On the prediction of geochemical parameters (TOC, S₁ and S₂) by considering well log parameters using ANFIS and LSSVM strategies

Abstract

Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty. Though, attaining these parameters, for instance total organic carbon (TOC), volatile and residual hydrocarbon (S₁ & S₂) is a challenge for geologists due to the high cost and time consumption. Therefore, addressing this issue has become an interesting subject for many researchers. As a result, on the ground of conventional well logs, vast kinds of methods, for example, back propagation artificial neural network (BPANN), have been introduced to solve this problem. Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime. However, the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum. Therefore, current study cope with this issue and alleviate the uncertainty, Least Squares Support Vector Machine (LSSVM) and Adaptive-Neuro Fuzzy Inference System (ANFIS) algorithms cooperating with the particle swarm optimization (PSO) were suggested as a suitable method to increase the precision of estimating geochemical factors. The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China, which consists of geochemical and well logging information. Outputs of this study illustrated that ANFIS-PSO and LSSVM-PSO have a great ability to estimate geochemical parameters. The values of R² obtained for these two models in order to predict the output parameters of TOC, S₁ and S₂ are equal to 0.6846 & 0.785, 0.6864 & 0.778, and 0.7343 & 0.8128, respectively. The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model. Also, a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.

Keywords

Least squares support vector machine / Total organic carbon / Well log parameters / Adaptive-neuro fuzzy inference system / Particle swarm optimization

Declaration of competing interests

The authors declare that they have no conflicts of interests.

References

[1] C. Zou, et al., Geological characteristics and resource potential of shale gas in China. Petroleum exploration and development 37 (6) (2010) 641-653.

[2] S. Lin, et al., Geochemical characteristics of the source rocks in mesozoic yanchang formation, central ordos basin, J. Earth Sci. 24 (5) (2013) 804-814.

[3] J.W. Schmoker, Determination of organic content of Appalachian Devonian shales from formation-density logs: geologic notes, AAPG (Am. Assoc. Pet. Geol.) Bull. 63 (9) (1979) 1504-1509.

[4] J.W. Schmoker, Organic content of devonian shale in western appalachian basin, AAPG (Am. Assoc. Pet. Geol.) Bull. 64 (12) (1980) 2156-2165.

[5] B. Meyer, M. Nederlof, Identification of source rocks on wireline logs by density/resistivity and sonic transit time/resistivity crossplots, AAPG (Am. Assoc. Pet. Geol.) Bull. 68 (1984) 121-129.

[6] Q. Passey, et al., A practical model for organic richness from porosity and resistivity logs. AAPG bulletin 74 (12) (1990) 1777-1794.

[7] H. Amiri Bakhtiar, et al., Estimating total organic carbon content and source rock evaluation, applying DlogR and neural network methods: ahwaz and Marun oilfields, SW of Iran. Petroleum Science and Technology 29 (16) (2011) 1691-1704.

[8] M. Khoshnoodkia, et al., TOC determination of Gadvan Formation in South Pars Gas field, using artificial intelligent systems and geochemical data. Journal of Petroleum Science and Engineering 78 (1) (2011) 119-130.

[9] E. Sfidari, A. Kadkhodaie-Ilkhchi, S. Najjari, Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems. Journal of Petroleum Science and Engineering 86 (2012) 190-205.

[10] P. Wang, et al., Revised models for determining TOC in shale play: example from Devonian Duvernay shale, Western Canada sedimentary basin, Mar. Petrol. Geol. 70 (2016) 304-319.

[11] A.A.A. Mahmoud, et al., Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network. International Journal of Coal Geology 179 (2017) 72-80.

[12] R. Hertzog, et al., Geochemical logging with spectrometry tools, SPE Form. Eval. 4 (2) (1989) 153-162.

[13] A. Autric, Resistivity, radioactivity and sonic transit time logs to evaluate the organic content of low permeability rocks, Log. Anal. 26 (3) (1985).

[14] A. Decker, D. Hill, D. Wicks, Log-based gas content and resource estimates for the Antrim shale, Michigan Basin. Low Permeability Reservoirs Symposium, Society of Petroleum Engineers, 1993.

[15] J.W. Schmoker, T.C. Hester, Organic carbon in Bakken formation, United States portion of Williston basin, AAPG Bull. 67 (12) (1983) 2165-2174.

[16] A. Kadkhodaie-Ilkhchi, M.R. Rezaee, H. Rahimpour-Bonab, A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf. Journal of Petroleum Science and Engineering 65 (1-2) (2009) 23-32.

