- Email: [email protected]
mineral: Application to CO2 geo-sequestration
Journal of Cleaner Production 239 (2019) 118101
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Modeling CO2 wettability behavior at the interface of brine/CO2/mineral: Application to CO2 geo-sequestration Amin Daryasafar a, Amin Keykhosravi b, Khalil Shahbazi a, * a
Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran Faculty of Petroleum and Natural Gas Engineering, Sahand Oil & Gas Research Institute (SOGRI), Sahand University of Technology, Sahand New City, Tabriz, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 March 2019 Received in revised form 16 August 2019 Accepted 18 August 2019 Available online 19 August 2019
Carbon capture and storage (CCS) has been introduced as an effective method for reduction of CO2 emissions to the atmosphere. While different aspects and controlling parameters of geological CO2 sequestration process are well understood, there is still large uncertainty regarding brine/CO2/rock wettability and no model has yet been presented for characterizing the contact angle of brine/CO2/rock system at different environmental conditions. In this study, various intelligent models have been developed for accurate estimation of brine/CO2/rock contact angles. Results demonstrated that the proposed models have the ability to accurately simulate the brine/CO2 wettability behavior for quartz, calcite, feldspar, and mica minerals at different pressures, temperatures and salinities. Additionally, results revealed that adaptive neuro-fuzzy interference system has the best performance compared with the other models and its superiority is shown through statistical and graphical analyses. Afterward, the most effective input parameters on brine/CO2/rock wettability were investigated by employing MonteCarlo algorithm, which showed that mineral type, salinity and pressure are the most sensitive variables for this process. As wettability can highly affect the residual and structural trapping mechanisms during carbon geo-sequestration, presenting reliable models for estimating brine/CO2/rock wettability is crucial. Therefore, the outcomes of this study can be useful for accurate estimation of these mechanisms capacity. © 2019 Elsevier Ltd. All rights reserved.
Handling editor. Prof. Jiri Jaromir Klemes Keywords: Carbon storage Contact angle Minerals Modeling Sensitivity analysis
1. Introduction Emission of CO2 into the atmosphere has been known as a major cause for climate change in recent years. It is predicted that these emissions are increasing due to the proceeding of global industrialization and increased energy demand. Therefore, regulations have been issued for reduction of CO2 emissions from industries. Different approaches such as using biofuels instead of fossil fuels (Dhinesh and Annamalai, 2018; Dhinesh et al., 2018; Vigneswaran et al., 2018), geological CO2 sequestration and etc. have been proposed for CO2 emissions reduction. One of the most recommended and effective methods for reduction of emitting greenhouse gases in to the atmosphere is geological storage and carbon dioxide (CO2) capture (Zhang and Huisingh, 2017). In this method, carbon dioxide is collected from different industrial sources and then injected into
* Corresponding author. E-mail address: [email protected] (K. Shahbazi). https://doi.org/10.1016/j.jclepro.2019.118101 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
deep geological beds such as saline aquifers, coal stratums, gas and oil reservoirs and etc., which have special characteristics that make them appropriate for CO2 storage (Arif et al., 2016a; Joshi et al., 2016). Among these geological beds, deep saline aquifers play a vital role and are the main targets for storage of CO2 (Nghiem et al., 2009; Seyyedi et al., 2016). Geological sequestration of carbon dioxide combined with water production from deep saline aquifers can significantly overcome the issues encountered by modern energy systems of CO2 emissions reduction and water intensity while providing secure, reliable, and affordable energy (Liu et al., 2016). Chen et al. (2018) analyzed both the carbon capture and storage (CCS) and CO2 enhanced water recovery (CO2-EWR) technology under various scenarios for CO2 injection and proposed an optimal scenario by performing numerical simulation. They found that by performing this scenario, more carbon dioxide can be stored in reservoir as well as brine can be produced by a single or multiple production wells. They also concluded that pre-production and coproduction of brine for the combined CO2-EWR are more effective in pressure perturbation controlling and increasing carbon dioxide
2
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
storage capacity. Additionally, increasing the number of wells can increase CO2 storage, but the revenue in applying this technology cannot compensate the costs of drilling wells. Jing et al. (2019) investigated the effect of salinity and dip angle on the storage amount and storage form of CO2 during geological storage of carbon dioxide. For this purpose, a 3D simulation model was used. Results demonstrated that larger dip angles yield greater migration distance for CO2 and greater salinities result in smaller total storage amount. They also added that higher salinity and dip angle significantly decrease CO2 geological safety and hence, a reservoir with lower salinity and smaller dip angle should be selected for CO2 storage. In carbon capture and storage (CCS), CO2 is often injected at 800m depth or higher and this is necessary for increasing the storage capacity since at these depths, CO2 is in supercritical state. However, supercritical CO2 migrates upward and it is buoyant (Pruess et al., 2003). There are different mechanisms that can prevent CO2 from escaping, namely mineral (Gaus, 2010), solubility (Iglauer, 2011), residual (Juanes et al., 2006), and structural trapping (Hesse et al., 2008). Performance of these mechanisms is a complex function of cap rock and aquifer parameters and each mechanism is active on various time scales, but it is believed that the most important mechanisms during the first years of a storage project are residual and structural trapping mechanisms (Iglauer et al., 2012; Al-Khdheeawi et al., 2016), Fig. 1. In structural trapping, carbon dioxide is trapped below a seal layer with an extremely low permeability and residual trapping is relevant to the cases in which the there is no cap rock (Qi et al., 2009). In residual trapping, capillary forces prevent CO2 to be mobilized and hence, this mechanisms capacity is a function of the contact angle of pore spaces (rock wettability), CO2-liquid/brine interfacial tension (IFT), initial carbon dioxide saturation, the morphology of the pores of the rock, etc. (Pentland et al., 2011; Raza et al., 2016). Based on this discussion, accurate knowledge of wettability is crucial for estimating the capacity of residual trapping and forecasting potential CO2 leakage. Wettability is tendency of the surface of rock to be in contact with a fluid more than the other ones and it can highly affect the residual CO2 saturation, capillary pressure, relative permeability, and mass transfer between CO2 and brine (Abdallah et al., 1986;
Anderson, 1987). Due to the importance of wettability and as it can vary significantly with changes in environmental conditions, several researchers have reported brine/CO2contact angle on different minerals as a function of temperature, pressure and salinity. Farokhpoor et al. (2013a) measured brine/CO2 contact angles on quartz, calcite, mica and feldspar as function of pressure, temperature and salinity. They demonstrated that contact angle on mica significantly changes with pressure from strongly water wet to less water wet and this wettability alteration is due to the intermolecular forces controlling the water film thickness and stability, while non-significant changes were observed for the other minerals. They also concluded that for quartz, calcite and feldspar, the maximum contact angle near critical pressure is found at 36 C. Chen et al. (2015) investigated the advancing, receding and static contact angles of brine/CO2 on quartz at various conditions of pressure, temperature and salinity and finally simulated the wettability behavior using molecular dynamic simulation. Their results showed that the water contact angle increases with ionic strength and temperature and pressure can insignificantly affect the contact angle values. Results of the molecular dynamic simulation were also in a very good agreement with the reported experimental data. Sarmadivaleh et al. (2015) depicted water-CO2 wettability on quartz minerals with surface roughness of 40 nm. The measured advancing contact angle by the authors was 0 at 0.1 MPa and 296K. Pressure and temperature could significantly increase the contact angle (q) and therefore, decrease the CO2 storage capacity. They also demonstrated the variation of waterCO2 interfacial tension and found that IFT increases with temperature and increases with pressure up to 10 MPa and then decreases. Arif et al. (2017) investigated the advancing and receding q for brine/CO2/mica and also brine-CO2 IFT values at different operating conditions. They illustrated that both receding and advancing contact angles decrease with temperature and increase with salinity and pressure. It can be seen from their measurements that at about 308K and 20 MPa, the wettability is changed to CO2 wetting with q of about 110 . Al-Yaseri et al. (2016) measured the system contact angles on quartz at environmental conditions of pressure, temperature, salinity, salt type, and surface roughness. They found lower advancing and receding q at higher surface roughness and higher contact angles at higher temperatures,
Fig. 1. Structural and residual trapping. Fluid configurations in pore scale for water wet condition.
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
salinities and pressures. They also concluded that different salt types have different effects on the amount of q changes and Mg2þ, Ca2þ, and Naþ have the highest changes, respectively. Mutailipu et al. (2019) investigated the brine-CO2 IFTs at different conditions and finally proposed an empirical equation to estimate the system IFT in wide range of pressures, temperatures and salinities. They also reported the contact angle values on limestone, Brea sandstone and quartz minerals for liquid, gaseous and supercritical CO2. Based on their results, IFT increases with temperature and salinity and decreases with pressure up to a value. However, salt type had a limited influence on the IFT. Brine/CO2 contact angles had different behavior with pressure and temperature changes and a sudden increase was demonstrated when carbon dioxide converted from subcritical to supercritical for all of the minerals. Additionally, the wettability of limestone and Berea sandstone were altered to less water wet at supercritical conditions while quartz showed a different trend. Summary of the above studies performed by different researchers on brine/CO2/mineral wettability is tabulated in Table 1. According to the above discussions, studying CO2 wettability is necessary for predicting the capacity of structural and residual trapping. There is a serious lack of analysis and modeling brine/ CO2/mineral wettability in literature and researchers have not proposed any model for predicting the brine/CO2/mineral contact angle at various operating conditions, yet. This may be due to the great uncertainties in the reported data by different authors. Developing precise and general models for estimating CO2 contact angles is of great importance since experimental procedures are so expensive and time-consuming. Nowadays, with computers aid, artificial intelligent tools have become an inseparable part of scientific and engineering calculations especially in petroleum and chemical engineering. In this paper, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy interference system (ANFIS), and least square support vector machine (LSSVM) were used for predicting brine/CO2 contact angle on quartz, calcite, feldspar and mica minerals. In order to reduce the uncertainties
existing in the experimental data, a new variable is defined. Using this variable as a model input, accurate models are developed and their superiority is proven in the current work. 2. Models theory In this section, the background of various models which have been used in this study is explained. 2.1. Multi-layer perceptron artificial neural network (MLP-ANN) Complicated problems can be modeled using different types of artificial intelligent tools. One of these tools is artificial neural network (ANN) which finds the connections between inputs and outputs based on the biological behavior of neurons. Structure of ANNs includes several computational units that aim to obtain the best model by finding the most suitable arrangement of weights and biases between neurons/units, Fig. 2. Parallel combinations and arrangement of computational neurons and also no need for exact and clear formulation for the problem are the highlighted features of this tool (Daryasafar et al., 2014). In a basic design of a multi-layer perceptron (MLP) neural network, there are three types of layers, namely input, hidden, and output layers. These layers contain neurons and the number of neurons in the hidden layer needs to be optimized. In the present study, mean square error (MSE) was used as a stopping criterion for evaluation of the designed MLP. The main drawbacks of MLP-ANN are the determination of number of hidden layers and corresponding neurons. Optimum selection of these factors with optimization algorithms results in an accurate model with satisfying predictions (Yegnanarayana, 2009). 2.2. Adaptive neuro-fuzzy interference system (ANFIS) Fuzzy logic (FL) was introduced for the first time by Zadeh (1965). The main feature of this approach is the ability of converting linguistic variables to mathematical formats. In some cases,
Table 1 Summary of different literature surveys on brine/CO2/mineral wettability. Reference
Key Findings
Farokhpoor et al. (2013a)
Investigated brine/CO2 contact angle on quartz, mica, calcite and feldspar Pressure affects mica contact angle significantly No significant changes were observed for other minerals Quartz, calcite and feldspar have their maximum contact angle near critical pressure Demonstrated advancing, receding and static contact angle on quartz Simulated wettability behavior using molecular dynamic simulation Contact angle increases with temperature and ionic strength Pressure affects contact angle values significantly Water-CO2 wettability on quartz minerals with surface roughness of 40 nm Contact angle increases with pressure and temperature Water-CO2 IFT increases with temperature Water-CO2 IFT increases with pressure up to 10 MPa Measured the system contact angle on quartz Surface roughness lowers advancing and receding contact angle Contact angle increases with pressure, temperature and salinity Mg2þ, Ca2þ, and Na þ have the highest effect on contact angle changes Advancing and receding contact angles for mica were investigated Water-CO2 IFT values were observed at different operating conditions Contact angles increase with salinity and pressure Contact angles decrease with temperature At 308K and 20 MPa, wettability changes to CO2 wet Investigated brine-CO2 IFT at different conditions Reported contact angles on quartz, limestone and Berea sandstone IFT increases with salinity and temperature IFT decreases with pressure up to a value Salt type had limited effect on IFT Sudden increase was observed in contact angles when conditions were changed from sub- to super-critical Berea sandstone and limestone wettability was altered to less water wet at supercritical conditions
Chen et al. (2015)
Sarmadivaleh et al. (2015)
Al-Yaseri et al. (2016)
Arif et al. (2017)
Mutailipu et al. (2019)
3
4
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 2. Flow diagram for developing a MLP-ANN model.
fuzzy logic fails to obtain appropriate outputs due to contrasts and mistakes in judgments or insufficient information (Dehghan Saee et al., 2018). In order to tackle this problem, other approaches such as ANNs must be used with FL for process modeling. The ANN and FL tools are jointed together and provide a system called Adaptive Neuro-Fuzzy Interference System (ANFIS). Coupling of these approaches is performed by defining membership functions (MFs) and IF-THEN rules for a structure called fuzzy interference system. The most well-known MFs are triangular, trapezoidal, Gaussian, generalized bell-shaped and etc. (Daryasafar et al., 2018).
