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Risk analysis in ultra deep scientific drilling project — A fuzzy synthetic evaluation approach
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International Journal of Project Management 31 (2013) 449 – 458 www.elsevier.com/locate/ijproman
Risk analysis in ultra deep scientific drilling project — A fuzzy synthetic evaluation approach Jialin Liu a,⁎, Quanxi Li b , Yuhan Wang a
c
School of Management, College of Mechanical Science and Engineering, Jilin University, No. 5988, Renmin Avenue, Changchun, Jilin Province, 130022, People's Republic of China b School of Management, Jilin University, No. 5988, Renmin Avenue, Changchun, Jilin Province, 130022, People's Republic of China c College of Geo-Exploration Science and Technology, Jilin University, Changchun, Jilin Province, 130022, People's Republic of China Received 19 April 2012; received in revised form 14 September 2012; accepted 20 September 2012
Abstract Exploration of deep earth requires ultra deep drilling attempts on the sea or continent, which is the main goal of scientific drilling projects currently established. Uncertain geological complexity, high requirement for R&D of critical equipment as well as high demand of practical performance has to be encountered during a scientific drilling project, making it full of challenge and risks. Risk management, therefore, is critically proposed for scientific drilling projects in order to reduce the risks. However, many traditional risk assessment methods may not perform well in the project due to lack of high quality data of historical record and sufficient information. This paper, therefore, proposes a fuzzy synthetic evaluation approach for scientific drilling project risk assessment. Four criteria — probability, severity, non-detectability and worsening factor are utilized to evaluate individual and overall risks comprehensively. Linguistic terms instead of numerical values are employed to evaluate each risk normally done by experts. AHP/ANP is used to determine sensible weights of each criterion. Values of risk indices are calculated to represent the level of each risk and the overall risk. Finally, a case study on risk analysis of SinoProbe-09 project conducted in Jilin University is tested to demonstrate the procedure of the method and to validate the proposed method. Results show that the risks of the scientific drilling project can be assessed effectively and efficiently. © 2012 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Risk analysis; Scientific drilling project; Fuzzy synthetic evaluation; AHP/ANP
1. Introduction Deep scientific drilling (DSD) is a fundamental attempt to sample deep secrets underneath in modern Earth science research, to provide direct information about deep earth. Because of the explorative nature, scientific drilling projects comprise not only the drilling phase, bust also other related phases such as research and develop (R&D) on critical equipment to satisfy the ultra deep drilling needs (Harms et al., 2007). With the increase of drilling
⁎ Corresponding author. E-mail address: [email protected] (J. Liu). 0263-7863/$36.00 © 2012 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2012.09.015
depth, challenges increase exponentially (Kelessidis, 2009). For example, ultra deep wells would encounter high pressures and high temperatures requiring much better, thicker and very costly metals for equipment, which may possibly make traditional drilling equipment fail and costly lost. Moreover, the nature of scientific drilling has made it full of challenging regime to be handled, e.g., constant changes on drilling site, direct exposure to hazardous sources, high uncertainties and unknown underground geological complexity. Therefore, scientific drilling project, perhaps more than others, has been plagued by various risks often resulting in project failure, even great economic or human losses. Thus, risk management is necessary for scientific drilling projects to improve the performance and secure the success of the projects.
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Risk analysis for scientific drilling project, however, is intricate, especially at the early stages of the project, because the nature of risk is usually affected by numerous factors including natural factors, technical factors, etc. Many risk assessment techniques proposed in the literature and used in practice, such as fault tree analysis, event tree analysis, Monte Carlo Simulation, Bayesian network, fuzzy reason inference, etc. (Zeng et al., 2007), are difficult to be applied in scientific drilling project risk assessment, if not impossible. One reason is that high quality data are required for effective applications of the methods, which is impossible for scientific drilling project. Moreover, even such data exist, they cannot be used to address the uncertainties associated with new projects, since conditions change and requirements increase dramatically with the increase of drilling depth. One risk that can be neglected in previous drilling project may become a huge one in another project. It is therefore essential to develop new risk analysis methods to assess scientific drilling project risk in an acceptable way. To overcome the difficulties mentioned above, this paper proposes an AHP/ANP based fuzzy synthetic evaluation (FSE) approach to evaluate risks in deep scientific drilling project. Risks are usually evaluated from the perspectives of probability occurrence and the impact to calculate the risk level by multiplying the two quantities (Ouédraogo et al., 2011a). As stated by Ouédraogo et al. (2011a), risk evaluation should include more criteria such as worsening factor, research specificities, and use other functional form to calculate the risk level, e.g., summation including the use of logarithm. Applying the same idea, we use four criteria to evaluate a particular risk, e.g. probability, severity, non-detectability and worsening factor. Due to the lack of data, we cannot calculate the risk level for each risk. Therefore, a fuzzy synthetic evaluation approach is applied to evaluate risks in this paper. The application of fuzzy synthetic evaluation technique provides a systematic tool to deal with qualitative and quantitative data and information. In the application of FSE, weights for each criterion should be determined, where AHP/ANP is applied. The proposed method is validated by the case of SinoProbe-09 project conducted by Jilin University. The remainder of the paper is organized as follows: Section 2 reviews the related researches. The new risk assessment model is presented in Section 3. A case study is conducted in Section 4 to test the proposed model. Conclusions are drawn in Section 5.
2. Literature review Risk management is beneficial if it is implemented in a systematic manner from planning stage through the project completion. It has gained strong interest from academia and practice since the 2000s (Kwak and Anbari, 2009). Taroun et al. (2011) review the results of construction project risk assessment critically. Various methods are reviewed, including risk breakdown structures, fuzzy set theory, Monte Carlo simulation, AHP, ANP, Probability–Impact model, etc. Most methods assess risks from the perspectives of probability, severity and/or detectability (Ouédraogo et al., 2011a). Various risk assessment techniques are
different in the way of combining the different aspects into one value. Risk analysis and management of petroleum exploration ventures are growing all over the world and many international oil companies have improved their exploration performance by using principles of risk analysis in combination with new technologies (Harbaugh et al., 1995; Rose, 2001). Jones and Hillis (2003) propose a fault-seal risk assessment model that integrates parameters from different aspects of fault-seal analysis. Coelho et al. (2005) use two different methodologies, a Monte Carlo simulation and a connectionist approach to estimate the total time assessment in drilling and completion operations of oil wells in deep waters. Cunha et al. (2005) present comments and some background on the use of risk analysis methods in the oil and gas industry. Their attention is given to applications developed specifically for drilling operations. However, scientific drilling project has essential differences with traditional oil and gas drilling, which hampers the applicability of many risk assessment methods used widely in oil and gas industry. First, each ultra deep scientific drilling project has its unique characteristic, so the experience of one project cannot be applied to other projects. Thus, historical data needed by other risk assessment approaches cannot be obtained. Second, the risks of ultra deep drilling are huge and full of the whole project. Although geographical data can provide some help, the main source of information provided for the risk assessment is the knowledge of experienced engineers and experts, most of which is not precise data but vague linguistic description. In addition, there are too many uncertain factors during the whole project. Because of these differences, we cannot use the old methods in the ultra deep drilling project. To conquer, the difficulties of acquiring high quality data, many researchers combine experts' knowledge into the risk assessment process by using fuzzy set theory which was proposed by Zaheh (1965). By using fuzzy set theory, data can be defined as linguistic terms instead of numerical values. Several studies propose fuzzy counterpart of classical risk assessment methods, such as fuzzy fault tree analysis (Fujino, 1994), and fuzzy event tree analysis (Cho et al., 2002). Also, new methods based on fuzzy set theory are proposed for risk assessment. Zeng et al. (2007) propose a risk assessment model based on fuzzy reasoning and AHP approach. Nieto-Morote and Ruz-Vila (2011) propose a fuzzy approach to construction project risk assessment, in which three risk factors – risk impact, risk probability and risk discrimination are used to assess the overall risk. For more fuzzy risk assessment approaches, please see Nieto-Morote and Ruz-Vila (2011) and literature therein. Although the high riskiness has been recognized in the deep scientific drilling project as mentioned in the introduction, to the best of our knowledge, little study is done to assess the risk from the perspective of whole project using fuzzy approaches. While drilling operation risk is investigated in literature, it is only one source of risk in scientific drilling project as shown in the case study of this paper. In this paper, an AHP/ANP based fuzzy synthetic evaluation method is proposed to assess the risk of scientific drilling project.
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3. Proposed risk assessment model A risk assessment model, based on fuzzy synthetic evaluation and AHP/ANP, is proposed as shown in Fig. 1. The model consists of three steps: preliminary step, evaluate each risk step and evaluate the overall risk step. The details are described in the following subsections. 3.1. Preliminary step 3.1.1. Establish a risk assessment group Risk assessment starts with the establishment of a risk assessment group whose members must be carefully selected. The selected experts must have different backgrounds/disciplines and essential experiences regarding the concerned project (NietoMorote and Ruz-Vila, 2011; Zeng et al., 2007). In a scientific drilling project, a risk assessment team must include the following
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experts: project managers, R&D scientists, drilling engineers, equipment suppliers, equipment operators, experts from outside the project team, etc. The members in the risk assessment team will undertake the risk identification, risk criteria determination, comparing relative importance between criteria, and evaluation of risks under each criterion. In addition, the risk assessment group will take in charge of the measurements of risk control according to the risk assessment. In our case study, the Chinese Government's Executive Program “Instrumentation development and field experimentation” (SinoProbe-09) is selected, which provides key information and capability of this article. The authors themselves are also members of the management staff of that program. The risk analysis reported in this article is a subproject of SinoProbe-09. And the program is totally aware of and also fully supports this. 3.1.2. Identify risk factors Risk identification is the process in which threats for the project concerned are investigated. Usually, in scientific drilling projects, the following question will be asked to the risk assessment group: “what can go wrong in the scientific drilling project” or “what accident could occur during the project”. This process is of great importance because the risk analysis and measures may be performed on identified potential risk. The identification of risks requires a deep understanding of the scope of the project process, related professional knowledge, experience as well as imagination. Because of the knowledge-intense property of scientific drilling project, multi-disciplined experts must be included in the group and identify the potential risks iteratively. Besides intuitive methods, there are some tools for risk identification, such as: checklist, influence diagrams, cause and effect diagrams, failure mode and effect analysis, etc. Detailed introduction to the methods are described by Ahmed et al.(2007). In scientific drilling projects, 6 categories—natural risk, R&D risk, management risk, drilling operation risk, drilling equipment risk, and environmental and security risk—of risks, including 25 risk factors, are identified by Liu et al., 2011 as shown in Table 1. 3.2. Evaluate a specific risk
Fig. 1. Risk assessment model.
