factor on oil project productivity. The questionnaire consists of 51 questions which 45 questions related to the influential factors
27
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and 6 questions are about the oil productivity criteria in upstream section. Respondents must be assigned a score between 0 and 20 to each question.
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As mentioned before, the concentration of this study is on the upstream oil projects in Iran. In this study, 150 questionnaires have been distributed among the experts of seven main companies, two active companies in exploration field, two companies of production and three
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companies of development of oil industry and then 88 questionnaires have been collected which is about 59% response rate. To test the validity of questionnaire, ANOVA is applied (the data
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distribute normally). To do this, two random groups of each question are selected and the equality of two group’s averages is examined by F-test (equality of two group’s averages is considered as null hypothesis). It should be noted that, this test is performed at 0.05 significance level. According to the obtained results, for each factor the null hypothesis which is the equality of the mean of two groups, does not rejected. As seen in Table 4, for all factors p-value is higher
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than 0.05. It means that, the obtained data through questionnaire are valid (Azadeh et al., 2011). In order to test the reliability of the collected data via questionnaire, Cronbach alpha test is used for each of the group factors. The calculated Cronbach alpha for each groups is 0.7 or higher
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than 0.7 which shows acceptable level of reliability of questionnaire. The results of the validity
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and reliability of questionnaire are shown in Table 4 (Appendix A) (See steps 3 to 6 of the proposed algorithm).
4.2. Removeing the factors with lower effect and determining the weight In this phase, first, the impact of each factor is evaluated by using one sample t-test. Based on expert’s opinions, value of the influential factor must be greater than 10. Therefore, null and alternative hypotheses are considered as
= 10 and 28
< 10 respectively. The results shows that
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the mean of all factors is equal or more than 10, and there is no factor with low effect. As shown in Table 5, all achieved p-values are higher than the significance level (0.05). This shows all
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factor are important in this case study. Table 5 shows the results (Appendix B) Then, the weights of the factors are calculated. As mentioned before, to this purpose, four types of DEA models which are CCR and BCC input and output oriented are applied. The results showed that BCC output oriented model is the least sensitive model to noise because the
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correlation between results of this model by main data and noisy data is high. Therefore, this
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DEA model is selected to calculate the weighs of factors. In this way, the DEA model has been run 45 times and the input factors are omitted one by one in each time and the relevant efficiency is calculated. The results of this step are shown in Table 6 (Appendix B) (See steps 8 and 9 of the proposed algorithm). It should be noted that each weight is calculated by dividing change in
4.3. Preparing Data
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efficiency score after and before factor omitting into total changes.
In this phase, data are prepared in order to use for ANN, ANFIS, RBF, CR, and FR. To do
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this, data converted into the range of 0 and 1. This scale helps the neural networks generalize better inputs (Portas and AbouRizk, 1997). Then, according to the calculated weights in step 9,
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the weighted mean of each group is computed (Steps 10 and 11 of the proposed algorithm). 4.4. Identify the relation between the influential factors of productivity and the productivity criteria of oil projects
As stated before, in this phase, different methods are used to determine the relation between inputs and outputs and the model with the minimum error is selected as the optimum model.
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According to the proposed algorithm, the inputs and outputs variables of the model is determined in step 12. The nine groups of factors which are human, management, project, HSE, material and equipment, regulatory and contractual, economic, political and governmental and natural factors
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are defined as the inputs of the model and the six defined productivity criteria are considered as outputs. To run the different architectures of MLP and RBF neural networks, ANFIS, CRs and
are used as test data (Steps 13 of the proposed algorithm).
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FRs, 70 to 90 percent of data are chosen randomly as the training data set and the leftover data
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The aim of the steps 14 to16 of the proposed algorithm is to find the appropriate architecture among the MLP, RBF and ANFIS models. To select the optimum architecture for MLP, by changing some parameters of ANN-MLP, 40 different architectures are defined (See Appendix C). In this model, there are different parameters that some of them are changed to find optimal structures. RBF neural network is also run for 40 different architectures that defined according to
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change two parameters of this networks (See Appendix C). To find the optimum structure of ANFIS, 64 models are tested and the related MAE is calculated. The defined architectures and the architecture with the least error for each output of MLP, RBF and ANFIS are presented in
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Tables 7 to 9 respectively (Appendix C).
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The relation between inputs and outputs are evaluated by three different regression methods which are linear, quadratic and exponential. The values of MAE are calculated for each output and the results are shown in Table10 (Step17). Table 10. The value of MAE for different kinds of regression method MAE Regression Number method Cri1 Cri2 Cri3 1
Linear
0.336
0.222
30
0.529
Cri4
Cri5
Cri6
0.425
0.177
0.216
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Table 10. The value of MAE for different kinds of regression method MAE Regression Number method Cri1 Cri2 Cri3
Cri4
Cri5
Cri6
Exponential
0.412
0.299
0.605
0.517
0.296
0.460
3
Quadratic
0.391
0.264
0.510
0.407
0.196
0.274
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2
In addition, FR is applied to evaluate the relation between inputs and each of the criteria
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(outputs of models) under different values of h (of Tanaka model) which changes between 0 to 1, 0.1 by 0.1 (interval) also, h and e (of improved Tanaka model) with three different values for
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each of them (h= 0, 0.4, 0.8 and e= 0.1, 0.5, 0.9). The results are presented in Table11. (Step18). Table11. Results of fuzzy regression
Improved Tanaka h* e* MAE h* MAE Cri1 0.4 0.5 0.683 0 0.673 Cri2 0.4 0.1 0.2 0.4090 0.461 Cri3 0.4 0.1 0.4 0.745 0.648 Cri4 0.8 0.9 0.3 0.705 0.798 Cri5 0.8 0.9 0.567 0.4 0.564 Cri6 0.4 0.1 0.4 0.304 0.328 h* and e*: the value of h and resulted in minimum MAE Tanaka
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Criterion
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For each outputs of model, the optimal model of MLP, RBF and ANFIS are selected and compared to the results of CR and FR methods. As shown in Table12, for all outputs, ANFIS method is given the least MAE, therefore this method is applied to identify the relation between the influential factors of productivity and the productivity criteria of oil projects and measure the productivity level. It is worth to mention that the presented values of MAE in Table 12 are related to the optimum architecture of each method , in addition, MAE value of the best method
31
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is less than 0.2, therefore it is not necessary to repeat the previous phases (See steps 19 and 20 of the proposed algorithm).
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Table12. Selection the best method among different methods MAE Method Cri1 Cri2 Cri3 Cri4 0.5371 0.4261 0.7074 0.6293 MLP 0.2714 0.2001 0.3769 0.3481 RBF 0.1782 0.1456 0.1980 0.1938 ANFIS Linear 0.3364 0.2215 0.5288 0.4245 regression Exponential regression 0.4118 0.2986 0.6051 0.5168 Quadratic regression 0.3908 0.2638 0.5103 0.4073
Fuzzy regression (Improved Tanaka)
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0.1775 0.2159 0.2963 0.4598 0.1956 0.2745
0.6726 0.4045 0.6481 0.7978 0.5640 0.3284
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Fuzzy regression (Tanaka)
Cri5 Cri6 0.3220 0.4987 0.1401 0.1894 0.1106 0.1748
0.683
4.5. Determine the level of productivity
0.461
0.745
0.705
0.567
0.304
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The optimum method (See Table 12) of each output is run for all data. The predicted values are compared to actual data in Fig 3 to Fig 8. These figures approve the accuracy of the best
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model for all criteria because the difference between the predicted and actual data is not significance. By using the best method, the productivity scores of each output are calculated
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based on Equations (2) to (6). As mentioned before, the total score of oil project productivity (total level of productivity) is the average of six scores of productivity based on 6 criteria which are determined according to the experts’ opinions under six questions (See steps 21 and 22 of the proposed algorithm).
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1.2 Actual value ANFIS value
0.8 0.6 0.4
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Value of Cri1
1
0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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Expert code Fig3. The results of selected ANFIS method and the actual data for Cri1
1.2
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Actual data
Value of Cri2
1 0.8 0.6 0.4
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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0.2
ANFIS value
Expert code
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Fig4. The results of selected ANFIS method and the actual data for Cri2
1.2
Actual value
1
0.8 0.6 0.4 0.2 0
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
Value of Cri3
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ANFIS value
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Fig5. The results of selected ANFIS method and the actual data for Cri3 33
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1.2
Actual value ANFIS value
0.8 0.6 0.4 0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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Value of Cri4
1
Expert code
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Fig6. The results of selected ANFIS method and the actual data for Cri4
1.2
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Value of Cri5
1 0.8 0.6 0.4 0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
Actual value ANFIS value
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Expert code
1.2
0.8 0.6 0.4
Actual value ANFIS value
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Value of Cri6
1
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Fig7. The results of selected ANFIS method and the actual data for Cri5
0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
Expert code Fig8. The results of selected ANFIS method and the actual data for Cri6
As presented in Fig 9, the results show the low level of productivity in upstream oil industry. As mentioned before, upstream oil industry consists of three different sectors including 34
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exploration, development and production. To discover the poor sector among the three ones of upstream oil industry the respondents of each sector are separated and repeated the computation of steps 13 to 22 for each one. The number of respondents in exploration, development and
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production sectors are 16, 19 and 53 respectively. The results show that the production sector has the least level of productivity among the three sectors of upstream oil industry which confirms the statement of the study of Azadi and Yarmohammad (2011). Fig10 to Fig12 show the level of
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productivity in three different sectors.
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0.8 0.6 0.4 0.2 0
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E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
Total score of productivirty
1
Expert code
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Fig9. Level of productivity in upstream oil industry
0.8 0.6
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Expert code
Fig10. Level of productivity in exploration sector of upstream oil industry
35
EX16
EX15
EX14
EX13
EX12
EX11
EX10
EX9
EX8
EX7
EX6
EX5
EX4
EX3
EX1
0
EX2
Total score of productivirty
1
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0.8 0.6
Expert code
ED19
ED18
ED17
ED16
ED14
ED13
ED12
ED11
ED10
ED9
ED8
ED7
ED6
ED5
ED4
ED3
ED2
0
ED15
0.2
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0.4
ED1
Total score of productivirty
1
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Fig11. Level of productivity in development sector of upstream oil industry
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0.8 0.6 0.4 0.2 0
EP1 EP3 EP5 EP7 EP9 EP11 EP13 EP15 EP17 EP19 EP21 EP23 EP25 EP27 EP29 EP31 EP33 EP35 EP37 EP39 EP41 EP43 EP45 EP47 EP49 EP51 EP53
Total score of productivirty
1
Expert code
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Fig12. Level of productivity in production sector of upstream oil industry
4.5.1 Validation the productivity results
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As mention in steps 23 and 24 of the proposed algorithm, four types of DEA methods are used which are CCR input-oriented, CCR output-oriented, BCC input-oriented and BCC output
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oriented. In addition, SDEA method in different level of U is applied. The results show that the rank of SDEA model at U = 0.6 is highly correlated with the rank of productivity scores which
the value of Spearmen correlation equals to 0.628. Table 13 (Appendix D) shows the obtained rank of each method and the calculated correlation with the rank of productivity scores. As seen in Table 13, the average correlation between obtained productivity scores through proposed
36
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algorithm and different DEA, SDEA models are about 0.6 (0.57). So, results are validated and acceptable.
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4.6. Determining the weight of each group factor The importance of each group of influential factors with regards to the level of productivity is determined in this section. To this purpose, the weight of each group of factors is calculated
Table14 and Fig13.
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Table 14.The weight of groups of Influential factors
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according to the explanations in step 25 of the proposed algorithm. The results are presented in
Number
Removed Group Factor
1 2 3 4 5 6 7 8 9
Human
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Management Factors Project HSE Material and Equipment Regulatory and Contractual Economic Political and Governmental factors
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Natural Factors
37
Average Level
Difference with main model
Percent of Weight
0.133 0.141 0.062 0.171 0.112 0.120 0.170 0.130 0.093
0.118 0.124 0.055 0.151 0.098 0.106 0.150 0.115 0.082
11.76 12.44 5.50 15.05 9.85 10.62 15.03 11.50 8.25
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Natural Factors, 8.25 Political and Governmental factors, 11.50
Management Factors, 12.44
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Human , 11.76
Project , 5.50
Economic, 15.03
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HSE, 15.05
Regulatory and Contractual, 10.62
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Material and Equipment, 9.85
Fig13. The weight of groups of Influential factors
The results show that HSE group is identified as the most priority group in this area with 15.09 percent weights which is expected, because of the high risk environment of these kinds of
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projects. As a result, providing the safety and health of workers in this risky environment will help improve productivity of oil upstream projects. Then, economic group is the most important group with 15.03 percent weights which consists of Inflation and economic downturn, stability
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of exchange rate and oil price factors. It should be noted that these factors are among the external factors and control and planning of them are affected by different conditions of world’s
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economy. Management group is located in the third degree of importance. Indeed, the successes and failures of programs of improving productivity depend on attitudes, strategies, policies and proceedings of management. After management group, human factors with regard to level of productivity are the most important factors. After that political factors with a slight difference by the human factor weight is placed in fifth degree of importance. The next three important factors are regulatory and contractual, material and equipment and natural factors respectively. The project group factor is the least influential factors on oil project productivity among the nine 38
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groups by 5.5 percent weight.The results of this section, will help planners and decision makers focus on more important factors in the way of improving productivity of upstream oil projects.
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5. Conclusion
Improving and optimizing productivity of upstream oil projects are challenging task. This research presented an intelligent algorithm for identification and optimization of influential
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factors for upstream oil projects. An actual case study in Iran was selected to experiment the proposed algorithm. The algorithm includes 25 steps. First, a questionnaire was designed, Consequently, the reliability and
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distributed amongst the experts of oil upstream projects.
validity of the questionnaire must be attained. Second, DEA was used to evaluate the weight of each input sub-factor. Then, data was prepared to be used in the next phase. Three intelligent methods, namely, MLP, RBF and ANFIS, and conventional and fuzzy regressions were used to
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evaluate relations between inputs (influential factors) and outputs (productivity criteria). Based on minimum value of MAE, the best ANFIS was selected to calculate productivity of upstream oil projects. The results (productivity scores) were validated by DEA and SDEA. The weight of
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each factor was calculated with regard to level of productivity of each criterion. The results showed that the upstream oil projects are in low level of productivity. The arrangement of the
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nine group in terms of importance was determined as follows: HSE, economic, management, human, political, regulatory and contractual, material and equipment, natural and project. The results of this research can help decision makers improve productivity of upstream projects by considering the identified factors. It will also identify weak and strength of such projects with respect to productivity. Table 15 compares the features of this research with previous studies.
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LIANG et al (2013)
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*√
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. Hui et al (2013)
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Enshassi et al. (2007) Dai et al. (2009) Barati and Sepasgozar (2011) Soekiman et al. (2011) Alinaitwe et al. (2012) Ghoddousi and Hosseini (2012) Nasirzadeh and Nojedehi (2013)
fuzzy regression
intelligent method
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Influential factors of Oil upstream projects
√
regression
Measuring the productivity of projects Qualitatively
This study Haghighatian and Ezati (2015) Heravi and Eslamdoost (2015)
Considering the influential factors comprehensively
Studies
Evaluate the relation between factors and productivity
Measuring the weight by considering the level of productivity
Table15: Features of this study versus previous ones
√ √
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*construction projects of oil industry
Appendix A: Results of phase one
1 2 3 4 5 6
Group name
human
Num
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Table 4. Reliability and validity analysis Validity analysis Factors definition
1. Experienced Workforce 2. Job satisfaction in the work force 3.Education level of the workforce 4. Motivating Employees 5. Job security 6. friendly atmosphere between staff
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Reliability
F
P-value
Cronbach’ s Alpha
1.91 2.9 2.4 0.08 0.59 0.23
0.175 0.097 0.13 0.778 0.448 0.633
0.801
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Table 4. Reliability and validity analysis
28 29 30
2.91 1.04 2.85 1.4 0.62 2.21 2.5 0.42 3.08 1.87 1.03 0.51 0.45 2.7 1.75
0.097 0.315 0.101 0.245 0.437 0.146 0.123 0.52 0.088 0.18 0.317 0.48 0.506 0.109 0.195
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0.445
0.75
0.393
1.62
0.211
5. Waste Management 1. Lack of materials and equipment 2. lack of access to spare parts
1.09 3.32 0.85
0.303 0.077 0.365
1.7
0.202
3.86
0.057
1.52
0.226
0.31
0.582
0.75 2.08
0.391 0.16
2.51
0.122
2.1
0.157
3. obstacles in the way of transfer of technology related to the oil industry 4. The distance between the storage location and the site 5. Delay in reaching Equipment 1. Conventional strategies that are used in upstream oil projects 2. Conventional methods in projects upstream 3. Contractual claims of the contract correspondents
regulatory and contractual
35
4. The rules on foreign investment 1. Inflation and economic downturn
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Reliability Cronbach’ s Alpha
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P-value
0.6
34
32 33
F
2. Environmental factors (inadequate lighting, poor ventilation, noise, etc.) 3. Occupational safety inspector at the project site 4. Safety analysis activities, identify risks and anticipated regulatory actions
no mi cal fac tor
31
8. Risk assessment in each phase of the project 9. Failure to follow the schedule 10. Empower project managers 11. Disputes between the actors of the project 1. the size of the project 2. complexity of oil projects 3. Difficult access to the place of oil projects 1. Cleanliness and amenities on the camp and site
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25 26 27
material and equipment
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1. Labor participation in the decision-making process 2. overtime hours 3. avoid any discrimination 4. Salary payment system 5. Weakness and delay in the inspection program 6. Properly monitor labor practices 7. Workforce training
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23
HSE
22
Factors definition
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management
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Group name
project
Num
Validity analysis
0.717
0.705
0.908
0.72
0.871
0.817
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Table 4. Reliability and validity analysis Group name
Factors definition F
37
3. oil price
38 39 40
1. Iran's relations with targeted countries 2. Internal political changes 3. Boycott 4. Create a competitive atmosphere and ample opportunities for active private contractors
0.111
2.79
0.103
1.6 1.02 0.45
0.213 0.319 0.506
2.78
0.104
1.58
0.216
0.34
0.562
2.59
0.116
2. Natural factors related to the oil field
1.06
0.311
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1. Economizing the resources
0.75
0.394
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2. The difference between the use of resources from the amount of planned resources
2.47
0.125
3. The difference between the actual project duration and the scheduled project duration
1.61
0.213
4. The difference between the actual progress of the project with advances predicted for it
0.1
0.75
1.65 0.22
0.208 0.642
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5. The correct definition of project activities 6. The proper conduct of project activities Total Cronbach’s Alpha (consists of total questions) = 0.919
Cronbach’ s Alpha
0.762
0.915
0.871
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50 51 52
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45
Productivity Criteria Measurement
44
Natural
43
5. Slowness in issuing permits and approvals 6. providing loans for contractors to develop infrastructure 1. Weather conditions in oil areas
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2.67
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2. stability of exchange rate
Political and governmental
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P-value
Reliability
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Num
Validity analysis
Appendix B. Results of phase two Table 5. Evaluation influential factors Num Factors 1 Experienced Workforce 2 Job satisfaction in the work force 3 Education level of the workforce
Mean 17.41 17.67 14.14
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STD 2.53 2.20 2.93
T-value 27.54 32.75 13.25
P-value 1 1 1
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T-value 46.73 18.76 35.50 23.11 3.91 44.71 35.90 18.49 20.19 17.55 18.40 21.58 15.22 18.15 4.67 6.56 0.63 14.20
P-value 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.735 1
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STD 1.65 3.04 1.88 2.35 4.62 1.67 2.09 2.86 2.67 2.80 2.88 2.89 3.53 2.98 4.68 4.52 4.57 3.43
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Mean 18.21 16.09 17.11 15.78 11.93 17.95 17.99 15.63 15.75 15.23 15.65 16.65 15.73 15.77 12.33 13.16 10.31 15.19
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Table 5. Evaluation influential factors Num Factors 4 Motivating Employees 5 Job security 6 Friendly atmosphere between staff 7 Labor participation in the decision-making process 8 Overtime hours 9 Avoid any discrimination 10 Salary and benefits 11 Weakness and delay in the inspection program 12 Properly monitor labor practices 13 Workforce training 14 Risk assessment in each phase of the project 15 Failure to follow the schedule 16 Empower project managers 17 Disputes between the actors of the project 18 Size of oil project 19 Complexity of oil projects 20 Difficult access to the place of oil projects 21 Cleanliness and amenities on the camp and site Environmental factors (inadequate lighting, poor ventilation, noise, etc.)
