Fracture zone prediction based on random forest algorithm
HE Jian1,2, WEN Xiaotao1,2, NIE Wen-liang1,3, LI Leihao1, YANG Jixin1
1. School of Geophysics, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 3. School of Electronic and Information Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing 404000, China
Abstract:Fracture zone prediction and characterization are of great significance for the exploration and development of fractured oil and gas reservoirs.In order to solve the multi-solution problem of the prediction methods using single attribute,multiple seismic attributes were used comprehensively.The relationships between fracture development degree and seismic attributes are often non-linear.Therefore,random forest algorithm was used to learn the correspondence between seismic attribute characteristics and fracture development degree,and then the fracture development degree in the study area was determined comprehensively according to the learning results,aiming to improve the prediction precision of fracture zone.The application in real data demonstrated that random forest algorithm achieved fracture zone prediction results with high accuracy,and the method is universal generally.
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HE Jian, WEN Xiaotao, NIE Wen-liang, LI Leihao, YANG Jixin. Fracture zone prediction based on random forest algorithm. Oil Geophysical Prospecting, 2020, 55(1): 161-166.
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