Two types of fuzzy interference systems are reported in literature, Mamdani and Takagi-Sugeno types (Jang, 1993). In this study, Takagi-Sugeno was utilized due to its ability to solve the nonlinearity between inputs and output variables. The main steps for designing an ANFIS structure are illustrated in Fig. 3. For different layers of ANFIS, different relations are performed. Clarifications of various layers are as follows (Fazeli et al., 2013): Transferring the raw input data into linguistic terms is performed in layer 1. Input data points are connected to the nodes in which the linguistic terms are defined. This definition is based on
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
5
O2i ¼ ui ¼ bAi ðxÞ:bBi ðxÞ
(2)
Where, u is the rule's firing strength, and MF is expressed by b. Normalization of ui in the previous layer is executed in layer 3. This normalization leads to discrimination between the total firing strength and each rule firing strengths. Layer 3 uses the following formulation for normalizing ui:
u ui
O3i ¼ ui ¼ P i
(3)
i
Characterization of the model's output linguistic terms is done in layer 4. The level of every rule impacts on the model's output is determined using the following expression:
O4i ¼ ui ðpi x1 þ qi x2 þ ri Þ
(4)
The linear variables as well as the parameters of the first layer are optimized by ANFIS. Layer 5 sums up the rules with output variable and converts them to a quantitative form as follows:
O5i
¼
X
P
ui fi
i ui fi ¼ P
i
i
(5)
ui
2.3. Least square support vector machine Random initialization and optimization criteria alteration of models based on ANN are some major drawbacks which lead to non-reproducibility of the model outcomes (Hagan et al., 1996). Support vector machine (SVM) was introduced as an alternative approach for overcoming the mentioned issues. SVM-based models have special features which show their supremacy in comparison to ANN models. These models have less adjustable or tunable variables, their generality is more exact, and degree of overtraining issue in these models is smaller (Suykens and Vandewalle, 1999). Quadratic programming is needed for finding the parameters of SVM. In order to lessen this complexity, a modified form of SVM was introduced by Suykens and Vandewalle (1999) which is called least square support vector machine (LSSVM). LSSVM characterizes regression error and function evaluation by solving a set of linear equations (Baghban et al., 2018). The following function is used as a cost function for finding hyper plane in LSSVM:
Fig. 3. Designing an optimized ANFIS system.
the membership functions. Gaussian membership function is one of the most popular and well-known MFs which is extensively used in ANFIS modeling due to its smoother behavior. This MF type was used in this study and its mathematical formulation is as:
N 1 1 X J ¼ uT u þ g e2 2 2 i¼1 i
(6)
The following constraint should be applied to the above relation:
O1i
¼ exp
ðx zÞ 2s2
2
yi ¼ uT 4ðxi Þ þ b þ ei
! (1)
In this equation, O is the output of layer 1, z and s are the Gaussian MF center and the variance term, respectively. In order to develop a model with an excellent performance, these parameters need to be optimized by ANFIS during learning process. Layer 2 is called the firing strength layer in which the adequacy and accuracy of the conditions applied to the previous sections are determined. The rule's firing strength of this layer is formulated as:
i ¼ 1; 2; :::; N
(7)
In the above equations, u is the weight factor, g is the tuning parameter, e is the error of the function, 4(xi) is the mapping function, and b is the bias vector. For determining the objective function, Lagrangian coefficients are employed in the equation as given below:
xðu; b; e; aÞ ¼ J
N X i¼1
n
ai uT 4ðxi Þ þ b þ ei yi
o
(8)
6
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Differentiating the above equation with respect to each of the parameters based on Karush-Kuhn-Tucker (KKT) leads to its optimum solution. N X vx ¼ 0/u ¼ ai 4ðxi Þ vu
(9)
i¼1
N X vx ¼ 0/ ai ¼ 0 vb i¼1
vx ¼ 0/ai ¼ gei ; vei
(10)
i ¼ 1; :::; N
vx ¼ 0/yi ¼ 4ðxi ÞuT þ b þ ei ; vai
(11)
i ¼ 1; :::; N
(12)
According to the Mercer's theory, LSSVM for nonlinear regression becomes as follows (Pelckmans et al., 2002):
y¼
N X
ai Kðx; xi Þ þ b
(13)
i¼1
Where, K(x,xi) is the Kernel function. Radial basis function (RBF) is the most well-known and widely used Kernel function that has just one tuning variable (Baghban et al., 2019). RBF was used in this study and is defined as (Wang et al., 2018):
. Kðx; xi Þ ¼ exp x x2i s2
(14)
In which s2 is the adjusting parameter and the smoothness of the function is controlled by this variable. Flowchart for constructing a LSSVM model is shown in Fig. 4. Based on the above equations, LSSVM has two adjusting parameters, s2 and g, which are tuned during the optimization process. The optimum values of these variables are obtained by utilizing a cost function. For this purpose, mean square error (MSE) was used in this study. MSE is defined as: N P
MSE ¼ i¼1
yi;exp yi;pred N
(15)
Where, yexp and ypred are experimental and estimated data. In order to minimize this cost function and finding the optimum values for the tuning parameters, a proper optimization algorithm is needed. In this paper, coupled simulated annealing (CSA) was used as a powerful algorithm for this purpose. 3. Data gathering Collection of accurate and comprehensive experimental data from reliable resources is necessary for developing an efficient and accurate model. Therefore, an extensive databank including 630 data points of CO2/brine/mineral contact angle was gathered for proposing accurate models in this study. The selected databank includes CO2/brine contact angles at wide operating conditions of pressure, temperature and salinity. It also consists of the static, advancing and receding contact angle values for various minerals including quartz, calcite, mica, and feldspar. Several researchers have measured the contact angle of CO2/brine on different kinds of minerals. However, significant uncertainty remains and the measured values by different researchers are different for a certain environmental condition. Variation between the measurements might be due to the surface roughness and surface contamination
Fig. 4. Flowchart of developing a LSSVM model.
(Iglauer et al., 2015). Surface roughness is a parameter that controls the hysteresis (Andrew et al., 2014) and there is a general understood that increasing surface roughness can decrease contact angle on hydrophilic surfaces and increase it on hydrophobic surfaces
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
(Bikkina, 2011; Palamara et al., 2015). Surface contamination has also been identified as a significant problem since different procedures have been proposed for cleaning the mineral surfaces (Iglauer et al., 2015). In this study, surface roughness was not considered as an input variable of the model since this factor is often unreported in published researches and only five studies (Sarmadivaleh et al., 2015; Jung and Wan, 2012; Saraji et al., 2013, 2014; Wang et al., 2013) have reported this factor. In order to apply these two factors, a parameter called “theta zero (q0)" was considered in this paper for developing different models. This parameter shows the wettability condition of the minerals surface in terms of surface contamination and roughness. Differences between the measurements of contact angle values by different researchers can be explained by this parameter. Theta zero is defined as follows:
q ¼ roundðqi =10Þ 0
the output layer was utilized for MLP-ANN modeling. Hyperbolic tangent sigmoid (tansig) transfer function was considered for the input and hidden layers while the linear (purelin) transfer function was used for the output layer. Neurons in the hidden layer (x) were found by learning the model for different numbers of neurons. Finally, the configuration with the least average absolute relative error (AARE%) and root mean square error (RMSE), and the highest Pearson correlation coefficient (R) was selected as the optimal network structure for modeling the contact angle of brine/CO2/ minerals.
tan sigðnÞ ¼
2 1 1 þ expð 2nÞ
qðadv=rec=stÞ¼f ðT;P;salinity;mineraltype;contactangletype;q0
(18) It must be concluded that the proposed models in this study can estimate static/advancing/receding contact angles of brine/CO2/ mineral by selecting 1 ¼ static, 2 ¼ advancing, and 3 ¼ receding as an input variable in the models for “contact angle type". Table 2 shows the analysis of input and output variables as well as the references for each set of the gathered databank. The dataset used in this study can be found in Appendix A. 4. Models development In this study, the selected databank for developing different models was randomly splitted into two subsets, namely training and testing data sets. 80% of the total data was considered as train set while the remaining 20% was selected as test set. Multi-layer perceptron networks have the ability to model a nonlinear function with only one hidden layer. Based on this and in order to reduce the computational time, only one hidden layer was considered for the proposed MLP network. A general structure of 20-x-1 with twenty neurons in the input layer and one neuron in
qexp i
N 0
(17) Hence, the model output is contact angle value (static or advancing or receding) and the input parameters of the developed models are pressure (MPa), temperature (K), salinity (M), type of mineral (quartz, calcite, mica, and feldspar), type of contact angle (static (st), advancing (adv), and receding (rec)), and theta zero (q0). All the experimental data points have been measured with NaCl brine and due to the lack of data, effect of salt type was not included in this modeling. Therefore, the function of the models' output is as given below:
(20)
N qpred qexp P ji j i
AAREð%Þ ¼ i¼1
@ ðambient temperature; ambient pressure; salinity ¼ 0Þ
(19)
purelinðnÞ ¼ n
(16)
qi ¼ brine=CO2 =mineral contact angle
7
N P
B B RMSE ¼ SQRT Bi¼1 @
N h P
100
(21)
exp qpred qi i
N
exp qexp ave qi i
2 1 C C C A
(22)
pred qpred ave qi i
i
i¼1 R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 P 2 N N P exp pred qexp ave qi : qpred ave qi i i i¼1
(23)
i¼1
In these relations, N is the total number of data, qexp is the actual i contact angle value, and qpred is the estimated contact angle value i by the model. Results of different networks during the optimization process for finding the number of neurons in the hidden layer (x) are shown in Fig. 5. As shown in this figure, the network with configuration of 20-13-1 has the performance compared to the other networks and hence, is used for further analyses. For ANFIS model, the Takagi-Sugeno type of FIS was used. This type of FIS can be obtained using different methods including lookup table method, grid partitioning, subtractive clustering and fuzzy c-means. In the developed ANFIS model, subtractive clustering and Gaussian membership function were utilized to the structure and MF type, respectively. Subtractive clustering finds the antecedent MFs and the number of rules by subclast function. Then, characterization of each rule equations are performed using the least squares estimation. Hybrid method was also used in this study to train the initial fuzzy interference system and the optimum structure was found at an iteration of 50. For the LSSVM structure, as mentioned before, there are two tuning parameters, g and s2, which are essentially needed to be tuned for the model design since the comprehensiveness and
Table 2 Details of the parameters used for developing different models. Mineral Temperature Pressure (K) (MPa)
Salinity Refs. (M)
Quartz
0e40
0e12.9
0.1e40 0.25e30 0.5e30
0e7.4 0e0.8 0e11.1
296e373
Calcite 298e343 Feldspar 309e339 Mica 308e343
[Farokhpoor et al., 2013a; 2013b; Chen et al., 2015; Sarmadivaleh et al., 2015; Al-Yaseri et al., 2016; Mutailipu et al., 2019; Jung and Wan, 2012; Saraji et al., 2013; Espinoza and Santamarina, 2010; Chiquet et al., 2007] [Farokhpoor et al., 2013a; 2013b; Arif et al., 2017; Espinoza and Santamarina, 2010] [Farokhpoor et al., 2013a; 2013b] [Farokhpoor et al., 2013a; Chiquet et al., 2007; Arif et al., 2016b]
8
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 5. Performance of various MLP-ANN models with different neurons in the hidden layer; a) AARE results, b) RMSE results, and c) Pearson coefficient (R) results.
accuracy of the LSSVM model are dependent on these factors. CSA algorithm was utilized for optimization of these parameters. According to the optimization process, the best values for g and s2 were found to be 70.406 and 1.122, respectively.