14.43
3.68
11.27
1
23
Occupational safety inspector at the project site
13.88
3.56
10.21
1
24
Safety analysis activities, identify risks and anticipated regulatory actions
14.70
3.09
14.29
1
25 26 27
Waste Management Lack of materials and equipment lack of access to spare parts
12.84 17.49 16.45
4.12 2.23 3.12
6.46 31.49 19.40
1 1 1
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Obstacles in the way of transfer of technology related to the oil industry
14.98
3.16
14.81
1
The distance between the storage location and the site
11.40
4.34
3.02
0.998
Delay in reaching Equipment
15.72
3.25
16.49
1
Conventional strategies that are used in upstream oil projects
14.16
3.28
11.90
1
Conventional methods in projects upstream Contractual claims of the contract correspondents The rules on foreign investment Inflation and economic downturn Stability of exchange rate
14.29 12.97 15.36 14.90 15.89
3.23 3.42 3.49 4.22 4.08
12.44 8.13 14.40 10.89 13.53
1 1 1 1 1
30 31 32 33 34 35 36
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Mean 13.60 15.56 15.21 16.73
Create a competitive atmosphere and ample opportunities for active private contractors
15.82
42
Slowness in issuing permits and approvals
16.67
43
Providing loans for contractors to develop infrastructure
14.18
44 45
Weather conditions in oil areas Natural factors related to the oil field
11.82 12.14
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Table 6. The primary weight of the factors
T-value 7.55 16.96 16.22 19.01
P-value 1 1 1 1
2.83
19.27
1
2.51
24.91
1
2.78
14.11
1
4.47 5.73
1 1
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STD 4.47 3.07 3.01 3.32
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Table 5. Evaluation influential factors Num Factors 37 Oil price 38 Iran's relations with targeted countries 39 Internal political changes 40 Boycott
3.83 3.50
Average efficiency
Factors
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Experienced Workforce Job satisfaction in the work force Education level of the workforce Motivating Employees Job security Friendly atmosphere between staff Labor participation in the decision-making process Overtime hours Avoid any discrimination Salary and benefits Weakness and delay in the inspection program Properly monitor labor practices Workforce training Risk assessment in each phase of the project Failure to follow the schedule Empower project managers Disputes between the actors of the project Size of oil project Complexity of oil projects
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EP
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Number
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1.0807 1.0803 1.0824 1.0825 1.0820 1.0822 1.0810 1.0817 1.0803 1.0810 1.0816 1.0822 1.0796 1.0824 1.0822 1.0812 1.0817 1.0822 1.0825
Difference with main model 0.0018 0.0022 0.0001 0.0000 0.0004 0.0003 0.0015 0.0008 0.0022 0.0014 0.0009 0.0003 0.0029 0.0001 0.0003 0.0012 0.0008 0.0002 0.0000
Weight 0.0309 0.0365 0.0017 0.0002 0.0076 0.0056 0.0259 0.0130 0.0377 0.0245 0.0151 0.0052 0.0496 0.0019 0.0056 0.0212 0.0133 0.0040 0.0002
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Table 6. The primary weight of the factors
22 23 24 25 26 27
Difficult access to the place of oil projects Cleanliness and amenities on the camp and site Environmental factors (inadequate lighting, poor ventilation, noise, etc.) Occupational safety inspector at the project site Safety analysis activities, identify risks and anticipated regulatory actions Waste Management Lack of materials and equipment lack of access to spare parts
1.0808 1.0823
Difference with main model 0.0017 0.0002
Weight 0.0287 0.0032
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20 21
Average efficiency
Factors
1.0816
0.0009
0.0147
1.0824
0.0001
0.0012
1.0820
0.0005
0.0087
1.0824 1.0811 1.0825
0.0001 0.0014 0.0000
0.0011 0.0233 0.0002
1.0822
0.0003
0.0044
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Number
Obstacles in the way of transfer of technology related to the oil industry
29
The distance between the storage location and the site
1.0821
0.0004
0.0072
30
Delay in reaching Equipment
1.0822
0.0003
0.0051
31
Conventional strategies that are used in upstream oil projects
1.0815
0.0010
0.0163
32 33 34 35 36 37 38 39 40
Conventional methods in projects upstream Contractual claims of the contract correspondents The rules on foreign investment Inflation and economic downturn Stability of exchange rate Oil price Iran's relations with targeted countries Internal political changes Boycott
1.0816 1.0821 1.0825 1.0801 1.0819 1.0820 1.0816 1.0820 1.0819
0.0009 0.0004 0.0000 0.0024 0.0005 0.0005 0.0009 0.0005 0.0006
0.0146 0.0067 0.0005 0.0406 0.0090 0.0083 0.0156 0.0083 0.0103
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Create a competitive atmosphere and ample opportunities for active private contractors
1.0823
0.0002
0.0031
Slowness in issuing permits and approvals
1.0811
0.0014
0.0240
Providing loans for contractors to develop infrastructure
1.0823
0.0001
0.0023
Weather conditions in oil areas Natural factors related to the oil field Economizing the resources
1.0821 1.0819 1.0760
0.0004 0.0006 0.0065
0.0060 0.0100 0.1102
The difference between the use of resources from the amount of planned resources
1.0759
0.0065
0.1108
43 44 45 46 47
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Table 6. The primary weight of the factors Number
Factors
Average efficiency
Difference with main model
Weight
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The difference between the actual project duration and the scheduled project duration
1.0801
0.0024
0.0399
Appendix C. Results of phase four
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50 51
The difference between the actual progress of the project with advances predicted for it The correct definition of project activities The proper conduct of project activities
1.0798
0.0027
0.0462
1.0789 1.0790
0.0036 0.0035
0.0608 0.0592
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Table 7. Different architectures of MLP neural networks and the relevant error
LM LM LM LM LM LM LM GD GD SCG SCG SCG SCG SCG SCG OSS OSS OSS BFG BFG BFG GDX GDX
log log tan log log tan log log log log log tan tan log tan log tan log log log tan tan tan
NFN in HL2
TF of output later
25 15 10 10 10 20 20 15 10 8 20 35 10 40 40 18 40 22 30 10 10 10 30
log log tan log log tan tan tan -
10 30 22 15 20 20 10 16 -
purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
TF of HL2
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TF
NFN in HL1
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Number
TF of HL1
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MAE
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
0.8478 0.8467 0.8150 0.7806 0.7640 0.6424 0.6948 0.8631 0.7847 0.7502 0.6854
0.6007 0.5898 0.7019 0.5625 0.4841 0.6339 0.5580 0.8188 0.5527 0.5328 0.6388 0.6192 0.5711 0.6210 0.6427 0.5720 0.5647 0.6849 0.5129 0.5355 0.5493 0.5707 0.5468
0.9888 1.0857 1.0299 1.2263 1.0540 1.1496 0.9944 1.2623 1.7324 0.9304 1.0905 0.9700 1.0296 0.8269 0.7602 0.9675 1.0703 1.0208 0.8404 1.2786 0.7074 1.0193 1.2854
0.8762 0.8604 1.3066 0.8253 0.6786 0.6493 0.8983 0.9894 0.7699 0.7122 0.8889 0.7737 0.6600 0.8097 0.7523 0.8853 0.7045 0.7270 0.9174 1.0310 0.7268 1.1087 1.1938
0.3303 0.5924 0.4934 0.4214 0.4581 0.5245 0.5003 0.6834 0.6319 0.4659 0.6357 0.5219 0.5205 0.4623
0.8123 0.9363 0.7414 0.8097 0.7653 0.7264 0.7712 1.1202 1.2616 0.6671 0.9488 0.9518 0.7687 0.9662 0.6656 0.9655 0.8312 0.7157 1.1914 0.9475 0.8662 0.9911 0.7622
0.5371 0.7955 0.6723 0.7974 0.6823 0.7908 0.7214 0.6619 0.7330 0.8273 0.7719 0.7918
0.3220 0.5629 0.5253 0.4833 0.5449 0.4284 0.5245 0.6497 0.5833
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Table 7. Different architectures of MLP neural networks and the relevant error Number
TF
TF of HL1
NFN in HL1
TF of HL2
NFN in HL2
TF of output later
MAE
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
EP
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GDX purelin 0.8531 0.6858 1.3605 0.8580 0.4397 0.9069 log 24 40 GDA purelin 0.6895 0.5268 0.9957 0.7646 0.5005 0.8288 log 25 10 GDA purelin 0.8291 0.6104 1.1964 0.9475 0.5934 0.8114 tan 26 30 GDM purelin 0.9868 0.7645 1.5855 1.3758 0.7113 1.1890 log 27 40 GDM purelin 0.8765 0.6625 0.9305 0.9103 0.8160 1.0803 log 28 10 GDX purelin 0.9095 0.6840 1.0319 1.0846 0.5449 1.2650 log 29 30 GDX purelin 0.7986 0.5814 1.1828 0.7058 0.6020 0.9110 30 tan 15 log 10 CGB purelin 0.7468 0.4640 0.9676 0.9448 0.3966 0.8059 tan 31 15 CGB purelin 0.7517 0.5429 1.4210 1.1016 0.5280 0.9547 log 32 12 CGB purelin 0.6785 0.5316 0.7570 0.6882 0.4146 0.4987 tan 33 22 log 15 CGF purelin 0.7705 0.4261 0.8104 0.9577 0.5080 1.0042 log 34 18 log 20 CGF purelin 0.6256 0.5323 0.8745 0.8877 0.4489 0.6128 log 35 20 CGF purelin 0.8198 0.5613 1.4231 1.0132 0.4705 0.6420 tan 36 25 RP purelin 0.6659 0.6502 0.7934 0.6293 0.4831 0.7158 log 37 25 tan 20 RP purelin 0.7058 0.5678 0.8192 0.8976 0.3759 0.7053 log 38 7 RP purelin 0.6739 0.6198 0.9653 1.1322 0.5018 0.7999 tan 39 10 RP purelin 0.6980 0.5155 0.8696 0.8598 0.4853 0.8147 log 40 40 Note: TF: transfer function; HL: hidden layer; NFN: number of neurons; CR: criterion; log: Log-sigmoid; tan: Hyperbolic tangent sigmoid; LM: Levenberg-Marquardt backpropagation; SCG: Scaled conjugate gradient backpropagation; OSS: One step secant backpropagation; BFG: BFGS quasi-Newton backpropagation; GD: Gradient descent backpropagation; GDX: Gradient descent with momentum and adaptive learning rule backpropagation; GDA: Gradient descent with adaptive learning rule backpropagation; GDM: Gradient descent with momentum backpropagation; CGB: Powell-Beale conjugate gradient backpropagation; CGF: Fletcher-Powell conjugate gradient backpropagation; RP: Resilient backpropagation.