After identifying the potential risks, we should evaluate the risks one by one, on which risk reduction decisions should be based. This is often fulfilled by ranking or prioritizing the risk priority number (RPN), which is usually calculated as the product of risk probability (RP), risk severity (RS) and /or detectability (RD). However, as stated by Bowles (2003), different combinations of ranking factors may produce the same RPN. To overcome the drawbacks of RPN, Ouédraogo et al.(2011a,b) propose a Lab Criticity Index (LCI), which is a rather comprehensive function of probability, severity, risk worsening factors, research specificities and hazard detectability. However, the lack of historical data and great uncertainties in scientific drilling project hamper the applicability of LCI. To deploy the merit of LCI, we use their idea of multiple evaluation criteria, but calculate the risk level by fuzzy synthetic evaluation (FSE) approach.
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Table 1 Risk factors in scientific drilling project. Natural risk
Geological factor
R&D risk
Corollary equipment supply The difficulties of critical techniques The stability of research team The capability of research team Management organization Management staff Financial management Management policy and implementation Stuck pipe Drilling string failure Wellbore stability Coring effect Lost circulation Junk in hole Crooked hole Cementing quality Blowout Operations of constructors Hook load Torsion Pressure and flow output of mud pump Well out of control
Management risk
Drilling operation risk
Drilling equipment risk
Environmental and security risk
FSE approach is a method to assess multi-criteria decision making (Xu et al., 2010), and has been adopted in many fields, including assessment of reservoir water quality (Lu et al., 1999), health risk assessment (Sadiq and Rodriguez, 2004), PPP project risk assessment (Xu et al., 2010), etc. These studies show that FSE approach has merits in handling complicated evaluation with multi-attributes and multi-levels. In this research, it is used to calculate the risk index of each risk and the overall risk index of scientific drilling project. The procedures of fuzzy synthetic evaluation approach are as follows (Xu et al., 2010): (1) Determine the set of basic criteria/factors π = {f1,f2, ⋯,fm}, where m is the number of criteria. (2) Determine the set of grade alternatives E = {e1,e2, ⋯,en}, where n is the number of alternatives. For example, e1 = very low; e2 = low; e3 x= mod erate; e4 = high; and e5 = very high. Grades will be given for each alternative, such as 1 = very low; 2 = low; 3 = mod erate; 4 = high; and 5 = very high. (3) Determine weight for each criterion/factor W = {w1,w2, ⋯, wm}. The weight of each criterion can be obtained by various approaches, for example, AHP/ANP used in this paper, expert scoring, etc. (4) For each criterion, an evaluation is a fuzzy subset of grade set, whose membership function can be established by the risk assessment group. For example, if the survey results on the probability of blowout risk indicate that 5% of the experts opined the probability of occurrence as very low, 15% as low, 45% as moderate, 35% as high and
0% as very high, then the membership function of the probability of blowout is given by Eq. (1):
f1 ¼
0:05 0:15 0:45 0:35 þ þ þ very low low moderate high þ
¼
ð1Þ
0:00 very high
0:05 0:15 0:45 0:35 0:00 þ þ þ þ : 1 2 3 4 5
It can also be written as (0.05, 0.15, 0.45, 0.35, 0.00). All the evaluations form a fuzzy evaluation matrix R ¼ r ij mn , where rij is the degree to which alternative ej satisfies the criterion fi. (5) Fuzzy synthetic evaluation. The results of the evaluation are obtained by calculating the fuzzy composition of the weighting vector and the fuzzy evaluation matrix as shown in Fig. 2, where ∘ is a fuzzy composition operator. The final evaluation denoted by D is also a fuzzy subset of the alternative set. Four composition operators can be used to determine the results of the evaluation. Operator 1: Mð∧; ∨Þ; dj ¼ ∨m dj ∈D i¼1 wi ∧r ij ; Operator 2: Mð]; ∨Þ; dj ¼ ∨m dj ∈D i¼1 wi r ij ; Operator 3: Mð]; ⊕Þ;
dj ¼ min 1;
m X
!
wi rij ;
dj ∈D
i¼1
Operator 4: Mð∧; þÞ; dj ¼
m X
wi ∧r ij ;
dj ∈D:
i¼1
The symbol ⊕ in Operator 3 denotes the summation of product of weight and membership function. Note that the four operators are suitable for different settings. Operators 1 and 2 can be applied for single-item problem in which only the major criteria could be considered while other minor criteria would be ignored. Operator 3 suits for the setting where many criteria are considered and the difference in the weights of each criterion is not great. Operator 4 will miss some information with small weights. In our study, Operator 3 will be used. (6) Normalize the fuzzy evaluation vector and calculate the risk index according to
RI ¼
n X
ð2Þ
d k ek :
k¼1
3.2.1. Determine the evaluation criteria Risk is usually be assessed by considering two fundamental risk parameters: risk likelihood and risk impact (Ahmed et al.,
W
R
D W R
Fig. 2. The Fuzzy synthetic evaluation process.
J. Liu et al. / International Journal of Project Management 31 (2013) 449–458
2007; Xu et al., 2010). However, the two criteria are not enough to evaluate the risk. For example, a particular risk is also dependent on many involved factors, e.g., human factors, workplace factors, etc. (Zeng et al., 2007). Also, these parameters do not take into account the impact of the risk to the overall framework of the project. A parameter called risk discrimination is proposed by Kendrick (2003) to address this issue (Cervone, 2006). Detectability is also used in risk management literature (Ouédraogo et al., 2011a). Ouédraogo et al. (2011a, b) suggest estimating risk in research environment as a combination of severity, probability, detectability, worsening factors and research specificities. The studies show that evaluation criteria play an important role in risk analysis. Different criteria should be developed for different projects. Our proposed risk assessment model is adaptive for the number of criteria. Through the discussions of risk assessment group on scientific drilling project, four criteria are proposed to evaluate a particular risk: probability, severity, non-detectability and worsening factors. Probability and severity are two commonly used criteria for evaluating risks. Probability investigates the likelihood that each specific risk will occur. The severity investigates the potential effect of the risk on economy, environment, human, etc. Non-detectability is a measure of risk detection. Risk can be detected in different ways during drilling operations, by using detectors, indicators or by human senses. Significant difference exists between risks on the detectability in scientific drilling project. R&D risk is less detectable than drilling operation risk. Effective measures can be performed before risky event occurs if it can be detected accurately and timely, which means this event is not such risky. To align with other criteria on the grades of evaluation, non-detectability instead of detectability is used in this paper. The risk worsening factors are conditions which can amplify direct consequences, hence the severity (Ouédraogo et al., 2011a). In scientific drilling project, the loss usually does not occur at once, but in a dynamic manner. Improper actions may amplify the loss exponentially (Knock, 2011). 3.2.2. Establish a set of grade alternatives To utilize the fuzzy synthetic evaluation approach, a set of grade alternatives or linguistic evaluations and scores should be established. In this paper, the grades are defined as E = {1,2,3,4,5}, where 1 = very low; 2 = low; 3 = moderate; 4 = high and 5 = very high. 3.2.3. Determine weight for each criterion When using fuzzy synthetic evaluation approach, one must set weight for each criterion. However, it is a difficult task to assign weight for each criterion. To overcome this difficulty, analytic hierarchy process (AHP), which was proposed by Saaty (1980), is applied. AHP provides a way to input judgments and measurements to derive ratio scale priorities for the distribution of influence among the factors and groups of factors in the decision. Since its development, successful applications in planning, selecting the best alternative and predict market share and so on are reported (Saaty and Vargas, 2006). In this paper, AHP is used to weight the criteria. The hierarchy structure of the problem is presented in Fig. 3.
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Fig. 3. The hierarchy structure of the evaluation problem.
Pairwise comparisons will be conducted by the members of risk assessment group using fundamental scales 1–9 as proposed by Saaty (1980). The output of this procedure is the weightings of the criteria. For the detailed procedures of AHP, please refer to Saaty and Vargas (2006). 3.2.4. Establish fuzzy evaluation matrix The fuzzy evaluation matrix is made up of membership functions of evaluations for the criteria of a specific risk. These membership functions can be formed by evaluations of experts. Each expert will make a judgment on the level of each criteria of a particular risk. Statistical results will be the membership function for the evaluation for each criterion. Example has been given in step (4) of the procedures of FSE. A complete case study will be conducted in Section 4. 3.2.5. Calculate risk index After establishing the weighting vector and fuzzy evaluation matrix, the evaluation for each risk can be conducted using fuzzy composition operators. In this paper, the Operator 3 will be used and the risk index for each risk can be calculated according to Eq. (2). The evaluation of each risk has been done now, and the risks can be rated by the numerical value of their risk index. The risks can be grouped into clusters by rounding their risk indices to nearest grades. 3.3. Evaluate overall risk For evaluating the overall risk, FSE approach is still used with a minor difference to that used for evaluating a specific risk. In this setting, the criteria/factors set becomes the set of risks, e.g. π = {f1,f2, ⋯,fm}, where fi is the ith risk. In this paper, fi is the ith risk in a scientific drilling project. The set of grade alternatives is the same as that for evaluating each risk. Thus, the fuzzy evaluation matrix is formed by the evaluations of risks. Table 2 Judgment matrix. Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
1 4 2 4
1/4 1 1/2 1
1/2 2 1 2
1/4 1 1/2 1
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Table 3 The fuzzy evaluation matrix.