5. Results and discussion In this study, different models have been developed for predicting the contact angle (static, advancing, and receding) of brine/
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101 Table 3 Analysis of different statistical parameters for various models. Model
Parameter
Train Data
Test Data
Total Data
MLP-ANN
AARE (%) RMSE R
8.366 3.510 0.984
8.950 2.927 0.981
8.464 3.418 0.984
ANFIS
AARE (%) RMSE R
0.523 0.895 0.999
9.393 3.986 0.980
2.021 1.830 0.996
LSSVM
AARE (%) RMSE R
8.916 3.989 0.980
7.398 2.973 0.983
8.645 3.833 0.981
CO2/minerals at different operating conditions of pressure, temperature, and salinity. The proposed models are able to estimate the contact angle brine/carbon dioxide system on quartz, calcite, feldspar, and mica. Accuracy and proficiency of the presented models were investigated through statistical and graphical analyses. For this purpose, statistical parameters of AARE, RMSE, and R (Eqs. (21)e(23)) were used in this research. The values of these parameters for MLP-ANN, ANFIS, and LSSVM are shown in Table 3. Comparison between these parameters is also presented graphically in Fig. 6. Table 3 and Fig. 6 indicate that the estimated results by the developed models are in a very good agreement with actual data; however, the ANFIS model exhibits higher accuracy with AARE, RMSE, and R of 2.021, 1.830, and 0.996, respectively for the total data (train and test subsets). In this study, the accuracy of various models in simulating the wettability behavior of brine/CO2 on different minerals surface was demonstrated. The predicted data for both train and test subsets were plotted versus actual values for the proposed models, Fig. 7.
9
This figure illustrates that predicted values of experimental brine/ CO2 contact angle by the presented models are mostly concentrated around the 450 slope line which shows the acceptable accuracy of these models. According to this figure, all the developed models are accurate enough and can be utilized for simulating the brine/CO2/ mineral wettability behavior; however, the ANFIS model is more proficient than the others. For further verifications, the relative errors of the models outcomes were plotted against the experimental data, Fig. 8. This figure shows that among different models, the relative errors of the ANFIS results are compacted around the zero error line, which proves the reliability of this model. According to the above statistical analyses for the ANFIS model, there is a very good compromise between the predicted and experimental values due to the great overlap between the estimated and target data points. In order to show the distribution of absolute relative errors of various presented models, the plot of cumulative data frequency that is defined as the percent of total data utilized has been drawn versus absolute relative error in Fig. 9 for all the models. This figure indicates that more than 90% of the predicted contact angles by the ANFIS model have absolute relative error lower than 8% whereas for the other models, less than 70% of the estimated values have error lower than 8%. For investigating the error distribution of different models over the input variables and accuracy verification of these models, Fig. 10(aed) is utilized. As it can be seen in this figure, the predicted results by the proposed ANFIS model are more concentrated around the error line of zero for various ranges of input variables compared to the estimated results of the other models. This illustrates the robustness of the presented ANFIS model for wide ranges of operating conditions. Selecting the best model for a specific range of input variables is one of the main issues for any parameter estimation. Fig. 10 shows the ability of the proposed models in
Fig. 6. Comparison between different models in predicting brine/CO2/minerals contact angle.
10
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 7. Crossplot of the estimations of the proposed models; a) ANFIS, b) MLP-ANN, c) LSSVM.
predicting brine/CO2/rock wettability for various ranges of operating conditions with high accuracy. In order to show the ability of the proposed models in trend prediction, effects of pressure, temperature, and salinity on the amount of brine/CO2 contact angles for different minerals were simulated using the developed MLP-ANN, ANFIS, and LSSVM. Experimental data with environmental conditions which were used for this purpose are indicated in Table 4.
Fig. 11 shows the studied systems contact angles versus pressure for different models. According to the measure values of contact angles, brine/CO2 contact angle increases with pressure for quartz, feldspar and mica, and decreases at high pressures for calcite. Among the proposed models, the ANFIS approach has the best performance and can predict the process trend with high accuracy for various materials. Effects of temperature on brine/CO2 contact angle as well as the
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
11
Fig. 8. Relative error of the predicted results of the developed approaches; a) ANFIS, b) MLP-ANN, c) LSSVM.
ability of the developed models in predicting the trend prediction of this process are demonstrated in Fig. 12. Based on the used datasets, increasing temperature increases contact angles for quartz and feldspar minerals and decreases those for calcite and mica. However, different results for these trends are reported by researchers, for example for the effect of temperature on brine/CO2/
quartz (Saraji et al., 2014). As indicated in Fig. 12, the developed ANFIS efficiently predicts the trend and behavior of contact angles against temperature. Performance of the presented MLP-ANN, ANFIS, and LSSVM models in trend prediction of contact angles changes with salinity are shown in Fig. 13. It is evident from this figure that ANFIS model
12
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 9. Distribution of absolute relative error for total data.
Fig. 10. Relative error distribution over input parameters for various models; a) Pressure, b) Temperature, C) Salinity, d) Theta_zero
is accurately enough for the trend predictions. According to this figure, increasing salinity (NaCl) increases contact angles for all the minerals and the proposed ANFIS can excellently simulate this behavior. Wettability of different minerals with respect to brine and CO2 is important in geological storage of CO2 since it can strongly affect the structural and residual trapping. Therefore, modeling the wettability behavior of brine/CO2/mineral system is highly crucial.
There is large uncertainty associated with experimental data published in different researches. In this study, efforts have been performed for reducing the uncertainties by defining new variables and considering the influencing parameters in the proposed models. Based on the results of the developed intelligent models, one can conclude that the ANFIS outperforms the other models and presents more reliable and accurate results. Sensitivity analysis was also performed in this paper. Finding the
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
13
Table 4 Experimental data used for simulating the trend prediction of brine/CO2/minerals contact angle by different models. Mineral Set 1 Quartz
Set 2 Calcite
Set 3 Feldspar
Set 4 Mica
Set 5 Quartz
Set 6 Calcite
Set 7 Feldspar Set 8 Mica
Set 9 Quartz
Set 10 Calcite
Set 11 Feldspar
Set 12 Mica
Pressure (MPa)
Temperature (K)
Salinity (M)
Contact angle type
Theta-zero
Experimental contact angle
Ref.
0.09 8.33 9.07 9.40 9.97 10.93 11.89 17.99 19.98 22.01 25.01
318 318 318 318 318 318 318 318 318 318 318
3 3 3 3 3 3 3 3 3 3 3
Static
3
45.1 57.2 58.1 58.8 59.1 59.1 59.2 59.1 59.6 59.5 60.0
Jung and Wan (2012)
0.30 1.99 9.98 19.93 30.00 39.99
309 309 309 309 309 309
0 0 0 0 0 0
Static
1
12.2 12.2 12.5 11.9 11.5 11.9
(Farokhpoor et al., 2013a, 2013b)
0.27 2.39 6.23 10.44 15.40 19.99 29.97
309 309 309 309 309 309 309
0.8 0.8 0.8 0.8 0.8 0.8 0.8
Static
1
14.6 15.4 15.9 17.3 17.8 18.9 19.8
(Farokhpoor et al., 2013a, 2013b)
1.53 2.00 2.53 3.54 6.05 8.03 8.95 9.90
308 308 308 308 308 308 308 308
1 1 1 1 1 1 1 1
Receding
1
41.2 41.2 44.1 48.4 57.1 67.1 69.2 66.1
Chiquet et al. (2007)
3.50 3.50 3.50
308 318 333
0 0 0
Advancing
1
11.9 15.9 56.9
Saraji et al. (2013)
0.10 0.10 0.10
308 323 343
0 0 0
Advancing
2
24.7 17.8 13.8
Arif et al. (2017)
10.43 10.43
309 339
0.8 0.8
Static
1
17.3 18.2
(Farokhpoor et al., 2013a, 2013b)
5.00 5.00 5.00
308 323 343
0 0 0
Advancing
0
38.84 35.92 31.90
Arif et al. (2016b)
10 10 10 10 10 10
323 323 323 323 323 323
0 1.13 2.19 3.68 7.36 12.88
Advancing
0
30.03 34.02 36.19 38.13 42.11 44.05
Al-Yaseri et al. (2016)
15 15 15 15 15
323 323 323 323 323
0 1.84 3.69 5.53 7.38
Advancing
2
85.19 86.06 91.08 93.94 94.98
Arif et al. (2017)
7.50 7.50 7.50
309 309 309
0 0.2 0.8
Static
1
16.97 16.51 19.43
(Farokhpoor et al., 2013a, 2013b)
15 15 15 15 15
323 323 323 323 323
0.02 1.87 3.72 7.39 11.09
Advancing
0
58.96 61.86 64.92 73.95 82.98
Arif et al. (2016b)
14
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 11. Performance of the proposed models in trend prediction of contact angle changes with pressure; a) Set 1, b) Set 2, c) Set 3, d) Set 4.
Fig. 12. Performance of the proposed models in trend prediction of contact angle changes with temperature; a) Set 5, b) Set 6, c) Set 7, d) Set 8.
effect of each input variable on the output parameter is the sensitivity analysis. This process can be done by performing various approaches such as response surface methodology (RSM), sampling-based methods like Monte-Carlo algorithm, differential analysis, fast probability integration, relevancy factor and etc. In this study, the impact of each input variable on the output (brine/ CO2/mineral wettability) is determined by using Monte-Carlo algorithm (Iman et al., 1981; Helton, 1993). In this algorithm, multiple model evaluations with probabilistic model inputs are performed and then, uncertainties are determined by using these results. Facilitating discontinuities and nonlinearities by sampling in a wide
range of uncertain variables, obtaining uncertainty results without needing to use the surrogated models and simplicity are the main abilities of this algorithm (Iman et al., 1981; Helton, 1993). The results of performing Monte-Carlo analysis for finding the sensitivity of each input variable (Mineral type, pressure, temperature, salinity and theta_zero) on brine/CO2/mineral contact angle are indicated in Fig. 14. This figure depicts the changes in the output variable relative to the changes in each input parameter. According to Fig. 11, mineral type, salinity and pressure can affect the system wettability very much.
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
15
Fig. 13. Performance of the proposed models in trend prediction of contact angle changes with salinity (NaCl); a) Set 9, b) Set 10, c) Set 11, d) Set 12.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.118101.
References
Fig. 14. Sensitivity analysis on brine/CO2/mineral wettability behavior.