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Table 8. Different architectures of RBF neural networks Number
1 2 3 4 5 6
Radius Spread 0.5 0.5 0.6 0.6 0.7 0.8
MAE
Max number of neurons 25 10 10 20 40 10
Cri1 0.3855 0.3633 0.3383 0.3441 0.5373 0.3386
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Cri2 0.2471 0.2538 0.2803 0.2433 0.2863 0.2785
Cri3 0.3769 0.4684 0.4733 0.4786 0.3961 0.5182
Cri4 0.3758 0.4283 0.4177 0.3803 0.3707 0.4269
Cri5 0.2958 0.1930 0.2314 0.2799 0.2945 0.1620
Cri6 0.2567 0.3089 0.2562 0.2857 0.3617 0.2517
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Table 8. Different architectures of RBF neural networks
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Cri4 0.3798 0.4338 0.4281 0.3659 0.4398 0.4114 0.3665 0.4256 0.4400 0.4011 0.4271 0.4033 0.4707 0.4120 0.4348 0.4286 0.4185 0.3506 0.3993 0.4100 0.3752 0.3839 0.4155 0.3771 0.3993 0.4184 0.4155 0.3992 0.3829 0.3481 0.4128 0.3992 0.4208 0.4514
Cri5 0.2234 0.1619 0.1517 0.1401 0.2860 0.3277 0.2428 0.1523 0.1476 0.2239 0.1597 0.1839 0.4223 0.1570 0.1876 0.2313 0.1711 0.2600 0.1594 0.1746 0.1731 0.2620 0.1866 0.3209 0.1506 0.1728 0.1796 0.1852 0.3221 0.3853 0.2199 0.1514 0.1748 0.2031
Cri6 0.2182 0.2699 0.3195 0.2329 0.3971 0.4194 0.2137 0.2141 0.2303 0.3069 0.2614 0.1983 0.3594 0.2109 0.2478 0.2835 0.2185 0.3008 0.2093 0.2422 0.2002 0.2906 0.2172 0.3117 0.2085 0.2391 0.2176 0.2201 0.2914 0.3934 0.2227 0.1894 0.2283 0.2592
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Cri3 0.4880 0.5207 0.4626 0.5154 0.4014 0.4507 0.4571 0.5202 0.5053 0.4092 0.4758 0.5357 0.5384 0.5419 0.5507 0.5332 0.5317 0.4215 0.5432 0.5419 0.5375 0.4914 0.5358 0.4097 0.5439 0.5487 0.5383 0.5378 0.5755 0.4979 0.4144 0.5445 0.5355 0.5506
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Cri2 0.2689 0.2621 0.2333 0.2623 0.2454 0.3593 0.2432 0.2068 0.2533 0.2604 0.2428 0.2384 0.3321 0.2216 0.2408 0.3749 0.2443 0.2200 0.2097 0.2372 0.3309 0.3170 0.2360 0.2764 0.2103 0.2489 0.2254 0.2202 0.2001 0.2813 0.3267 0.2109 0.2584 0.2853
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15 10 20 10 40 35 18 5 10 20 15 7 40 5 10 15 8 20 5 10 15 20 7 25 5 10 7 6 20 30 18 5 10 12
Cri1 0.2714 0.3092 0.3226 0.3539 0.6001 0.6160 0.2949 0.3185 0.3376 0.4194 0.3040 0.3370 0.5893 0.3113 0.3225 0.3599 0.3646 0.5095 0.3118 0.3250 0.3734 0.5194 0.3425 0.4197 0.3121 0.3579 0.3420 0.3639 0.3965 0.4769 0.3297 0.3123 0.3626 0.3207
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0.8 0.9 0.9 1 1 1.2 1.3 1.5 1.5 1.5 1.8 1.9 1.9 2 2 2 2.1 2.2 2.5 2.5 2.5 2.6 2.7 2.9 3 3 3.2 3.5 3.5 3.6 3.8 4 4 4
MAE
Max number of neurons
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7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 CR: criterion
Radius Spread
EP
Number
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Table 9. Different architectures of ANFIS Number
Initial FIS
NMF
R
GP
2
3
SC
0.1
4
SC
5
imp
MAE
agg Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
max
0.2809
0.2061
0.4473
0.4206
0.2493
0.3162
BP
min
max
prod
BP
prod
max
prod
sum
0.3077
0.2582
0.4996
0.4503
0.2254
0.2944
BP
min
max
prod
max
0.2503
0.2646
0.3301
0.2458
0.1675
0.2365
0.1
BP
prod
max
prod
sum
0.2536
0.1566
0.2453
0.2565
0.1709
0.2177
SC
0.1
H
prod
probor
prod
max
0.2623
0.3050
0.2492
0.2709
0.1579
0.2205
6
SC
0.1
H
min
max
prod
probor
0.5178
0.4152
0.2648
0.2510
0.1530
0.2856
7
SC
0.2
BP
min
max
prod
probor
0.2119
0.2259
0.3305
0.2804
0.1438
0.2413
8
SC
0.2
BP
prod
probor
prod
probor
0.2430
0.1662
0.2449
0.2542
0.1680
0.2145
9
SC
0.2
H
min
probor
prod
probor
0.2967
0.1795
0.3141
0.2639
0.1186
0.2942
10
SC
0.2
H
prod
probor
prod
max
0.2572
0.1933
0.2627
0.2409
0.1463
0.1884
11
SC
0.3
BP
prod
max
prod
sum
0.2052
0.1767
0.1980
0.2670
0.1481
0.2069
12
SC
0.3
BP
min
max
min
sum
0.1954
0.1933
0.3418
0.3209
0.1341
0.2227
13
SC
0.3
H
prod
max
prod
max
0.2757
0.1720
0.2698
0.2416
0.1312
0.2205
14
SC
0.3
H
min
probor
prod
probor
0.1939
0.2352
0.3212
0.2324
0.1940
0.5308
15
SC
0.4
BP
min
probor
min
probor
0.1850
0.2073
0.3352
0.3108
0.1106
0.2312
16
SC
0.4
BP
prod
probor
prob
probor
0.1818
0.1529
0.2568
0.2632
0.1249
0.2191
17
SC
0.4
H
min
max
prod
sum
0.1984
0.2364
0.3739
0.2477
0.2348
0.3367
18
SC
0.4
H
prod
max
prod
max
0.2752
0.1843
0.2894
0.2455
0.1233
0.1758
19
SC
0.45
BP
min
max
prod
sum
0.2004
0.2127
0.3135
0.2638
0.1425
0.1748
20
SC
0.45
BP
prod
probor
min
max
0.1912
0.1981
0.2425
0.2562
0.1699
0.2601
21
SC
0.45
H
min
max
prod
sum
0.5071
0.2127
0.3590
0.2355
0.2858
0.3169
22
SC
0.45
H
prod
probor
min
sum
0.2311
0.1651
0.2693
0.2297
0.1488
0.2271
23
SC
0.5
H
prod
probor
min
sum
0.2174
0.1456
0.2862
0.2859
0.1192
0.1764
24
SC
0.5
25
SC
26
SC
27
SC
28 29
RI PT
2
Or
SC
2
and
M AN U
GP
Opt method
TE D
1
NCl
min
max
prod
max
0.3682
0.3003
0.3570
0.2303
0.1869
0.3481
BP
min
max
prod
sum
0.1885
0.2615
0.3354
0.2444
0.1543
0.1917
0.5
BP
prod
max
min
probor
0.1867
0.1702
0.2644
0.3029
0.3452
0.2796
0.52
BP
prod
max
min
sum
0.2141
0.2031
0.3308
0.3338
0.2778
0.2919
SC
0.6
BP
prod
max
prod
sum
0.1782
1.5721
0.3834
0.4791
0.3846
0.7129
SC
0.6
BP
min
max
min
probor
0.2258
0.8502
0.4028
0.3929
0.2553
0.3090
30
SC
0.6
H
min
probor
prod
max
0.4674
0.1974
0.3705
0.2559
0.2232
0.4913
31
SC
0.6
H
prod
probor
min
sum
0.2240
0.2366
0.3535
0.1938
0.1380
0.3445
32
SC
0.8
BP
min
probor
min
sum
1.4191
0.3560
1.3055
0.3655
0.2023
0.5934
33
SC
0.8
BP
prod
probor
prod
max
1.7699
0.2964
0.6221
0.3456
0.2641
0.4774
34
SC
0.8
H
min
probor
prod
sum
1.1877
0.3598
1.1641
0.3363
0.2401
0.4932
35
SC
0.8
H
prod
probor
min
max
0.3107
0.3104
0.8968
0.3065
0.5475
0.4323
36
SC
0.7
H
prod
max
prod
max
0.2860
1.0336
0.3202
0.5583
0.2837
0.2916
37
SC
0.7
H
min
max
prod
sum
0.5350
0.8268
0.6349
0.4604
1.0090
0.5859
AC C
EP
H
0.5
49
ACCEPTED MANUSCRIPT
Table 9. Different architectures of ANFIS Number
Initial FIS
NMF
R
NCl
Opt method
and
Or
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
probor
0.4048
0.2863
0.4781
0.3790
0.2527
0.2406
FCM
5
BP
min
39
FCM
5
BP
prod
max
prod
max
0.4242
0.3131
0.4330
0.3898
0.1712
0.2269
40
FCM
5
BP
prod
probor
prod
probor
0.4289
0.3050
0.4193
0.3980
0.1938
0.2542
41
FCM
5
H
min
max
prod
probor
1.3157
0.9790
0.8944
0.8046
1.0365
0.7714
42
FCM
10
BP
min
max
min
sum
0.3438
0.3354
0.4489
0.3635
0.1929
0.2590
43
FCM
8
BP
prod
max
min
sum
0.3624
0.3018
0.4383
0.4111
0.1688
0.2436
44
FCM
14
BP
min
max
min
max
0.3686
0.3020
0.4495
0.3695
0.1930
0.2556
45
FCM
14
BP
prod
max
prod
max
0.3823
0.2951
0.4065
0.4105
0.1895
0.2185
46
FCM
16
BP
min
probor
prod
sum
0.3940
0.2688
0.4362
0.3945
0.2017
0.2378
47
FCM
16
BP
prod
probor
prod
sum
0.3974
0.2913
0.4119
0.4084
0.1975
0.2423
48
FCM
16
H
min
max
prod
max
2.6855
0.7592
2.4405
0.7939
0.9386
0.5472
49
FCM
16
H
prod
max
prod
max
0.3379
0.2982
0.4059
0.3765
0.2093
0.2896
50
FCM
18
BP
min
max
prod
probor
0.3961
0.3035
0.4486
0.3925
0.3925
0.2328
51
FCM
18
BP
prod
max
prod
probor
0.3920
0.2849
0.4083
0.4061
0.1818
0.2261
52
FCM
3
BP
prod
max
prod
sum
0.3355
0.3331
0.4444
0.3861
0.1951
0.3152
53
FCM
3
BP
min
probor
prod
probor
0.3994
0.3081
0.4264
0.4035
0.1456
0.2591
54
FCM
6
BP
min
probor
min
max
0.3193
0.3056
0.4565
0.3584
0.1921
0.2583
55
FCM
9
BP
prod
probor
min
max
0.3629
0.3189
0.4462
0.4088
0.1894
0.2387
56
FCM
24
BP
prod
max
prod
max
0.3856
0.2849
0.4243
0.4244
0.2079
0.2382
57
FCM
20
BP
min
max
min
sum
0.3912
0.3135
0.4659
0.3746
0.1814
0.2536
58
FCM
20
H
min
probor
prod
probor
2.3337
0.8155
2.2844
0.6657
0.7010
0.4965
59
FCM
27
BP
min
max
min
sum
0.3661
0.2839
0.5010
0.4111
0.1850
0.2359
60
FCM
27
H
prod
max
prod
max
0.3489
0.2917
0.4001
0.3407
0.2194
0.2716
61
FCM
32
BP
prod
probor
prod
probor
0.3894
0.2710
0.4242
0.4077
0.2220
0.2376
62
FCM
30
BP
min
probor
prod
probor
0.3879
0.3193
0.4826
0.4424
0.2022
0.2403
63
FCM
35
BP
min
probor
prod
probor
0.3533
0.2942
0.4777
0.4274
0.2145
0.2284
64
FCM
30
H
prod
probor
prod
probor
0.3479
0.2657
0.4090
0.3267
0.2323
0.2670
TE D
EP
SC
RI PT
38
AC C
prod
MAE
agg
M AN U
max
imp
Note. FIS: Fuzzy inference system; NMF: Number of member functions, R:Radius; NCl: Number of clusters; imp:Implication method; Agg.: Aggregation method;Fuzzy c-means (FCM) clustering; GP: Grid partition; SC: Subtractive clustering; H: Hybrid; BP: Backpropagation;
Appendix D.
Table 13. The results of model validation
65
0.05 85
0.1 86
0.2 87
0.3 87
SDEA model k 0.4 0.5 0.6 87 87 87
53
53
14
14
15
43
41
40
40
40
38
37
36
35
40
82
50
50
34
34
22
30
32
33
36
37
37
38
41
45
65
87
51
51
32
32
Expert code
RPS
E1 E2 E3
50
0.7 87
0.8 85
0.9 81
0.95 86
DEA-CCR
DEA-BCC
In
OUT
In
OUT
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
74
0.05 75
0.1 75
0.2 75
0.3 75
SDEA model k 0.4 0.5 0.6 76 76 76
E5
47
50
50
50
49
50
50
50
52
58
E6
63
77
77
78
78
78
78
79
80
80
E7
66
67
68
70
69
69
70
70
72
72
E8
30
1
1
1
1
1
1
15
16
17
E9
26
15
16
15
16
15
2
1
1
1
E10
71
44
45
45
46
46
48
48
48
51
E11
34
17
17
16
17
16
3
14
14
14
E12
24
24
24
24
24
24
4
23
23
E13
5
18
18
18
18
17
5
2
2
E14
18
35
37
37
39
39
40
41
40
E15
12
26
26
26
26
26
0.8 78
0.9 87
0.95 85
68
74
6
18
22
DEA-CCR
DEA-BCC
In
OUT
In
OUT
68
68
53
53
49
49
57
57
RI PT
E4
0.7 78
86
69
79
79
69
69
80
71
70
70
79
79
18
36
15
15
18
18
1
26
13
13
10
10
56
40
47
47
61
61
15
31
31
31
47
47
23
25
35
17
17
19
19
2
2
1
3
3
1
1
37
37
10
37
37
58
58
21
4
32
32
44
44
SC
RPS
M AN U
Expert code
22
6
16
2
17
2
18
7
3
3
3
3
8
4
4
6
6
E17
57
88
88
88
88
88
88
88
88
88
84
88
63
63
15
15
E18
17
12
13
12
13
12
30
21
18
19
19
12
16
16
21
21
E19
87
61
61
61
61
61
60
57
53
47
43
38
48
48
39
39
E20
79
83
82
81
81
81
81
80
79
74
66
47
75
75
59
59
E21
51
69
69
63
63
63
63
62
57
53
51
83
87
87
87
87
E22
42
81
81
82
82
82
82
82
82
82
69
44
83
83
78
78
E23
56
82
83
83
83
83
83
83
85
87
78
57
81
81
86
86
E24
67
2
3
2
3
2
9
16
15
15
16
49
12
12
16
16
E25
11
31
30
30
29
29
10
25
25
25
28
50
18
18
20
20
E26
86
70
70
64
64
64
64
63
61
54
52
46
65
65
73
73
E27
70
37
36
36
35
33
11
26
26
27
29
51
25
25
33
33
E28
55
3
4
3
4
3
12
29
29
29
31
84
14
14
13
13
E29
19
4
5
4
5
4
13
4
4
4
4
81
5
5
7
7
E30
50
41
39
39
37
E31
52
E32
EP
TE D
E16
35
32
30
30
33
73
35
35
51
51
6
5
6
5
14
28
28
28
30
76
20
20
24
24
21
28
28
28
27
27
15
27
27
26
23
13
23
23
25
25
E33
77
78
79
79
79
80
80
81
81
86
82
64
84
84
83
83
E34
4
40
42
43
44
44
45
47
50
55
57
41
45
45
65
65
E35
9
20
20
20
20
20
17
34
35
39
53
52
26
26
35
35
E36
80
74
74
74
74
75
75
75
75
76
79
56
86
86
75
75
E37
14
6
7
6
7
6
19
19
19
18
14
53
19
19
8
8
E38
29
42
43
42
42
42
42
43
45
49
55
42
40
40
42
42
E39
25
47
47
46
45
45
44
44
44
46
47
37
42
42
46
46
E40
27
7
8
7
8
7
20
5
5
5
5
2
11
11
17
17
AC C
36
5
51
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
46
0.05 32
0.1 31
0.2 31
0.3 30
SDEA model k 0.4 0.5 0.6 30 21 17
E42
23
45
44
44
43
41
41
39
34
33
E43
76
36
35
34
33
32
22
13
13
13
RPS
E41
0.7 17
0.8 16
0.9 17
0.95 7
26
9
2
8
9
8
9
8
23
6
6
6
E45
58
80
80
80
80
79
79
78
74
73
E46
78
54
52
51
51
49
46
42
42
38
E47
81
64
64
65
66
66
66
67
68
68
E48
73
63
63
68
68
68
69
71
71
71
E49
31
10
11
10
11
10
25
20
20
E50
32
55
55
54
53
52
51
51
49
E51
10
34
34
35
34
34
26
31
32
E52
1
11
12
11
12
11
27
7
7
E53
69
76
76
76
77
77
77
77
77
12
3
DEA-BCC
In
OUT
In
OUT
22
22
26
26
39
39
49
49
45
45
33
33
6
18
1
1
3
3
58
59
61
61
23
23
32
19
44
44
60
60
64
60
64
64
55
55
71
39
71
71
72
72
21
20
54
7
7
11
11
48
50
34
52
52
54
54
32
34
30
34
34
43
43
7
7
5
2
2
4
4
77
85
70
78
78
84
84
8
22
22
22
22
22
34
9
9
9
9
63
6
6
2
2
E55
59
58
58
58
57
56
56
56
59
59
49
28
59
59
71
71
E56
64
79
78
77
76
74
73
69
67
60
39
17
76
76
68
68
E57
33
23
23
23
23
23
28
33
33
36
41
29
24
24
28
28
E58
28
73
71
71
71
70
68
66
66
62
48
24
74
74
77
77
E59
83
68
67
66
65
65
65
65
64
61
45
21
67
67
66
66
E60
53
53
56
57
58
58
58
60
63
65
67
75
58
58
30
30
E61
60
46
46
47
47
47
47
45
43
42
42
55
43
43
38
38
E62
43
65
65
67
67
67
67
68
70
70
59
43
72
72
62
62
E63
61
52
53
55
56
57
57
59
62
66
63
66
56
56
52
52
E64
39
21
21
21
21
21
29
8
8
8
8
6
10
10
12
12
E65
68
62
62
E66
54
38
38
E67
16
27
27
E68
48
39
40
41
41
E69
62
29
29
29
E70
82
51
51
E71
44
49
E72
49
E73
35
E74
62
62
62
62
61
56
50
46
25
62
62
63
63
38
38
38
39
40
38
34
36
22
38
38
56
56
27
28
28
31
22
21
20
22
11
30
30
48
48
AC C
TE D
E54
EP
M AN U
SC
E44
DEA-CCR
RI PT
Expert code
43
43
46
47
56
73
77
41
41
31
31
31
35
36
36
37
41
60
78
36
36
29
29
52
52
53
54
55
58
63
62
48
57
57
70
70
49
48
48
48
49
49
51
57
70
79
46
46
41
41
13
14
13
14
13
32
12
12
12
13
45
21
21
22
22
66
66
69
70
71
71
73
73
75
83
65
77
77
85
85
36
60
60
60
60
60
61
64
65
67
74
67
66
66
74
74
E75
7
48
48
49
50
51
52
54
60
64
76
72
60
60
76
76
E76
3
14
15
14
15
14
33
11
11
11
11
15
9
9
9
9
E77
20
33
33
32
32
31
18
24
24
24
27
33
28
28
27
27
E78
13
9
10
9
10
9
24
10
10
10
10
14
8
8
5
5
52
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
84
0.05 72
0.1 72
0.2 72
0.3 72
SDEA model k 0.4 0.5 0.6 72 72 72
E80
75
71
73
73
73
73
74
74
76
79
E81
88
57
57
56
55
55
55
53
54
52
E82
72
84
84
84
84
84
84
84
83
81
E83
85
86
87
86
86
86
86
86
86
84
E84
37
59
59
59
59
59
59
58
55
44
E85
45
56
54
53
54
54
53
52
46
40
E86
41
87
85
85
85
85
85
85
84
83
E87
40
25
25
25
25
25
8
30
31
E88
38
19
19
19
19
19
16
35
39
0.617
0.622
0.612
0.615
0.610
0.590
0.628
0.617
correlation coefficient
0.8 69
0.9 54
0.95 32
88
80
DEA-CCR
DEA-BCC
In
OUT
In
OUT
69
69
67
67
88
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0.7 69
44
27
54
54
64
64
72
61
82
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68
80
80
82
82
35
20
73
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16
55
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77
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85
85
81
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31
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27
36
36
43
61
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29
29
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0.595
0.521
0.297
0.606
0.606
0.527
0.527
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RPS
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Expert code
Note: RPS: Rank of productivity score; In: input-oriented; Out: output-oriented.