Geological factor
Corollary equipment supply
The difficulties of critical techniques
The stability of research team
The capability of research team
Management organization
Management staff
Financial management
Management policy and implementation
Stuck pipe
Drilling string failure
Wellbore stability
Coring effect
Lost circulation
Junk in hole
Crooked hole
Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
Very low
Low
Moderate
High
Very high
0.00 0.00 0.10 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.58 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.23 0.00 0.69 0.00 0.02 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.21 0.00
0.14 0.00 0.30 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.75 0.00 0.00 0.00 0.45 0.00 0.00 0.34 0.42 0.00 0.00 0.00 0.49 0.00 0.00 0.00 0.27 0.00 0.31 0.00 0.61 0.00 0.20 0.00 0.24 0.00 0.02 0.00 0.86 0.00 0.00 0.00 0.58 0.00 0.00 0.00 0.59 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.79 0.00
0.55 0.30 0.60 0.00 0.09 0.23 0.05 0.22 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.30 0.00 0.50 0.30 0.32 0.66 0.00 0.16 0.00 0.64 0.41 0.00 0.00 0.79 0.50 0.01 0.00 0.03 0.37 0.00 0.66 0.00 0.00 0.15 0.69 0.00 0.04 0.00 0.02 0.07 0.36 0.00 0.16 0.00 0.27 0.00 0.00 0.12 0.59 0.00 0.00 0.01 0.00 0.00
0.30 0.65 0.00 0.55 0.86 0.70 0.00 0.64 0.10 0.40 0.00 0.50 0.80 0.82 0.00 0.03 0.60 0.85 0.00 0.02 0.61 0.66 0.00 0.70 0.49 0.00 0.00 0.77 0.57 0.36 0.00 0.13 0.85 0.18 0.00 0.61 0.00 0.79 0.00 0.02 0.14 0.01 0.00 0.80 0.29 0.89 0.00 0.63 0.94 0.90 0.00 0.09 0.64 0.83 0.00 0.21 0.07 0.67 0.00 0.19 0.61 0.66 0.00 0.10
0.01 0.05 0.00 0.45 0.05 0.07 0.00 0.14 0.90 0.00 0.00 0.50 0.20 0.18 0.00 0.96 0.40 0.15 0.00 0.98 0.09 0.34 0.00 0.00 0.19 0.00 0.00 0.07 0.43 0.00 0.00 0.87 0.15 0.03 0.00 0.38 0.00 0.18 0.00 0.98 0.00 0.99 0.00 0.05 0.00 0.11 0.00 0.37 0.04 0.03 0.00 0.91 0.20 0.17 0.00 0.79 0.93 0.21 0.00 0.81 0.39 0.33 0.00 0.90
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Table 3 (continued)
Cementing quality
Blowout
Operations of constructors
Hook load
Torsion
Pressure and flow output of mud pump
Well out of control
Harm of sulfured hydrogen
Discharge of waste drilling fluids
Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
3.3.1. Determine weights for each risk To assess the overall risk, the weightings for each risk should be set. However, as shown in Liu et al. (2011), the risks are not independent from each other. In this case, AHP loses its power. Instead of AHP, we use ANP – analytic network process – which is proposed by Saaty (1996). ANP is a generalization of AHP by considering the dependence between the elements of the hierarchy. The steps of using ANP are described briefly as follows. For details, please refer to Saaty and Vargas (2006). Step 1. Determine the network structure Step 2. Pairwise comparison between elements to form the supermatrix Through pairwise comparisons a priority vector is derived, which represents the impact of a given set of elements in a component on another element in the system. The priorities are entered as parts of the columns of a supermatrix, which denotes the influence priority of an element on the left of the matrix on an element at the top of the matrix with respect to a particular control criterion. Step 3. Construct the cluster matrix The cluster themselves must be compared to establish their relative importance and must use it to weigh the corresponding
Very low
Low
Moderate
High
Very high
0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.89 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.23 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.00 0.21 0.20 0.50 0.00 0.00 0.60 0.01 0.00 0.00 0.00 0.00 0.00 0.14 0.30 0.00 0.00 0.10 0.10 0.00 0.00 0.00 0.91 0.00 0.10 0.00 0.12 0.00
0.62 0.12 0.01 0.16 0.00 0.00 0.00 0.00 0.10 0.57 0.00 0.50 0.53 0.56 0.32 0.28 0.00 0.60 0.01 0.00 0.09 0.64 0.70 0.53 0.02 0.00 0.01 0.00 0.00 0.20 0.00 0.00 0.45 0.36 0.65 0.30
0.38 0.86 0.81 0.78 0.76 0.00 0.00 0.51 0.78 0.22 0.00 0.00 0.41 0.20 0.00 0.59 0.65 0.39 0.74 0.81 0.71 0.22 0.00 0.38 0.65 0.40 0.00 0.79 0.89 0.61 0.00 0.88 0.45 0.62 0.00 0.54
0.00 0.02 0.18 0.06 0.24 1.00 0.00 0.49 0.12 0.00 0.00 0.00 0.06 0.24 0.00 0.12 0.35 0.01 0.25 0.19 0.20 0.00 0.00 0.09 0.33 0.50 0.00 0.21 0.11 0.19 0.00 0.12 0.00 0.02 0.00 0.16
blocks of the supermatrix to make it column stochastic. The clusters linked from the source cluster are pairwise compared for the importance of their impact on it with respect to the control criteria. Step 4. Construct the weighted supermatrix The weighted supermatrix is obtained by multiplying each entry in a block of the component at the top of the supermatrix by the priority of influence of the component on the left from the cluster matrix. Step 5. Limit supermatrix In the final step, the weighted supermatrix is raised to powers to get the global limit priority vectors. If the supermatrix has the effect of cyclicity, the Cesaro sum is used to get the average priority weights. The resulting priority weights will be the input of the fuzzy synthetic evaluation approach. 3.3.2. Evaluate the overall risk by FSE Fuzzy operators will be performed on the weighting vector and the fuzzy evaluation matrix to produce the evaluation of the overall risk. Note that if the risks belong to different clusters, using the same procedure can produce the evaluation of overall
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Table 4 The membership function and risk for each risk.
Geological factor Corollary equipment supply The difficulties of critical techniques The stability of research team The capability of research team Management organization Management staff Financial management Management policy and implementation Stuck pipe Drilling string failure Wellbore stability Coring effect Lost circulation Junk in hole Crooked hole Cementing quality Blowout Operations of constructors Hook load Torsion Pressure and flow output of mud pump Well out of control Harm of sulfured hydrogen Discharge of waste drilling fluids
Membership function
Risk index
Ranking
Weighting
(0.018, 0.068, 0.271, 0.462, 0.181) (0.081, 0.09, 0.18, 0.568, 0.081) (0.081, 0.099, 0.216, 0.334, 0.27) (0.09, 0.09, 0.0036, 0.38, 0.4304) (0.045, 0.135, 0, 0.3732, 0.4468) (0.009, 0.081, 0.228, 0.551, 0.131) (0.104, 0.198, 0.327, 0.326, 0.044) (0.018, 0.088, 0.304, 0.233, 0.356) (0.041, 0.049,0.378, 0.369, 0.163) (0.073, 0.141,0.077, 0.292, 0.418) (0.137, 0.063,0.12, 0.306, 0.374) (0.018, 0.157,0.076, 0.576, 0.173) (0.011, 0.104,0.092, 0.45, 0.342) (0.025, 0.106,0.065, 0.438, 0.366) (0.036, 0.038,0.149, 0.317, 0.46) (0.038, 0.142, 0.004, 0.335, 0.482) (0, 0, 0.165, 0.774,0.061) (0.121, 0.059,0.26,0.56) (0.144, 0.292, 0.395, 0.157, 0.012) (0.014, 0.112, 0.413, 0.325, 0.136) (0, 0, 0.218, 0.63, 0.152) (0, 0.104,0.556, 0.287, 0.052) (0.16, 0.054, 0.004,0.493, 0.289) (0.016, 0.164, 0.072, 0.625, 0.123) (0.041, 0.032, 0.4, 0.463, 0.065)
3.72 3.4776 3.613 3.9768 4.0418 3.7144 3.0078 3.8216 3.5632 3.8408 3.7176 3.729 4.0092 4.013 4.1272 4.0804 3.8966 4.0798 2.6016 3.4562 3.9342 3.2874 3.6962 3.6744 3.4778
13 21 18 7 4 15 24 11 19 10 14 12 6 5 1 2 9 3 25 22 8 23 16 17 20
0 0.025799 0.069318 0.054026 0.065915 0.041579 0.045384 0.049522 0.054076 0.027981 0.021359 0.01809 0.021832 0.017844 0.018907 0.017708 0.019258 0.019978 0.019781 0.07898 0.101404 0.080584 0.027123 0.051381 0.052168
risk in that cluster. For simplicity, we omit this step to produce the overall risk directly. 3.3.3. Calculate overall risk index The method used to calculate the overall risk index is the same as that for a specific risk. The overall risk level can be obtained by translating the overall risk index into the risk levels.
by experts from related areas is built up. The judgments used in the proposed methodology are collected from both the risk assessment group and the consulting team members. 4.1.2. Identify risk factors On the basis of their experience and qualification, the two teams identify 25 risks as listed in Table 1. The risks have been explained in a companion paper (Liu et al., 2011) and are omitted here.
4. Case study: risk assessment of Sinoprobe-09 project To illustrate the applicability of the proposed method, a case study of risk assessment on SinoProble-09 project conducted by Jilin University is presented in this section. This project aims at the ten thousand-meter well drilling. It is of great challenge due to the high uncertainties and complexities. There are many risk sources which can lead to significant losses including economic loss, environmental impact, human safety, etc. Thus, risk assessment is necessary at the beginning of the project. However, the risks are difficult to measure due to the lack of practical data and information. Therefore, the proposed method has been used to assess the risks of the scientific drilling project.
4.2. Evaluate a specific risk 4.2.1. Determine evaluation criteria For each risk, the risk assessment group agrees to use four criteria to evaluate it, e.g., probability, severity, non-detectability and worsening factor. 4.2.2. Establish a set of grade alternatives In this project, the five-point Likert scale (1 = very low, 5 = very high) is used to calculate the mean score for each risk, e.g., the set of grade alternatives is defined as E = {1, 2, 3, 4, 5}, where1 = very low; 2 = low; 3 = moderate; 4 = high; and 5 = very high (for all the four criteria).
4.1. Preliminary step 4.1.1. Establish a risk assessment group An eight-person risk assessment group is established, including a project manager, two R&D scientists, two Geoscience experts, and three engineers. Moreover, a consulting team formed
4.2.3. Determine weight for each criterion The four criteria can be constructed as a criteria hierarchy as shown in Fig. 3. The relative importance to the overall risk is pair-wise compared by the risk assessment group. The judgment matrix is shown in Table 2. Use the standard calculation procedure
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of AHP, we can compute the weight for each criterion as (0.10, 0.36, 0.18, 0.36).