6. Conclusions Three models, namely MLP-ANN, ANFIS and LSSVM, were developed for simulating the wettability behavior of brine/CO2/ rock system at different operating conditions of pressure, temperature and salinity. The proposed models are capable to predict advancing, receding and static contact angles of brine/CO2 on quartz, calcite, feldspar and mica minerals. The reliability and proficiency of the developed models were shown using statistical analyses. Results showed that the proposed ANFIS model provides more accurate q values and it can predict the wettability behavior against the operating conditions excellently. The presented models are important for characterization of wettability of brine/CO2/rock systems and reducing the operational costs by omitting the highly expensive and time consuming experimental tests. Finally, MonteCarlo simulator was used to find the impact of input variables on the output variable, i.e. brine/CO2/rock contact angle. Results of sensitivity analysis demonstrated that mineral type, salinity, and pressure have the highest impact and should be considered during CO2 geo-sequestration process.
Abdallah, W., Buckley, J.S., Carnegie, A., Edwards, J., Herold, B., Fordham, E., Graue, A., Habashy, T., Seleznev, N., Signer, C., 1986. Fundamentals of wettability. Technol. 38, 1125e1144. Al-Khdheeawi, E.A., Vialle, S., Barifcani, A., Sarmadivaleh, M., Iglauer, S., 2016. Influence of rock wettability on CO2 migration and storage capacity in deep saline aquifers. In: 13th International Conference on Greenhouse Gas Control Technologies. GHGT-13, Lausanne, Switzerland. Al-Yaseri, A.Z., Lebedev, M., Barifcani, A., Iglauer, S., 2016. Receding and advancing (CO2 þ brine þ quartz) contact angles as a function of pressure, temperature, surface roughness, salt type and salinity. J. Chem. Thermodyn. 93, 416e423. Anderson, W.G., 1987. Wettability literature survey-part 6: the effects of wettability on waterflooding. J. Pet. Technol. 39, 1605e1622. Andrew, M., Bijeljic, B., Blunt, M.J., 2014. Pore-scale contact angle measurements at reservoir conditions using X-ray microtomography. Adv. Water Resour. 68, 24e31. Arif, M., Barifcani, A., Lebedev, M., Iglauer, S., 2016a. CO2-wettability of low to high rank coal seams: implications for carbon sequestration and enhanced methane recovery. Fuel 181, 680e689. Arif, M., Al-Yaseri, A.Z., Barifcani, A., Lebedev, M., Iglauer, S., 2016b. Impact of pressure and temperature on CO2ebrineemica contact angles and CO2ebrine interfacial tension: implications for carbon geo-sequestration. J. Colloid Interface Sci. 462, 208e215. Arif, M., Lebedev, M., Barifcani, A., Iglauer, S., 2017. CO2 storage in carbonates: wettability of calcite. Int. J. Greenh. Gas Contr. 62, 113e121. Baghban, A., Sasanipour, J., Habibzadeh, S., Zhang, Z., 2019. Sulfur dioxide solubility prediction in ionic liquids by a group contribution-LSSVM model. Chem. Eng. Res. Des. 142, 44e52. Baghban, A., Sasanipour, J., Zhang, Z., 2018. A new chemical structure-based model to estimate solid compound solubility in supercritical CO2. J. CO2 Util. 26, 262e270. Bikkina, P.K., 2011. Contact angle measurements of CO2ewaterequartz/calcite systems in the perspective of carbon sequestration. Int. J. Greenh. Gas Contr. 5, 1259e1271. Chen, C., Wan, J., Li, W., Song, Y., 2015. Water contact angles on quartz surfaces under supercritical CO2 sequestration conditions: experimental and molecular dynamics simulation studies. Int. J. Greenh. Gas Contr. 42, 655e665. Chen, L., Liu, D., Agarwal, R., 2018. Numerical simulation of enhancement in CO2 sequestration with various water production schemes under multiple well scenarios. J. Clean. Prod. 184, 12e20.
16
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Chiquet, P., Broseta, D., Thibeau, S., 2007. Wettability alteration of cap rock minerals by carbon dioxide. Geofluids 7, 112e122. Daryasafar, A., Ahadi, A., Kharrat, A., 2014. Modeling of steam distillation mechanisms during steam injection process using artificial intelligence. Sci. World J. 2014, 1e8. Daryasafar, A., Daryasafar, N., Madani, M., Kalantari Meybodi, M., Joukar, M., 2018. Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide. Neural Comput. Appl. 29, 295e305. Dehghan Saee, A., Baghban, A., Zarei, F., Zhang, Z., Habibzadeh, S., 2018. ANFIS based evolutionary concept for estimating nucleate pool boiling heat transfer of refrigerant-ester oil containing nanoparticles. Int. J. Refrig. 96, 38e49. Dhinesh, B., Annamalai, M., 2018. A study on performance, combustion and emission behaviour of diesel engine powered by novel nano nerium oleander biofuel. J. Clean. Prod. 196, 74e83. Dhinesh, B., Raj, Y.M.A., Kalaiselvan, C., KrishnaMoorthy, R., 2018. A numerical and experimental assessment of a coated diesel engine powered by highperformance nano biofuel. Energy Convers. Manag. 171, 815e824. Espinoza, D.N., Santamarina, J.C., 2010. WatereCO2emineral systems: interfacial tension, contact angle, and diffusiondimplications to CO2 geological storage. Water Resour. Res. 46, 1e10. Farokhpoor, R., Bjorkvik, B.J.A., Lindeberg, E., Torsater, O., 2013a. Wettability behavior of CO2 at storage conditions. Int. J. Greenh. Gas Contr. 12, 18e25. Farokhpoor, R., Bjorkvik, B.J.A., Lindeberg, E., Torsater, O., 2013b. CO2 wettability behavior during CO2 sequestration in saline aquifer -An Experimental study on minerals representing sandstone and carbonate. Energy Procedia 37, 5339e5351. Fazeli, H., Soleimani, R., Ahmadi, M.A., Badrnezhad, R., Mohammadi, A.H., 2013. Experimental study and modeling of ultrafiltration of refinery effluents using a hybrid intelligent approach. Energy Fuels 27, 3523e3537. Gaus, I., 2010. Role and impact of CO2erock interactions during CO2 storage in sedimentary rocks. Int. J. Greenh. Gas Contr. 4, 73. Hagan, M.T., Demuth, H.B., Beale, M.H., 1996. Neural Network Design. Pws Pub, Boston. Helton, J.C., 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive-waste disposal. Reliab. Eng. Syst. Saf. 42, 327e367. Hesse, M.A., Orr, F.M., Tchelepi, H.A., 2008. Gravity currents with residual trapping. J. Fluid Mech. 611, 35. Iglauer, S., 2011. In: Nakajima, H. (Ed.), Mass Transfer. InTech, Rijeka, p. 233. Iglauer, S., Mathew, M.S., Bresme, F., 2012. Molecular dynamics computations of brineeCO2 interfacial tensions and brineeCO2equartz contact angles and their effects on structural and residual trapping mechanisms in carbon geo-sequestration. J. Colloid Interface Sci. 386, 405e414. Iglauer, S., Pentland, C.H., Busch, A., 2015. CO2 wettability of seal and reservoir rocks and the implications for carbon geo-sequestration. Water Resour. Res. 51, 729e774. Iman, R.L., Helton, J.C., Campbell, J.E., 1981. An approach to sensitivity analysis of computer models: Part II - ranking of input variables, response surface validation, distribution effect and technique synopsis. J. Qual. Technol. 13, 232e240. Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (3), 665e685. Jing, J., Yang, Y., Tang, Z., 2019. Effects of formation dip angle and salinity on the safety of CO2 geological storage - a case study of Shiqianfeng strata with low porosity and low permeability in the Ordos Basin, China. J. Clean. Prod. 226, 874e891. Joshi, A., Gangadharan, S., Leonenko, Y., 2016. Modeling of pressure evolution during multiple well injection of CO2 in saline aquifers. J. Nat. Gas Sci. Eng. 36, 1070e1079. Juanes, R., Spiteri, E.J., Orr, F.M., Blunt, M.J., 2006. Impact of relative permeability hysteresis on geological CO2 storage. Water Resour. Res. 42.
Jung, J.W., Wan, J., 2012. Supercritical CO2 and ionic strength effects on wettability of silica surfaces: equilibrium contact angle measurements. Energy Fuels 26, 6053e6059. Liu, D., Agarwal, R., Li, Y., 2016. Numerical simulation and optimization of CO2enhanced water recovery by employing a genetic algorithm. J. Clean. Prod. 133, 994e1007. Mutailipu, M., Liu, Y., Jiang, L., Zhang, Y., 2019. Measurement and estimation of CO2ebrine interfacial tension and rock wettability under CO2 sub- and supercritical conditions. J. Colloid Interface Sci. 534, 605e617. Nghiem, L., Shrivastava, V., Tran, D., Kohse, B., Hassam, M., Yang, C., 2009. Simulation of CO2 storage in saline aquifers. In: SPE Paper 125848, Proceeding of SPE/ EAGE Reservoir Characterization & Simulation Conference, Abu Dhabi, UAE. Palamara, D.R., Neeman, T., Golab, A.N., Sheppard, A., 2015. A statistical analysis of the effects of pressure, temperature and salinity on contact angles in CO2ebrineequartz systems. Int. J. Greenh. Gas Contr. 42, 516e524. Pelckmans, K., Suykens, J.A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., Vandewalle, J., 2002. LS-SVMlab: a Matlab/c Toolbox for Least Squares Support Vector Machines, Tutorial, vol. 142. KULeuven-ESAT, Leuven, Belgium, pp. 1e2. Pentland, C.H., El-Maghraby, R., Iglauer, S., Blunt, M.J., 2011. Measurements of the capillary trapping of super-critical carbon dioxide in Berea sandstone. Geophys. Res. Lett. 38. Pruess, K., Xu, T., Apps, J., Garcia, J., 2003. Numerical modelling of aquifer disposal of CO2. SPE J. 8, 49e60. Qi, R., LaForce, T.C., Blunt, M.J., 2009. Design of carbon dioxide storage in aquifers. Int. J. Greenh. Gas Contr. 3, 195. Raza, A., Gholami, R., Sarmadivaleh, M., Tarom, N., Rezaee, R., Han Bing, C., Nagarajan, R., Hamid, M.A., Elochukwu, H., 2016. Integrity analysis of CO2 storage sites concerning geochemical-geomechanical interactions in saline aquifers. J. Nat. Gas Sci. Eng. 36, 224e240. Saraji, S., Goual, L., Piri, M., Plancher, H., 2013. Wettability of supercritical carbon dioxide/water/quartz systems: simultaneous measurement of contact angle and interfacial tension at reservoir conditions. Langmuir 29, 6856e6866. Saraji, S., Piri, M., Goual, L., 2014. The effects of SO2 contamination, brine salinity, pressure, and temperature on dynamic contact angles and interfacial tension of supercritical CO2/brine/quartz systems. Int. J. Greenh. Gas Contr. 28, 147e155. Sarmadivaleh, M., Al-Yaseri, A.Z., Iglauer, S., 2015. Influence of temperature and pressure on quartzewatereCO2 contact angle and CO2ewater interfacial tension. J. Colloid Interface Sci. 441, 59e64. Seyyedi, M., Rostami, B., Pasdar, M., Pazhoohan, J., 2016. Experimental and numerical study of the effects of formation brine salinity and reservoir temperature on convection mechanism during CO2 storage in saline aquifers. J. Nat. Gas Sci. Eng. 36, 950e962. Suykens, J.A., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Process. Lett. 9, 293e300. Vigneswaran, R., Annamalai, K., Dhinesh, B., Krishnamoorthy, R., 2018. Experimental investigation of unmodified diesel engine performance, combustion and emission with multipurpose additive along with water-in-diesel emulsion fuel. Energy Convers. Manag. 172, 370e380. Wang, P., Peng, S., He, T., 2018. A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China. J. Nat. Gas Sci. Eng. 55, 1e15. Wang, S., Edwards, I.M., Clarens, A.F., 2013. Wettability phenomena at the CO2ebrineemineral interface: implications for geologic carbon sequestration. Environ. Sci. Technol. 47, 234e241. Yegnanarayana, B., 2009. Artificial Neural Networks. PHI Learning Pvt. Ltd. Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338. Zhang, Z., Huisingh, D., 2017. Carbon dioxide storage schemes: technology, assessment and deployment. J. Clean. Prod. 142, 1055e1064.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Modeling CO2 wettability behavior at the interface of brine/CO2/mineral: Application to CO2 geo-sequestration Amin Daryasafar a, Amin Keykhosravi b, Khalil Shahbazi a, * a
Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran Faculty of Petroleum and Natural Gas Engineering, Sahand Oil & Gas Research Institute (SOGRI), Sahand University of Technology, Sahand New City, Tabriz, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 March 2019 Received in revised form 16 August 2019 Accepted 18 August 2019 Available online 19 August 2019
Carbon capture and storage (CCS) has been introduced as an effective method for reduction of CO2 emissions to the atmosphere. While different aspects and controlling parameters of geological CO2 sequestration process are well understood, there is still large uncertainty regarding brine/CO2/rock wettability and no model has yet been presented for characterizing the contact angle of brine/CO2/rock system at different environmental conditions. In this study, various intelligent models have been developed for accurate estimation of brine/CO2/rock contact angles. Results demonstrated that the proposed models have the ability to accurately simulate the brine/CO2 wettability behavior for quartz, calcite, feldspar, and mica minerals at different pressures, temperatures and salinities. Additionally, results revealed that adaptive neuro-fuzzy interference system has the best performance compared with the other models and its superiority is shown through statistical and graphical analyses. Afterward, the most effective input parameters on brine/CO2/rock wettability were investigated by employing MonteCarlo algorithm, which showed that mineral type, salinity and pressure are the most sensitive variables for this process. As wettability can highly affect the residual and structural trapping mechanisms during carbon geo-sequestration, presenting reliable models for estimating brine/CO2/rock wettability is crucial. Therefore, the outcomes of this study can be useful for accurate estimation of these mechanisms capacity. © 2019 Elsevier Ltd. All rights reserved.