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Upstream projects play important role in oil industry A unique intelligent algorithm to optimize productivity of upstream oil projects A continuous improvement approach for upstream oil projects HSE, economic and management are determined as the most influential factors
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• • • •
S0920-4105(18)30291-2
DOI:
10.1016/j.petrol.2018.03.099
Reference:
PETROL 4840
To appear in:
Journal of Petroleum Science and Engineering
Received Date: 5 April 2017 Revised Date:
17 January 2018
Accepted Date: 27 March 2018
Please cite this article as: Azadeh, A., Pourebrahim Ahvazi, M., Motevali Haghighi, S., An intelligent algorithm for determination and optimization of productivity factors in upstream oil projects, Journal of Petroleum Science and Engineering (2018), doi: 10.1016/j.petrol.2018.03.099. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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An Intelligent Algorithm for Determination and Optimization of Productivity Factors in Upstream Oil Projects 1
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A. Azadeha , M. Pourebrahim Ahvazi2a, S. Motevali Haghighia&b a
Abstract
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School of Industrial and Systems Engineering, Center of Excellence for Intelligent-Based Experimental Mechanic and Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran b Department of Industrial Engineering, Esfarayen University of Technology, Esfarayen, 9661998195, Iran
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This paper presents a unique intelligent algorithm to identify and optimize factors affecting the productivity of upstream oil projects. Oil industry is the most important economic sector in the world and upstream projects play an important role in this industry. The proposed algorithm is composed of analysis of variance (ANOVA), data envelopment analysis (DEA), artificial neural
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network (ANN), adaptive network based fuzzy inference system (ANFIS), conventional regression (CR), and fuzzy regression (FR). In addition, each stage of the algorithm is equipped with verification and validation mechanism. First, a standard questionnaire containing
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productivity factors is designed and completed by experts of actual upstream oil projects. Then, DEA is used to identify the weights of all influential factors. The relationships between the
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influential factors and each of the criteria of productivity are then evaluated by ANN, ANFIS, CR and FR through relative error. Finally, ANFIS is selected as the preferred methods for optimization of the actual case of this study according to mean absolute error (MAE). Health, safety and environment (HSE), economic and management are determined as the most influential factors through sensitivity analysis. This is the first study that presents an intelligent algorithm 1
He Passed away on June 27, 2017 Corresponding author: Marziye Pourebrahim Ahvazi E-mail address: [email protected]
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for determination and optimization of productivity factors in an actual upstream oil projects. It can be used as a continuous improvement solution for upstream oil projects. Keywords: Upstream Oil Projects; Productivity; Artificial Neural Network (ANN); Adaptive
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Network Based Fuzzy Inference Systems (ANFIS); Analysis of Variance (ANOVA); Data Envelopment Analysis (DEA); Stochastic DEA 1. Introduction
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Energy is one of the most significant factors in developing of economy and society (Azadeh et al., 2015). Oil industry is one of the most influential industries in the world which provides 60
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percent of energy needs (Berends, 2007). After Saudi Arabia, Iraq, UAE and Kuwait, Iran ranks fifth in terms of oil reserves in the world and contain 9.2 percent of total oil revisers of the world (Hessari, 2005). It is a major supplier of oil in the world during different centuries and has a significant impact on the global energy (Rabbani et al., 2014). Oil and gas industry are the main
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provider of energy in Iran. It is one of the most important suppliers of revenues, the main source of public funding (Kiani and Pourfakhraei, 2010), and also has a major share of GDP in Iran. Iran's economy is highly dependent on oil exports (Hessari, 2005).
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Cost overruns and delays of schedule are some inevitable issues that these projects face with them (Jergeas, 2009) and resulted in decreasing productivity. By considering the importance of
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oil industry in the world, especially in Iran, low level of productivity in this industry, makes adverse consequences on the economic activities of the country. To improve the productivity, it is necessary to identify the positive or negative factors affecting the productivity and try to decrease the effect of negative factors and enhance the positive ones (Ghoddousi and Hosseini, 2012). The aims of this study are to determine the oil productivity measures and evaluating the factors that affect the upstream oil project productivity. Determining the most significant factors by considering the relation between the factors and the productivity measures is the final purpose 2
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of this study. In this regards, artificial neural networks, conventional regressions and fuzzy regression methods are applied to find the relation between the influential factors and the criteria of productivity.
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1.1. Upstream Oil Industry in Iran
Iran is a regional power in southwest Asia and occupies and has an important position in the global economy due to having the oil industry, petrochemical industry and natural gas. Iran has
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great potential in oil production and produces about 4 million barrels per day and is capable of producing more than 6 million barrels per day (Rabbani et al., 2014). The process flow of oil
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industry includes discovering the fields of oil, performing the operation and development of oil, transferring the oil from the wells to refineries, oil refining, transporting products to the place of use, marketing and supplying oil products to consumers. Oil and gas projects are divided into three broad categories that include upstream, midstream and downstream projects. The process
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of each category are shown in summary in Fig1. The oil industry is generally divided into three parts: upstream, downstream and midstream. The midstream industry is usually categorized in downstream section. Upstream industry includes the exploration of oil fields, drilling oil wells,
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development of oil fields and oil production. The projects of the oil industry categorized as the mega project. Considerable interrelationship and dependences, the inherent complexity, costly
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and risky are some of the characteristics of these kinds of projects (Barati and Sepasgozar, 2015). As mentioned before, the focus of this research is on upstream projects in Iran. National Iranian Oil Company includes several subsidiaries which about 14 main companies in upstream oil industry.
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Fig1. The selected oil industry
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1.2. Motivation and Significance
Previous studies do not consider full factors productivity analysis of upstream oil projects. Most recent studies in this area use qualitative approaches. This study is the first one which
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comprehensively identifies and optimizes factors affecting upstream oil project productivity. In addition, this is the first research which determines productivity of upstream oil project by a
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combined qualitative and quantitative algorithm. It considers six distinct and useful criteria in exploration, development and production of oil industry sectors. Determining the degree of influence of each group of factors by considering the level of productivity is another motivation of this study.
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This study is organized as follows: Section 2 reviews the related articles. In Section 3, the methodology of this study is presented. Section 4 describes the numerical results and discuss
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them. Finally, in Section 5 the study is concluded. 2. Literature Review
Identification the influential factors of the productivity of projects and determination the
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most significant of them are the challenging issues for researchers in recent years. There are many researches which evaluated influential factors of productivity in various industries in order
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to enhance productivity in that field. Most of the studies are done in the field of construction projects. For example, Alinaitwe et al (2007) evaluated 36 factors impacting the construction productivity of workers and incompetent supervisors, lack of skills of the workers, rework, lack of tools/equipment and poor construction method were identified as the most influential factors
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which resulted in poor productivity. Enshasi et al (2007) identified 45 factors of building projects in Gaza strip classified them in 10 groups and determined the importance of each group as follows: materials/tools, supervision, leadership, quality, time, manpower, project, external
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factors, motivation and safety. Dai et al (2009) tried to identify 83 factors impacting labor productivity by the viewpoints of 1996 craft workers in United States and 10 latent factors of
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these 83 factors were identified by applying principal factor analyses. The aim of the study presented by Soekiman et al (2011) was to identify factors affecting the project completion in Indonesia and its results could be useful in order to promote efficiency. In this study 113 factors through 15 groups were identified. The results showed that supervision, materials, execution plan and design are the most important group factors affecting labor productivity. Jarkas and Bitar (2011) identified 45 factors affecting labor productivity in construction projects in Kuwait. They grouped these factors into 4 categories which as follows: management, technological, labor and 5
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external group factors. They ranked the factors by using the relative importance index. The clarification technical specifications, the variation or change orders during execution, the level of coordination among design disciplines, lack of labor supervision and the proportion
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of work subcontracted were identified as the most influential factors. Ghoddousi and Hosseini (2012) identified 31 factors that had a negative impact on productivity construction projects in Iran. The factors classified into 7 groups, including materials and construction tools, technology
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and manufacturing techniques, management and planning, supervision, rework, weather and site conditions. The group of material and tool was identified as the most influential factors. Kazaz
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and Acıkara (2015) compared the factors affecting the labor productivity from the view point of project managers and craft workers by applying relative importance index (RII) method. They classified the factors in 4 groups as follows: organizational, economic, Socio-psychological and physical group factors. The project managers ranked the four group factors as follow:
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organizational, economic, physical and Socio-psychological group factors. Organizational, economic, Socio-psychological and physical group factors were respectively identified as the important group factors from craft workers’ perspective. Haghighatian and Ezati (2015)
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evaluated the effect of 21 factors on human resource productivity by calculating correlation coefficient and applying regression model. Training human resources, managerial support for
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successful accomplishment of affairs and having job security are identified as the factors which had the most relationship with productivity. In the field of oil industry, Managi et al (2006) evaluated the effect of technology changes on exploration, development and production of oil in United State and estimated total factor productivity in this area. Jergeas (2009) introduced 10 areas for improving construction productivity in oil and gas industry which are as follows: labor management, conditions and 6
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relations, project planning, management of construction and support, engineering management, effective supervision and leadership, communication, contractual strategy and contractor selection, constructability in engineering design, government influence, modularization and
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prefabrication. Taiwo (2010) evaluated the effect of work environment on labor productivity in oil and gas industry in Nigeria. In this regard, four hypotheses were tested to analysis the bad work environment, improvement in work environment, conducive work environment and
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employee productivity problems which may lead to increase or decrease productivity. Trade liberalization, currency stabilization, deregulation, privatization and nature features of oil
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reservoirs such as pressure of wells and chemicals used to separate oil and gas from water, are some effective factors on oil productivity that pointed out by Bridgman et al (2011). Chanmeka et al (2012) evaluated the 11 key factors affecting the performance and the productivity of oil and gas industry. Some of these factors are as follows: total cost of project, project duration,
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percent contingency budget, percent modularization, percent offsite work hours, percent construction indirect / direct work hours, work force predictability, percent scaffolding / direct work hours. LIANG et al (2013) employed three methods, including information amount theory,
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gray correlative method and orthogonal experimental design to evaluate the impact of some physical factors (permeability, porosity, formation pressure, in-situ oil viscosity, horizontal
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section length, fracture length, fracture width and fracturing stage number) on well’s productivity in the Bakken tight oil reservoir. The results of the three methods showed that the fracturing stage number, fracture length, horizontal section length, and permeability factors are the main influential factors on productivity of the Bakken tight oil reservoir. Hui et al (2013) Evaluated productivity and influential factors in south Azadegan oil field. They analyzed the effect of physical properties and fluid parameters on productivity which were oil pressure, choke size,
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interlayer distribution, longitudinal conductivity, permeability, natural aquifer size. They presented a model in order to evaluate the productivity. Barati and Sepasgozar (2015) identified 63 effective factors on the productivity of oil and gas industries. These factors were categorized
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in five groups as follows: project, external, organizational, labor and management factors. The results showed that the management factors are the most important ones.
There are different models in order to identify the relations between influential factors of
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productivity and the productivity measurement and predict the productivity such as regression
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methods and artificial neural networks which are used to predict productivity and determine the relation between factors and productivity in several studies (Tam et al., 2002; Lu and Yeung, 2003). Neural networks are able to identify similarities between a new model of inputs and outputs in a new pattern. They are capable of deal with the incomplete information. This method is very efficient especially when the relationship between inputs and outputs is not clear (Heravi
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and Eslamdoost, 2015). Oral and Oral (2010) used self-organizing maps (a type of artificial neural network) to predict construction labor productivity. They considered the size of crew, crew experience, age of the crew, the system of payment, working hours in one week, working
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hours in one day and distance between the accommodations of the crew members from the site as
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the factors affected the productivity. Concrete pouring, reinforcement and formwork activities were considered as the criteria of productivity measurement. Moselhi and Khan (2012) evaluated 9 influential factors on productivity through 3 groups in construction industry which were weather, workforce and project groups. They ranked them by applying three method which were fuzzy subtractive clustering, neural network and regression method. Finally, temperature (weather group), work type (project group) and floor level (project group) are identified as the most important factors. Nasirzadeh and Nojedehi (2013) used system dynamic approach to
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determine the inter-relations between the effective factors and also the relationship between the effective factors and the labor productivity. The aim of the research of Heravi and Eslamdoost (2015) was to identify the factors affecting the labor productivity. They developed a model to
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measure and predict the productivity by using artificial neural networks. Five factors which are labor competence, poor decision making, motivation of labor, proper site layout and suitable site planning have been identified as the most influential factors.
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As presented in this section, although there are many studies that concentrated on
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identifying influential factors of productivity of projects, in the field of oil and gas industries, there is no study which comprehensively presented the factors affecting on productivity of projects. Most of this study focused on the construction oil project and the other studies have minor points not comprehensive on project productivity in upstream oil industry. In addition,
upstream oil industry. 3. Methodology
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there is no study which evaluates the level of productivity qualitatively in different sector of
The proposed algorithm of this research is presented in Fig 2. This consists of 25 steps
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which are as follow:
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Step 1: Identify factors affecting on oil project productivity. Step 2: Identify oil project productivity criteria. Step 3: Design the questionnaire by considering productivity criteria and influential factors. Step 4: Is the validity of questionnaire content confirmed? If yes go to Step 5. Otherwise go to Step 3. Step 5: Data collection and analysis through experts of upstream oil projects. 9
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Step 6: Validity of questionnaire can be checked by using ANOVA or Kruskal-Wallis experiments (depending on normality of data) in order to test the equality of means of two sample of data for each question. If the null hypothesis is not rejected, the validity of
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questionnaire can be proven.
Step 7: The reliability of questionnaire is measured by using Cronbach alpha. The value of Cronbach alpha is from 0 to 1 and this test can be performed to show reliability of factors
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achieved by questionnaire (Santos, 1999). This reliability is computed by Equation (1) (DeVellis,
N
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2016).
σ2
∑ Xi N (1 − i =1 2 ) α= N −1 σY
(1)
2 Which Y= X1+X2+….+XN , and σ Xi and σ Y2 show the variance of the each component
literature (Nunally, 1978).
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(Xi) and total variance of Y. 0.7 has introduced as acceptable value for Cronbach alpha in
data.
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If validity and reliability are proven go to next step. Otherwise go to Step 5 and collect more
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Step 8: Remove the factors with lower effect By using the one sample t-test, influential factors will be determined. This test is used to investigate if mean values exceed certain limits? (Denni-Fiberesimam and Rani, 2011). The null hypothesis is considered as follows:
: =
: <
.
If the null hypothesis is not
rejected, the related factor will be influential and applied in the next calculations; otherwise, the factor is determined as the less influential factors and removed from the next calculation. 11
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Step 9: Determine the weight of each factor by data envelopment analysis (DEA) DEA is applied to determine weight of each influential productivity factor. Four types of
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DEA models are applied which are CCR and BCC input-oriented and output-oriented models. Forty five influential factors are considered as inputs and six productivity criteria of oil projects are considered as the outputs of DEA. Perturbation analysis is carried out to identify the best DEA model. The model with least sensitive to the noise is selected as the best model and is
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applied to calculate the primary weights.
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In order to calculate the weighs of each factor, the best DEA model is performed with 51 indexes which includes 45 influential factors as inputs and 6 productivity criteria of oil projects as outputs. Then, DEA model is run 45 times by eliminating each of the inputs one by one. The difference between average efficiency of each removing input and the main model is calculated
weights of each factor.
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and the result is divided by sum of these differences. The obtained values are considered as the
Step 10: Data conversion between zero and one.
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Step 11: Calculate the weighted average of each group of influential factors.
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Identify the relation between the influential factors of productivity and the productivity criteria of oil projects (Steps 12 to 20) Through the steps 12 to 20, by applying intelligent methods, conventional regression and fuzzy regression, the relation between the influential factors of productivity and productivity criteria of
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oil projects is evaluated. The method with the lowest mean absolute error (MAE)3 is selected as the best ones to predict the level of productivity. These steps are as follows:
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Step 12: Determine the input and output variables of the model. Step 13: Determine the train and test data in order to use by artificial neural network ANN-MLP, ANN-RBF and ANFIS, CRs and FRs. The train and test data are selected randomly for each of
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the applied method. Applying intelligent methods (Steps 14 to 16)
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Step 14: Define different architectures for ANN-MLP, train the model for each architecture by train data, calculate the MAE by test data and select the best architecture with lowest MAE (MLP*).
Step 15: Repeat Step 14 for ANN-RBF to select the best architecture with lowest MAE (RBF*).
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Step 16: Repeat Step 15 for ANFIS to select the best architecture with lowest MAE (ANFIS*). Step 17: Run linear, quadratic and exponential regressions to evaluate the relations between inputs and outputs variables. The MAE values are calculated for the test data of each output.
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Step 18: Run fuzzy regressions (Tanaka and improved Tanaka models) to identify the relation
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between the inputs and outputs.
Step 19: Select the best method from the previous steps according to the minimum MAE for each output.
Step 20: If the MAE of the best method is less than 0.2, then go to the next phase. Otherwise, distribute questionnaires to more operators. With respect to questionnaires, a maximum error of 0.20 is chosen to allow the significant variations between respondents due to skills, education 3
=1
∑
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and experience. This is a reasonable and logical estimate. It is worth to mention, the steps 14 to 20 are repeated for each output. After selecting the best method to evaluate the relation between the influential factors of
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productivity and productivity criteria of oil projects, steps 21 and 22 are applied in order to measure the productivity score of the system. These steps are repeated for each outputs. Step 21: Run the model by applying the best method for each output.
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Step 22: Calculate the productivity scores (level of productivity) and rank the respondents. For this purpose, first the difference between the actual output (!
#
=!
(#)
& ∗)
and the output of the best
is calculated for all respondents as follows (Equation 2), (Azadeh et al., 2007):
− !%
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method (!%
(#) )
& ∗ (2)
In order to obtain the impact of the largest positive error, the frontier function is shifted from the optimal model as follows (Equation 3), (Azadeh et al., 2007): =
#
!%
& ∗
(3)
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* #
This feature does not contain the largest value of error, but calculates by considering the
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respondent scale (Constant Returns to Scale (CRS)). In this way, find the largest indicates the respondent with the highest level of productivity. Suppose that the largest
* #
* #
which belong
* ,
=
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to respondent k, then (Equation 4), (Azadeh et al., 2007): -. ( #* ) (4)
Therefore, the value of the shift is different for each respondent and is measured as follows (Equation 5), (Azadeh et al., 2007): 0ℎ# =
∗ !(% !(% &
* ,
& ∗ )#
∗ ),
(5)
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In this algorithm, the impact of the respondent’s scale is considered and the unit which applied for the correction, is selected by considering its scale (Azadeh et al., 2007; Costa and Mahrkellos, 1997; Delgado, 2005). The level of productivity for each respondent is calculated by
2007): 3# =
!(%
!#
& )#
+ 0ℎ#
(6)
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the Equation 5. The range of productivity score is between 0 to 1 (Equation 6), (Azadeh et al.,
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This score (3# ) is calculated for each output according to its appropriate model (the model with
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minimum MAE). Total score of productivity is obtained by calculating the mean of 3# of six outputs.
Step 23: Validate the productivity results by applying DEA and SDEA methods. In order to
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validate the results of the previous step under deterministic and stochastic conditions, DEA and SDEA methods are applied. In this way, four types of DEA method are used which are CCR and BCC input and output oriented models. In addition, SDEA method in different levels of risk is
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applied.
Step24: The results of ranking in step 21 is compared to the results of ranking by DEA and
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SDEA methods. The high correlation between total score of productivity and DEA and SDEA results show the validity.