[17] P. Wang, S. Peng, T. He, A novel approach to total organic carbon content prediction in shale gas reservoirs with well logs data, Tonghua Basin, China. Journal of Natural Gas Science and Engineering 55 (2018) 1-15.

[18] B. Alizadeh, S. Najjari, A. Kadkhodaie-Ilkhchi, Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: a case study of the South Pars Gas Field, Persian Gulf, Iran, Comput. Geosci. 45 (2012) 261-269.

[19] B. Alizadeh, K. Maroufi, M.H. Heidarifard, Estimating source rock parameters using wireline data: an example from dezful embayment, south west of Iran. Journal of petroleum science and engineering 167 (2018) 857-868.

[20] M. Tan, Q. Liu, S. Zhang, A dynamic adaptive radial basis function approach for total organic carbon content prediction in organic shale, Geophysics 78 (6) (2013) D445-D459.

[21] V. Bolandi, A. Kadkhodaie, R. Farzi, Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: a case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran. Journal of Petroleum Science and Engineering 151 (2017) 224-234.

[22] L. Zhu, et al., Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves. Journal of Geophysics and Engineering 15 (3) (2018) 1050-1061.

[23] A.A. Mahmoud, et al., Evaluation of the total organic carbon (TOC) using different artificial intelligence techniques, Sustainability 11 (20) (2019) 5643.

[24] A.A. Mahmoud, et al., Estimation of the total organic carbon using functional neural networks and support vector machine,in:International Petroleum Technology Conference, International Petroleum Technology Conference, 2020.

[25] A.A. Mahmoud, et al., New robust model to evaluate the total organic carbon using fuzzy logic. SPE Kuwait Oil & Gas Show and Conference, Society of Petroleum Engineers, 2019.

[26] N. Nabipour, et al., Estimating biofuel density via a soft computing approach based on intermolecular interactions, Renew. Energy 152 (2020) 1086-1098.

[27] M.B. Vanani, R. Daneshfar, E. Khodapanah, A novel MLP approach for estimating asphaltene content of crude oil. Petroleum Science and Technology 37 (22) (2019) 2238-2245.

[28] R. Daneshfar, et al., Estimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms, Appl. Sci. 10 (18) (2020) 6432.

[29] A. Baghban, et al., Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. International Journal of Heat and Mass Transfer 128 (2019) 825-835.

[30] A. Baghban, et al., Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids. Engineering Applications of Computational Fluid Mechanics 13 (1) (2019) 26-39.

[31] M.H. Ahmadi, et al., Evolving connectionist approaches to compute thermal conductivity of TiO₂/water nanofluid, Phys. Stat. Mech. Appl. 540 (2020) 122489.

[32] M.H. Ahmadi, et al., An insight into the prediction of TiO₂/water nanofluid viscosity through intelligence schemes, J. Therm. Anal. Calorim. 139 (3) (2020) 2381-2394.

[33] R. Daneshfar, et al., A neural computing strategy to estimate dew-point pressure of gas condensate reservoirs. Petroleum Science and Technology 38 (10) (2020) 706-712.

[34] H. Wang, et al., An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. Journal of Petroleum Science and Engineering 176 (2019) 664-678.

[35] X. Shi, et al., Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs. Journal of Natural Gas Science and Engineering 33 (2016) 687-702.

[36] Y.A. Nezhad, A. Moradzadeh, M.R. Kamali, A new approach to evaluate Organic Geochemistry Parameters by geostatistical methods: a case study from western Australia. Journal of Petroleum Science and Engineering 169 (2018) 813-824.

[37] M. Tan, et al., Support-vector-regression machine technology for total organic carbon content prediction from wireline logs in organic shale: a comparative study. Journal of Natural Gas Science and Engineering 26 (2015) 792-802.

[38] H. Dembicki Jr., Three common source rock evaluation errors made by geologists during prospect or play appraisals, AAPG Bull. 93 (3) (2009) 341-356.

[39] R.T. Ryder, et al., ShahejieeShahejie/Guantao/Wumishan and carboniferous/Permian coalePaleozoic total petroleum systems in the Bohaiwan basin, China ( based on geologic studies for the 2000 world energy assessment Project of the US geological survey). Reston, Virginia: US geological survey, 2012.

[40] L.A. Zadeh, Fuzzy sets. Information and control 8 (3) (1965) 338-353.

[41] J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control 42 (10) (1997) 1482-1484.

[42] K.H. Lee, Springer science & business media, First Course Fuzzy Theor. Appl. 27 (2004).

[43] H.A. Zamani, et al., Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio. Journal of Natural Gas Science and Engineering 25 (2015) 325-334.

[44] H. Takagi, I. Hayashi, NN-driven fuzzy reasoning. International journal of approximate reasoning 5 (3) (1991) 191-212.

