Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir
Luo Wei-Ping1,2, Li Hong-Qi1,2, and Shi Ning1,2
1. College of Geophysics and Information Engineering, China University of Petroleum (Beijing), Beijing 102200, China.
2. Beijing Key Laboratory of Petroleum Data Mining (No. 121109212008), China University of Petroleum (Beijing), Beijing 102200, China.
Abstract At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semi-supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.
This research work was supported by the "12th Five Year Plan" National Science and Technology Major Special Subject: Well Logging Data and Seismic Data Fusion Technology Research (No. 2011ZX05023-005-006).
Cite this article:
. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir[J]. APPLIED GEOPHYSICS, 2016, 13(2): 406-415.
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2016, Vol. 13 