4.2.4. Establish fuzzy evaluation matrix For each risk, the membership function can be formed by the evaluations of experts in risk assessment group and consulting group. Statistics are conducted on surveys to form the fuzzy evaluation matrix as shown in Table 3.
457
function of the overall risk, which is (0.045, 0.094, 0.227, 0.413, 0.222). The calculation procedure is as follows: DA ¼ W A ∘RA ¼ ð0:045; 0:094; 0:227; 0:413; 0:222Þ;
where DA is the membership of the overall risk, W A ¼ ð0; 0:258; 0:0693; 0:0540; 0:0659; 0:0416; 0:0454; 0:0495; 0:0541; 0:0280; 0:0214; 0:0181; 0:0218; 0:0178; 0:0189; 0:0177; 0:0193; 0:0200; 0:0198; 0:0790; 0:1014; 0:0806; 0:0271; 0:0514; 0:0522Þ
4.2.5. Calculate risk index The evaluation of each risk is derived from the Operator 3 as mentioned in Section 3.2. Take the geological factor as an example; its membership function is as follows: 0
0:00 B 0:00 B ð0:10; 0:36; 0:18; 0:36Þ∘@ 0:10 0:00
0:14 0:00 0:30 0:00
0:55 0:30 0:60 0:00
0:30 0:65 0:00 0:55
1 0:01 0:05 C C 0:00 A 0:45
¼ ð minð1; 0:10 0:00 þ 0:36 0:00 þ 0:18 0:10 þ 0:36 0:00Þ; minð1; 0:10 0:14 þ 0:36 0:00 þ 0:18 0:30 þ 0:36 0:00Þ; minð1; 0:10 0:55 þ 0:36 0:30 þ 0:18 0:60 þ 0:36 0:00Þ; minð1; 0:10 0:30 þ 0:36 0:65 þ 0:18 0:00 þ 0:36 0:55Þ; minð1; 0:10 0:01 þ 0:36 0:05 þ 0:18 0:00 þ 0:36 0:45ÞÞ ¼ ð0:018; 0:068; 0:271; 0:462; 0:181Þ:
The risk index is calculated according to Eq. (2) as follows, 0:018 1 þ 0:068 2 þ 0:271 3 þ 0:462 4 þ 0:181 5 ¼ 3:72:
The final evaluation for each risk is listed in Table 4, with membership function listed in the second column, risk index listed in the third column, and the ranking listed in the forth column. From Table 4, we can see that ‘junk in hole’ has the highest risk index, meaning that particular attention should be paid on junk in hole and effective measures should be adopted to protect the accident occurring. Moreover, the risk index of “junk in hole” is 4.13, which can be seen as “high”. 4.3. Evaluate overall risk In this case, the factor set includes all the risks, e.g. π = {f1, f2, ⋯,f25} where fi is the ith risk. The set of grade alternatives is the same as that mentioned above. 4.3.1. Determine weight for each risk As our earlier study (Liu et al., 2011), the risks are not independent with each other. Therefore, AHP cannot be applied in this setting. In turn, we use the extension version of AHP, e.g., ANP. The relative importance of each risk has been reported in Liu et al. (2011).We omit the detailed computation procedures and use the results directly as shown in the last column in Table 4. 4.3.2. Evaluate the overall risk The fuzzy evaluations for each risk in Table 4 of Section 4.2.5 are used to form the fuzzy evaluation matrix. Operator 3 is used to composite the weighting vector and fuzzy evaluation matrix to get the fuzzy evaluation for the overall risk, e.g., the membership
2
3 0:018 0:068 0:271 0:462 0:181 6 0:081 0:09 0:18 0:568 0:081 7 6 7 6 0:081 0:099 0:216 0:334 0:27 7 6 7 6 0:09 0:09 0:0036 0:38 0:4304 7 6 7 6 0:045 0:135 0 0:3732 0:4468 7 6 7 6 0:009 0:081 0:228 0:551 0:131 7 6 7 6 0:104 0:198 0:327 0:326 0:044 7 6 7 6 0:018 0:088 0:304 0:233 0:356 7 6 7 6 0:041 0:049 0:378 0:369 0:163 7 6 7 6 0:073 0:141 0:077 0:292 0:418 7 6 7 6 0:137 0:063 0:12 0:306 0:374 7 6 7 0:576 0:173 7 RA ¼ 6 6 0:018 0:157 0:076 7: 6 0:011 0:104 0:092 0:45 0:342 7 6 7 6 0:025 0:106 0:065 0:438 0:366 7 6 7 6 0:038 0:142 0:004 0:355 0:482 7 6 7 6 0 0 0:165 0:774 0:016 7 6 7 6 0:121 0:059 0 0:26 0:56 7 6 7 6 0:144 0:112 0:413 0:325 0:136 7 6 7 6 0 0 0:218 0:63 0:152 7 6 7 6 0 0:104 0:556 0:287 0:052 7 6 7 6 0:16 0:054 0:004 0:493 0:289 7 6 7 4 0:016 0:164 0:072 0:625 0:123 5 0:014 0:032 0:4 0:463 0:065
The overall risk index is calculated according to Eq. (2) as the following: 0:045 1 þ 0:094 2 þ 0:227 3 þ 0:413 4 þ 0:222 5 ¼ 3:673:
ð3Þ The results (Table 4 and Eq. (3)) show that the overall risk index of this scientific drilling project is 3.673, which can be regarded as high. Therefore, the risk level for the project can be construed as “high”. Moreover, among various risks, “junk in hole” is the most critical risk, with risk index equal to 4.127; with “crooked hole” being the second, with risk index equal to 4.08; and with “blowout” being the third, with risk index equal to 4.0798. Moreover, among the 25 risk factors, 19 risk factors can be seen as high risk, which made the project more risky. 5. Conclusions The nature of deep scientific drilling makes many welldeveloped risk assessment methods invalid. This paper adopts a novel approach to develop a practical risk assessment model for scientific drilling project to deal with risks associated with the projects in the complicated situations in which information to assess risks is non-obtainable. This approach uses probability, severity, non-detectability, and worsening factor to comprehensively evaluate a particular risk. Moreover, this approach allows
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members in the risk assessment group and other experts to make their judgments by means of linguistic terms instead of real numbers. Due to the different importance of criteria in evaluating risks, AHP/ANP is adopted to determine the weightings of criteria. Fuzzy synthetic evaluation approach is applied to obtain the evaluation of each risk and a risk index is calculated to indicate the level of each risk. This approach can also assess the overall risk level of the whole project by applying similar procedures. To illustrate how the approach works, a real case on risk assessment of SinoProbe-09 project conducted by Jilin University is presented. The findings show that it is an effective and efficient method for assessing scientific drilling risk. The proposed approach provides a comprehensive, objective, reliable and practical risk assessment tool for scientific drilling project. It evaluates risks from multiple perspectives. Moreover, it can handle experts' knowledge by manipulating linguistic terms directly. Furthermore, it can produce a risk index for each risk and the overall risk, which can be used to indicate the risk level. According to the progress of the project, our method in the implementation of the project continues to be implemented. It validates the applicability of the four standard evaluation methods we proposed in this article. As the project progresses, the actual situation is also confirmed in our judgment. Acknowledgment This research was supported by the Chinese Government's Executive Program “Instrumentation development and field experimentation” (SinoProbe-09). References Ahmed, A., Kayis, B., Amornsawadwatana, S., 2007. A review of techniques for risk management in projects. Benchmarking: An International Journal 14 (1), 22–36. Bowles, J.B., 2003. An assessment of RPN prioritization in a failure modes effects and criticality analysis. Reliability and Maintainability Symposium, pp. 380–386. Cervone, H.F., 2006. Project risk management. OCLC Systems and Services 22 (4), 256–262. Cho, H.-N., Choi, H.-H., Kim, Y.-B., 2002. A risk assessment methodology for incorporating uncertainties using fuzzy concepts. Reliability Engineering and System Safety 78 (2), 173–183. Coelho, D.K., Roisenberg, M., de Freitas Filho, P.J., Jacinto, C.M.C., 2005. Risk assessment of drilling and completion operations in petroleum wells using a Monte Carlo and a neural network approach. In: Kuhl, M.E., Steiger, N.M., Armstrong, F.B., Joines, J.A. (Eds.), Proceedings of the 2005 Winter Simulation Conference. Cunha, J.C., Demirdal, B., Gui, P., 2005. Quantitative risk analysis for uncertainty quantification on drilling operations: review and lessons learned. SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro.
Fujino, T., 1994. The Development of a Method for Investigation Construction Site Accidents using Fuzzy Fault Tree Analysis. The Ohio State University, USA. Harbaugh, J.W., Davis, J.C., Wendebourg, C., 1995. Computing Risk for Oil Prospects: Principles and Programs. Kidlington, Pergamon. Harms, U., Koeberl, C., Zoback, M.D., 2007. Continental Scientific Drilling— A Decade of Progress, and Challenges for the Future. Springer, Berlin Heidelberg. Jones, R.M., Hillis, R.R., 2003. An integrated, quantitative approach to assessing fault-seal risk. AAPG Bulletin 87 (3), 507–524. Kelessidis, V.C., 2009. Challenges for very deep oil and gas drilling — will there ever be a depth limit? 3rd AMIREG International Conference. Althens, Greece. Kendrick, T., 2003. Identifying and Managing Project Risk. American Management Association, New York. Knock, R., 2011. Risk assessments become risky. Oil & Gas Network (http:// oilgas.net/feature-article/risk-assessments-become-risky). Kwak, Y.H., Anbari, F.T., 2009. Analyzing project management research: perspectives from top management journals. International Journal of Project Management 27, 435–446. Liu, J., Li, Q., Wang, Y., 2011. Risk Assessment of Scientific Drilling Project: An Integrated Approach of ISM and ANP. Working Paper. Jilin University. Lu, R.S., Lo, S.L., Hu, J.Y., 1999. Analysis of reservoir water quality using fuzzy synthetic evaluation. Stochastic Environmental Research and Risk Assessment 13 (5), 327–336. Nieto-Morote, A., Ruz-Vila, F., 2011. A fuzzy approach to construction project risk assessment. International Journal of Project Management 29 (2), 220–231. Ouédraogo, A., Groso, A., Meyer, T., 2011a. Risk analysis in research environment — part I: modeling Lab Criticity Index using Improved Risk Priority Number. Safety Science 49 (6), 778–784. Ouédraogo, A., Groso, A., Meyer, T., 2011b. Risk analysis in research environment — part II: weighting lab criticity index using the analytic hierarchy process. Safety Science 49 (6), 785–793. Rose, P., 2001. Risk analysis and management of petroleum exploration ventures. American Association of Petroleum Geologists, Tulsa, Oklahoma. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Saaty, T.L., 1996. Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh. Saaty, T.L., Vargas, L.G., 2006. Decision Making with the Analytic Network Process. Springer, New York. Sadiq, R., Rodriguez, M.J., 2004. Fuzzy synthetic evaluation of disinfection byproducts: a risk-based indexing system. Journal of Environmental Management 73 (1), 1–13. Taroun, A., Yang, J.B., Lowe, D., 2011. Construction risk modelling and assessment: insights from a literature review. The Built & Human Environment Review 4 (Special Issue 1), 87–97. Xu, Y., Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., Wang, S., Ke, Y., 2010. Developing a risk assessment model for PPP projects in China — a fuzzy synthetic evaluation approach. Automation in Construction 19 (7), 929–943. Zaheh, L.A., 1965. Fuzzy sets. Information and Control 8 (3), 338–353. Zeng, J., An, M., Smith, N.J., 2007. Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management 25 (6), 589–600.