Handling editor. Prof. Jiri Jaromir Klemes Keywords: Carbon storage Contact angle Minerals Modeling Sensitivity analysis
1. Introduction Emission of CO2 into the atmosphere has been known as a major cause for climate change in recent years. It is predicted that these emissions are increasing due to the proceeding of global industrialization and increased energy demand. Therefore, regulations have been issued for reduction of CO2 emissions from industries. Different approaches such as using biofuels instead of fossil fuels (Dhinesh and Annamalai, 2018; Dhinesh et al., 2018; Vigneswaran et al., 2018), geological CO2 sequestration and etc. have been proposed for CO2 emissions reduction. One of the most recommended and effective methods for reduction of emitting greenhouse gases in to the atmosphere is geological storage and carbon dioxide (CO2) capture (Zhang and Huisingh, 2017). In this method, carbon dioxide is collected from different industrial sources and then injected into
* Corresponding author. E-mail address: [email protected] (K. Shahbazi). https://doi.org/10.1016/j.jclepro.2019.118101 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
deep geological beds such as saline aquifers, coal stratums, gas and oil reservoirs and etc., which have special characteristics that make them appropriate for CO2 storage (Arif et al., 2016a; Joshi et al., 2016). Among these geological beds, deep saline aquifers play a vital role and are the main targets for storage of CO2 (Nghiem et al., 2009; Seyyedi et al., 2016). Geological sequestration of carbon dioxide combined with water production from deep saline aquifers can significantly overcome the issues encountered by modern energy systems of CO2 emissions reduction and water intensity while providing secure, reliable, and affordable energy (Liu et al., 2016). Chen et al. (2018) analyzed both the carbon capture and storage (CCS) and CO2 enhanced water recovery (CO2-EWR) technology under various scenarios for CO2 injection and proposed an optimal scenario by performing numerical simulation. They found that by performing this scenario, more carbon dioxide can be stored in reservoir as well as brine can be produced by a single or multiple production wells. They also concluded that pre-production and coproduction of brine for the combined CO2-EWR are more effective in pressure perturbation controlling and increasing carbon dioxide
2
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
storage capacity. Additionally, increasing the number of wells can increase CO2 storage, but the revenue in applying this technology cannot compensate the costs of drilling wells. Jing et al. (2019) investigated the effect of salinity and dip angle on the storage amount and storage form of CO2 during geological storage of carbon dioxide. For this purpose, a 3D simulation model was used. Results demonstrated that larger dip angles yield greater migration distance for CO2 and greater salinities result in smaller total storage amount. They also added that higher salinity and dip angle significantly decrease CO2 geological safety and hence, a reservoir with lower salinity and smaller dip angle should be selected for CO2 storage. In carbon capture and storage (CCS), CO2 is often injected at 800m depth or higher and this is necessary for increasing the storage capacity since at these depths, CO2 is in supercritical state. However, supercritical CO2 migrates upward and it is buoyant (Pruess et al., 2003). There are different mechanisms that can prevent CO2 from escaping, namely mineral (Gaus, 2010), solubility (Iglauer, 2011), residual (Juanes et al., 2006), and structural trapping (Hesse et al., 2008). Performance of these mechanisms is a complex function of cap rock and aquifer parameters and each mechanism is active on various time scales, but it is believed that the most important mechanisms during the first years of a storage project are residual and structural trapping mechanisms (Iglauer et al., 2012; Al-Khdheeawi et al., 2016), Fig. 1. In structural trapping, carbon dioxide is trapped below a seal layer with an extremely low permeability and residual trapping is relevant to the cases in which the there is no cap rock (Qi et al., 2009). In residual trapping, capillary forces prevent CO2 to be mobilized and hence, this mechanisms capacity is a function of the contact angle of pore spaces (rock wettability), CO2-liquid/brine interfacial tension (IFT), initial carbon dioxide saturation, the morphology of the pores of the rock, etc. (Pentland et al., 2011; Raza et al., 2016). Based on this discussion, accurate knowledge of wettability is crucial for estimating the capacity of residual trapping and forecasting potential CO2 leakage. Wettability is tendency of the surface of rock to be in contact with a fluid more than the other ones and it can highly affect the residual CO2 saturation, capillary pressure, relative permeability, and mass transfer between CO2 and brine (Abdallah et al., 1986;
Anderson, 1987). Due to the importance of wettability and as it can vary significantly with changes in environmental conditions, several researchers have reported brine/CO2contact angle on different minerals as a function of temperature, pressure and salinity. Farokhpoor et al. (2013a) measured brine/CO2 contact angles on quartz, calcite, mica and feldspar as function of pressure, temperature and salinity. They demonstrated that contact angle on mica significantly changes with pressure from strongly water wet to less water wet and this wettability alteration is due to the intermolecular forces controlling the water film thickness and stability, while non-significant changes were observed for the other minerals. They also concluded that for quartz, calcite and feldspar, the maximum contact angle near critical pressure is found at 36 C. Chen et al. (2015) investigated the advancing, receding and static contact angles of brine/CO2 on quartz at various conditions of pressure, temperature and salinity and finally simulated the wettability behavior using molecular dynamic simulation. Their results showed that the water contact angle increases with ionic strength and temperature and pressure can insignificantly affect the contact angle values. Results of the molecular dynamic simulation were also in a very good agreement with the reported experimental data. Sarmadivaleh et al. (2015) depicted water-CO2 wettability on quartz minerals with surface roughness of 40 nm. The measured advancing contact angle by the authors was 0 at 0.1 MPa and 296K. Pressure and temperature could significantly increase the contact angle (q) and therefore, decrease the CO2 storage capacity. They also demonstrated the variation of waterCO2 interfacial tension and found that IFT increases with temperature and increases with pressure up to 10 MPa and then decreases. Arif et al. (2017) investigated the advancing and receding q for brine/CO2/mica and also brine-CO2 IFT values at different operating conditions. They illustrated that both receding and advancing contact angles decrease with temperature and increase with salinity and pressure. It can be seen from their measurements that at about 308K and 20 MPa, the wettability is changed to CO2 wetting with q of about 110 . Al-Yaseri et al. (2016) measured the system contact angles on quartz at environmental conditions of pressure, temperature, salinity, salt type, and surface roughness. They found lower advancing and receding q at higher surface roughness and higher contact angles at higher temperatures,
Fig. 1. Structural and residual trapping. Fluid configurations in pore scale for water wet condition.
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
salinities and pressures. They also concluded that different salt types have different effects on the amount of q changes and Mg2þ, Ca2þ, and Naþ have the highest changes, respectively. Mutailipu et al. (2019) investigated the brine-CO2 IFTs at different conditions and finally proposed an empirical equation to estimate the system IFT in wide range of pressures, temperatures and salinities. They also reported the contact angle values on limestone, Brea sandstone and quartz minerals for liquid, gaseous and supercritical CO2. Based on their results, IFT increases with temperature and salinity and decreases with pressure up to a value. However, salt type had a limited influence on the IFT. Brine/CO2 contact angles had different behavior with pressure and temperature changes and a sudden increase was demonstrated when carbon dioxide converted from subcritical to supercritical for all of the minerals. Additionally, the wettability of limestone and Berea sandstone were altered to less water wet at supercritical conditions while quartz showed a different trend. Summary of the above studies performed by different researchers on brine/CO2/mineral wettability is tabulated in Table 1. According to the above discussions, studying CO2 wettability is necessary for predicting the capacity of structural and residual trapping. There is a serious lack of analysis and modeling brine/ CO2/mineral wettability in literature and researchers have not proposed any model for predicting the brine/CO2/mineral contact angle at various operating conditions, yet. This may be due to the great uncertainties in the reported data by different authors. Developing precise and general models for estimating CO2 contact angles is of great importance since experimental procedures are so expensive and time-consuming. Nowadays, with computers aid, artificial intelligent tools have become an inseparable part of scientific and engineering calculations especially in petroleum and chemical engineering. In this paper, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy interference system (ANFIS), and least square support vector machine (LSSVM) were used for predicting brine/CO2 contact angle on quartz, calcite, feldspar and mica minerals. In order to reduce the uncertainties
existing in the experimental data, a new variable is defined. Using this variable as a model input, accurate models are developed and their superiority is proven in the current work. 2. Models theory In this section, the background of various models which have been used in this study is explained. 2.1. Multi-layer perceptron artificial neural network (MLP-ANN) Complicated problems can be modeled using different types of artificial intelligent tools. One of these tools is artificial neural network (ANN) which finds the connections between inputs and outputs based on the biological behavior of neurons. Structure of ANNs includes several computational units that aim to obtain the best model by finding the most suitable arrangement of weights and biases between neurons/units, Fig. 2. Parallel combinations and arrangement of computational neurons and also no need for exact and clear formulation for the problem are the highlighted features of this tool (Daryasafar et al., 2014). In a basic design of a multi-layer perceptron (MLP) neural network, there are three types of layers, namely input, hidden, and output layers. These layers contain neurons and the number of neurons in the hidden layer needs to be optimized. In the present study, mean square error (MSE) was used as a stopping criterion for evaluation of the designed MLP. The main drawbacks of MLP-ANN are the determination of number of hidden layers and corresponding neurons. Optimum selection of these factors with optimization algorithms results in an accurate model with satisfying predictions (Yegnanarayana, 2009). 2.2. Adaptive neuro-fuzzy interference system (ANFIS) Fuzzy logic (FL) was introduced for the first time by Zadeh (1965). The main feature of this approach is the ability of converting linguistic variables to mathematical formats. In some cases,
Table 1 Summary of different literature surveys on brine/CO2/mineral wettability. Reference
Key Findings
Farokhpoor et al. (2013a)
Investigated brine/CO2 contact angle on quartz, mica, calcite and feldspar Pressure affects mica contact angle significantly No significant changes were observed for other minerals Quartz, calcite and feldspar have their maximum contact angle near critical pressure Demonstrated advancing, receding and static contact angle on quartz Simulated wettability behavior using molecular dynamic simulation Contact angle increases with temperature and ionic strength Pressure affects contact angle values significantly Water-CO2 wettability on quartz minerals with surface roughness of 40 nm Contact angle increases with pressure and temperature Water-CO2 IFT increases with temperature Water-CO2 IFT increases with pressure up to 10 MPa Measured the system contact angle on quartz Surface roughness lowers advancing and receding contact angle Contact angle increases with pressure, temperature and salinity Mg2þ, Ca2þ, and Na þ have the highest effect on contact angle changes Advancing and receding contact angles for mica were investigated Water-CO2 IFT values were observed at different operating conditions Contact angles increase with salinity and pressure Contact angles decrease with temperature At 308K and 20 MPa, wettability changes to CO2 wet Investigated brine-CO2 IFT at different conditions Reported contact angles on quartz, limestone and Berea sandstone IFT increases with salinity and temperature IFT decreases with pressure up to a value Salt type had limited effect on IFT Sudden increase was observed in contact angles when conditions were changed from sub- to super-critical Berea sandstone and limestone wettability was altered to less water wet at supercritical conditions
Chen et al. (2015)
Sarmadivaleh et al. (2015)
Al-Yaseri et al. (2016)
Arif et al. (2017)
Mutailipu et al. (2019)
3
4
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 2. Flow diagram for developing a MLP-ANN model.
fuzzy logic fails to obtain appropriate outputs due to contrasts and mistakes in judgments or insufficient information (Dehghan Saee et al., 2018). In order to tackle this problem, other approaches such as ANNs must be used with FL for process modeling. The ANN and FL tools are jointed together and provide a system called Adaptive Neuro-Fuzzy Interference System (ANFIS). Coupling of these approaches is performed by defining membership functions (MFs) and IF-THEN rules for a structure called fuzzy interference system. The most well-known MFs are triangular, trapezoidal, Gaussian, generalized bell-shaped and etc. (Daryasafar et al., 2018).