Step25: Determine the weight of each group of the factors by considering the productivity score. The weight of each group factor is determined in this phase. To this purpose, each group factor is removed one by one and the F score (level of productivity) is calculated for each removal. The
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difference between average F score of main model (the model with all factors) and the removed factor is calculated. The result is divided by total differences.
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3.1. Productivity and Influential Factors 3.1.1. Influential Factors
In this study, identifying factors affecting productivity is done by referring to previous studies and using expert opinions. As mentioned before, because of insufficient studies in the
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field of assessing the influential factors of oil project productivity in oil upstream industry, in
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addition to these studies, we use the studies in other fields to identify primary factors. In this way, at first, we used several papers in order to gather different factors. Then, some factors are added or omitted by the viewpoints of oil experts. Finally, 45 factors are identified and classified into 9 groups which as follows: human factors, management factors, project factors, health, safety, environment (HSE) factors, regulatory and contractual factors, factors related to material
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and equipment, economical factors, political and governmental factors and natural factors. Grouping the factors is done according to the previous researches and modified by expert
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opinions. Identified factors and their relative group are shown in Table 1. Table 1. Identification the factors Group Number Factors definition name
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Reference Liberda et al.(2003);Enshassi et al. (2007);Barati and Sepasgozar (2015)
2. Job satisfaction in the workforce
Enshassi et al. (2007);Kazaz and Ulubeyli (2007);Barati and Sepasgozar (2015)
3.Education level of the workforce
Barati and Sepasgozar (2015)
4
4. Motivating Employees
Jergeas (2009)
5
5. Job security
Nasir (2013)
6
6. Friendly atmosphere between staff 1. Labor participation in the decisionmaking process
Enshassi et al. (2007)
3
7
man agem ent
2
human
1. Experienced Workforce
1
16
Kazaz وUlubeyli (2007)
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Table 1. Identification the factors Group Number Factors definition name
Reference
2. Overtime hours
Liberda et al. (2003); Enshassi et al. (2007); Jergeas (2009)
9
3. Avoid any discrimination
Nasir (2013)
10
4. Salary payment system
Liberda et al. (2003)
11
5. Weakness and delay in the inspection program
Liberda et al.(2003);Enshassi et al. (2007); Soekiman et al., (2011);Attar et al. (2012)
12
6. Properly monitor labor practices
Liberda et al.(2003); Enshassi et al. (2007);Attar et al. (2012);Barati and Sepasgozar (2015)
13
7. Workforce training
Liberda et al. (2003); Enshassi et al. (2007); Jergeas (2009); Attar et al. (2012); Barati and Sepasgozar (2015);
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8. Risk assessment in each phase of the project 9. Failure to follow the schedule
14 15 16
10. Empower project managers 11. Disputes between the actors of the project
17
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8
Jergeas (2009);Barati and Sepasgozar (2015) Enshassi et al. (2007); Attar et al. (2012) Jergeas (2009)
Jergeas (2009)
2. Complexity of oil projects
Liberda et al.(2003); Barati and Sepasgozar (2015); Barati and Sepasgozar (2015)
20
3. Difficult access to the place of oil projects
Liberda et al.(2003); Jergeas (2009); Barati and Sepasgozar (2015)
21
Liberda et al. (2003); Enshassi et al. 1. Cleanliness and amenities on the camp (2007);Jergeas (2009); Barati and Sepasgozar and site (2015)
24 25 26 27 28
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2. Environmental factors (inadequate lighting, poor ventilation, noise, etc.) 3. Occupational safety inspector at the project site 4. Safety analysis activities, identify risks and anticipated regulatory actions
Enshassi et al. (2007)
5. Waste Management
expert opinion
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23
HSE
22
material and equipment
19
1. The size of the projects project
18
1. Lack of materials and equipment 2. Lack of access to spare parts 3. obstacles in the way of transfer of technology related to the oil industry
17
Liberda et al.,(2003); Enshassi et al. (2007)
Nasir (2013)
Enshassi et al. (2007); Osmundsen et al. (2010); Attar et al. (2012); Soekiman et al. (2011); Attar et al. (2012) expert opinion
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Table 1. Identification the factors Group Number Factors definition name
Reference
30
5. Delay in reaching Equipment
35
1. Inflation and downturn
41 42 43
44 45
expert opinion
1. Iran's relations with targeted countries 2. Internal political changes
Barati and Sepasgozar (2015)
3. Oil price
3. Sanction 4. Create a competitive atmosphere and ample opportunities for active private contractors 5. Slowness in issuing permits and approvals
expert opinion
6. Providing loans for contractors to develop infrastructure
expert opinion
1. Weather conditions in oil areas
Liberda et al. (2003); Enshassi et al.(2007);Jergeas (2009); Barati and Sepasgozar (2015)
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40
Natural
39
Political and governmental
38
Barati and Sepasgozar (2015)
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37
Nasir (2013); Ghandi and Lin (2012).
Bridgman et al. (2011), Barati and Sepasgozar (2015) Barros and Managi (2009); Bridgman et al. (2011); Khan (2017) Barati and Sepasgozar (2015)
2. Stability of exchange rate
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36
Liberda et al. (2003)
Barati and Sepasgozar (2015)
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33
economical factors
32
regulatory and contractual
34
1. Conventional strategies that are used in upstream oil projects 2. Conventional methods in projects upstream 3. Contractual claims of the contract correspondents 4. The rules on foreign investment
31
Enshassi et al. (2007); Soekiman et al. (2011);Attar et al. (2012) Attar et al. (2012); Barati and Sepasgozar (2015)
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4. The distance between the storage location and the site
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29
2. Natural factors related to the oil field
expert opinion
Nasir (2013)
Bridgman et al. (2011)
3.1.2. Identification oil project productivity criteria According to Asian Productivity Organization (APO), productivity is the combination of efficiency and effectiveness. Drucker (1963) expressed the difference between efficiency and effectiveness. Doing things right and doing the right things are the definitions of efficiency and 18
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effectiveness respectively. In the other words, productivity is to achieve efficiency and effectiveness at the same time. In addition, in the field of the productivity of projects, there are many measurements in literature. Project duration is considered as productivity measurement by
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Roll and Rosenblatt (1983). Williams (1980) suggested that using the resources consumed in the best way, will improve productivity. In addition, the indicators such as the completion time of the project, delays and exceeding the schedule are some measures of the project productivity.
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Thomas et al (1990) expressed that productivity can be considered as performance index and calculated by dividing the estimated unit rate to actual unit rate. Nasir (2013) defined the
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productivity factor as follows: dividing estimated productivity to actual productivity. Estimated productivity is calculated by dividing the estimated work hours to estimated quantity of outputs and actual productivity is measured by dividing actual work hours to actual quantities. In addition, Heravi and Eslamdoost (2015) considered the productivity index as the rate of actual
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work hours to estimate work hours which is also pointed out by Yi and Chan (2013). In this study, six qualitative criteria are defined as productivity measurements in order to evaluate the productivity level qualitatively. Table 2 shows these criteria.
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Table 2. Productivity criteria definition Number Criteria definition 1 Economizing the resources
Symbol Cri1
The difference between the use of resources from the amount of planned resources
Cri2
3
The difference between the actual project duration and the scheduled project duration
Cri3
4
The difference between the actual progress of the project with advances predicted for it
Cri4
5 6
The correct definition of project activities The proper conduct of project activities
Cri5 Cri6
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3.2. Data Envelopment Analysis The concept of evaluating the level of efficiency among a group of decision making units
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(DMUs) is indicated by data envelopment analysis (DEA). This is a nonparametric frontier method that the efficiency of each DMU is calculated in comparison with the highest yield. Determining the efficiency by using the nonparametric method was done by Farrel (1957) for the first time. He fitted a production function on a set of inputs and output of a group of DMUs and
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resulted in a piecewise linear function. Based on previous studies, Charnes, Cooper and Rhodes
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(1978) presented a new method to calculate the efficiency of decision-making namely CCR model.Then, Banker, Charnes and Cooper presented BCC model in 1984 (Banker et al., 1984). In general, there are two strategies to improve the performance of non-efficient units and get them to efficient frontier (Charnes and Cooper, 1984). The first strategy is to reduce inputs without reducing output by the time the DMUs get to the efficient frontier namely input-oriented
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and the second strategy is to increase the outputs until getting the DMUs on the efficient frontier without using more inputs. This strategy is known as output-oriented. In addition, it is necessity to consider the changes of inputs to outputs which namely ratio of returns to scale. If by
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increasing the inputs, the ratio of outputs to inputs remains constant, it is constant returns to scale
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(CRS), and if the ratio of outputs to inputs changes, it is variable returns-to-scale (VRS). CCR and BCC methods cover the CRS and VRS respectively. The BCC and CCR input and outputoriented models that are applied in this study, introduced by Charnes et al. (1978) and Banker et al. (1984), are presented in models 7 to 10 in Table 3. It should be noted that we have 9 inputs and 6 categories outputs as shown in models 7 to 10.
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Table3. DEA models
Output-oriented 6 7 = 8 #:
9
. =. 8 > ? @ − 8 :
J
8> ? :
#: @
# .#@
#
9
L6 7 = 8
≤ 0 CDE F = 1, … ,88
#:
≥ 0 # .#
(9)
. =. 8 > ? @ − 8 :
9
8
# .#@
> , J
J
. =. 8 N ? :
@
#
≥ 0
describe the input and output of jth DMU and > ,
:
≤ 1 CDE F = 1, … ,88
J
8 N = 1 :
N ≥ 0
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≤ 0 CDE F = 1, … ,88
= 1 CDE F = 1, … ,88
N# ≥ 0
where .#@ and ?
# .#@
#:
-.7 = 8 N ? (10)
8 N# = 1 #:
9
#:
. =. 8 N# .#@ ≥ 1 CDE F = 1, … ,88 #:
9
J
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BCC
:
= 1 CDE F = 1, … ,88
> ,
9
-.7 = 8 > ? (8)
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CCR
(7)
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J
# .#
J
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9
Input-oriented
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Model
#
are the weights of output
and input respectively and r, i and j represent the number of outputs, inputs and DMUs. 3.3. Stochastic Data Envelopment Analysis Stochastic data envelopment analysis (SDEA) model is applied for evaluation of different DMU in stochastic environment to help decision makers for accurate decisions. In this model, 21
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the inputs (or outputs) can be deterministic. It means that it is unable to control the input (or outputs) because they are affected by external factors which are out of controls. In this study, the SDEA model which is introduced by Sueyoshi (2000), is used in order to validate the results of
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level of productivity by considering stochastic outputs. This model (model 11) is presented in the following equations. J
=. 8 9
#:
8 #:
# .#,
=1
# PQ@ .#@ R −
J
8 > S? @ + T ,@ 3 :
P1 − U@ RV ≥ 0
#
≥0
where T
,@
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CDE F = 1,2, … ,88 , > ,
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9
:
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-. 8 > ?O , (11)
is standard deviation of ?
,
and ?O
,
is the mean of ? , . βj is the expected
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efficiency level of the jth DMU (Sueyoshi, 2000) which is considered equal to 1 in this study. In this model, αj is a parameter to show the degree of risk in environment considered as a risk criterion, this parameter shows degree of risk in environment and the higher value of αj illustrates
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the higher risk of environment. The description of .#@ , ?
method.
@
> and
#
are the same as DEA
3.4. Artificial Neural Network The artificial neural networks are such tools for processing the information that are inspired by biological nervous systems. They processes the information such as the brain and are able to
22
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learn and generalize from experience (Zhang et al., 1998). ANNs consist of simple processing units with weighted connections to make different structures that are capable of learning the relationships between sets of variables. They are useful for nonlinear processes with unknown
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functional form (Azadeh et al., 2011). Pattern recognition, data mining, classification, forecasting and process modeling are such applications of ANNs (Azadeh et al., 2010).
In the way of solving an ANN problem, three steps should be passed which are training,
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generalization and implementation. The learning process in order to recognize the current pattern of input data is called training process. Back propagation learning algorithm is one the most
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popular learning technique which introduce by Werbos (1974). In this algorithm the gradient steepest decent method is used to minimize the error function. In order to fit the connecting weight, the obtained output in the output layer is compared with the desired output (Kuo et al., 2010). Each ANN network uses a set of training rules that define training method in order to
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evaluate the network capability in extracting feasible solution when the network faces the unknown inputs which are not trained to network (Azadeh et al., 2010). For a successful implementation, it is necessary to determine the proper configuration and parameters which is an
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iterative experimentation process. Although there are several possible solutions, in order to obtain the best results, the precise experimentation must be performed. There are many ANN
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models, however in this research, three types of ANN are applied which as follows: multilayer perceptron (MLP), radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS).
MLP consists of three types of layers of nodes. The first type of layers, namely input layer, receives external information of input data and distributes them to hidden layer. Hidden layer is the second type of layers which describes the relations between the input layer and output layer.
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In this layer, after receiving the inputs from all of the neurons of input layer, the values are added through applied weights and converted to an output value by an activation function. Then the output moves to all of the neurons in the next layer, preparing a feed forward path to the output
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layer. Output layer is the third type of layers of nodes and demonstrates the output nodes of variables (Kuo et al., 2010; Zhang et al., 1998). The more hidden layers lead to more complex networks. In this research, definition of different architectures of MLP neural network is based
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on different training function, two transfer function and the number of neurons in hidden layers. One of the most powerful neural network used in the estimation function is RBF neural
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network which uses radial basic function as the activation function. Free of “local minimum trap and overtraining” and short training time are some of the advantages of RBF (Lau et al., 2010). In these networks, unlike the common practice in other networks, the entire input space will not be responded similarly. Herein, first the center of input space is calculated and then the inputs
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that are close enough to the center are responded. As a result, these networks respond to inputs locally. RBF networks typically have three layers: an input layer, a hidden layer with a radial basic activation function and a linear output layer. The structure of RBF network is similar to
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MLP network but it has just one hidden layer (Principe et al., 2000). Definition of different structures of RBF network is according to two parameters which include the spread of radian
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basis function and the maximum number of neurons. Adaptive network based fuzzy inference system (ANFIS) is a kind of fuzzy neural network. The combination of artificial neural networks and fuzzy logic is resulted in neuro-fuzzy. The adaptive network is a network including of nodes and connections which through the nodes are linked and actually it is a superset of all kinds of feed-forward neural networks (Jang, 1993). According to study of Werbos (1974) the gradient descent is the basic learning rule of adaptive
24
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network. Solving slowly and tendency to trap in local minima are the disadvantages of this method. Jang (1993) introduced a hybrid learning rule in order to accelerate the learning process which combines the gradient method and the least squares estimate to identify parameters. By
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using feed-forward network, ANFIS finds the proper fuzzy decision rules and by applying the given dataset of inputs and output, it is able to create a fuzzy inference system that its membership function is fitted by back-propagation algorithm with a least squares method
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(Azadeh et al., 2013). In this study, in order to obtain the network with the best quality of training and evaluation, the networks with different parameters will be evaluated. In this regards,
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different architectures will be defined according to three types of fuzzy inference systems, two optimization methods and some of the inference options. 3.5. Fuzzy Regression
The aim of applying regression analysis is to find the relation between the variations of a
(Equation 12):
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depended variable (Y) with the explanatory variables (X) and can be estimated as follows
C(.) = - . + ⋯ + - . + T ( 12)
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where f is a crisp linear function and - , -X , … , - are real values. If there is uncertainty relation
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between inputs and outputs, fuzzy set are used. In fuzzy linear regression analysis, relationship is determined by a fuzzy function (Charfeddine et al., 2004). The fuzzy regression models are divided into two classifications. The first classification is the model with the fuzzy relationship between the variables, in other words, the coefficients of regression model are considered as fuzzy values and the observations are crisp in this model classification. The second type of fuzzy regression models is the model with the fuzzy observation and crisp coefficients.
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The general form of fuzzy regression model is as follows (Equation 13), (Charfeddine et al., 2004):
YZ = [ + [ . + ⋯ + [ . (13)
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Where YZ is a fuzzy output, [# 6 = 1,2, … , is indicated fuzzy coefficient, and \ is the non-
fuzzy input vector.
A triangular membership function can be attributed to the coefficient. [ # is a symmetric fuzzy
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number indicated by (]# , ^# ) where ]@ and ^@ are its center and width respectively. The
membership function of Y is as follows (Charfeddine et al., 2004): ) d|c| ( ) ` _ ? = 1 6C \ = 0 , ? ≠ 0 f (14) 0 6C \ = 0 , ? = 0 |_ bc|
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-. (0,1 −
The aim of fuzzy regression with non-fuzzy observation is to determine the [# coefficient so
that, first, the membership degree for the fuzzy output for all y is at least as large as h (
_ (?)
≥
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ℎ). Then the fuzzy coefficients would be determined in a manner that minimizes the fuzziness of
the fuzzy output which is achieved by minimizing the spread of fuzzy coefficient (^@ ) (Shapiro
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and Koissi, 2008).
In this study, two kinds of fuzzy regression models which belong to the first classification
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of fuzzy regression methods (fuzzy ccoefficients) are applied. The first model which is introduced by Tanaka et al (1982) is as follows (model 15): %
6 ^ + 8 8 ^# .#@ (15) @: #:
] + ∑#: ]# .#@ − (1 − ℎ)(^ + ∑#: ^# .#@ ) ≤ ?@
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] + 8 ]# .#@ + (1 − ℎ) g^ + 8 ^# .#@ h ≥ ?@ #:
#:
Tanaka and Watada, (1988) improved the above model by adding e parameters in the constraints
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which presented the spread of jth collected data.
Here, the actual data points fall within the interval ?@ ± (1 − ℎ)j@ . The constraints of this
(Equations 16 and 17), (Tanaka and Watada, 1988):
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improved model are as follows (the objective function is the same as the above model)
(16)
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% ] + ∑% #: ]# .#@ − (1 − ℎ)P^ + ∑#: ^# .#@ R ≤ ?@ − (1 − ℎ)j@
] + 8 ]# .#@ + (1 − ℎ) g^ + 8 ^# .#@ h ≥ ?@ + (1 − ℎ)j@ (17) #:
#:
4. Results and Discussion
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The numerical results of the proposed algorithm are presented in this section. In this regards, each step of the proposed algorithm is applied in order to analyze the level of productivity of oil projects and determine the importance of each factor. In this algorithm the factors impacting the
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productivity of oil projects are considered comprehensively and the importance of them is determined by considering the level of productivity.