International Journal of Project Management 31 (2013) 449 – 458 www.elsevier.com/locate/ijproman
Risk analysis in ultra deep scientific drilling project — A fuzzy synthetic evaluation approach Jialin Liu a,⁎, Quanxi Li b , Yuhan Wang a
c
School of Management, College of Mechanical Science and Engineering, Jilin University, No. 5988, Renmin Avenue, Changchun, Jilin Province, 130022, People's Republic of China b School of Management, Jilin University, No. 5988, Renmin Avenue, Changchun, Jilin Province, 130022, People's Republic of China c College of Geo-Exploration Science and Technology, Jilin University, Changchun, Jilin Province, 130022, People's Republic of China Received 19 April 2012; received in revised form 14 September 2012; accepted 20 September 2012
Abstract Exploration of deep earth requires ultra deep drilling attempts on the sea or continent, which is the main goal of scientific drilling projects currently established. Uncertain geological complexity, high requirement for R&D of critical equipment as well as high demand of practical performance has to be encountered during a scientific drilling project, making it full of challenge and risks. Risk management, therefore, is critically proposed for scientific drilling projects in order to reduce the risks. However, many traditional risk assessment methods may not perform well in the project due to lack of high quality data of historical record and sufficient information. This paper, therefore, proposes a fuzzy synthetic evaluation approach for scientific drilling project risk assessment. Four criteria — probability, severity, non-detectability and worsening factor are utilized to evaluate individual and overall risks comprehensively. Linguistic terms instead of numerical values are employed to evaluate each risk normally done by experts. AHP/ANP is used to determine sensible weights of each criterion. Values of risk indices are calculated to represent the level of each risk and the overall risk. Finally, a case study on risk analysis of SinoProbe-09 project conducted in Jilin University is tested to demonstrate the procedure of the method and to validate the proposed method. Results show that the risks of the scientific drilling project can be assessed effectively and efficiently. © 2012 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Risk analysis; Scientific drilling project; Fuzzy synthetic evaluation; AHP/ANP
1. Introduction Deep scientific drilling (DSD) is a fundamental attempt to sample deep secrets underneath in modern Earth science research, to provide direct information about deep earth. Because of the explorative nature, scientific drilling projects comprise not only the drilling phase, bust also other related phases such as research and develop (R&D) on critical equipment to satisfy the ultra deep drilling needs (Harms et al., 2007). With the increase of drilling
⁎ Corresponding author. E-mail address: [email protected] (J. Liu). 0263-7863/$36.00 © 2012 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2012.09.015
depth, challenges increase exponentially (Kelessidis, 2009). For example, ultra deep wells would encounter high pressures and high temperatures requiring much better, thicker and very costly metals for equipment, which may possibly make traditional drilling equipment fail and costly lost. Moreover, the nature of scientific drilling has made it full of challenging regime to be handled, e.g., constant changes on drilling site, direct exposure to hazardous sources, high uncertainties and unknown underground geological complexity. Therefore, scientific drilling project, perhaps more than others, has been plagued by various risks often resulting in project failure, even great economic or human losses. Thus, risk management is necessary for scientific drilling projects to improve the performance and secure the success of the projects.
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Risk analysis for scientific drilling project, however, is intricate, especially at the early stages of the project, because the nature of risk is usually affected by numerous factors including natural factors, technical factors, etc. Many risk assessment techniques proposed in the literature and used in practice, such as fault tree analysis, event tree analysis, Monte Carlo Simulation, Bayesian network, fuzzy reason inference, etc. (Zeng et al., 2007), are difficult to be applied in scientific drilling project risk assessment, if not impossible. One reason is that high quality data are required for effective applications of the methods, which is impossible for scientific drilling project. Moreover, even such data exist, they cannot be used to address the uncertainties associated with new projects, since conditions change and requirements increase dramatically with the increase of drilling depth. One risk that can be neglected in previous drilling project may become a huge one in another project. It is therefore essential to develop new risk analysis methods to assess scientific drilling project risk in an acceptable way. To overcome the difficulties mentioned above, this paper proposes an AHP/ANP based fuzzy synthetic evaluation (FSE) approach to evaluate risks in deep scientific drilling project. Risks are usually evaluated from the perspectives of probability occurrence and the impact to calculate the risk level by multiplying the two quantities (Ouédraogo et al., 2011a). As stated by Ouédraogo et al. (2011a), risk evaluation should include more criteria such as worsening factor, research specificities, and use other functional form to calculate the risk level, e.g., summation including the use of logarithm. Applying the same idea, we use four criteria to evaluate a particular risk, e.g. probability, severity, non-detectability and worsening factor. Due to the lack of data, we cannot calculate the risk level for each risk. Therefore, a fuzzy synthetic evaluation approach is applied to evaluate risks in this paper. The application of fuzzy synthetic evaluation technique provides a systematic tool to deal with qualitative and quantitative data and information. In the application of FSE, weights for each criterion should be determined, where AHP/ANP is applied. The proposed method is validated by the case of SinoProbe-09 project conducted by Jilin University. The remainder of the paper is organized as follows: Section 2 reviews the related researches. The new risk assessment model is presented in Section 3. A case study is conducted in Section 4 to test the proposed model. Conclusions are drawn in Section 5.
2. Literature review Risk management is beneficial if it is implemented in a systematic manner from planning stage through the project completion. It has gained strong interest from academia and practice since the 2000s (Kwak and Anbari, 2009). Taroun et al. (2011) review the results of construction project risk assessment critically. Various methods are reviewed, including risk breakdown structures, fuzzy set theory, Monte Carlo simulation, AHP, ANP, Probability–Impact model, etc. Most methods assess risks from the perspectives of probability, severity and/or detectability (Ouédraogo et al., 2011a). Various risk assessment techniques are
different in the way of combining the different aspects into one value. Risk analysis and management of petroleum exploration ventures are growing all over the world and many international oil companies have improved their exploration performance by using principles of risk analysis in combination with new technologies (Harbaugh et al., 1995; Rose, 2001). Jones and Hillis (2003) propose a fault-seal risk assessment model that integrates parameters from different aspects of fault-seal analysis. Coelho et al. (2005) use two different methodologies, a Monte Carlo simulation and a connectionist approach to estimate the total time assessment in drilling and completion operations of oil wells in deep waters. Cunha et al. (2005) present comments and some background on the use of risk analysis methods in the oil and gas industry. Their attention is given to applications developed specifically for drilling operations. However, scientific drilling project has essential differences with traditional oil and gas drilling, which hampers the applicability of many risk assessment methods used widely in oil and gas industry. First, each ultra deep scientific drilling project has its unique characteristic, so the experience of one project cannot be applied to other projects. Thus, historical data needed by other risk assessment approaches cannot be obtained. Second, the risks of ultra deep drilling are huge and full of the whole project. Although geographical data can provide some help, the main source of information provided for the risk assessment is the knowledge of experienced engineers and experts, most of which is not precise data but vague linguistic description. In addition, there are too many uncertain factors during the whole project. Because of these differences, we cannot use the old methods in the ultra deep drilling project. To conquer, the difficulties of acquiring high quality data, many researchers combine experts' knowledge into the risk assessment process by using fuzzy set theory which was proposed by Zaheh (1965). By using fuzzy set theory, data can be defined as linguistic terms instead of numerical values. Several studies propose fuzzy counterpart of classical risk assessment methods, such as fuzzy fault tree analysis (Fujino, 1994), and fuzzy event tree analysis (Cho et al., 2002). Also, new methods based on fuzzy set theory are proposed for risk assessment. Zeng et al. (2007) propose a risk assessment model based on fuzzy reasoning and AHP approach. Nieto-Morote and Ruz-Vila (2011) propose a fuzzy approach to construction project risk assessment, in which three risk factors – risk impact, risk probability and risk discrimination are used to assess the overall risk. For more fuzzy risk assessment approaches, please see Nieto-Morote and Ruz-Vila (2011) and literature therein. Although the high riskiness has been recognized in the deep scientific drilling project as mentioned in the introduction, to the best of our knowledge, little study is done to assess the risk from the perspective of whole project using fuzzy approaches. While drilling operation risk is investigated in literature, it is only one source of risk in scientific drilling project as shown in the case study of this paper. In this paper, an AHP/ANP based fuzzy synthetic evaluation method is proposed to assess the risk of scientific drilling project.
