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| QUANTIFYING OIL CONTENT OF OIL SHALE FROM WELL LOGS USING SUPPORT VECTOR REGRESSION |
| CHEN Jing-wu1, ZHU Jian-wei1,2,3, SUN Ping-chang1,2 |
1. College of Earth Science, Jilin University, Changchun 130061, China;
2. Key Laboratory for Oil Shale and Coexistent Energy Minerals of Jilin Province, Changchun 130061, China;
3. Key Lab for Evolution of Past Life and Environment in Northeast Asia, Ministry of Education, Changchun 130061, China |
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Abstract The oil content is an important evaluation index for oil shale resources. Traditionally, calculation of the oil content from well logs of oil shale is performed by regression model, which, however, has the limitation and weakness of big error or over-fitting. This paper attempts to integrate the classical data mining algorithm with "Big Data" concept and logging application knowledge for oil content quantification to improve the accuracy and generalize the model. The explanatory variables of DTs, DENs and GRs for analysis are obtained by the improved ΔlogR technique. The data mining algorithm of support vector regression (SVR) can greatly improve the model generalization and precision in the oil content quantification. The R2 score of training samples in the model is 0.82. A high fitting precision is achieved in the test samples, of which the R2 score is 0.70. The SVR model is more generalized than traditional regression model, and can avoid over-fitting problem and well applied.
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Received: 13 January 2017
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2017, Vol. 26 