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4.1. Data Collection
A standard questionnaire is designed by using the extracted factors from literature and interviewing with the experts of upstream oil industry in Iran. It is tried to design a comprehensive questionnaire to cover most of the influential factor on oil project productivity. The questionnaire consists of 51 questions which 45 questions related to the influential factors
27
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and 6 questions are about the oil productivity criteria in upstream section. Respondents must be assigned a score between 0 and 20 to each question.
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As mentioned before, the concentration of this study is on the upstream oil projects in Iran. In this study, 150 questionnaires have been distributed among the experts of seven main companies, two active companies in exploration field, two companies of production and three
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companies of development of oil industry and then 88 questionnaires have been collected which is about 59% response rate. To test the validity of questionnaire, ANOVA is applied (the data
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distribute normally). To do this, two random groups of each question are selected and the equality of two group’s averages is examined by F-test (equality of two group’s averages is considered as null hypothesis). It should be noted that, this test is performed at 0.05 significance level. According to the obtained results, for each factor the null hypothesis which is the equality of the mean of two groups, does not rejected. As seen in Table 4, for all factors p-value is higher
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than 0.05. It means that, the obtained data through questionnaire are valid (Azadeh et al., 2011). In order to test the reliability of the collected data via questionnaire, Cronbach alpha test is used for each of the group factors. The calculated Cronbach alpha for each groups is 0.7 or higher
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than 0.7 which shows acceptable level of reliability of questionnaire. The results of the validity
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and reliability of questionnaire are shown in Table 4 (Appendix A) (See steps 3 to 6 of the proposed algorithm).
4.2. Removeing the factors with lower effect and determining the weight In this phase, first, the impact of each factor is evaluated by using one sample t-test. Based on expert’s opinions, value of the influential factor must be greater than 10. Therefore, null and alternative hypotheses are considered as
= 10 and 28
< 10 respectively. The results shows that
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the mean of all factors is equal or more than 10, and there is no factor with low effect. As shown in Table 5, all achieved p-values are higher than the significance level (0.05). This shows all
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factor are important in this case study. Table 5 shows the results (Appendix B) Then, the weights of the factors are calculated. As mentioned before, to this purpose, four types of DEA models which are CCR and BCC input and output oriented are applied. The results showed that BCC output oriented model is the least sensitive model to noise because the
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correlation between results of this model by main data and noisy data is high. Therefore, this
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DEA model is selected to calculate the weighs of factors. In this way, the DEA model has been run 45 times and the input factors are omitted one by one in each time and the relevant efficiency is calculated. The results of this step are shown in Table 6 (Appendix B) (See steps 8 and 9 of the proposed algorithm). It should be noted that each weight is calculated by dividing change in
4.3. Preparing Data
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efficiency score after and before factor omitting into total changes.
In this phase, data are prepared in order to use for ANN, ANFIS, RBF, CR, and FR. To do
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this, data converted into the range of 0 and 1. This scale helps the neural networks generalize better inputs (Portas and AbouRizk, 1997). Then, according to the calculated weights in step 9,
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the weighted mean of each group is computed (Steps 10 and 11 of the proposed algorithm). 4.4. Identify the relation between the influential factors of productivity and the productivity criteria of oil projects
As stated before, in this phase, different methods are used to determine the relation between inputs and outputs and the model with the minimum error is selected as the optimum model.
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According to the proposed algorithm, the inputs and outputs variables of the model is determined in step 12. The nine groups of factors which are human, management, project, HSE, material and equipment, regulatory and contractual, economic, political and governmental and natural factors
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are defined as the inputs of the model and the six defined productivity criteria are considered as outputs. To run the different architectures of MLP and RBF neural networks, ANFIS, CRs and
are used as test data (Steps 13 of the proposed algorithm).
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FRs, 70 to 90 percent of data are chosen randomly as the training data set and the leftover data
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The aim of the steps 14 to16 of the proposed algorithm is to find the appropriate architecture among the MLP, RBF and ANFIS models. To select the optimum architecture for MLP, by changing some parameters of ANN-MLP, 40 different architectures are defined (See Appendix C). In this model, there are different parameters that some of them are changed to find optimal structures. RBF neural network is also run for 40 different architectures that defined according to
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change two parameters of this networks (See Appendix C). To find the optimum structure of ANFIS, 64 models are tested and the related MAE is calculated. The defined architectures and the architecture with the least error for each output of MLP, RBF and ANFIS are presented in
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Tables 7 to 9 respectively (Appendix C).
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The relation between inputs and outputs are evaluated by three different regression methods which are linear, quadratic and exponential. The values of MAE are calculated for each output and the results are shown in Table10 (Step17). Table 10. The value of MAE for different kinds of regression method MAE Regression Number method Cri1 Cri2 Cri3 1
Linear
0.336
0.222
30
0.529
Cri4
Cri5
Cri6
0.425
0.177
0.216
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Table 10. The value of MAE for different kinds of regression method MAE Regression Number method Cri1 Cri2 Cri3
Cri4
Cri5
Cri6
Exponential
0.412
0.299
0.605
0.517
0.296
0.460
3
Quadratic
0.391
0.264
0.510
0.407
0.196
0.274
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2
In addition, FR is applied to evaluate the relation between inputs and each of the criteria
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(outputs of models) under different values of h (of Tanaka model) which changes between 0 to 1, 0.1 by 0.1 (interval) also, h and e (of improved Tanaka model) with three different values for
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each of them (h= 0, 0.4, 0.8 and e= 0.1, 0.5, 0.9). The results are presented in Table11. (Step18). Table11. Results of fuzzy regression
Improved Tanaka h* e* MAE h* MAE Cri1 0.4 0.5 0.683 0 0.673 Cri2 0.4 0.1 0.2 0.4090 0.461 Cri3 0.4 0.1 0.4 0.745 0.648 Cri4 0.8 0.9 0.3 0.705 0.798 Cri5 0.8 0.9 0.567 0.4 0.564 Cri6 0.4 0.1 0.4 0.304 0.328 h* and e*: the value of h and resulted in minimum MAE Tanaka
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Criterion
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For each outputs of model, the optimal model of MLP, RBF and ANFIS are selected and compared to the results of CR and FR methods. As shown in Table12, for all outputs, ANFIS method is given the least MAE, therefore this method is applied to identify the relation between the influential factors of productivity and the productivity criteria of oil projects and measure the productivity level. It is worth to mention that the presented values of MAE in Table 12 are related to the optimum architecture of each method , in addition, MAE value of the best method
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is less than 0.2, therefore it is not necessary to repeat the previous phases (See steps 19 and 20 of the proposed algorithm).
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Table12. Selection the best method among different methods MAE Method Cri1 Cri2 Cri3 Cri4 0.5371 0.4261 0.7074 0.6293 MLP 0.2714 0.2001 0.3769 0.3481 RBF 0.1782 0.1456 0.1980 0.1938 ANFIS Linear 0.3364 0.2215 0.5288 0.4245 regression Exponential regression 0.4118 0.2986 0.6051 0.5168 Quadratic regression 0.3908 0.2638 0.5103 0.4073
Fuzzy regression (Improved Tanaka)
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0.1775 0.2159 0.2963 0.4598 0.1956 0.2745
0.6726 0.4045 0.6481 0.7978 0.5640 0.3284
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Fuzzy regression (Tanaka)
Cri5 Cri6 0.3220 0.4987 0.1401 0.1894 0.1106 0.1748
0.683
4.5. Determine the level of productivity
0.461
0.745
0.705
0.567
0.304
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The optimum method (See Table 12) of each output is run for all data. The predicted values are compared to actual data in Fig 3 to Fig 8. These figures approve the accuracy of the best
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model for all criteria because the difference between the predicted and actual data is not significance. By using the best method, the productivity scores of each output are calculated
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based on Equations (2) to (6). As mentioned before, the total score of oil project productivity (total level of productivity) is the average of six scores of productivity based on 6 criteria which are determined according to the experts’ opinions under six questions (See steps 21 and 22 of the proposed algorithm).
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1.2 Actual value ANFIS value
0.8 0.6 0.4
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Value of Cri1
1
0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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Expert code Fig3. The results of selected ANFIS method and the actual data for Cri1
1.2
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Actual data
Value of Cri2
1 0.8 0.6 0.4
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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0.2
ANFIS value
Expert code
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Fig4. The results of selected ANFIS method and the actual data for Cri2
1.2
Actual value
1
0.8 0.6 0.4 0.2 0
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
Value of Cri3
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ANFIS value
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Fig5. The results of selected ANFIS method and the actual data for Cri3 33
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1.2
Actual value ANFIS value
0.8 0.6 0.4 0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
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Value of Cri4
1
Expert code
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Fig6. The results of selected ANFIS method and the actual data for Cri4
1.2
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Value of Cri5
1 0.8 0.6 0.4 0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
Actual value ANFIS value
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Expert code
1.2
0.8 0.6 0.4
Actual value ANFIS value
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Value of Cri6
1
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Fig7. The results of selected ANFIS method and the actual data for Cri5
0.2
E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
0
Expert code Fig8. The results of selected ANFIS method and the actual data for Cri6
As presented in Fig 9, the results show the low level of productivity in upstream oil industry. As mentioned before, upstream oil industry consists of three different sectors including 34
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exploration, development and production. To discover the poor sector among the three ones of upstream oil industry the respondents of each sector are separated and repeated the computation of steps 13 to 22 for each one. The number of respondents in exploration, development and
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production sectors are 16, 19 and 53 respectively. The results show that the production sector has the least level of productivity among the three sectors of upstream oil industry which confirms the statement of the study of Azadi and Yarmohammad (2011). Fig10 to Fig12 show the level of
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productivity in three different sectors.
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0.8 0.6 0.4 0.2 0
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E1 E5 E9 E13 E17 E21 E25 E29 E33 E37 E41 E45 E49 E53 E57 E61 E65 E69 E73 E77 E81 E85
Total score of productivirty
1
Expert code
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Fig9. Level of productivity in upstream oil industry
0.8 0.6
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Fig10. Level of productivity in exploration sector of upstream oil industry
35
EX16
EX15
EX14
EX13
EX12
EX11
EX10
EX9
EX8
EX7
EX6
EX5
EX4
EX3
EX1
0
EX2
Total score of productivirty
1
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0.8 0.6
Expert code
ED19
ED18
ED17
ED16
ED14
ED13
ED12
ED11
ED10
ED9
ED8
ED7
ED6
ED5
ED4
ED3
ED2
0
ED15
0.2
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0.4
ED1
Total score of productivirty
1
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Fig11. Level of productivity in development sector of upstream oil industry
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0.8 0.6 0.4 0.2 0
EP1 EP3 EP5 EP7 EP9 EP11 EP13 EP15 EP17 EP19 EP21 EP23 EP25 EP27 EP29 EP31 EP33 EP35 EP37 EP39 EP41 EP43 EP45 EP47 EP49 EP51 EP53
Total score of productivirty
1
Expert code
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Fig12. Level of productivity in production sector of upstream oil industry
4.5.1 Validation the productivity results
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As mention in steps 23 and 24 of the proposed algorithm, four types of DEA methods are used which are CCR input-oriented, CCR output-oriented, BCC input-oriented and BCC output
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oriented. In addition, SDEA method in different level of U is applied. The results show that the rank of SDEA model at U = 0.6 is highly correlated with the rank of productivity scores which
the value of Spearmen correlation equals to 0.628. Table 13 (Appendix D) shows the obtained rank of each method and the calculated correlation with the rank of productivity scores. As seen in Table 13, the average correlation between obtained productivity scores through proposed
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algorithm and different DEA, SDEA models are about 0.6 (0.57). So, results are validated and acceptable.
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4.6. Determining the weight of each group factor The importance of each group of influential factors with regards to the level of productivity is determined in this section. To this purpose, the weight of each group of factors is calculated
Table14 and Fig13.
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Table 14.The weight of groups of Influential factors
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according to the explanations in step 25 of the proposed algorithm. The results are presented in
Number
Removed Group Factor
1 2 3 4 5 6 7 8 9
Human
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Management Factors Project HSE Material and Equipment Regulatory and Contractual Economic Political and Governmental factors
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Natural Factors
37
Average Level
Difference with main model
Percent of Weight
0.133 0.141 0.062 0.171 0.112 0.120 0.170 0.130 0.093
0.118 0.124 0.055 0.151 0.098 0.106 0.150 0.115 0.082
11.76 12.44 5.50 15.05 9.85 10.62 15.03 11.50 8.25
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Natural Factors, 8.25 Political and Governmental factors, 11.50
Management Factors, 12.44
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Human , 11.76
Project , 5.50
Economic, 15.03
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HSE, 15.05
Regulatory and Contractual, 10.62
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Material and Equipment, 9.85
Fig13. The weight of groups of Influential factors
The results show that HSE group is identified as the most priority group in this area with 15.09 percent weights which is expected, because of the high risk environment of these kinds of
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projects. As a result, providing the safety and health of workers in this risky environment will help improve productivity of oil upstream projects. Then, economic group is the most important group with 15.03 percent weights which consists of Inflation and economic downturn, stability
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of exchange rate and oil price factors. It should be noted that these factors are among the external factors and control and planning of them are affected by different conditions of world’s
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economy. Management group is located in the third degree of importance. Indeed, the successes and failures of programs of improving productivity depend on attitudes, strategies, policies and proceedings of management. After management group, human factors with regard to level of productivity are the most important factors. After that political factors with a slight difference by the human factor weight is placed in fifth degree of importance. The next three important factors are regulatory and contractual, material and equipment and natural factors respectively. The project group factor is the least influential factors on oil project productivity among the nine 38
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groups by 5.5 percent weight.The results of this section, will help planners and decision makers focus on more important factors in the way of improving productivity of upstream oil projects.
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5. Conclusion
Improving and optimizing productivity of upstream oil projects are challenging task. This research presented an intelligent algorithm for identification and optimization of influential
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factors for upstream oil projects. An actual case study in Iran was selected to experiment the proposed algorithm. The algorithm includes 25 steps. First, a questionnaire was designed, Consequently, the reliability and
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distributed amongst the experts of oil upstream projects.
validity of the questionnaire must be attained. Second, DEA was used to evaluate the weight of each input sub-factor. Then, data was prepared to be used in the next phase. Three intelligent methods, namely, MLP, RBF and ANFIS, and conventional and fuzzy regressions were used to
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evaluate relations between inputs (influential factors) and outputs (productivity criteria). Based on minimum value of MAE, the best ANFIS was selected to calculate productivity of upstream oil projects. The results (productivity scores) were validated by DEA and SDEA. The weight of
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each factor was calculated with regard to level of productivity of each criterion. The results showed that the upstream oil projects are in low level of productivity. The arrangement of the
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nine group in terms of importance was determined as follows: HSE, economic, management, human, political, regulatory and contractual, material and equipment, natural and project. The results of this research can help decision makers improve productivity of upstream projects by considering the identified factors. It will also identify weak and strength of such projects with respect to productivity. Table 15 compares the features of this research with previous studies.
