Support vector machine method, a new technique for lithology prediction in an Iranian heterogeneous carbonate reservoir using petrophysical well logs

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.