Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method

Xinmao Zhou, Yawen Li, Xiaodong Song, Lingxuan Jin and Xixin Wang ()
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Xinmao Zhou: Research Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 100083, China
Yawen Li: College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China
Xiaodong Song: College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China
Lingxuan Jin: College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China
Xixin Wang: School of Geosciences, Yangtze University, Wuhan 430100, China

Energies, 2023, vol. 16, issue 4, 1-12

Abstract: A reservoir with a thickness less than 0.5 m is generally considered to be a thin reservoir, in which it is difficult to directly identify oil-water layers with conventional logging data, and the identify result coincidence rate is low. Therefore, a support vector machine method (SVM) is introduced in the field of oil-water-dry layer identification. The basic approach is to map the nonlinear problem (input space) to a new high-dimensional feature space through the introduction of a kernel function, and then construct the optimal decision surface in the high-dimensional feature space and conduct sample classification. There are plenty of thin reservoirs in Wangguantun oilfield. Therefore, 63 samples are established by integrating general logging data and oil testing data from the study area, including 42 learning samples and 21 prediction samples, which are normalized. Then, the kernel function is selected, based on previous experience, and the fluid identification model of the thin reservoir is built. The model is used to identify 21 prediction samples; 18 are correct, and the prediction accuracy reaches 85.7%. The results show that the SVM method is feasible for fluid identification in thin reservoirs.

Keywords: support vector machine; thin reservoir; fluid identification; Wangguantun oilfield (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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