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3. Proposed risk assessment model A risk assessment model, based on fuzzy synthetic evaluation and AHP/ANP, is proposed as shown in Fig. 1. The model consists of three steps: preliminary step, evaluate each risk step and evaluate the overall risk step. The details are described in the following subsections. 3.1. Preliminary step 3.1.1. Establish a risk assessment group Risk assessment starts with the establishment of a risk assessment group whose members must be carefully selected. The selected experts must have different backgrounds/disciplines and essential experiences regarding the concerned project (NietoMorote and Ruz-Vila, 2011; Zeng et al., 2007). In a scientific drilling project, a risk assessment team must include the following
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experts: project managers, R&D scientists, drilling engineers, equipment suppliers, equipment operators, experts from outside the project team, etc. The members in the risk assessment team will undertake the risk identification, risk criteria determination, comparing relative importance between criteria, and evaluation of risks under each criterion. In addition, the risk assessment group will take in charge of the measurements of risk control according to the risk assessment. In our case study, the Chinese Government's Executive Program “Instrumentation development and field experimentation” (SinoProbe-09) is selected, which provides key information and capability of this article. The authors themselves are also members of the management staff of that program. The risk analysis reported in this article is a subproject of SinoProbe-09. And the program is totally aware of and also fully supports this. 3.1.2. Identify risk factors Risk identification is the process in which threats for the project concerned are investigated. Usually, in scientific drilling projects, the following question will be asked to the risk assessment group: “what can go wrong in the scientific drilling project” or “what accident could occur during the project”. This process is of great importance because the risk analysis and measures may be performed on identified potential risk. The identification of risks requires a deep understanding of the scope of the project process, related professional knowledge, experience as well as imagination. Because of the knowledge-intense property of scientific drilling project, multi-disciplined experts must be included in the group and identify the potential risks iteratively. Besides intuitive methods, there are some tools for risk identification, such as: checklist, influence diagrams, cause and effect diagrams, failure mode and effect analysis, etc. Detailed introduction to the methods are described by Ahmed et al.(2007). In scientific drilling projects, 6 categories—natural risk, R&D risk, management risk, drilling operation risk, drilling equipment risk, and environmental and security risk—of risks, including 25 risk factors, are identified by Liu et al., 2011 as shown in Table 1. 3.2. Evaluate a specific risk
Fig. 1. Risk assessment model.
After identifying the potential risks, we should evaluate the risks one by one, on which risk reduction decisions should be based. This is often fulfilled by ranking or prioritizing the risk priority number (RPN), which is usually calculated as the product of risk probability (RP), risk severity (RS) and /or detectability (RD). However, as stated by Bowles (2003), different combinations of ranking factors may produce the same RPN. To overcome the drawbacks of RPN, Ouédraogo et al.(2011a,b) propose a Lab Criticity Index (LCI), which is a rather comprehensive function of probability, severity, risk worsening factors, research specificities and hazard detectability. However, the lack of historical data and great uncertainties in scientific drilling project hamper the applicability of LCI. To deploy the merit of LCI, we use their idea of multiple evaluation criteria, but calculate the risk level by fuzzy synthetic evaluation (FSE) approach.
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Table 1 Risk factors in scientific drilling project. Natural risk
Geological factor
R&D risk
Corollary equipment supply The difficulties of critical techniques The stability of research team The capability of research team Management organization Management staff Financial management Management policy and implementation Stuck pipe Drilling string failure Wellbore stability Coring effect Lost circulation Junk in hole Crooked hole Cementing quality Blowout Operations of constructors Hook load Torsion Pressure and flow output of mud pump Well out of control
Management risk
Drilling operation risk
Drilling equipment risk
Environmental and security risk
FSE approach is a method to assess multi-criteria decision making (Xu et al., 2010), and has been adopted in many fields, including assessment of reservoir water quality (Lu et al., 1999), health risk assessment (Sadiq and Rodriguez, 2004), PPP project risk assessment (Xu et al., 2010), etc. These studies show that FSE approach has merits in handling complicated evaluation with multi-attributes and multi-levels. In this research, it is used to calculate the risk index of each risk and the overall risk index of scientific drilling project. The procedures of fuzzy synthetic evaluation approach are as follows (Xu et al., 2010): (1) Determine the set of basic criteria/factors π = {f1,f2, ⋯,fm}, where m is the number of criteria. (2) Determine the set of grade alternatives E = {e1,e2, ⋯,en}, where n is the number of alternatives. For example, e1 = very low; e2 = low; e3 x= mod erate; e4 = high; and e5 = very high. Grades will be given for each alternative, such as 1 = very low; 2 = low; 3 = mod erate; 4 = high; and 5 = very high. (3) Determine weight for each criterion/factor W = {w1,w2, ⋯, wm}. The weight of each criterion can be obtained by various approaches, for example, AHP/ANP used in this paper, expert scoring, etc. (4) For each criterion, an evaluation is a fuzzy subset of grade set, whose membership function can be established by the risk assessment group. For example, if the survey results on the probability of blowout risk indicate that 5% of the experts opined the probability of occurrence as very low, 15% as low, 45% as moderate, 35% as high and
0% as very high, then the membership function of the probability of blowout is given by Eq. (1):
f1 ¼
0:05 0:15 0:45 0:35 þ þ þ very low low moderate high þ
¼
ð1Þ
0:00 very high
0:05 0:15 0:45 0:35 0:00 þ þ þ þ : 1 2 3 4 5
It can also be written as (0.05, 0.15, 0.45, 0.35, 0.00). All the evaluations form a fuzzy evaluation matrix R ¼ r ij mn , where rij is the degree to which alternative ej satisfies the criterion fi. (5) Fuzzy synthetic evaluation. The results of the evaluation are obtained by calculating the fuzzy composition of the weighting vector and the fuzzy evaluation matrix as shown in Fig. 2, where ∘ is a fuzzy composition operator. The final evaluation denoted by D is also a fuzzy subset of the alternative set. Four composition operators can be used to determine the results of the evaluation. Operator 1: Mð∧; ∨Þ; dj ¼ ∨m dj ∈D i¼1 wi ∧r ij ; Operator 2: Mð]; ∨Þ; dj ¼ ∨m dj ∈D i¼1 wi r ij ; Operator 3: Mð]; ⊕Þ;
dj ¼ min 1;
m X
!
wi rij ;
dj ∈D
i¼1
Operator 4: Mð∧; þÞ; dj ¼
m X
wi ∧r ij ;
dj ∈D:
i¼1
The symbol ⊕ in Operator 3 denotes the summation of product of weight and membership function. Note that the four operators are suitable for different settings. Operators 1 and 2 can be applied for single-item problem in which only the major criteria could be considered while other minor criteria would be ignored. Operator 3 suits for the setting where many criteria are considered and the difference in the weights of each criterion is not great. Operator 4 will miss some information with small weights. In our study, Operator 3 will be used. (6) Normalize the fuzzy evaluation vector and calculate the risk index according to
RI ¼
n X
ð2Þ
d k ek :
k¼1
3.2.1. Determine the evaluation criteria Risk is usually be assessed by considering two fundamental risk parameters: risk likelihood and risk impact (Ahmed et al.,
W
R
D W R
Fig. 2. The Fuzzy synthetic evaluation process.
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2007; Xu et al., 2010). However, the two criteria are not enough to evaluate the risk. For example, a particular risk is also dependent on many involved factors, e.g., human factors, workplace factors, etc. (Zeng et al., 2007). Also, these parameters do not take into account the impact of the risk to the overall framework of the project. A parameter called risk discrimination is proposed by Kendrick (2003) to address this issue (Cervone, 2006). Detectability is also used in risk management literature (Ouédraogo et al., 2011a). Ouédraogo et al. (2011a, b) suggest estimating risk in research environment as a combination of severity, probability, detectability, worsening factors and research specificities. The studies show that evaluation criteria play an important role in risk analysis. Different criteria should be developed for different projects. Our proposed risk assessment model is adaptive for the number of criteria. Through the discussions of risk assessment group on scientific drilling project, four criteria are proposed to evaluate a particular risk: probability, severity, non-detectability and worsening factors. Probability and severity are two commonly used criteria for evaluating risks. Probability investigates the likelihood that each specific risk will occur. The severity investigates the potential effect of the risk on economy, environment, human, etc. Non-detectability is a measure of risk detection. Risk can be detected in different ways during drilling operations, by using detectors, indicators or by human senses. Significant difference exists between risks on the detectability in scientific drilling project. R&D risk is less detectable than drilling operation risk. Effective measures can be performed before risky event occurs if it can be detected accurately and timely, which means this event is not such risky. To align with other criteria on the grades of evaluation, non-detectability instead of detectability is used in this paper. The risk worsening factors are conditions which can amplify direct consequences, hence the severity (Ouédraogo et al., 2011a). In scientific drilling project, the loss usually does not occur at once, but in a dynamic manner. Improper actions may amplify the loss exponentially (Knock, 2011). 3.2.2. Establish a set of grade alternatives To utilize the fuzzy synthetic evaluation approach, a set of grade alternatives or linguistic evaluations and scores should be established. In this paper, the grades are defined as E = {1,2,3,4,5}, where 1 = very low; 2 = low; 3 = moderate; 4 = high and 5 = very high. 3.2.3. Determine weight for each criterion When using fuzzy synthetic evaluation approach, one must set weight for each criterion. However, it is a difficult task to assign weight for each criterion. To overcome this difficulty, analytic hierarchy process (AHP), which was proposed by Saaty (1980), is applied. AHP provides a way to input judgments and measurements to derive ratio scale priorities for the distribution of influence among the factors and groups of factors in the decision. Since its development, successful applications in planning, selecting the best alternative and predict market share and so on are reported (Saaty and Vargas, 2006). In this paper, AHP is used to weight the criteria. The hierarchy structure of the problem is presented in Fig. 3.
453
Fig. 3. The hierarchy structure of the evaluation problem.