Two types of fuzzy interference systems are reported in literature, Mamdani and Takagi-Sugeno types (Jang, 1993). In this study, Takagi-Sugeno was utilized due to its ability to solve the nonlinearity between inputs and output variables. The main steps for designing an ANFIS structure are illustrated in Fig. 3. For different layers of ANFIS, different relations are performed. Clarifications of various layers are as follows (Fazeli et al., 2013): Transferring the raw input data into linguistic terms is performed in layer 1. Input data points are connected to the nodes in which the linguistic terms are defined. This definition is based on
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
5
O2i ¼ ui ¼ bAi ðxÞ:bBi ðxÞ
(2)
Where, u is the rule's firing strength, and MF is expressed by b. Normalization of ui in the previous layer is executed in layer 3. This normalization leads to discrimination between the total firing strength and each rule firing strengths. Layer 3 uses the following formulation for normalizing ui:
u ui
O3i ¼ ui ¼ P i
(3)
i
Characterization of the model's output linguistic terms is done in layer 4. The level of every rule impacts on the model's output is determined using the following expression:
O4i ¼ ui ðpi x1 þ qi x2 þ ri Þ
(4)
The linear variables as well as the parameters of the first layer are optimized by ANFIS. Layer 5 sums up the rules with output variable and converts them to a quantitative form as follows:
O5i
¼
X
P
ui fi
i ui fi ¼ P
i
i
(5)
ui
2.3. Least square support vector machine Random initialization and optimization criteria alteration of models based on ANN are some major drawbacks which lead to non-reproducibility of the model outcomes (Hagan et al., 1996). Support vector machine (SVM) was introduced as an alternative approach for overcoming the mentioned issues. SVM-based models have special features which show their supremacy in comparison to ANN models. These models have less adjustable or tunable variables, their generality is more exact, and degree of overtraining issue in these models is smaller (Suykens and Vandewalle, 1999). Quadratic programming is needed for finding the parameters of SVM. In order to lessen this complexity, a modified form of SVM was introduced by Suykens and Vandewalle (1999) which is called least square support vector machine (LSSVM). LSSVM characterizes regression error and function evaluation by solving a set of linear equations (Baghban et al., 2018). The following function is used as a cost function for finding hyper plane in LSSVM:
Fig. 3. Designing an optimized ANFIS system.
the membership functions. Gaussian membership function is one of the most popular and well-known MFs which is extensively used in ANFIS modeling due to its smoother behavior. This MF type was used in this study and its mathematical formulation is as:
N 1 1 X J ¼ uT u þ g e2 2 2 i¼1 i
(6)
The following constraint should be applied to the above relation:
O1i
¼ exp
ðx zÞ 2s2
2
yi ¼ uT 4ðxi Þ þ b þ ei
! (1)
In this equation, O is the output of layer 1, z and s are the Gaussian MF center and the variance term, respectively. In order to develop a model with an excellent performance, these parameters need to be optimized by ANFIS during learning process. Layer 2 is called the firing strength layer in which the adequacy and accuracy of the conditions applied to the previous sections are determined. The rule's firing strength of this layer is formulated as:
i ¼ 1; 2; :::; N
(7)
In the above equations, u is the weight factor, g is the tuning parameter, e is the error of the function, 4(xi) is the mapping function, and b is the bias vector. For determining the objective function, Lagrangian coefficients are employed in the equation as given below:
xðu; b; e; aÞ ¼ J
N X i¼1
n
ai uT 4ðxi Þ þ b þ ei yi
o
(8)
6
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Differentiating the above equation with respect to each of the parameters based on Karush-Kuhn-Tucker (KKT) leads to its optimum solution. N X vx ¼ 0/u ¼ ai 4ðxi Þ vu
(9)
i¼1
N X vx ¼ 0/ ai ¼ 0 vb i¼1
vx ¼ 0/ai ¼ gei ; vei
(10)
i ¼ 1; :::; N
vx ¼ 0/yi ¼ 4ðxi ÞuT þ b þ ei ; vai
(11)
i ¼ 1; :::; N
(12)
According to the Mercer's theory, LSSVM for nonlinear regression becomes as follows (Pelckmans et al., 2002):
y¼
N X
ai Kðx; xi Þ þ b
(13)
i¼1
Where, K(x,xi) is the Kernel function. Radial basis function (RBF) is the most well-known and widely used Kernel function that has just one tuning variable (Baghban et al., 2019). RBF was used in this study and is defined as (Wang et al., 2018):
. Kðx; xi Þ ¼ exp x x2i s2
(14)
In which s2 is the adjusting parameter and the smoothness of the function is controlled by this variable. Flowchart for constructing a LSSVM model is shown in Fig. 4. Based on the above equations, LSSVM has two adjusting parameters, s2 and g, which are tuned during the optimization process. The optimum values of these variables are obtained by utilizing a cost function. For this purpose, mean square error (MSE) was used in this study. MSE is defined as: N P
MSE ¼ i¼1
yi;exp yi;pred N
(15)
Where, yexp and ypred are experimental and estimated data. In order to minimize this cost function and finding the optimum values for the tuning parameters, a proper optimization algorithm is needed. In this paper, coupled simulated annealing (CSA) was used as a powerful algorithm for this purpose. 3. Data gathering Collection of accurate and comprehensive experimental data from reliable resources is necessary for developing an efficient and accurate model. Therefore, an extensive databank including 630 data points of CO2/brine/mineral contact angle was gathered for proposing accurate models in this study. The selected databank includes CO2/brine contact angles at wide operating conditions of pressure, temperature and salinity. It also consists of the static, advancing and receding contact angle values for various minerals including quartz, calcite, mica, and feldspar. Several researchers have measured the contact angle of CO2/brine on different kinds of minerals. However, significant uncertainty remains and the measured values by different researchers are different for a certain environmental condition. Variation between the measurements might be due to the surface roughness and surface contamination
Fig. 4. Flowchart of developing a LSSVM model.
(Iglauer et al., 2015). Surface roughness is a parameter that controls the hysteresis (Andrew et al., 2014) and there is a general understood that increasing surface roughness can decrease contact angle on hydrophilic surfaces and increase it on hydrophobic surfaces
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
(Bikkina, 2011; Palamara et al., 2015). Surface contamination has also been identified as a significant problem since different procedures have been proposed for cleaning the mineral surfaces (Iglauer et al., 2015). In this study, surface roughness was not considered as an input variable of the model since this factor is often unreported in published researches and only five studies (Sarmadivaleh et al., 2015; Jung and Wan, 2012; Saraji et al., 2013, 2014; Wang et al., 2013) have reported this factor. In order to apply these two factors, a parameter called “theta zero (q0)" was considered in this paper for developing different models. This parameter shows the wettability condition of the minerals surface in terms of surface contamination and roughness. Differences between the measurements of contact angle values by different researchers can be explained by this parameter. Theta zero is defined as follows:
q ¼ roundðqi =10Þ 0
the output layer was utilized for MLP-ANN modeling. Hyperbolic tangent sigmoid (tansig) transfer function was considered for the input and hidden layers while the linear (purelin) transfer function was used for the output layer. Neurons in the hidden layer (x) were found by learning the model for different numbers of neurons. Finally, the configuration with the least average absolute relative error (AARE%) and root mean square error (RMSE), and the highest Pearson correlation coefficient (R) was selected as the optimal network structure for modeling the contact angle of brine/CO2/ minerals.
tan sigðnÞ ¼
2 1 1 þ expð 2nÞ
qðadv=rec=stÞ¼f ðT;P;salinity;mineraltype;contactangletype;q0
(18) It must be concluded that the proposed models in this study can estimate static/advancing/receding contact angles of brine/CO2/ mineral by selecting 1 ¼ static, 2 ¼ advancing, and 3 ¼ receding as an input variable in the models for “contact angle type". Table 2 shows the analysis of input and output variables as well as the references for each set of the gathered databank. The dataset used in this study can be found in Appendix A. 4. Models development In this study, the selected databank for developing different models was randomly splitted into two subsets, namely training and testing data sets. 80% of the total data was considered as train set while the remaining 20% was selected as test set. Multi-layer perceptron networks have the ability to model a nonlinear function with only one hidden layer. Based on this and in order to reduce the computational time, only one hidden layer was considered for the proposed MLP network. A general structure of 20-x-1 with twenty neurons in the input layer and one neuron in
qexp i
N 0
(17) Hence, the model output is contact angle value (static or advancing or receding) and the input parameters of the developed models are pressure (MPa), temperature (K), salinity (M), type of mineral (quartz, calcite, mica, and feldspar), type of contact angle (static (st), advancing (adv), and receding (rec)), and theta zero (q0). All the experimental data points have been measured with NaCl brine and due to the lack of data, effect of salt type was not included in this modeling. Therefore, the function of the models' output is as given below:
(20)
N qpred qexp P ji j i
AAREð%Þ ¼ i¼1
@ ðambient temperature; ambient pressure; salinity ¼ 0Þ
(19)
purelinðnÞ ¼ n
(16)
qi ¼ brine=CO2 =mineral contact angle
7
N P
B B RMSE ¼ SQRT Bi¼1 @
N h P
100
(21)
exp qpred qi i
N
exp qexp ave qi i
2 1 C C C A
(22)
pred qpred ave qi i
i
i¼1 R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 P 2 N N P exp pred qexp ave qi : qpred ave qi i i i¼1
(23)
i¼1
In these relations, N is the total number of data, qexp is the actual i contact angle value, and qpred is the estimated contact angle value i by the model. Results of different networks during the optimization process for finding the number of neurons in the hidden layer (x) are shown in Fig. 5. As shown in this figure, the network with configuration of 20-13-1 has the performance compared to the other networks and hence, is used for further analyses. For ANFIS model, the Takagi-Sugeno type of FIS was used. This type of FIS can be obtained using different methods including lookup table method, grid partitioning, subtractive clustering and fuzzy c-means. In the developed ANFIS model, subtractive clustering and Gaussian membership function were utilized to the structure and MF type, respectively. Subtractive clustering finds the antecedent MFs and the number of rules by subclast function. Then, characterization of each rule equations are performed using the least squares estimation. Hybrid method was also used in this study to train the initial fuzzy interference system and the optimum structure was found at an iteration of 50. For the LSSVM structure, as mentioned before, there are two tuning parameters, g and s2, which are essentially needed to be tuned for the model design since the comprehensiveness and
Table 2 Details of the parameters used for developing different models. Mineral Temperature Pressure (K) (MPa)
Salinity Refs. (M)
Quartz
0e40
0e12.9
0.1e40 0.25e30 0.5e30
0e7.4 0e0.8 0e11.1
296e373
Calcite 298e343 Feldspar 309e339 Mica 308e343
[Farokhpoor et al., 2013a; 2013b; Chen et al., 2015; Sarmadivaleh et al., 2015; Al-Yaseri et al., 2016; Mutailipu et al., 2019; Jung and Wan, 2012; Saraji et al., 2013; Espinoza and Santamarina, 2010; Chiquet et al., 2007] [Farokhpoor et al., 2013a; 2013b; Arif et al., 2017; Espinoza and Santamarina, 2010] [Farokhpoor et al., 2013a; 2013b] [Farokhpoor et al., 2013a; Chiquet et al., 2007; Arif et al., 2016b]
8
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 5. Performance of various MLP-ANN models with different neurons in the hidden layer; a) AARE results, b) RMSE results, and c) Pearson coefficient (R) results.
accuracy of the LSSVM model are dependent on these factors. CSA algorithm was utilized for optimization of these parameters. According to the optimization process, the best values for g and s2 were found to be 70.406 and 1.122, respectively.