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√
√
LIANG et al (2013)
√
√
√
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√
M AN U
√ √
*√
√ √ √ √
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. Hui et al (2013)
√
√
Enshassi et al. (2007) Dai et al. (2009) Barati and Sepasgozar (2011) Soekiman et al. (2011) Alinaitwe et al. (2012) Ghoddousi and Hosseini (2012) Nasirzadeh and Nojedehi (2013)
fuzzy regression
intelligent method
√
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Influential factors of Oil upstream projects
√
regression
Measuring the productivity of projects Qualitatively
This study Haghighatian and Ezati (2015) Heravi and Eslamdoost (2015)
Considering the influential factors comprehensively
Studies
Evaluate the relation between factors and productivity
Measuring the weight by considering the level of productivity
Table15: Features of this study versus previous ones
√ √
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*construction projects of oil industry
Appendix A: Results of phase one
1 2 3 4 5 6
Group name
human
Num
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Table 4. Reliability and validity analysis Validity analysis Factors definition
1. Experienced Workforce 2. Job satisfaction in the work force 3.Education level of the workforce 4. Motivating Employees 5. Job security 6. friendly atmosphere between staff
40
Reliability
F
P-value
Cronbach’ s Alpha
1.91 2.9 2.4 0.08 0.59 0.23
0.175 0.097 0.13 0.778 0.448 0.633
0.801
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Table 4. Reliability and validity analysis
28 29 30
2.91 1.04 2.85 1.4 0.62 2.21 2.5 0.42 3.08 1.87 1.03 0.51 0.45 2.7 1.75
0.097 0.315 0.101 0.245 0.437 0.146 0.123 0.52 0.088 0.18 0.317 0.48 0.506 0.109 0.195
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M AN U
0.445
0.75
0.393
1.62
0.211
5. Waste Management 1. Lack of materials and equipment 2. lack of access to spare parts
1.09 3.32 0.85
0.303 0.077 0.365
1.7
0.202
3.86
0.057
1.52
0.226
0.31
0.582
0.75 2.08
0.391 0.16
2.51
0.122
2.1
0.157
3. obstacles in the way of transfer of technology related to the oil industry 4. The distance between the storage location and the site 5. Delay in reaching Equipment 1. Conventional strategies that are used in upstream oil projects 2. Conventional methods in projects upstream 3. Contractual claims of the contract correspondents
regulatory and contractual
35
4. The rules on foreign investment 1. Inflation and economic downturn
41
Reliability Cronbach’ s Alpha
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P-value
0.6
34
32 33
F
2. Environmental factors (inadequate lighting, poor ventilation, noise, etc.) 3. Occupational safety inspector at the project site 4. Safety analysis activities, identify risks and anticipated regulatory actions
no mi cal fac tor
31
8. Risk assessment in each phase of the project 9. Failure to follow the schedule 10. Empower project managers 11. Disputes between the actors of the project 1. the size of the project 2. complexity of oil projects 3. Difficult access to the place of oil projects 1. Cleanliness and amenities on the camp and site
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25 26 27
material and equipment
24
1. Labor participation in the decision-making process 2. overtime hours 3. avoid any discrimination 4. Salary payment system 5. Weakness and delay in the inspection program 6. Properly monitor labor practices 7. Workforce training
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23
HSE
22
Factors definition
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management
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Group name
project
Num
Validity analysis
0.717
0.705
0.908
0.72
0.871
0.817
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Table 4. Reliability and validity analysis Group name
Factors definition F
37
3. oil price
38 39 40
1. Iran's relations with targeted countries 2. Internal political changes 3. Boycott 4. Create a competitive atmosphere and ample opportunities for active private contractors
0.111
2.79
0.103
1.6 1.02 0.45
0.213 0.319 0.506
2.78
0.104
1.58
0.216
0.34
0.562
2.59
0.116
2. Natural factors related to the oil field
1.06
0.311
46
1. Economizing the resources
0.75
0.394
47
2. The difference between the use of resources from the amount of planned resources
2.47
0.125
3. The difference between the actual project duration and the scheduled project duration
1.61
0.213
4. The difference between the actual progress of the project with advances predicted for it
0.1
0.75
1.65 0.22
0.208 0.642
48 49
5. The correct definition of project activities 6. The proper conduct of project activities Total Cronbach’s Alpha (consists of total questions) = 0.919
Cronbach’ s Alpha
0.762
0.915
0.871
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50 51 52
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45
Productivity Criteria Measurement
44
Natural
43
5. Slowness in issuing permits and approvals 6. providing loans for contractors to develop infrastructure 1. Weather conditions in oil areas
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42
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41
2.67
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2. stability of exchange rate
Political and governmental
36
P-value
Reliability
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Num
Validity analysis
Appendix B. Results of phase two Table 5. Evaluation influential factors Num Factors 1 Experienced Workforce 2 Job satisfaction in the work force 3 Education level of the workforce
Mean 17.41 17.67 14.14
42
STD 2.53 2.20 2.93
T-value 27.54 32.75 13.25
P-value 1 1 1
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T-value 46.73 18.76 35.50 23.11 3.91 44.71 35.90 18.49 20.19 17.55 18.40 21.58 15.22 18.15 4.67 6.56 0.63 14.20
P-value 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.735 1
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STD 1.65 3.04 1.88 2.35 4.62 1.67 2.09 2.86 2.67 2.80 2.88 2.89 3.53 2.98 4.68 4.52 4.57 3.43
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Mean 18.21 16.09 17.11 15.78 11.93 17.95 17.99 15.63 15.75 15.23 15.65 16.65 15.73 15.77 12.33 13.16 10.31 15.19
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Table 5. Evaluation influential factors Num Factors 4 Motivating Employees 5 Job security 6 Friendly atmosphere between staff 7 Labor participation in the decision-making process 8 Overtime hours 9 Avoid any discrimination 10 Salary and benefits 11 Weakness and delay in the inspection program 12 Properly monitor labor practices 13 Workforce training 14 Risk assessment in each phase of the project 15 Failure to follow the schedule 16 Empower project managers 17 Disputes between the actors of the project 18 Size of oil project 19 Complexity of oil projects 20 Difficult access to the place of oil projects 21 Cleanliness and amenities on the camp and site Environmental factors (inadequate lighting, poor ventilation, noise, etc.)
14.43
3.68
11.27
1
23
Occupational safety inspector at the project site
13.88
3.56
10.21
1
24
Safety analysis activities, identify risks and anticipated regulatory actions
14.70
3.09
14.29
1
25 26 27
Waste Management Lack of materials and equipment lack of access to spare parts
12.84 17.49 16.45
4.12 2.23 3.12
6.46 31.49 19.40
1 1 1
28
Obstacles in the way of transfer of technology related to the oil industry
14.98
3.16
14.81
1
The distance between the storage location and the site
11.40
4.34
3.02
0.998
Delay in reaching Equipment
15.72
3.25
16.49
1
Conventional strategies that are used in upstream oil projects
14.16
3.28
11.90
1
Conventional methods in projects upstream Contractual claims of the contract correspondents The rules on foreign investment Inflation and economic downturn Stability of exchange rate
14.29 12.97 15.36 14.90 15.89
3.23 3.42 3.49 4.22 4.08
12.44 8.13 14.40 10.89 13.53
1 1 1 1 1
30 31 32 33 34 35 36
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Mean 13.60 15.56 15.21 16.73
Create a competitive atmosphere and ample opportunities for active private contractors
15.82
42
Slowness in issuing permits and approvals
16.67
43
Providing loans for contractors to develop infrastructure
14.18
44 45
Weather conditions in oil areas Natural factors related to the oil field
11.82 12.14
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Table 6. The primary weight of the factors
T-value 7.55 16.96 16.22 19.01
P-value 1 1 1 1
2.83
19.27
1
2.51
24.91
1
2.78
14.11
1
4.47 5.73
1 1
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41
STD 4.47 3.07 3.01 3.32
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Table 5. Evaluation influential factors Num Factors 37 Oil price 38 Iran's relations with targeted countries 39 Internal political changes 40 Boycott
3.83 3.50
Average efficiency
Factors
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Experienced Workforce Job satisfaction in the work force Education level of the workforce Motivating Employees Job security Friendly atmosphere between staff Labor participation in the decision-making process Overtime hours Avoid any discrimination Salary and benefits Weakness and delay in the inspection program Properly monitor labor practices Workforce training Risk assessment in each phase of the project Failure to follow the schedule Empower project managers Disputes between the actors of the project Size of oil project Complexity of oil projects
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EP
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Number
44
1.0807 1.0803 1.0824 1.0825 1.0820 1.0822 1.0810 1.0817 1.0803 1.0810 1.0816 1.0822 1.0796 1.0824 1.0822 1.0812 1.0817 1.0822 1.0825
Difference with main model 0.0018 0.0022 0.0001 0.0000 0.0004 0.0003 0.0015 0.0008 0.0022 0.0014 0.0009 0.0003 0.0029 0.0001 0.0003 0.0012 0.0008 0.0002 0.0000
Weight 0.0309 0.0365 0.0017 0.0002 0.0076 0.0056 0.0259 0.0130 0.0377 0.0245 0.0151 0.0052 0.0496 0.0019 0.0056 0.0212 0.0133 0.0040 0.0002
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Table 6. The primary weight of the factors
22 23 24 25 26 27
Difficult access to the place of oil projects Cleanliness and amenities on the camp and site Environmental factors (inadequate lighting, poor ventilation, noise, etc.) Occupational safety inspector at the project site Safety analysis activities, identify risks and anticipated regulatory actions Waste Management Lack of materials and equipment lack of access to spare parts
1.0808 1.0823
Difference with main model 0.0017 0.0002
Weight 0.0287 0.0032
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20 21
Average efficiency
Factors
1.0816
0.0009
0.0147
1.0824
0.0001
0.0012
1.0820
0.0005
0.0087
1.0824 1.0811 1.0825
0.0001 0.0014 0.0000
0.0011 0.0233 0.0002
1.0822
0.0003
0.0044
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Number
Obstacles in the way of transfer of technology related to the oil industry
29
The distance between the storage location and the site
1.0821
0.0004
0.0072
30
Delay in reaching Equipment
1.0822
0.0003
0.0051
31
Conventional strategies that are used in upstream oil projects
1.0815
0.0010
0.0163
32 33 34 35 36 37 38 39 40
Conventional methods in projects upstream Contractual claims of the contract correspondents The rules on foreign investment Inflation and economic downturn Stability of exchange rate Oil price Iran's relations with targeted countries Internal political changes Boycott
1.0816 1.0821 1.0825 1.0801 1.0819 1.0820 1.0816 1.0820 1.0819
0.0009 0.0004 0.0000 0.0024 0.0005 0.0005 0.0009 0.0005 0.0006
0.0146 0.0067 0.0005 0.0406 0.0090 0.0083 0.0156 0.0083 0.0103
41
Create a competitive atmosphere and ample opportunities for active private contractors
1.0823
0.0002
0.0031
Slowness in issuing permits and approvals
1.0811
0.0014
0.0240
Providing loans for contractors to develop infrastructure
1.0823
0.0001
0.0023
Weather conditions in oil areas Natural factors related to the oil field Economizing the resources
1.0821 1.0819 1.0760
0.0004 0.0006 0.0065
0.0060 0.0100 0.1102
The difference between the use of resources from the amount of planned resources
1.0759
0.0065
0.1108
43 44 45 46 47
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42
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28
45
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Table 6. The primary weight of the factors Number
Factors
Average efficiency
Difference with main model
Weight
48
The difference between the actual project duration and the scheduled project duration
1.0801
0.0024
0.0399
Appendix C. Results of phase four
RI PT
50 51
The difference between the actual progress of the project with advances predicted for it The correct definition of project activities The proper conduct of project activities
1.0798
0.0027
0.0462
1.0789 1.0790
0.0036 0.0035
0.0608 0.0592
SC
49
Table 7. Different architectures of MLP neural networks and the relevant error
LM LM LM LM LM LM LM GD GD SCG SCG SCG SCG SCG SCG OSS OSS OSS BFG BFG BFG GDX GDX
log log tan log log tan log log log log log tan tan log tan log tan log log log tan tan tan
NFN in HL2
TF of output later
25 15 10 10 10 20 20 15 10 8 20 35 10 40 40 18 40 22 30 10 10 10 30
log log tan log log tan tan tan -
10 30 22 15 20 20 10 16 -
purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin purelin
M AN U
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
TF of HL2
TE D
TF
NFN in HL1
AC C
EP
Number
TF of HL1
46
MAE
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
0.8478 0.8467 0.8150 0.7806 0.7640 0.6424 0.6948 0.8631 0.7847 0.7502 0.6854
0.6007 0.5898 0.7019 0.5625 0.4841 0.6339 0.5580 0.8188 0.5527 0.5328 0.6388 0.6192 0.5711 0.6210 0.6427 0.5720 0.5647 0.6849 0.5129 0.5355 0.5493 0.5707 0.5468
0.9888 1.0857 1.0299 1.2263 1.0540 1.1496 0.9944 1.2623 1.7324 0.9304 1.0905 0.9700 1.0296 0.8269 0.7602 0.9675 1.0703 1.0208 0.8404 1.2786 0.7074 1.0193 1.2854
0.8762 0.8604 1.3066 0.8253 0.6786 0.6493 0.8983 0.9894 0.7699 0.7122 0.8889 0.7737 0.6600 0.8097 0.7523 0.8853 0.7045 0.7270 0.9174 1.0310 0.7268 1.1087 1.1938
0.3303 0.5924 0.4934 0.4214 0.4581 0.5245 0.5003 0.6834 0.6319 0.4659 0.6357 0.5219 0.5205 0.4623
0.8123 0.9363 0.7414 0.8097 0.7653 0.7264 0.7712 1.1202 1.2616 0.6671 0.9488 0.9518 0.7687 0.9662 0.6656 0.9655 0.8312 0.7157 1.1914 0.9475 0.8662 0.9911 0.7622
0.5371 0.7955 0.6723 0.7974 0.6823 0.7908 0.7214 0.6619 0.7330 0.8273 0.7719 0.7918
0.3220 0.5629 0.5253 0.4833 0.5449 0.4284 0.5245 0.6497 0.5833
ACCEPTED MANUSCRIPT
Table 7. Different architectures of MLP neural networks and the relevant error Number
TF
TF of HL1
NFN in HL1
TF of HL2
NFN in HL2
TF of output later
MAE
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
EP
TE D
M AN U
SC
RI PT
GDX purelin 0.8531 0.6858 1.3605 0.8580 0.4397 0.9069 log 24 40 GDA purelin 0.6895 0.5268 0.9957 0.7646 0.5005 0.8288 log 25 10 GDA purelin 0.8291 0.6104 1.1964 0.9475 0.5934 0.8114 tan 26 30 GDM purelin 0.9868 0.7645 1.5855 1.3758 0.7113 1.1890 log 27 40 GDM purelin 0.8765 0.6625 0.9305 0.9103 0.8160 1.0803 log 28 10 GDX purelin 0.9095 0.6840 1.0319 1.0846 0.5449 1.2650 log 29 30 GDX purelin 0.7986 0.5814 1.1828 0.7058 0.6020 0.9110 30 tan 15 log 10 CGB purelin 0.7468 0.4640 0.9676 0.9448 0.3966 0.8059 tan 31 15 CGB purelin 0.7517 0.5429 1.4210 1.1016 0.5280 0.9547 log 32 12 CGB purelin 0.6785 0.5316 0.7570 0.6882 0.4146 0.4987 tan 33 22 log 15 CGF purelin 0.7705 0.4261 0.8104 0.9577 0.5080 1.0042 log 34 18 log 20 CGF purelin 0.6256 0.5323 0.8745 0.8877 0.4489 0.6128 log 35 20 CGF purelin 0.8198 0.5613 1.4231 1.0132 0.4705 0.6420 tan 36 25 RP purelin 0.6659 0.6502 0.7934 0.6293 0.4831 0.7158 log 37 25 tan 20 RP purelin 0.7058 0.5678 0.8192 0.8976 0.3759 0.7053 log 38 7 RP purelin 0.6739 0.6198 0.9653 1.1322 0.5018 0.7999 tan 39 10 RP purelin 0.6980 0.5155 0.8696 0.8598 0.4853 0.8147 log 40 40 Note: TF: transfer function; HL: hidden layer; NFN: number of neurons; CR: criterion; log: Log-sigmoid; tan: Hyperbolic tangent sigmoid; LM: Levenberg-Marquardt backpropagation; SCG: Scaled conjugate gradient backpropagation; OSS: One step secant backpropagation; BFG: BFGS quasi-Newton backpropagation; GD: Gradient descent backpropagation; GDX: Gradient descent with momentum and adaptive learning rule backpropagation; GDA: Gradient descent with adaptive learning rule backpropagation; GDM: Gradient descent with momentum backpropagation; CGB: Powell-Beale conjugate gradient backpropagation; CGF: Fletcher-Powell conjugate gradient backpropagation; RP: Resilient backpropagation.
