Application of random forests for regression to seismic reservoir prediction
Song Jianguo1,2, Gao Qiangshan1, Li Zhe3
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China;
2. Laboratory for Marine Mineral Resources, Qing-dao National Laboratory for Marine Science and Technology, Qingdao, Shandong 266071, China;
3. College of Information and Control Engineering, China University of Petroleum(East China), Qingdao, Shandong 266580, China
Abstract：Aiming at the nonlinear and stability issue of reservoir prediction,we introduced random forests for regression algorithm to seismic reservoir prediction by constructing the nonlinear relationship between seismic attributes and reservoir feature parameter.Several different kinds of attributes used as foundation,we predict reservoir parameter through fitting seismic attributes data along wells with feature parameter,regarding the root-mean-square error of predicted values and real values as evaluation standard,and then analyze the character of random forests regress algorithm applied in seismic reservoir prediction.This method is applied to predicting spontaneous potential for an inland survey and predicting natural gamma for an offshore survey.Meanwhile,we compare the prediction results with supported vector regression machine.The comparing result reveals that although the seismic data affected by strong noise,random forests method had better depiction of delta front depositional feature and perform better stability and accuracy.
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