Pairwise comparisons will be conducted by the members of risk assessment group using fundamental scales 1–9 as proposed by Saaty (1980). The output of this procedure is the weightings of the criteria. For the detailed procedures of AHP, please refer to Saaty and Vargas (2006). 3.2.4. Establish fuzzy evaluation matrix The fuzzy evaluation matrix is made up of membership functions of evaluations for the criteria of a specific risk. These membership functions can be formed by evaluations of experts. Each expert will make a judgment on the level of each criteria of a particular risk. Statistical results will be the membership function for the evaluation for each criterion. Example has been given in step (4) of the procedures of FSE. A complete case study will be conducted in Section 4. 3.2.5. Calculate risk index After establishing the weighting vector and fuzzy evaluation matrix, the evaluation for each risk can be conducted using fuzzy composition operators. In this paper, the Operator 3 will be used and the risk index for each risk can be calculated according to Eq. (2). The evaluation of each risk has been done now, and the risks can be rated by the numerical value of their risk index. The risks can be grouped into clusters by rounding their risk indices to nearest grades. 3.3. Evaluate overall risk For evaluating the overall risk, FSE approach is still used with a minor difference to that used for evaluating a specific risk. In this setting, the criteria/factors set becomes the set of risks, e.g. π = {f1,f2, ⋯,fm}, where fi is the ith risk. In this paper, fi is the ith risk in a scientific drilling project. The set of grade alternatives is the same as that for evaluating each risk. Thus, the fuzzy evaluation matrix is formed by the evaluations of risks. Table 2 Judgment matrix. Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
1 4 2 4
1/4 1 1/2 1
1/2 2 1 2
1/4 1 1/2 1
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Table 3 The fuzzy evaluation matrix.
Geological factor
Corollary equipment supply
The difficulties of critical techniques
The stability of research team
The capability of research team
Management organization
Management staff
Financial management
Management policy and implementation
Stuck pipe
Drilling string failure
Wellbore stability
Coring effect
Lost circulation
Junk in hole
Crooked hole
Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
Very low
Low
Moderate
High
Very high
0.00 0.00 0.10 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.58 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.23 0.00 0.69 0.00 0.02 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.06 0.00 0.00 0.00 0.14 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.21 0.00
0.14 0.00 0.30 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.55 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.75 0.00 0.00 0.00 0.45 0.00 0.00 0.34 0.42 0.00 0.00 0.00 0.49 0.00 0.00 0.00 0.27 0.00 0.31 0.00 0.61 0.00 0.20 0.00 0.24 0.00 0.02 0.00 0.86 0.00 0.00 0.00 0.58 0.00 0.00 0.00 0.59 0.00 0.00 0.00 0.21 0.00 0.00 0.00 0.79 0.00
0.55 0.30 0.60 0.00 0.09 0.23 0.05 0.22 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.30 0.00 0.50 0.30 0.32 0.66 0.00 0.16 0.00 0.64 0.41 0.00 0.00 0.79 0.50 0.01 0.00 0.03 0.37 0.00 0.66 0.00 0.00 0.15 0.69 0.00 0.04 0.00 0.02 0.07 0.36 0.00 0.16 0.00 0.27 0.00 0.00 0.12 0.59 0.00 0.00 0.01 0.00 0.00
0.30 0.65 0.00 0.55 0.86 0.70 0.00 0.64 0.10 0.40 0.00 0.50 0.80 0.82 0.00 0.03 0.60 0.85 0.00 0.02 0.61 0.66 0.00 0.70 0.49 0.00 0.00 0.77 0.57 0.36 0.00 0.13 0.85 0.18 0.00 0.61 0.00 0.79 0.00 0.02 0.14 0.01 0.00 0.80 0.29 0.89 0.00 0.63 0.94 0.90 0.00 0.09 0.64 0.83 0.00 0.21 0.07 0.67 0.00 0.19 0.61 0.66 0.00 0.10
0.01 0.05 0.00 0.45 0.05 0.07 0.00 0.14 0.90 0.00 0.00 0.50 0.20 0.18 0.00 0.96 0.40 0.15 0.00 0.98 0.09 0.34 0.00 0.00 0.19 0.00 0.00 0.07 0.43 0.00 0.00 0.87 0.15 0.03 0.00 0.38 0.00 0.18 0.00 0.98 0.00 0.99 0.00 0.05 0.00 0.11 0.00 0.37 0.04 0.03 0.00 0.91 0.20 0.17 0.00 0.79 0.93 0.21 0.00 0.81 0.39 0.33 0.00 0.90
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Table 3 (continued)
Cementing quality
Blowout
Operations of constructors
Hook load
Torsion
Pressure and flow output of mud pump
Well out of control
Harm of sulfured hydrogen
Discharge of waste drilling fluids
Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor Probability Severity Non-detectability Worsening factor
3.3.1. Determine weights for each risk To assess the overall risk, the weightings for each risk should be set. However, as shown in Liu et al. (2011), the risks are not independent from each other. In this case, AHP loses its power. Instead of AHP, we use ANP – analytic network process – which is proposed by Saaty (1996). ANP is a generalization of AHP by considering the dependence between the elements of the hierarchy. The steps of using ANP are described briefly as follows. For details, please refer to Saaty and Vargas (2006). Step 1. Determine the network structure Step 2. Pairwise comparison between elements to form the supermatrix Through pairwise comparisons a priority vector is derived, which represents the impact of a given set of elements in a component on another element in the system. The priorities are entered as parts of the columns of a supermatrix, which denotes the influence priority of an element on the left of the matrix on an element at the top of the matrix with respect to a particular control criterion. Step 3. Construct the cluster matrix The cluster themselves must be compared to establish their relative importance and must use it to weigh the corresponding
Very low
Low
Moderate
High
Very high
0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.89 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.23 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.00 0.21 0.20 0.50 0.00 0.00 0.60 0.01 0.00 0.00 0.00 0.00 0.00 0.14 0.30 0.00 0.00 0.10 0.10 0.00 0.00 0.00 0.91 0.00 0.10 0.00 0.12 0.00
0.62 0.12 0.01 0.16 0.00 0.00 0.00 0.00 0.10 0.57 0.00 0.50 0.53 0.56 0.32 0.28 0.00 0.60 0.01 0.00 0.09 0.64 0.70 0.53 0.02 0.00 0.01 0.00 0.00 0.20 0.00 0.00 0.45 0.36 0.65 0.30
0.38 0.86 0.81 0.78 0.76 0.00 0.00 0.51 0.78 0.22 0.00 0.00 0.41 0.20 0.00 0.59 0.65 0.39 0.74 0.81 0.71 0.22 0.00 0.38 0.65 0.40 0.00 0.79 0.89 0.61 0.00 0.88 0.45 0.62 0.00 0.54
0.00 0.02 0.18 0.06 0.24 1.00 0.00 0.49 0.12 0.00 0.00 0.00 0.06 0.24 0.00 0.12 0.35 0.01 0.25 0.19 0.20 0.00 0.00 0.09 0.33 0.50 0.00 0.21 0.11 0.19 0.00 0.12 0.00 0.02 0.00 0.16
blocks of the supermatrix to make it column stochastic. The clusters linked from the source cluster are pairwise compared for the importance of their impact on it with respect to the control criteria. Step 4. Construct the weighted supermatrix The weighted supermatrix is obtained by multiplying each entry in a block of the component at the top of the supermatrix by the priority of influence of the component on the left from the cluster matrix. Step 5. Limit supermatrix In the final step, the weighted supermatrix is raised to powers to get the global limit priority vectors. If the supermatrix has the effect of cyclicity, the Cesaro sum is used to get the average priority weights. The resulting priority weights will be the input of the fuzzy synthetic evaluation approach. 3.3.2. Evaluate the overall risk by FSE Fuzzy operators will be performed on the weighting vector and the fuzzy evaluation matrix to produce the evaluation of the overall risk. Note that if the risks belong to different clusters, using the same procedure can produce the evaluation of overall
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Table 4 The membership function and risk for each risk.
Geological factor Corollary equipment supply The difficulties of critical techniques The stability of research team The capability of research team Management organization Management staff Financial management Management policy and implementation Stuck pipe Drilling string failure Wellbore stability Coring effect Lost circulation Junk in hole Crooked hole Cementing quality Blowout Operations of constructors Hook load Torsion Pressure and flow output of mud pump Well out of control Harm of sulfured hydrogen Discharge of waste drilling fluids
Membership function
Risk index
Ranking
Weighting
(0.018, 0.068, 0.271, 0.462, 0.181) (0.081, 0.09, 0.18, 0.568, 0.081) (0.081, 0.099, 0.216, 0.334, 0.27) (0.09, 0.09, 0.0036, 0.38, 0.4304) (0.045, 0.135, 0, 0.3732, 0.4468) (0.009, 0.081, 0.228, 0.551, 0.131) (0.104, 0.198, 0.327, 0.326, 0.044) (0.018, 0.088, 0.304, 0.233, 0.356) (0.041, 0.049,0.378, 0.369, 0.163) (0.073, 0.141,0.077, 0.292, 0.418) (0.137, 0.063,0.12, 0.306, 0.374) (0.018, 0.157,0.076, 0.576, 0.173) (0.011, 0.104,0.092, 0.45, 0.342) (0.025, 0.106,0.065, 0.438, 0.366) (0.036, 0.038,0.149, 0.317, 0.46) (0.038, 0.142, 0.004, 0.335, 0.482) (0, 0, 0.165, 0.774,0.061) (0.121, 0.059,0.26,0.56) (0.144, 0.292, 0.395, 0.157, 0.012) (0.014, 0.112, 0.413, 0.325, 0.136) (0, 0, 0.218, 0.63, 0.152) (0, 0.104,0.556, 0.287, 0.052) (0.16, 0.054, 0.004,0.493, 0.289) (0.016, 0.164, 0.072, 0.625, 0.123) (0.041, 0.032, 0.4, 0.463, 0.065)
3.72 3.4776 3.613 3.9768 4.0418 3.7144 3.0078 3.8216 3.5632 3.8408 3.7176 3.729 4.0092 4.013 4.1272 4.0804 3.8966 4.0798 2.6016 3.4562 3.9342 3.2874 3.6962 3.6744 3.4778
13 21 18 7 4 15 24 11 19 10 14 12 6 5 1 2 9 3 25 22 8 23 16 17 20
0 0.025799 0.069318 0.054026 0.065915 0.041579 0.045384 0.049522 0.054076 0.027981 0.021359 0.01809 0.021832 0.017844 0.018907 0.017708 0.019258 0.019978 0.019781 0.07898 0.101404 0.080584 0.027123 0.051381 0.052168
risk in that cluster. For simplicity, we omit this step to produce the overall risk directly. 3.3.3. Calculate overall risk index The method used to calculate the overall risk index is the same as that for a specific risk. The overall risk level can be obtained by translating the overall risk index into the risk levels.
by experts from related areas is built up. The judgments used in the proposed methodology are collected from both the risk assessment group and the consulting team members. 4.1.2. Identify risk factors On the basis of their experience and qualification, the two teams identify 25 risks as listed in Table 1. The risks have been explained in a companion paper (Liu et al., 2011) and are omitted here.
