Abstract
Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Ahmadi, M.A. and Shadizadeh, S.R., 2012, New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel, 102, 716–723.
Al-Anazi, A. and Gates, I., 2010a, A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Engineering Geology, 114, 267–277.
Al-Anazi, A. and Gates, I., 2010b, Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study. Computers & Geosciences, 36, 1494–1503.
Al-Anazi, A. and Gates, I., 2012, Support vector regression to predict porosity and permeability: Effect of sample size. Computers & Geosciences, 39, 64–76.
Alizadeh, N., Mighani, S., Hashemi kiasari, H., Hemmati-Sarapardeh, A., and Kamari, A., 2003, Application of Fast-SAGD in Naturally Fractured Heavy Oil Reservoirs: A Case Study. Proceedings of the 18th Middle East Oil & Gas Show and Conference: Transforming the Energy Future (MEOS), Manama, March 10–13, 3 p. 1946–1953.
Balan, B., Mohaghegh, S., and Ameri, S., 1995, State-of-the-art in permeability determination from well log data: Part 1-A comparative study, model development. SPE, 30978, 17–21.
Bhatt, A. and Helle, H.B., 2002, Committee neural networks for porosity and permeability prediction from well logs. Geophysical Prospecting, 50, 645–660.
Chamkalani, A., Amani, M., Kiani, M.A., and Chamkalani, R., 2013, Assessment of asphaltene deposition due to titration technique. Fluid Phase Equilibria, 339, 72–80.
Chen, G., Fu, K., Liang, Z., Sema, T., Li, C., Tontiwachwuthikul, P., and Idem, R., 2014, The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel, 126, 202–212.
Choisy, C. and Belaid, A., 2001, Handwriting recognition using local methods for normalization and global methods for recognition. Proceedings of 6th International Conference on Document Analysis and Recognition, Seattle, Sep. 10–13, p. 23–27.
El-Sebakhy, E.A., 2009, Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. Journal of Petroleum Science and Engineering, 64, 25–34.
Esfahani, S., Baselizadeh, S., and Hemmati-Sarapardeh, A., 2015, On determination of natural gas density: Least square support vector machine modeling approach. Journal of Natural Gas Science and Engineering, 22, 348–358.
Eslamimanesh, A., Gharagheizi, F., Illbeigi, M., Mohammadi, A.H., Fazlali, A., and Richon, D., 2012, Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen + water soluble organic promoters using Support Vector Machine algorithm. Fluid Phase Equilibria, 316, 34–45.
Fathinasab, M., Ayatollahi, S., and Hemmati-Sarapardeh, A., 2015, A Rigorous Approach to Predict Nitrogen-Crude Oil Minimum Miscibility Pressure of Pure and Nitrogen Mixtures. Fluid Phase Equilibria, 399, 30–39.
Fayazi, A., Arabloo, M., Shokrollahi, A., Zargari, M.H., and Ghazanfari, M.H., 2013, State of the Art of Least Square Support Vector Machine for Accurate Determination of Natural Gas Viscosity. Industrial & Engineering Chemistry Research, 53, 945–958.
Ganguly, S., 2003, Prediction of VLE data using radial basis function network. Computers & chemical engineering, 27, 1445–1454.
Gao, D., Zhou, J., and Xin, L., 2001, SVM-based detection of moving vehicles for automatic traffic monitoring. Proceedings of Intelligent Transportation Systems, Oakland, Aug. 25–29, p. 745–749.
Gharagheizi, F., Eslamimanesh, A., Farjood, F., Mohammadi, A.H., and Richon, D., 2011, Solubility parameters of nonelectrolyte organic compounds: determination using quantitative structureproperty relationship strategy. Industrial & Engineering Chemistry Research, 50, 11382–11395.
Gharbi, R., 1997, Estimating the isothermal compressibility coefficient of undersaturated Middle East crudes using neural networks. Energy & Fuels, 11, 372–378.
Ghiasi, M.M., Bahadori, A., Zendehboudi, S., Jamili, A., and Rezaei-Gomari, S., 2013, Novel methods predict equilibrium vapor methanol content during gas hydrate inhibition. Journal of Natural Gas Science and Engineering, 15, 69–75.
Hashemi-Kiasari, H., Hemmati-Sarapardeh, A., Mighani, S., Mohammadi, A.H., and Sedaee-Sola, B., 2014, Effect of operational parameters on SAGD performance in a dip heterogeneous fractured reservoir. Fuel, 122, 82–93.
