Prediction of Water Saturation by FSVM using Well Logs in a Gas Field

Document Type : Research Article

Authors

1 Department of Earth Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran. E-mail: nastaran.moosavi@gmail.com

2 Corresponding Author, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: majidbagheri@ut.ac.ir

3 Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran. E-mail: mnbhendi@ut.ac.ir

4 Department of Earth Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran. E-mail: r.heidari61@gmail.com

Abstract

Water saturation is one of the key petrophysical parameters that mainly affects the accuracy of initial oil estimation related to a hydrocarbon reservoir. Approximation of this parameter is inevitable since it has a high effect on economic development of hydrocarbon reservoirs. In this paper, we approximate a function, using two wells with two core data sets belonging to each well, to predict water saturation by means of Support Vector Machine (SVM) algorithm in one of the gas reservoirs in the Persian Gulf. Due to the inevitability of noise and outliers in the measured data, SVM is modified to Fuzzy SVM (FSVM). For this purpose, a membership function is applied on the points, so each data point receives a membership degree. In this case, each input point is able to contribute to the learning of decision function. In other words, FSVM is able to enhance SVM by devoting less value to noise and outliers, as a result, better models compared to SVM can be produced. In this study, application of SVM for regression purpose (Support Vector Regression) is carried out on eight logs of DT, GR, RHOB, NPHI, LLD, LLS, MSFL, PEF as input with relevant core data belonging to a gas zone. Then, we determine the coefficients based on the comparison between predicted water saturation (using both SVR and fuzzy SVR algorithm) and core data. Our results show that the predicted water saturation from fuzzy SVR and SVR are 95% and 71%, respectively (higher for fuzzy SVR than SVR).

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References

Adeniran, A., Elshafei, M., & Hamada, G. (2009). Functional network soft sensor for formation porosity and water saturation in oil wells, Instrumentation and Measurement Technology Conference, I2MTC '09. IEEE, Singapore, 113-1143.
American Petroleum Institute. (1998). Recommended Practices for Core Analysis Handbook, API publications, http://www.api.org.
Archie, G.E. (1942). The electrical resistivity log as an aid in determining some reservoir characteristics. Petroleum Transactions of AIME, 146, 54-62.
Asquith, G., & Krygowski, D. (2004). AAGP Methods in Exploration, No. 16, Chapter 5: Resistivity Logs. Pages 77-101.
Bagheripour, P., & Asoodeh, M. (2014). Genetic implanted fuzzy model for water saturation determination. Journal of Applied Geophysics, 103, 232-236.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys (CSUR), 35, 268-308.
Hsu, C.W., Chang, C.C., & Lin, C.J. (2003). A practical guide to support vector classification.
Jafari Kenari, S.A., & Mashohor, S. (2013). Robust committee machine for water saturation prediction. Journal of Petroleum Science and Engineering, 104, 1-10.
Jia, J., Ke, Sh., Li, J., Kang, Zh, Ma, X., Li, M., & Cue, J. (2020). Estimation of Permeability and Saturation Based on Imaginary Component of Complex Resistivity Spectra: A Laboratory Study. Open Geoscience, 12, 299-306.
Keerthi, S.S., Sindhwani, V., & Chapelle, O. (2007). An efficient method for gradient based adaptation of hyperparameters in SVM models, in: Advances in neural information processing systems, 673-680.
Le, V.H., Liu, F., & Tran. D.K. (2009). Fuzzy Linguistic Logic Programming and its Applications. Theory and Practice of Logic Programming (TPLP).
Li, Z., Xie, Y., Li, X., & Zhao, W. (2021). Prediction and application of porosity based on support vector regression model optimized by adaptive dragonfly algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 43(9), 1073-1086.
Lim, K.M., Sim, Y.C., & Oh, K.W. (2002). A Face Recognition System Using Fuzzy Logic and Artificial Neural Network”, IEEE. [1992 Proceedings] IEEE International Conference on Fuzzy Systems, USA.
Luthi, E.M. (2001). Geological well logs, their use in reservoir modeling, Springer-Verlag Berlin Heidelberg, Germany.
Okwu, M., & Nwachukwu, A.N. (2019). A review of fuzzy logic applications in petroleum exploration, production and distribution operations. Journal of Petroleum Exploration and Production Technology, 9(2), 1555–1568.
Pickett, G.R. (1966). A Review of Current Techniques for Determination of Water Saturation from Logs, SPE 1446.
Schlumberger. (1991). Log Interpretation Principles/Applications. Schlumberger Wireline & Testing, SMP- 7017, Sugar Land, Texas.
Talbi, E.G. (2009). Methaheuristics: from Design to Implementation. Volume 74. John Wiley & Sons.
Tonstad, S.L., Boordsen, H., & Ringen, J.K. (1990). Alternative Methods for Determining Water Saturation in Core Plugs, Advances in Core Analysis, Gorden and Breach, Sc., 345-409.
Vapnik, V.N. (1995). The Nature of Statitical learning Theory. Springer, New York.
Walther, H.C. (1967). Saturation from Logs-Laberatory Measurements of Logging Parameters, SPE 42nd Annual Meeting Huston, Tex. Oct, 1-4.
Yu, H., & Kim, S. (2012). SVM tutorial: classification, regression, and ranking, Handbook of Natural Computing. Springer Berlin Heidelberg, 479-506.
Zadeh, L.A. (1965). Fuzzy Sets, Information and Control, 8(3), 338–353.
Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., & Zhuang, J. (2018), Modeling and Simulating of Reservoir operation Using the Artificial Neural Network, Support Vector Regression, Deep Learning Algorithm. Journal of Hydrology, 565, 720-736.
Journal of the Earth and Space Physics
Volume 48, Issue 4
online publication date: 5 March 2023
March 2023
Pages 77-86

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