AC C
Table 8. Different architectures of RBF neural networks Number
1 2 3 4 5 6
Radius Spread 0.5 0.5 0.6 0.6 0.7 0.8
MAE
Max number of neurons 25 10 10 20 40 10
Cri1 0.3855 0.3633 0.3383 0.3441 0.5373 0.3386
47
Cri2 0.2471 0.2538 0.2803 0.2433 0.2863 0.2785
Cri3 0.3769 0.4684 0.4733 0.4786 0.3961 0.5182
Cri4 0.3758 0.4283 0.4177 0.3803 0.3707 0.4269
Cri5 0.2958 0.1930 0.2314 0.2799 0.2945 0.1620
Cri6 0.2567 0.3089 0.2562 0.2857 0.3617 0.2517
ACCEPTED MANUSCRIPT
Table 8. Different architectures of RBF neural networks
48
Cri4 0.3798 0.4338 0.4281 0.3659 0.4398 0.4114 0.3665 0.4256 0.4400 0.4011 0.4271 0.4033 0.4707 0.4120 0.4348 0.4286 0.4185 0.3506 0.3993 0.4100 0.3752 0.3839 0.4155 0.3771 0.3993 0.4184 0.4155 0.3992 0.3829 0.3481 0.4128 0.3992 0.4208 0.4514
Cri5 0.2234 0.1619 0.1517 0.1401 0.2860 0.3277 0.2428 0.1523 0.1476 0.2239 0.1597 0.1839 0.4223 0.1570 0.1876 0.2313 0.1711 0.2600 0.1594 0.1746 0.1731 0.2620 0.1866 0.3209 0.1506 0.1728 0.1796 0.1852 0.3221 0.3853 0.2199 0.1514 0.1748 0.2031
Cri6 0.2182 0.2699 0.3195 0.2329 0.3971 0.4194 0.2137 0.2141 0.2303 0.3069 0.2614 0.1983 0.3594 0.2109 0.2478 0.2835 0.2185 0.3008 0.2093 0.2422 0.2002 0.2906 0.2172 0.3117 0.2085 0.2391 0.2176 0.2201 0.2914 0.3934 0.2227 0.1894 0.2283 0.2592
RI PT
Cri3 0.4880 0.5207 0.4626 0.5154 0.4014 0.4507 0.4571 0.5202 0.5053 0.4092 0.4758 0.5357 0.5384 0.5419 0.5507 0.5332 0.5317 0.4215 0.5432 0.5419 0.5375 0.4914 0.5358 0.4097 0.5439 0.5487 0.5383 0.5378 0.5755 0.4979 0.4144 0.5445 0.5355 0.5506
SC
Cri2 0.2689 0.2621 0.2333 0.2623 0.2454 0.3593 0.2432 0.2068 0.2533 0.2604 0.2428 0.2384 0.3321 0.2216 0.2408 0.3749 0.2443 0.2200 0.2097 0.2372 0.3309 0.3170 0.2360 0.2764 0.2103 0.2489 0.2254 0.2202 0.2001 0.2813 0.3267 0.2109 0.2584 0.2853
M AN U
15 10 20 10 40 35 18 5 10 20 15 7 40 5 10 15 8 20 5 10 15 20 7 25 5 10 7 6 20 30 18 5 10 12
Cri1 0.2714 0.3092 0.3226 0.3539 0.6001 0.6160 0.2949 0.3185 0.3376 0.4194 0.3040 0.3370 0.5893 0.3113 0.3225 0.3599 0.3646 0.5095 0.3118 0.3250 0.3734 0.5194 0.3425 0.4197 0.3121 0.3579 0.3420 0.3639 0.3965 0.4769 0.3297 0.3123 0.3626 0.3207
TE D
0.8 0.9 0.9 1 1 1.2 1.3 1.5 1.5 1.5 1.8 1.9 1.9 2 2 2 2.1 2.2 2.5 2.5 2.5 2.6 2.7 2.9 3 3 3.2 3.5 3.5 3.6 3.8 4 4 4
MAE
Max number of neurons
AC C
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 CR: criterion
Radius Spread
EP
Number
ACCEPTED MANUSCRIPT
Table 9. Different architectures of ANFIS Number
Initial FIS
NMF
R
GP
2
3
SC
0.1
4
SC
5
imp
MAE
agg Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
max
0.2809
0.2061
0.4473
0.4206
0.2493
0.3162
BP
min
max
prod
BP
prod
max
prod
sum
0.3077
0.2582
0.4996
0.4503
0.2254
0.2944
BP
min
max
prod
max
0.2503
0.2646
0.3301
0.2458
0.1675
0.2365
0.1
BP
prod
max
prod
sum
0.2536
0.1566
0.2453
0.2565
0.1709
0.2177
SC
0.1
H
prod
probor
prod
max
0.2623
0.3050
0.2492
0.2709
0.1579
0.2205
6
SC
0.1
H
min
max
prod
probor
0.5178
0.4152
0.2648
0.2510
0.1530
0.2856
7
SC
0.2
BP
min
max
prod
probor
0.2119
0.2259
0.3305
0.2804
0.1438
0.2413
8
SC
0.2
BP
prod
probor
prod
probor
0.2430
0.1662
0.2449
0.2542
0.1680
0.2145
9
SC
0.2
H
min
probor
prod
probor
0.2967
0.1795
0.3141
0.2639
0.1186
0.2942
10
SC
0.2
H
prod
probor
prod
max
0.2572
0.1933
0.2627
0.2409
0.1463
0.1884
11
SC
0.3
BP
prod
max
prod
sum
0.2052
0.1767
0.1980
0.2670
0.1481
0.2069
12
SC
0.3
BP
min
max
min
sum
0.1954
0.1933
0.3418
0.3209
0.1341
0.2227
13
SC
0.3
H
prod
max
prod
max
0.2757
0.1720
0.2698
0.2416
0.1312
0.2205
14
SC
0.3
H
min
probor
prod
probor
0.1939
0.2352
0.3212
0.2324
0.1940
0.5308
15
SC
0.4
BP
min
probor
min
probor
0.1850
0.2073
0.3352
0.3108
0.1106
0.2312
16
SC
0.4
BP
prod
probor
prob
probor
0.1818
0.1529
0.2568
0.2632
0.1249
0.2191
17
SC
0.4
H
min
max
prod
sum
0.1984
0.2364
0.3739
0.2477
0.2348
0.3367
18
SC
0.4
H
prod
max
prod
max
0.2752
0.1843
0.2894
0.2455
0.1233
0.1758
19
SC
0.45
BP
min
max
prod
sum
0.2004
0.2127
0.3135
0.2638
0.1425
0.1748
20
SC
0.45
BP
prod
probor
min
max
0.1912
0.1981
0.2425
0.2562
0.1699
0.2601
21
SC
0.45
H
min
max
prod
sum
0.5071
0.2127
0.3590
0.2355
0.2858
0.3169
22
SC
0.45
H
prod
probor
min
sum
0.2311
0.1651
0.2693
0.2297
0.1488
0.2271
23
SC
0.5
H
prod
probor
min
sum
0.2174
0.1456
0.2862
0.2859
0.1192
0.1764
24
SC
0.5
25
SC
26
SC
27
SC
28 29
RI PT
2
Or
SC
2
and
M AN U
GP
Opt method
TE D
1
NCl
min
max
prod
max
0.3682
0.3003
0.3570
0.2303
0.1869
0.3481
BP
min
max
prod
sum
0.1885
0.2615
0.3354
0.2444
0.1543
0.1917
0.5
BP
prod
max
min
probor
0.1867
0.1702
0.2644
0.3029
0.3452
0.2796
0.52
BP
prod
max
min
sum
0.2141
0.2031
0.3308
0.3338
0.2778
0.2919
SC
0.6
BP
prod
max
prod
sum
0.1782
1.5721
0.3834
0.4791
0.3846
0.7129
SC
0.6
BP
min
max
min
probor
0.2258
0.8502
0.4028
0.3929
0.2553
0.3090
30
SC
0.6
H
min
probor
prod
max
0.4674
0.1974
0.3705
0.2559
0.2232
0.4913
31
SC
0.6
H
prod
probor
min
sum
0.2240
0.2366
0.3535
0.1938
0.1380
0.3445
32
SC
0.8
BP
min
probor
min
sum
1.4191
0.3560
1.3055
0.3655
0.2023
0.5934
33
SC
0.8
BP
prod
probor
prod
max
1.7699
0.2964
0.6221
0.3456
0.2641
0.4774
34
SC
0.8
H
min
probor
prod
sum
1.1877
0.3598
1.1641
0.3363
0.2401
0.4932
35
SC
0.8
H
prod
probor
min
max
0.3107
0.3104
0.8968
0.3065
0.5475
0.4323
36
SC
0.7
H
prod
max
prod
max
0.2860
1.0336
0.3202
0.5583
0.2837
0.2916
37
SC
0.7
H
min
max
prod
sum
0.5350
0.8268
0.6349
0.4604
1.0090
0.5859
AC C
EP
H
0.5
49
ACCEPTED MANUSCRIPT
Table 9. Different architectures of ANFIS Number
Initial FIS
NMF
R
NCl
Opt method
and
Or
Cri1
Cri2
Cri3
Cri4
Cri5
Cri6
probor
0.4048
0.2863
0.4781
0.3790
0.2527
0.2406
FCM
5
BP
min
39
FCM
5
BP
prod
max
prod
max
0.4242
0.3131
0.4330
0.3898
0.1712
0.2269
40
FCM
5
BP
prod
probor
prod
probor
0.4289
0.3050
0.4193
0.3980
0.1938
0.2542
41
FCM
5
H
min
max
prod
probor
1.3157
0.9790
0.8944
0.8046
1.0365
0.7714
42
FCM
10
BP
min
max
min
sum
0.3438
0.3354
0.4489
0.3635
0.1929
0.2590
43
FCM
8
BP
prod
max
min
sum
0.3624
0.3018
0.4383
0.4111
0.1688
0.2436
44
FCM
14
BP
min
max
min
max
0.3686
0.3020
0.4495
0.3695
0.1930
0.2556
45
FCM
14
BP
prod
max
prod
max
0.3823
0.2951
0.4065
0.4105
0.1895
0.2185
46
FCM
16
BP
min
probor
prod
sum
0.3940
0.2688
0.4362
0.3945
0.2017
0.2378
47
FCM
16
BP
prod
probor
prod
sum
0.3974
0.2913
0.4119
0.4084
0.1975
0.2423
48
FCM
16
H
min
max
prod
max
2.6855
0.7592
2.4405
0.7939
0.9386
0.5472
49
FCM
16
H
prod
max
prod
max
0.3379
0.2982
0.4059
0.3765
0.2093
0.2896
50
FCM
18
BP
min
max
prod
probor
0.3961
0.3035
0.4486
0.3925
0.3925
0.2328
51
FCM
18
BP
prod
max
prod
probor
0.3920
0.2849
0.4083
0.4061
0.1818
0.2261
52
FCM
3
BP
prod
max
prod
sum
0.3355
0.3331
0.4444
0.3861
0.1951
0.3152
53
FCM
3
BP
min
probor
prod
probor
0.3994
0.3081
0.4264
0.4035
0.1456
0.2591
54
FCM
6
BP
min
probor
min
max
0.3193
0.3056
0.4565
0.3584
0.1921
0.2583
55
FCM
9
BP
prod
probor
min
max
0.3629
0.3189
0.4462
0.4088
0.1894
0.2387
56
FCM
24
BP
prod
max
prod
max
0.3856
0.2849
0.4243
0.4244
0.2079
0.2382
57
FCM
20
BP
min
max
min
sum
0.3912
0.3135
0.4659
0.3746
0.1814
0.2536
58
FCM
20
H
min
probor
prod
probor
2.3337
0.8155
2.2844
0.6657
0.7010
0.4965
59
FCM
27
BP
min
max
min
sum
0.3661
0.2839
0.5010
0.4111
0.1850
0.2359
60
FCM
27
H
prod
max
prod
max
0.3489
0.2917
0.4001
0.3407
0.2194
0.2716
61
FCM
32
BP
prod
probor
prod
probor
0.3894
0.2710
0.4242
0.4077
0.2220
0.2376
62
FCM
30
BP
min
probor
prod
probor
0.3879
0.3193
0.4826
0.4424
0.2022
0.2403
63
FCM
35
BP
min
probor
prod
probor
0.3533
0.2942
0.4777
0.4274
0.2145
0.2284
64
FCM
30
H
prod
probor
prod
probor
0.3479
0.2657
0.4090
0.3267
0.2323
0.2670
TE D
EP
SC
RI PT
38
AC C
prod
MAE
agg
M AN U
max
imp
Note. FIS: Fuzzy inference system; NMF: Number of member functions, R:Radius; NCl: Number of clusters; imp:Implication method; Agg.: Aggregation method;Fuzzy c-means (FCM) clustering; GP: Grid partition; SC: Subtractive clustering; H: Hybrid; BP: Backpropagation;
Appendix D.
Table 13. The results of model validation
65
0.05 85
0.1 86
0.2 87
0.3 87
SDEA model k 0.4 0.5 0.6 87 87 87
53
53
14
14
15
43
41
40
40
40
38
37
36
35
40
82
50
50
34
34
22
30
32
33
36
37
37
38
41
45
65
87
51
51
32
32
Expert code
RPS
E1 E2 E3
50
0.7 87
0.8 85
0.9 81
0.95 86
DEA-CCR
DEA-BCC
In
OUT
In
OUT
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
74
0.05 75
0.1 75
0.2 75
0.3 75
SDEA model k 0.4 0.5 0.6 76 76 76
E5
47
50
50
50
49
50
50
50
52
58
E6
63
77
77
78
78
78
78
79
80
80
E7
66
67
68
70
69
69
70
70
72
72
E8
30
1
1
1
1
1
1
15
16
17
E9
26
15
16
15
16
15
2
1
1
1
E10
71
44
45
45
46
46
48
48
48
51
E11
34
17
17
16
17
16
3
14
14
14
E12
24
24
24
24
24
24
4
23
23
E13
5
18
18
18
18
17
5
2
2
E14
18
35
37
37
39
39
40
41
40
E15
12
26
26
26
26
26
0.8 78
0.9 87
0.95 85
68
74
6
18
22
DEA-CCR
DEA-BCC
In
OUT
In
OUT
68
68
53
53
49
49
57
57
RI PT
E4
0.7 78
86
69
79
79
69
69
80
71
70
70
79
79
18
36
15
15
18
18
1
26
13
13
10
10
56
40
47
47
61
61
15
31
31
31
47
47
23
25
35
17
17
19
19
2
2
1
3
3
1
1
37
37
10
37
37
58
58
21
4
32
32
44
44
SC
RPS
M AN U
Expert code
22
6
16
2
17
2
18
7
3
3
3
3
8
4
4
6
6
E17
57
88
88
88
88
88
88
88
88
88
84
88
63
63
15
15
E18
17
12
13
12
13
12
30
21
18
19
19
12
16
16
21
21
E19
87
61
61
61
61
61
60
57
53
47
43
38
48
48
39
39
E20
79
83
82
81
81
81
81
80
79
74
66
47
75
75
59
59
E21
51
69
69
63
63
63
63
62
57
53
51
83
87
87
87
87
E22
42
81
81
82
82
82
82
82
82
82
69
44
83
83
78
78
E23
56
82
83
83
83
83
83
83
85
87
78
57
81
81
86
86
E24
67
2
3
2
3
2
9
16
15
15
16
49
12
12
16
16
E25
11
31
30
30
29
29
10
25
25
25
28
50
18
18
20
20
E26
86
70
70
64
64
64
64
63
61
54
52
46
65
65
73
73
E27
70
37
36
36
35
33
11
26
26
27
29
51
25
25
33
33
E28
55
3
4
3
4
3
12
29
29
29
31
84
14
14
13
13
E29
19
4
5
4
5
4
13
4
4
4
4
81
5
5
7
7
E30
50
41
39
39
37
E31
52
E32
EP
TE D
E16
35
32
30
30
33
73
35
35
51
51
6
5
6
5
14
28
28
28
30
76
20
20
24
24
21
28
28
28
27
27
15
27
27
26
23
13
23
23
25
25
E33
77
78
79
79
79
80
80
81
81
86
82
64
84
84
83
83
E34
4
40
42
43
44
44
45
47
50
55
57
41
45
45
65
65
E35
9
20
20
20
20
20
17
34
35
39
53
52
26
26
35
35
E36
80
74
74
74
74
75
75
75
75
76
79
56
86
86
75
75
E37
14
6
7
6
7
6
19
19
19
18
14
53
19
19
8
8
E38
29
42
43
42
42
42
42
43
45
49
55
42
40
40
42
42
E39
25
47
47
46
45
45
44
44
44
46
47
37
42
42
46
46
E40
27
7
8
7
8
7
20
5
5
5
5
2
11
11
17
17
AC C
36
5
51
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
46
0.05 32
0.1 31
0.2 31
0.3 30
SDEA model k 0.4 0.5 0.6 30 21 17
E42
23
45
44
44
43
41
41
39
34
33
E43
76
36
35
34
33
32
22
13
13
13
RPS
E41
0.7 17
0.8 16
0.9 17
0.95 7
26
9
2
8
9
8
9
8
23
6
6
6
E45
58
80
80
80
80
79
79
78
74
73
E46
78
54
52
51
51
49
46
42
42
38
E47
81
64
64
65
66
66
66
67
68
68
E48
73
63
63
68
68
68
69
71
71
71
E49
31
10
11
10
11
10
25
20
20
E50
32
55
55
54
53
52
51
51
49
E51
10
34
34
35
34
34
26
31
32
E52
1
11
12
11
12
11
27
7
7
E53
69
76
76
76
77
77
77
77
77
12
3
DEA-BCC
In
OUT
In
OUT
22
22
26
26
39
39
49
49
45
45
33
33
6
18
1
1
3
3
58
59
61
61
23
23
32
19
44
44
60
60
64
60
64
64
55
55
71
39
71
71
72
72
21
20
54
7
7
11
11
48
50
34
52
52
54
54
32
34
30
34
34
43
43
7
7
5
2
2
4
4
77
85
70
78
78
84
84
8
22
22
22
22
22
34
9
9
9
9
63
6
6
2
2
E55
59
58
58
58
57
56
56
56
59
59
49
28
59
59
71
71
E56
64
79
78
77
76
74
73
69
67
60
39
17
76
76
68
68
E57
33
23
23
23
23
23
28
33
33
36
41
29
24
24
28
28
E58
28
73
71
71
71
70
68
66
66
62
48
24
74
74
77
77
E59
83
68
67
66
65
65
65
65
64
61
45
21
67
67
66
66
E60
53
53
56
57
58
58
58
60
63
65
67
75
58
58
30
30
E61
60
46
46
47
47
47
47
45
43
42
42
55
43
43
38
38
E62
43
65
65
67
67
67
67
68
70
70
59
43
72
72
62
62
E63
61
52
53
55
56
57
57
59
62
66
63
66
56
56
52
52
E64
39
21
21
21
21
21
29
8
8
8
8
6
10
10
12
12
E65
68
62
62
E66
54
38
38
E67
16
27
27
E68
48
39
40
41
41
E69
62
29
29
29
E70
82
51
51
E71
44
49
E72
49
E73
35
E74
62
62
62
62
61
56
50
46
25
62
62
63
63
38
38
38
39
40
38
34
36
22
38
38
56
56
27
28
28
31
22
21
20
22
11
30
30
48
48
AC C
TE D
E54
EP
M AN U
SC
E44
DEA-CCR
RI PT
Expert code
43
43
46
47
56
73
77
41
41
31
31
31
35
36
36
37
41
60
78
36
36
29
29
52
52
53
54
55
58
63
62
48
57
57
70
70
49
48
48
48
49
49
51
57
70
79
46
46
41
41
13
14
13
14
13
32
12
12
12
13
45
21
21
22
22
66
66
69
70
71
71
73
73
75
83
65
77
77
85
85
36
60
60
60
60
60
61
64
65
67
74
67
66
66
74
74
E75
7
48
48
49
50
51
52
54
60
64
76
72
60
60
76
76
E76
3
14
15
14
15
14
33
11
11
11
11
15
9
9
9
9
E77
20
33
33
32
32
31
18
24
24
24
27
33
28
28
27
27
E78
13
9
10
9
10
9
24
10
10
10
10
14
8
8
5
5
52
ACCEPTED MANUSCRIPT
Table 13. The results of model validation
84
0.05 72
0.1 72
0.2 72
0.3 72
SDEA model k 0.4 0.5 0.6 72 72 72
E80
75
71
73
73
73
73
74
74
76
79
E81
88
57
57
56
55
55
55
53
54
52
E82
72
84
84
84
84
84
84
84
83
81
E83
85
86
87
86
86
86
86
86
86
84
E84
37
59
59
59
59
59
59
58
55
44
E85
45
56
54
53
54
54
53
52
46
40
E86
41
87
85
85
85
85
85
85
84
83
E87
40
25
25
25
25
25
8
30
31
E88
38
19
19
19
19
19
16
35
39
0.617
0.622
0.612
0.615
0.610
0.590
0.628
0.617
correlation coefficient
0.8 69
0.9 54
0.95 32
88
80
DEA-CCR
DEA-BCC
In
OUT
In
OUT
69
69
67
67
88
88
88
88
RI PT
E79
0.7 69
44
27
54
54
64
64
72
61
82
82
80
80
75
68
80
80
82
82
35
20
73
73
37
37
38
16
55
55
50
50
77
62
85
85
81
81
31
24
23
27
27
36
36
43
61
58
29
29
40
40
0.595
0.521
0.297
0.606
0.606
0.527
0.527
SC
RPS
M AN U
Expert code
Note: RPS: Rank of productivity score; In: input-oriented; Out: output-oriented.
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Upstream projects play important role in oil industry A unique intelligent algorithm to optimize productivity of upstream oil projects A continuous improvement approach for upstream oil projects HSE, economic and management are determined as the most influential factors
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• • • •