5. Results and discussion In this study, different models have been developed for predicting the contact angle (static, advancing, and receding) of brine/
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101 Table 3 Analysis of different statistical parameters for various models. Model
Parameter
Train Data
Test Data
Total Data
MLP-ANN
AARE (%) RMSE R
8.366 3.510 0.984
8.950 2.927 0.981
8.464 3.418 0.984
ANFIS
AARE (%) RMSE R
0.523 0.895 0.999
9.393 3.986 0.980
2.021 1.830 0.996
LSSVM
AARE (%) RMSE R
8.916 3.989 0.980
7.398 2.973 0.983
8.645 3.833 0.981
CO2/minerals at different operating conditions of pressure, temperature, and salinity. The proposed models are able to estimate the contact angle brine/carbon dioxide system on quartz, calcite, feldspar, and mica. Accuracy and proficiency of the presented models were investigated through statistical and graphical analyses. For this purpose, statistical parameters of AARE, RMSE, and R (Eqs. (21)e(23)) were used in this research. The values of these parameters for MLP-ANN, ANFIS, and LSSVM are shown in Table 3. Comparison between these parameters is also presented graphically in Fig. 6. Table 3 and Fig. 6 indicate that the estimated results by the developed models are in a very good agreement with actual data; however, the ANFIS model exhibits higher accuracy with AARE, RMSE, and R of 2.021, 1.830, and 0.996, respectively for the total data (train and test subsets). In this study, the accuracy of various models in simulating the wettability behavior of brine/CO2 on different minerals surface was demonstrated. The predicted data for both train and test subsets were plotted versus actual values for the proposed models, Fig. 7.
9
This figure illustrates that predicted values of experimental brine/ CO2 contact angle by the presented models are mostly concentrated around the 450 slope line which shows the acceptable accuracy of these models. According to this figure, all the developed models are accurate enough and can be utilized for simulating the brine/CO2/ mineral wettability behavior; however, the ANFIS model is more proficient than the others. For further verifications, the relative errors of the models outcomes were plotted against the experimental data, Fig. 8. This figure shows that among different models, the relative errors of the ANFIS results are compacted around the zero error line, which proves the reliability of this model. According to the above statistical analyses for the ANFIS model, there is a very good compromise between the predicted and experimental values due to the great overlap between the estimated and target data points. In order to show the distribution of absolute relative errors of various presented models, the plot of cumulative data frequency that is defined as the percent of total data utilized has been drawn versus absolute relative error in Fig. 9 for all the models. This figure indicates that more than 90% of the predicted contact angles by the ANFIS model have absolute relative error lower than 8% whereas for the other models, less than 70% of the estimated values have error lower than 8%. For investigating the error distribution of different models over the input variables and accuracy verification of these models, Fig. 10(aed) is utilized. As it can be seen in this figure, the predicted results by the proposed ANFIS model are more concentrated around the error line of zero for various ranges of input variables compared to the estimated results of the other models. This illustrates the robustness of the presented ANFIS model for wide ranges of operating conditions. Selecting the best model for a specific range of input variables is one of the main issues for any parameter estimation. Fig. 10 shows the ability of the proposed models in
Fig. 6. Comparison between different models in predicting brine/CO2/minerals contact angle.
10
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 7. Crossplot of the estimations of the proposed models; a) ANFIS, b) MLP-ANN, c) LSSVM.
predicting brine/CO2/rock wettability for various ranges of operating conditions with high accuracy. In order to show the ability of the proposed models in trend prediction, effects of pressure, temperature, and salinity on the amount of brine/CO2 contact angles for different minerals were simulated using the developed MLP-ANN, ANFIS, and LSSVM. Experimental data with environmental conditions which were used for this purpose are indicated in Table 4.
Fig. 11 shows the studied systems contact angles versus pressure for different models. According to the measure values of contact angles, brine/CO2 contact angle increases with pressure for quartz, feldspar and mica, and decreases at high pressures for calcite. Among the proposed models, the ANFIS approach has the best performance and can predict the process trend with high accuracy for various materials. Effects of temperature on brine/CO2 contact angle as well as the
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
11
Fig. 8. Relative error of the predicted results of the developed approaches; a) ANFIS, b) MLP-ANN, c) LSSVM.
ability of the developed models in predicting the trend prediction of this process are demonstrated in Fig. 12. Based on the used datasets, increasing temperature increases contact angles for quartz and feldspar minerals and decreases those for calcite and mica. However, different results for these trends are reported by researchers, for example for the effect of temperature on brine/CO2/
quartz (Saraji et al., 2014). As indicated in Fig. 12, the developed ANFIS efficiently predicts the trend and behavior of contact angles against temperature. Performance of the presented MLP-ANN, ANFIS, and LSSVM models in trend prediction of contact angles changes with salinity are shown in Fig. 13. It is evident from this figure that ANFIS model
12
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 9. Distribution of absolute relative error for total data.
Fig. 10. Relative error distribution over input parameters for various models; a) Pressure, b) Temperature, C) Salinity, d) Theta_zero
is accurately enough for the trend predictions. According to this figure, increasing salinity (NaCl) increases contact angles for all the minerals and the proposed ANFIS can excellently simulate this behavior. Wettability of different minerals with respect to brine and CO2 is important in geological storage of CO2 since it can strongly affect the structural and residual trapping. Therefore, modeling the wettability behavior of brine/CO2/mineral system is highly crucial.
There is large uncertainty associated with experimental data published in different researches. In this study, efforts have been performed for reducing the uncertainties by defining new variables and considering the influencing parameters in the proposed models. Based on the results of the developed intelligent models, one can conclude that the ANFIS outperforms the other models and presents more reliable and accurate results. Sensitivity analysis was also performed in this paper. Finding the
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
13
Table 4 Experimental data used for simulating the trend prediction of brine/CO2/minerals contact angle by different models. Mineral Set 1 Quartz
Set 2 Calcite
Set 3 Feldspar
Set 4 Mica
Set 5 Quartz
Set 6 Calcite
Set 7 Feldspar Set 8 Mica
Set 9 Quartz
Set 10 Calcite
Set 11 Feldspar
Set 12 Mica
Pressure (MPa)
Temperature (K)
Salinity (M)
Contact angle type
Theta-zero
Experimental contact angle
Ref.
0.09 8.33 9.07 9.40 9.97 10.93 11.89 17.99 19.98 22.01 25.01
318 318 318 318 318 318 318 318 318 318 318
3 3 3 3 3 3 3 3 3 3 3
Static
3
45.1 57.2 58.1 58.8 59.1 59.1 59.2 59.1 59.6 59.5 60.0
Jung and Wan (2012)
0.30 1.99 9.98 19.93 30.00 39.99
309 309 309 309 309 309
0 0 0 0 0 0
Static
1
12.2 12.2 12.5 11.9 11.5 11.9
(Farokhpoor et al., 2013a, 2013b)
0.27 2.39 6.23 10.44 15.40 19.99 29.97
309 309 309 309 309 309 309
0.8 0.8 0.8 0.8 0.8 0.8 0.8
Static
1
14.6 15.4 15.9 17.3 17.8 18.9 19.8
(Farokhpoor et al., 2013a, 2013b)
1.53 2.00 2.53 3.54 6.05 8.03 8.95 9.90
308 308 308 308 308 308 308 308
1 1 1 1 1 1 1 1
Receding
1
41.2 41.2 44.1 48.4 57.1 67.1 69.2 66.1
Chiquet et al. (2007)
3.50 3.50 3.50
308 318 333
0 0 0
Advancing
1
11.9 15.9 56.9
Saraji et al. (2013)
0.10 0.10 0.10
308 323 343
0 0 0
Advancing
2
24.7 17.8 13.8
Arif et al. (2017)
10.43 10.43
309 339
0.8 0.8
Static
1
17.3 18.2
(Farokhpoor et al., 2013a, 2013b)
5.00 5.00 5.00
308 323 343
0 0 0
Advancing
0
38.84 35.92 31.90
Arif et al. (2016b)
10 10 10 10 10 10
323 323 323 323 323 323
0 1.13 2.19 3.68 7.36 12.88
Advancing
0
30.03 34.02 36.19 38.13 42.11 44.05
Al-Yaseri et al. (2016)
15 15 15 15 15
323 323 323 323 323
0 1.84 3.69 5.53 7.38
Advancing
2
85.19 86.06 91.08 93.94 94.98
Arif et al. (2017)
7.50 7.50 7.50
309 309 309
0 0.2 0.8
Static
1
16.97 16.51 19.43
(Farokhpoor et al., 2013a, 2013b)
15 15 15 15 15
323 323 323 323 323
0.02 1.87 3.72 7.39 11.09
Advancing
0
58.96 61.86 64.92 73.95 82.98
Arif et al. (2016b)
14
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Fig. 11. Performance of the proposed models in trend prediction of contact angle changes with pressure; a) Set 1, b) Set 2, c) Set 3, d) Set 4.
Fig. 12. Performance of the proposed models in trend prediction of contact angle changes with temperature; a) Set 5, b) Set 6, c) Set 7, d) Set 8.
effect of each input variable on the output parameter is the sensitivity analysis. This process can be done by performing various approaches such as response surface methodology (RSM), sampling-based methods like Monte-Carlo algorithm, differential analysis, fast probability integration, relevancy factor and etc. In this study, the impact of each input variable on the output (brine/ CO2/mineral wettability) is determined by using Monte-Carlo algorithm (Iman et al., 1981; Helton, 1993). In this algorithm, multiple model evaluations with probabilistic model inputs are performed and then, uncertainties are determined by using these results. Facilitating discontinuities and nonlinearities by sampling in a wide
range of uncertain variables, obtaining uncertainty results without needing to use the surrogated models and simplicity are the main abilities of this algorithm (Iman et al., 1981; Helton, 1993). The results of performing Monte-Carlo analysis for finding the sensitivity of each input variable (Mineral type, pressure, temperature, salinity and theta_zero) on brine/CO2/mineral contact angle are indicated in Fig. 14. This figure depicts the changes in the output variable relative to the changes in each input parameter. According to Fig. 11, mineral type, salinity and pressure can affect the system wettability very much.
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
15
Fig. 13. Performance of the proposed models in trend prediction of contact angle changes with salinity (NaCl); a) Set 9, b) Set 10, c) Set 11, d) Set 12.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.118101.
References
Fig. 14. Sensitivity analysis on brine/CO2/mineral wettability behavior.
6. Conclusions Three models, namely MLP-ANN, ANFIS and LSSVM, were developed for simulating the wettability behavior of brine/CO2/ rock system at different operating conditions of pressure, temperature and salinity. The proposed models are capable to predict advancing, receding and static contact angles of brine/CO2 on quartz, calcite, feldspar and mica minerals. The reliability and proficiency of the developed models were shown using statistical analyses. Results showed that the proposed ANFIS model provides more accurate q values and it can predict the wettability behavior against the operating conditions excellently. The presented models are important for characterization of wettability of brine/CO2/rock systems and reducing the operational costs by omitting the highly expensive and time consuming experimental tests. Finally, MonteCarlo simulator was used to find the impact of input variables on the output variable, i.e. brine/CO2/rock contact angle. Results of sensitivity analysis demonstrated that mineral type, salinity, and pressure have the highest impact and should be considered during CO2 geo-sequestration process.