Hemmati-Sarapardeh, A., Alipour-Yeganeh-Marand, R., Naseri, A., Safiabadi, A., Gharagheizi, F., Ilani-Kashkouli, P., and Mohammadi, A.H., 2013, Asphaltene precipitation due to natural depletion of reservoir: Determination using a SARA fraction based intelligent model. Fluid Phase Equilibria, 354, 177–184.
Hemmati-Sarapardeh, A., Majidi, S.-M.-J., Mahmoudi, B., Ahmad Ramazani, S.A., and Mohammadi, A., 2014, Experimental measurement and modeling of saturated reservoir oil viscosity. Korean Journal of Chemical Engineering, 31, 1253–1264.
Hosseinzadeh, M. and Hemmati-Sarapardeh, A., 2014, Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids. Journal of Molecular Liquids, 200, 340–348.
Kamari, A., Gharagheizi, F., Bahadori, A., Mohammadi, A.H., 2014, Rigorous Modeling for Prediction of Barium Sulfate (Barite) Deposition in Oilfield Brines. Fluid Phase Equilibria, 366, 117–126.
Kamari, A., Hemmati-Sarapardeh, A., Mirabbasi, S.-M., Nikookar, M., and Mohammadi, A.H., 2013a, Prediction of sour gas compressibility factor using an intelligent approach. Fuel Processing Technology, 116, 209–216.
Kamari, A., Khaksar-Manshad, A., Gharagheizi, F., Mohammadi, A.H., and Ashoori, S., 2013b, Robust Model for the Determination of Wax Deposition in Oil Systems. Industrial & Engineering Chemistry Research, 52, 15664–15672.
Kamari, A., Bahadori, A., Mohammadi, A.H., and Zendehboudi, S., 2014a, Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations. Petroleum Science and Technology, 32, 2961–2968.
Kamari, A., Mohammadi, A., Bahadori, A., and Zendehboudi, S., 2014b, A Reliable Model for Estimating the Wax Deposition Rate During Crude Oil Production and Processing. Petroleum Science and Technology, 32, 2837–2844.
Kamari, A., Mohammadi, A.H., Bahadori, A., and Zendehboudi, S., 2014c, Prediction of Air Specific Heat Ratios at Elevated Pressures Using a Novel Modeling Approach. Chemical Engineering & Technology, 37, 2047–2055.
Kamari, A., Safiri, A., and Mohammadi, A.H., 2015, A Compositional Model for Estimating Asphaltene Precipitation Conditions in Live Reservoir Oil Systems. Journal of Dispersion Science and Technology, 36, 301–309.
Kamari, A., Arabloo, M., Shokrollahi, A., Gharagheizi, F., and Mohammadi, A.H., 2015a, Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO2 flooding. Fuel, 153, 310–319.
Kamari, A., Bahadori, A., Mohammadi, A.H., and Zendehboudi, S., 2015b, New tools predict monoethylene glycol injection rate for natural gas hydrate inhibition. Journal of Loss Prevention in the Process Industries, 33, 222–231.
Kamari, A., Hemmati-Sarapardeh, A., Mohammadi, A.H., Hashemi-Kiasari, H., and Mohagheghian, E., 2015c, On the evaluation of Fast-SAGD process in naturally fractured heavy oil reservoir. Fuel, 143, 155–164.
Karimpouli, S., Fathianpour, N., and Roohi, J., 2010, A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). Journal of Petroleum Science and Engineering, 73, 227–232.
Kaviani, D., Bui, T., Jensen, J.L., and Hanks, C., 2008, The Application of Artificial Neural Networks With Small Data Sets: An Example for Analysis of Fracture Spacing in the Lisburne Formation Northeastern Alaska. SPE Reservoir Evaluation & Engineering, 11, 598–605.
Laugier, S. and Richon, D., 2003, Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data. Fluid Phase Equilibria, 210, 247–255.
Li, Z., Weida, Z., and Licheng, J., 2000, Radar target recognition based on support vector machine. Proceedings of 5th International Conference on Signal Processing, Beijing, Aug. 21–25, p. 1453–1456.
Ma, C., Randolph, M.A., and Drish, J., 2001, A support vector machines-based rejection technique for speech recognition. Proceedings of Acoustics, Speech, and Signal, Salt Lake City, May 7–11, p. 381–384.