4. Case study: risk assessment of Sinoprobe-09 project To illustrate the applicability of the proposed method, a case study of risk assessment on SinoProble-09 project conducted by Jilin University is presented in this section. This project aims at the ten thousand-meter well drilling. It is of great challenge due to the high uncertainties and complexities. There are many risk sources which can lead to significant losses including economic loss, environmental impact, human safety, etc. Thus, risk assessment is necessary at the beginning of the project. However, the risks are difficult to measure due to the lack of practical data and information. Therefore, the proposed method has been used to assess the risks of the scientific drilling project.
4.2. Evaluate a specific risk 4.2.1. Determine evaluation criteria For each risk, the risk assessment group agrees to use four criteria to evaluate it, e.g., probability, severity, non-detectability and worsening factor. 4.2.2. Establish a set of grade alternatives In this project, the five-point Likert scale (1 = very low, 5 = very high) is used to calculate the mean score for each risk, e.g., the set of grade alternatives is defined as E = {1, 2, 3, 4, 5}, where1 = very low; 2 = low; 3 = moderate; 4 = high; and 5 = very high (for all the four criteria).
4.1. Preliminary step 4.1.1. Establish a risk assessment group An eight-person risk assessment group is established, including a project manager, two R&D scientists, two Geoscience experts, and three engineers. Moreover, a consulting team formed
4.2.3. Determine weight for each criterion The four criteria can be constructed as a criteria hierarchy as shown in Fig. 3. The relative importance to the overall risk is pair-wise compared by the risk assessment group. The judgment matrix is shown in Table 2. Use the standard calculation procedure
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of AHP, we can compute the weight for each criterion as (0.10, 0.36, 0.18, 0.36).
4.2.4. Establish fuzzy evaluation matrix For each risk, the membership function can be formed by the evaluations of experts in risk assessment group and consulting group. Statistics are conducted on surveys to form the fuzzy evaluation matrix as shown in Table 3.
457
function of the overall risk, which is (0.045, 0.094, 0.227, 0.413, 0.222). The calculation procedure is as follows: DA ¼ W A ∘RA ¼ ð0:045; 0:094; 0:227; 0:413; 0:222Þ;
where DA is the membership of the overall risk, W A ¼ ð0; 0:258; 0:0693; 0:0540; 0:0659; 0:0416; 0:0454; 0:0495; 0:0541; 0:0280; 0:0214; 0:0181; 0:0218; 0:0178; 0:0189; 0:0177; 0:0193; 0:0200; 0:0198; 0:0790; 0:1014; 0:0806; 0:0271; 0:0514; 0:0522Þ
4.2.5. Calculate risk index The evaluation of each risk is derived from the Operator 3 as mentioned in Section 3.2. Take the geological factor as an example; its membership function is as follows: 0
0:00 B 0:00 B ð0:10; 0:36; 0:18; 0:36Þ∘@ 0:10 0:00
0:14 0:00 0:30 0:00
0:55 0:30 0:60 0:00
0:30 0:65 0:00 0:55
1 0:01 0:05 C C 0:00 A 0:45
¼ ð minð1; 0:10 0:00 þ 0:36 0:00 þ 0:18 0:10 þ 0:36 0:00Þ; minð1; 0:10 0:14 þ 0:36 0:00 þ 0:18 0:30 þ 0:36 0:00Þ; minð1; 0:10 0:55 þ 0:36 0:30 þ 0:18 0:60 þ 0:36 0:00Þ; minð1; 0:10 0:30 þ 0:36 0:65 þ 0:18 0:00 þ 0:36 0:55Þ; minð1; 0:10 0:01 þ 0:36 0:05 þ 0:18 0:00 þ 0:36 0:45ÞÞ ¼ ð0:018; 0:068; 0:271; 0:462; 0:181Þ:
The risk index is calculated according to Eq. (2) as follows, 0:018 1 þ 0:068 2 þ 0:271 3 þ 0:462 4 þ 0:181 5 ¼ 3:72:
The final evaluation for each risk is listed in Table 4, with membership function listed in the second column, risk index listed in the third column, and the ranking listed in the forth column. From Table 4, we can see that ‘junk in hole’ has the highest risk index, meaning that particular attention should be paid on junk in hole and effective measures should be adopted to protect the accident occurring. Moreover, the risk index of “junk in hole” is 4.13, which can be seen as “high”. 4.3. Evaluate overall risk In this case, the factor set includes all the risks, e.g. π = {f1, f2, ⋯,f25} where fi is the ith risk. The set of grade alternatives is the same as that mentioned above. 4.3.1. Determine weight for each risk As our earlier study (Liu et al., 2011), the risks are not independent with each other. Therefore, AHP cannot be applied in this setting. In turn, we use the extension version of AHP, e.g., ANP. The relative importance of each risk has been reported in Liu et al. (2011).We omit the detailed computation procedures and use the results directly as shown in the last column in Table 4. 4.3.2. Evaluate the overall risk The fuzzy evaluations for each risk in Table 4 of Section 4.2.5 are used to form the fuzzy evaluation matrix. Operator 3 is used to composite the weighting vector and fuzzy evaluation matrix to get the fuzzy evaluation for the overall risk, e.g., the membership
2
3 0:018 0:068 0:271 0:462 0:181 6 0:081 0:09 0:18 0:568 0:081 7 6 7 6 0:081 0:099 0:216 0:334 0:27 7 6 7 6 0:09 0:09 0:0036 0:38 0:4304 7 6 7 6 0:045 0:135 0 0:3732 0:4468 7 6 7 6 0:009 0:081 0:228 0:551 0:131 7 6 7 6 0:104 0:198 0:327 0:326 0:044 7 6 7 6 0:018 0:088 0:304 0:233 0:356 7 6 7 6 0:041 0:049 0:378 0:369 0:163 7 6 7 6 0:073 0:141 0:077 0:292 0:418 7 6 7 6 0:137 0:063 0:12 0:306 0:374 7 6 7 0:576 0:173 7 RA ¼ 6 6 0:018 0:157 0:076 7: 6 0:011 0:104 0:092 0:45 0:342 7 6 7 6 0:025 0:106 0:065 0:438 0:366 7 6 7 6 0:038 0:142 0:004 0:355 0:482 7 6 7 6 0 0 0:165 0:774 0:016 7 6 7 6 0:121 0:059 0 0:26 0:56 7 6 7 6 0:144 0:112 0:413 0:325 0:136 7 6 7 6 0 0 0:218 0:63 0:152 7 6 7 6 0 0:104 0:556 0:287 0:052 7 6 7 6 0:16 0:054 0:004 0:493 0:289 7 6 7 4 0:016 0:164 0:072 0:625 0:123 5 0:014 0:032 0:4 0:463 0:065
The overall risk index is calculated according to Eq. (2) as the following: 0:045 1 þ 0:094 2 þ 0:227 3 þ 0:413 4 þ 0:222 5 ¼ 3:673:
ð3Þ The results (Table 4 and Eq. (3)) show that the overall risk index of this scientific drilling project is 3.673, which can be regarded as high. Therefore, the risk level for the project can be construed as “high”. Moreover, among various risks, “junk in hole” is the most critical risk, with risk index equal to 4.127; with “crooked hole” being the second, with risk index equal to 4.08; and with “blowout” being the third, with risk index equal to 4.0798. Moreover, among the 25 risk factors, 19 risk factors can be seen as high risk, which made the project more risky. 5. Conclusions The nature of deep scientific drilling makes many welldeveloped risk assessment methods invalid. This paper adopts a novel approach to develop a practical risk assessment model for scientific drilling project to deal with risks associated with the projects in the complicated situations in which information to assess risks is non-obtainable. This approach uses probability, severity, non-detectability, and worsening factor to comprehensively evaluate a particular risk. Moreover, this approach allows
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members in the risk assessment group and other experts to make their judgments by means of linguistic terms instead of real numbers. Due to the different importance of criteria in evaluating risks, AHP/ANP is adopted to determine the weightings of criteria. Fuzzy synthetic evaluation approach is applied to obtain the evaluation of each risk and a risk index is calculated to indicate the level of each risk. This approach can also assess the overall risk level of the whole project by applying similar procedures. To illustrate how the approach works, a real case on risk assessment of SinoProbe-09 project conducted by Jilin University is presented. The findings show that it is an effective and efficient method for assessing scientific drilling risk. The proposed approach provides a comprehensive, objective, reliable and practical risk assessment tool for scientific drilling project. It evaluates risks from multiple perspectives. Moreover, it can handle experts' knowledge by manipulating linguistic terms directly. Furthermore, it can produce a risk index for each risk and the overall risk, which can be used to indicate the risk level. According to the progress of the project, our method in the implementation of the project continues to be implemented. It validates the applicability of the four standard evaluation methods we proposed in this article. As the project progresses, the actual situation is also confirmed in our judgment. Acknowledgment This research was supported by the Chinese Government's Executive Program “Instrumentation development and field experimentation” (SinoProbe-09). References Ahmed, A., Kayis, B., Amornsawadwatana, S., 2007. A review of techniques for risk management in projects. Benchmarking: An International Journal 14 (1), 22–36. Bowles, J.B., 2003. An assessment of RPN prioritization in a failure modes effects and criticality analysis. Reliability and Maintainability Symposium, pp. 380–386. Cervone, H.F., 2006. Project risk management. OCLC Systems and Services 22 (4), 256–262. Cho, H.-N., Choi, H.-H., Kim, Y.-B., 2002. A risk assessment methodology for incorporating uncertainties using fuzzy concepts. Reliability Engineering and System Safety 78 (2), 173–183. Coelho, D.K., Roisenberg, M., de Freitas Filho, P.J., Jacinto, C.M.C., 2005. Risk assessment of drilling and completion operations in petroleum wells using a Monte Carlo and a neural network approach. In: Kuhl, M.E., Steiger, N.M., Armstrong, F.B., Joines, J.A. (Eds.), Proceedings of the 2005 Winter Simulation Conference. Cunha, J.C., Demirdal, B., Gui, P., 2005. Quantitative risk analysis for uncertainty quantification on drilling operations: review and lessons learned. SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro.
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