Title: The use of support vector machine for oil and gas identification in low-porosity and low-permeability reservoirs

Authors: Guang-Ren Shi

Addresses: Research Institute of Petroleum Exploration and Development, 20# Xueyuan Road, Beijing 100083, P.R. China

Abstract: Well-log interpretation becomes very complicated in low-porosity and low-permeability reservoirs, due to the strong non-linear relationship between oil/gas identification and well-log interpretation results. To find a method for predicting oil/gas identification, multiple regression analysis (MRA), backpropagation neural network (BPNN) and support vector machine (SVM) have been applied to two case studies based on well-log interpretation results and oil/gas test data. The specific MRA adopted is the technique of successive regression analysis, and the particular SVM employed is the technique of C-SVM binary classifier. The two case studies show that: 1) for the learning samples, the results of SVM and BPNN show a far more precise fit than MRA; 2) for the prediction samples, the SVM predictions coincide with oil/gas test results and in fact correct some erroneous well-log interpretations, but the predictions of both MRA and BPNN do not.

Keywords: multiple regression analysis; MRA; artificial neural networks; ANNs; support vector machines; SVM; method comparison; well-log interpretation; oil identification; gas identification; low-porosity reservoirs; low-permeability reservoirs.

DOI: 10.1504/IJMMNO.2009.030088

International Journal of Mathematical Modelling and Numerical Optimisation, 2009 Vol.1 No.1/2, pp.75 - 87

Published online: 09 Dec 2009 *

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