Abstract
Lithology prediction is one of the most important issues in the petroleum geology and geological studies of petroleum engineering. Since well logging responses are very analogous for heterogeneous carbonate and evaporite sequences, a precisionist lithology prediction at predetermined depths becomes extremely critical. In this work, a combination of conventional petrophysical-based method and artificial intelligent approaches are used for lithological characterization of these layered reservoirs. Support vector machines (SVMs) are based on statistical learning theory and the principles of structural and empirical risk minimization use a non-heuristic analytical approach for prediction. SVM classification method is adopted for lithology prediction from petrophysical well logs based on core analysis data in an Iranian heterogeneous carbonate reservoir consisting of limestone, dolomite and anhydrite sequences. Normalization and attribute selection are conducted for data preparation purposes and the effect of kernel functions types on SVM performance is then investigated. Results show that SVM is a useful approach for lithology prediction and the radial basis function kernel is more accurate as compared to other kernel functions since it yields minimum misclassification rate error.
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Abbreviations
- b :
-
Bias constant
- C :
-
Penalty parameter
- e :
-
Exponent parameter of polynomial kernel
- H :
-
Hessian matrix
- k :
-
Kernel function
- l :
-
Number of instances
- L :
-
Lagrangian equation
- X :
-
Input vector of attributes
- y :
-
Output vector of class label
- α, β :
-
Lagrangian multipliers
- σ :
-
RBF parameter
- ξ :
-
Slack variable
- W :
-
Normal vector
- Φ :
-
Mapping function from input space to feature space
- d :
-
Dual
- i, j :
-
Indices
- n :
-
Input space dimension
- s :
-
Optimal
- p :
-
Primal
- SV:
-
Support vectors
- T :
-
Transpose
References
Alpaydin E (2010) Introduction to machine learning, 2nd edn. The Massachusetts Institute of Technology Press, Cambridge
Amari S-i, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783–789
Borsaru M, Zhou B, Aizawa T, Karashima H, Hashimoto T (2006) Automated lithology prediction from PGNAA and other geophysical logs. Appl Radiat Isot 64(2):272–282
Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory, ACM, pp 144–152
Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26(1):5–14
Busch JM, Fortney WG, Berry LN (1987) Determination of lithology from well logs by statistical analysis. SPE Form Eval 2(4):412–418. doi:10.2118/14301-pa
Carrasquilla A, Silvab JD, Flexa R (2008) Associating fuzzy logic, neural networks and multivariable statistic methodologies in the automatic identification of oil reservoir lithologies through well logs. Revista de Geologia 21(1):27–34
Chang H-C, Kopaska-Merkel DC, Chen H-C (2002) Identification of lithofacies using Kohonen self-organizing maps. Comput Geosci 28(2):223–229
Cherkassky V, Mulier FM (2007) Learning from data: concepts, theory, and methods. Wiley, Hoboken
Chikhi S, Batouche M (2005) Using probabilistic unsupervised neural method for lithofacies identification. Int Arab J Inf Technol 2(1):58–66
Chikhi S, Batouche M (2007) Hybrid neural network methods for lithology identification in the Algerian Sahara. World Acad Sci Eng Technol 4:774–782
Cuddy SJ (2000) Litho-facies and permeability prediction from electrical logs using fuzzy logic. SPE Reserv Eval Eng 3(4):319–324. doi:10.2118/65411-pa
Delfiner P, Peyret O, Serra O (1987) Automatic determination of lithology from well logs. SPE Form Eval 2(3):303–310. doi:10.2118/13290-pa
Ding S (2011) Spectral and wavelet-based feature selection with particle swarm optimization for hyperspectral classification. J Softw 6(7):1248–1256
Ellis DV, Singer JM (2008) Well logging for Earth scientists. Springer, Dordrecht
Gifford CM, Agah A (2010) Collaborative multi-agent rock facies classification from wireline well log data. Eng Appl Artif Intell 23(7):1158–1172
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update, v3.6.9, 2013 edn. SIGKDD explorations, vol 11, issue 1
Hamel L (2009) Knowledge discovery with support vector machines. Wiley, Hoboken
Hua S, Sun Z (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 308(2):397–408
Huerta EB, Duval B, Hao J-K (2006) A hybrid GA/SVM approach for gene selection and classification of microarray data. In: Applications of evolutionary computing. Springer, Berlin, pp 34–44
Katz SA, Vernik L, Chilingar GV (1999) Prediction of porosity and lithology in siliciclastic sedimentary rock using cascade neural assemblies. J Pet Sci Eng 22(1):141–150
Kecman V (2005) Support vector machines—an introduction. In: Wang L (ed) Support vector machines: theory and applications, vol 177. Studies in fuzziness and soft computing. Springer: Berlin, pp 1–47. doi:10.1007/10984697_1
Lee Y, Lin Y, Wahba G (2004) Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data. J Am Stat Assoc 99(465):67–81
Lim J-S, Kang JM, Kim J (1999) Interwell log correlation using artificial intelligence approach and multivariate statistical analysis. Paper presented at the SPE Asia Pacific oil and gas conference and exhibition, Jakarta, Indonesia, 20–22 April 1999
Mezghani D, Boujelbene S, Ellouze N (2010) Evaluation of SVM kernels and conventional machine learning algorithms for speaker identification. Int J Hybrid Inf Technol 3(3):23–34
Raeesi M, Moradzadeh A, Doulati Ardejani F, Rahimi M (2012) Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data and artificial neural networks. J Pet Sci Eng 82:151–165
Ravikumar B, Thukaram D, Khincha H (2008) Application of support vector machines for fault diagnosis in power transmission system. Gener Transm Distrib IET 2(1):119–130
Rider M (2002) The geological interpretation of well logs, 2nd edn. Rider-French Consulting Ltd., Sutherland
Shin C, Kim K, Park M, Kim H (2000) Support vector machine-based text detection in digital video. In: Neural networks for signal processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, 2000. IEEE, pp 634–641
Tang H (2009) Successful carbonate well log facies prediction using an artificial neural network method: Wafra Maastrichtian reservoir, partitioned neutral zone (PNZ), Saudi Arabia and Kuwait. Paper presented at the SPE annual technical conference and exhibition, New Orleans, Louisiana
Tang H, White CD (2008) Multivariate statistical log log-facies classification on a shallow marine reservoir. J Pet Sci Eng 61(2):88–93
Teh W, Willhite GP, Doveton J (2012) Improved reservoir characterization in the Ogallah field using petrophysical classifiers within electrofacies. In: SPE Improved oil recovery symposium, Tulsa, Oklahoma, USA
Wang Z, Sun X (2011) An efficient discriminant analysis algorithm for document classification. J Softw 6(7):1265–1272
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques
Acknowledgments
The Authors would like to thank Exploration Directorate of National Iranian Oil Company (NIOC) for providing data to apply in this research.
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Sebtosheikh, M.A., Motafakkerfard, R., Riahi, M.A. et al. Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs. Carbonates Evaporites 30, 59–68 (2015). https://doi.org/10.1007/s13146-014-0199-0
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DOI: https://doi.org/10.1007/s13146-014-0199-0