Mohaghegh, S., Arefi, R., Ameri, S., and Hefner, M.H., 1994, A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks. Proceedings of SPE Annual Technical Conference and Exhibition, New Orleans, Sep. 25–28, SPE 28394.
Mohaghegh, S., Arefi, R., Bilgesu, I., Ameri, S., and Rose, D., 1995, Design and development of an artificial neural network for estimation of formation permeability. SPE Computer Applications, 7, 151–154.
Mohaghegh, S., 2000, Virtual intelligence and its applications in petroleum engineering. Journal of Petroleum Technology. Distinguished Author Series, 52. http://dx.doi.org/10.2118/58046-JPT
Montgomery, D.C., 2008, Design and analysis of experiments (7th edition). John Wiley & Sons Inc., Hoboken, 656 p.
Nejatian, I., Kanani, M., Arabloo, M., Bahadori, A., and Zendehboudi, S., 2014, Prediction of natural gas flow through chokes using support vector machine algorithm. Journal of Natural Gas Science and Engineering, 18, 155–163.
Nowroozi, S., Ranjbar, M., Hashemipour, H., and Schaffie, M., 2009, Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs. Fuel Processing Technology, 90, 452–457.
Pelckmans, K., Suykens, J.A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., and Vandewalle, J., 2002, LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial. KULeuven-ESAT. Leuven, Belgium, 8 p.
Ramgulam, A., 2006, Utilization of artificial neural networks in the optimization of history matching. M.Sc. Thesis, The Pennsylvania State University, University Park, 118 p.
Saeedi, A., Camarda, K.V., and Liang, J.-T., 2007, Using Neural Networks for Candidate Selection and Well Performance Prediction in Water-Shutoff Treatments Using Polymer Gels-A Field-Case Study. SPE Production & Operations, 22, 417–424.
Saemi, M., Ahmadi, M., and Varjani, A.Y., 2007, Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. Journal of Petroleum Science and Engineering, 59, 97–105.
Shafiei, A., Dusseault, M.B., Zendehboudi, S., and Chatzis, I., 2013, A new screening tool for evaluation of steamflooding performance in Naturally Fractured Carbonate Reservoirs. Fuel, 108, 502–514.
Suykens, J.A. and Vandewalle, J., 1999, Least squares support vector machine classifiers. Neural processing letters, 9, 293–300.
Suykens, J.A., De Brabanter, J., Lukas, L., and Vandewalle, J., 2002a, Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48, 85–105.
Suykens, J.A., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., Suykens, J., and Van Gestel, T., 2002b, Least squares support vector machines. World Scientific, Singapore, 308 p.
Tahmasebi, P. and Hezarkhani, A., 2012, A fast and independent architecture of artificial neural network for permeability prediction. Journal of Petroleum Science and Engineering, 86, 118–126.
Talebi, R., Ghiasi, M.M., Talebi, H., Mohammadyian, M., Zendehboudi, S., Arabloo, M., and Bahadori, A., 2014, Application of soft computing approaches for modeling saturation pressure of reservoir oils. Journal of Natural Gas Science and Engineering, 20, 8–15.
Van Gestel, T., Suykens, J.A., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., and Vandewalle, J., 2001, Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12, 809–821.
Wong, P.M., Jang, M., Cho, S., and Gedeon, T.D., 2000, Multiple permeability predictions using an observational learning algorithm. Computers & Geosciences, 26, 907–913.
Zendehboudi, S., Chatzis, I., Mohsenipour, A.A., and Elkamel, A., 2011, Dimensional analysis and scale-up of immiscible twophase flow displacement in fractured porous media under controlled gravity drainage. Energy & Fuels, 25, 1731–1750.
Zendehboudi, S., Shafiei, A., Bahadori, A., James, L.A., Elkamel, A., and Lohi, A., 2014, Asphaltene precipitation and deposition in oil reservoirs–Technical aspects, experimental and hybrid neural network predictive tools. Chemical Engineering Research and Design, 92, 857–875.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kamari, A., Moeini, F., Shamsoddini-Moghadam, MJ. et al. Modeling the permeability of heterogeneous oil reservoirs using a robust method. Geosci J 20, 259–271 (2016). https://doi.org/10.1007/s12303-015-0033-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12303-015-0033-2