[45] J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, Cybernetics 23 (3) (1993) 665-685.

[46] H. Safari, et al., Assessing the dynamic viscosity of NaeKeCaeCleH₂O aqueous solutions at high-pressure and high-temperature conditions. Industrial & Engineering Chemistry Research 53 (28) (2014) 11488-11500.

[47] A. Daryasafar, A. Keykhosravi, K. Shahbazi, Modeling CO2 wettability behavior at the interface of brine/CO2/mineral: Application to CO2 geo-sequestration. Journal of Cleaner Production 239 (2019) 118101.

[48] M.H. Ahmadi, et al., Evaluation of electrical efficiency of photovoltaic thermal solar collector. Engineering Applications of Computational Fluid Mechanics 14 (1) (2020) 545-565.

[49] A. Baghban, et al., Towards experimental and modeling study of heat transfer performance of water-SiO 2 nanofluid in quadrangular cross-section channels. Engineering applications of computational fluid mechanics 13 (1) (2019) 453-469.

[50] A. Bemani, A. Baghban, A.H. Mohammadi, An insight into the modeling of sulfur content of sour gases in supercritical region. Journal of Petroleum Science and Engineering 184 (2020) 106459.

[51] R.M. Balabin, E.I. Lomakina, Support vector machine regression (LS-SVM)dan alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys. Chem. Chem. Phys. 13 (24) (2011) 11710-11718.

[52] K. Pelckmans, et al., LS-SVMlab: a matlab/c toolbox for least squares support vector machines, Tutorial. KULeuven-ESAT. Leuven, Belgium 142 (1-2) (2002).

[53] M. Arabloo, et al., Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy. Journal of the Taiwan Institute of Chemical Engineers 50 (2015) 123-130.

[54] A. Kamari, et al., Integrating a robust model for predicting surfactantepolymer flooding performance. Journal of Petroleum Science and Engineering 137 (2016) 87-96.

[55] N.M. Mahmoodi, M. Arabloo, J. Abdi, Laccase immobilized manganese ferrite nanoparticle: synthesis and LSSVM intelligent modeling of decolorization, Water Res. 67 (2014) 216-226.

[56] S. Faghihi, A. Keykhosravi, K. Shahbazi, Modeling of kinetic adsorption of natural surfactants on sandstone minerals: spotlight on accurate prediction and data evaluation. Colloid and Interface Science Communications 33 (2019) 100208.

[57] M.N. Kardani, A. Baghban, Utilization of LSSVM strategy to predict water content of sweet natural gas. Petroleum Science and Technology 35 (8) (2017) 761-767.

[58] N. Kardani, et al., Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotechnical and Geological Engineering 38 (2) (2020) 2271-2291.

[59] N. Tanoumand, A. Hemmati-Sarapardeh, A. Bahadori, A CSA-LSSVM model to estimate diluted heavy oil viscosity in the presence of kerosene. Petroleum Science and Technology 33 (10) (2015) 1085-1092.

[60] ( Ieee R. Eberhart J. Kennedy,A new optimizer using particle swarm theory, MHS’95. Proc. Sixth Int. Sympos. Micro Machine Human Sci. (1995) 39-43.

[61] J. Kennedy, R. Eberhart, Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 1995.

[62] J.-S. Chiou, S.-H. Tsai, M.-T. Liu, A PSO-based adaptive fuzzy PID-controllers. Simulation modelling practice and theory 26 (2012) 49-59.

[63] P. Coulibaly, C.K. Baldwin, Nonstationary hydrological time series forecasting using nonlinear dynamic methods, J. Hydrol. 307 (1-4) (2005) 164-174.

[64] Y. Shi, R. Eberhart,A modified particle swarm optimizer, in:1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), IEEE, 1998.

[65] Z. Chen, et al., Organic matter, mineral composition, pore size, and gas sorption capacity of lacustrine mudstones: implications for the shale oil and gas exploration in the Dongying depression, eastern China. AAPG Bulletin 102 (8) (2018) 1565-1600.

[66] A.A. Mahmoud, et al., Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence, Energies 12 (19) (2019) 3671.

[67] A.A. Mahmoud, et al., Estimation of static young's modulus for sandstone formation using artificial neural networks, Energies 12 (11) (2019) 2125.

[68] A. Eslamimanesh, et al., Assessment test of sulfur content of gases, Fuel Process. Technol. 110 (2013) 133-140.

[69] C.R. Goodall,13 Comput. QR Decompos. (1993).

[70] M. Mesbah, et al., Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach. The Journal of Supercritical Fluids 90 (2014) 110-125.