Abdallah, W., Buckley, J.S., Carnegie, A., Edwards, J., Herold, B., Fordham, E., Graue, A., Habashy, T., Seleznev, N., Signer, C., 1986. Fundamentals of wettability. Technol. 38, 1125e1144. Al-Khdheeawi, E.A., Vialle, S., Barifcani, A., Sarmadivaleh, M., Iglauer, S., 2016. Influence of rock wettability on CO2 migration and storage capacity in deep saline aquifers. In: 13th International Conference on Greenhouse Gas Control Technologies. GHGT-13, Lausanne, Switzerland. Al-Yaseri, A.Z., Lebedev, M., Barifcani, A., Iglauer, S., 2016. Receding and advancing (CO2 þ brine þ quartz) contact angles as a function of pressure, temperature, surface roughness, salt type and salinity. J. Chem. Thermodyn. 93, 416e423. Anderson, W.G., 1987. Wettability literature survey-part 6: the effects of wettability on waterflooding. J. Pet. Technol. 39, 1605e1622. Andrew, M., Bijeljic, B., Blunt, M.J., 2014. Pore-scale contact angle measurements at reservoir conditions using X-ray microtomography. Adv. Water Resour. 68, 24e31. Arif, M., Barifcani, A., Lebedev, M., Iglauer, S., 2016a. CO2-wettability of low to high rank coal seams: implications for carbon sequestration and enhanced methane recovery. Fuel 181, 680e689. Arif, M., Al-Yaseri, A.Z., Barifcani, A., Lebedev, M., Iglauer, S., 2016b. Impact of pressure and temperature on CO2ebrineemica contact angles and CO2ebrine interfacial tension: implications for carbon geo-sequestration. J. Colloid Interface Sci. 462, 208e215. Arif, M., Lebedev, M., Barifcani, A., Iglauer, S., 2017. CO2 storage in carbonates: wettability of calcite. Int. J. Greenh. Gas Contr. 62, 113e121. Baghban, A., Sasanipour, J., Habibzadeh, S., Zhang, Z., 2019. Sulfur dioxide solubility prediction in ionic liquids by a group contribution-LSSVM model. Chem. Eng. Res. Des. 142, 44e52. Baghban, A., Sasanipour, J., Zhang, Z., 2018. A new chemical structure-based model to estimate solid compound solubility in supercritical CO2. J. CO2 Util. 26, 262e270. Bikkina, P.K., 2011. Contact angle measurements of CO2ewaterequartz/calcite systems in the perspective of carbon sequestration. Int. J. Greenh. Gas Contr. 5, 1259e1271. Chen, C., Wan, J., Li, W., Song, Y., 2015. Water contact angles on quartz surfaces under supercritical CO2 sequestration conditions: experimental and molecular dynamics simulation studies. Int. J. Greenh. Gas Contr. 42, 655e665. Chen, L., Liu, D., Agarwal, R., 2018. Numerical simulation of enhancement in CO2 sequestration with various water production schemes under multiple well scenarios. J. Clean. Prod. 184, 12e20.
16
A. Daryasafar et al. / Journal of Cleaner Production 239 (2019) 118101
Chiquet, P., Broseta, D., Thibeau, S., 2007. Wettability alteration of cap rock minerals by carbon dioxide. Geofluids 7, 112e122. Daryasafar, A., Ahadi, A., Kharrat, A., 2014. Modeling of steam distillation mechanisms during steam injection process using artificial intelligence. Sci. World J. 2014, 1e8. Daryasafar, A., Daryasafar, N., Madani, M., Kalantari Meybodi, M., Joukar, M., 2018. Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide. Neural Comput. Appl. 29, 295e305. Dehghan Saee, A., Baghban, A., Zarei, F., Zhang, Z., Habibzadeh, S., 2018. ANFIS based evolutionary concept for estimating nucleate pool boiling heat transfer of refrigerant-ester oil containing nanoparticles. Int. J. Refrig. 96, 38e49. Dhinesh, B., Annamalai, M., 2018. A study on performance, combustion and emission behaviour of diesel engine powered by novel nano nerium oleander biofuel. J. Clean. Prod. 196, 74e83. Dhinesh, B., Raj, Y.M.A., Kalaiselvan, C., KrishnaMoorthy, R., 2018. A numerical and experimental assessment of a coated diesel engine powered by highperformance nano biofuel. Energy Convers. Manag. 171, 815e824. Espinoza, D.N., Santamarina, J.C., 2010. WatereCO2emineral systems: interfacial tension, contact angle, and diffusiondimplications to CO2 geological storage. Water Resour. Res. 46, 1e10. Farokhpoor, R., Bjorkvik, B.J.A., Lindeberg, E., Torsater, O., 2013a. Wettability behavior of CO2 at storage conditions. Int. J. Greenh. Gas Contr. 12, 18e25. Farokhpoor, R., Bjorkvik, B.J.A., Lindeberg, E., Torsater, O., 2013b. CO2 wettability behavior during CO2 sequestration in saline aquifer -An Experimental study on minerals representing sandstone and carbonate. Energy Procedia 37, 5339e5351. Fazeli, H., Soleimani, R., Ahmadi, M.A., Badrnezhad, R., Mohammadi, A.H., 2013. Experimental study and modeling of ultrafiltration of refinery effluents using a hybrid intelligent approach. Energy Fuels 27, 3523e3537. Gaus, I., 2010. Role and impact of CO2erock interactions during CO2 storage in sedimentary rocks. Int. J. Greenh. Gas Contr. 4, 73. Hagan, M.T., Demuth, H.B., Beale, M.H., 1996. Neural Network Design. Pws Pub, Boston. Helton, J.C., 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive-waste disposal. Reliab. Eng. Syst. Saf. 42, 327e367. Hesse, M.A., Orr, F.M., Tchelepi, H.A., 2008. Gravity currents with residual trapping. J. Fluid Mech. 611, 35. Iglauer, S., 2011. In: Nakajima, H. (Ed.), Mass Transfer. InTech, Rijeka, p. 233. Iglauer, S., Mathew, M.S., Bresme, F., 2012. Molecular dynamics computations of brineeCO2 interfacial tensions and brineeCO2equartz contact angles and their effects on structural and residual trapping mechanisms in carbon geo-sequestration. J. Colloid Interface Sci. 386, 405e414. Iglauer, S., Pentland, C.H., Busch, A., 2015. CO2 wettability of seal and reservoir rocks and the implications for carbon geo-sequestration. Water Resour. Res. 51, 729e774. Iman, R.L., Helton, J.C., Campbell, J.E., 1981. An approach to sensitivity analysis of computer models: Part II - ranking of input variables, response surface validation, distribution effect and technique synopsis. J. Qual. Technol. 13, 232e240. Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (3), 665e685. Jing, J., Yang, Y., Tang, Z., 2019. Effects of formation dip angle and salinity on the safety of CO2 geological storage - a case study of Shiqianfeng strata with low porosity and low permeability in the Ordos Basin, China. J. Clean. Prod. 226, 874e891. Joshi, A., Gangadharan, S., Leonenko, Y., 2016. Modeling of pressure evolution during multiple well injection of CO2 in saline aquifers. J. Nat. Gas Sci. Eng. 36, 1070e1079. Juanes, R., Spiteri, E.J., Orr, F.M., Blunt, M.J., 2006. Impact of relative permeability hysteresis on geological CO2 storage. Water Resour. Res. 42.
Jung, J.W., Wan, J., 2012. Supercritical CO2 and ionic strength effects on wettability of silica surfaces: equilibrium contact angle measurements. Energy Fuels 26, 6053e6059. Liu, D., Agarwal, R., Li, Y., 2016. Numerical simulation and optimization of CO2enhanced water recovery by employing a genetic algorithm. J. Clean. Prod. 133, 994e1007. Mutailipu, M., Liu, Y., Jiang, L., Zhang, Y., 2019. Measurement and estimation of CO2ebrine interfacial tension and rock wettability under CO2 sub- and supercritical conditions. J. Colloid Interface Sci. 534, 605e617. Nghiem, L., Shrivastava, V., Tran, D., Kohse, B., Hassam, M., Yang, C., 2009. Simulation of CO2 storage in saline aquifers. In: SPE Paper 125848, Proceeding of SPE/ EAGE Reservoir Characterization & Simulation Conference, Abu Dhabi, UAE. Palamara, D.R., Neeman, T., Golab, A.N., Sheppard, A., 2015. A statistical analysis of the effects of pressure, temperature and salinity on contact angles in CO2ebrineequartz systems. Int. J. Greenh. Gas Contr. 42, 516e524. Pelckmans, K., Suykens, J.A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., Vandewalle, J., 2002. LS-SVMlab: a Matlab/c Toolbox for Least Squares Support Vector Machines, Tutorial, vol. 142. KULeuven-ESAT, Leuven, Belgium, pp. 1e2. Pentland, C.H., El-Maghraby, R., Iglauer, S., Blunt, M.J., 2011. Measurements of the capillary trapping of super-critical carbon dioxide in Berea sandstone. Geophys. Res. Lett. 38. Pruess, K., Xu, T., Apps, J., Garcia, J., 2003. Numerical modelling of aquifer disposal of CO2. SPE J. 8, 49e60. Qi, R., LaForce, T.C., Blunt, M.J., 2009. Design of carbon dioxide storage in aquifers. Int. J. Greenh. Gas Contr. 3, 195. Raza, A., Gholami, R., Sarmadivaleh, M., Tarom, N., Rezaee, R., Han Bing, C., Nagarajan, R., Hamid, M.A., Elochukwu, H., 2016. Integrity analysis of CO2 storage sites concerning geochemical-geomechanical interactions in saline aquifers. J. Nat. Gas Sci. Eng. 36, 224e240. Saraji, S., Goual, L., Piri, M., Plancher, H., 2013. Wettability of supercritical carbon dioxide/water/quartz systems: simultaneous measurement of contact angle and interfacial tension at reservoir conditions. Langmuir 29, 6856e6866. Saraji, S., Piri, M., Goual, L., 2014. The effects of SO2 contamination, brine salinity, pressure, and temperature on dynamic contact angles and interfacial tension of supercritical CO2/brine/quartz systems. Int. J. Greenh. Gas Contr. 28, 147e155. Sarmadivaleh, M., Al-Yaseri, A.Z., Iglauer, S., 2015. Influence of temperature and pressure on quartzewatereCO2 contact angle and CO2ewater interfacial tension. J. Colloid Interface Sci. 441, 59e64. Seyyedi, M., Rostami, B., Pasdar, M., Pazhoohan, J., 2016. Experimental and numerical study of the effects of formation brine salinity and reservoir temperature on convection mechanism during CO2 storage in saline aquifers. J. Nat. Gas Sci. Eng. 36, 950e962. Suykens, J.A., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Process. Lett. 9, 293e300. Vigneswaran, R., Annamalai, K., Dhinesh, B., Krishnamoorthy, R., 2018. Experimental investigation of unmodified diesel engine performance, combustion and emission with multipurpose additive along with water-in-diesel emulsion fuel. Energy Convers. Manag. 172, 370e380. Wang, P., Peng, S., He, T., 2018. A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China. J. Nat. Gas Sci. Eng. 55, 1e15. Wang, S., Edwards, I.M., Clarens, A.F., 2013. Wettability phenomena at the CO2ebrineemineral interface: implications for geologic carbon sequestration. Environ. Sci. Technol. 47, 234e241. Yegnanarayana, B., 2009. Artificial Neural Networks. PHI Learning Pvt. Ltd. Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338. Zhang, Z., Huisingh, D., 2017. Carbon dioxide storage schemes: technology, assessment and deployment. J. Clean. Prod. 142, 1055e1064.