Abstract：Undefined factors during the prediction of oil and gas by seismic exploration include selection of seismic attributes, selection of predicting algorithm, environmental noises and surveying errors of raw data.In order to eliminate these undefined factors,the paper utilized supporting vector computer and information-merging theory to predict oil and gas by using seismic exploration,supporting vector computer first transforms the input space into high-dimensional spaces by using non-linear transformation defined in inner product and solve the generalized optimal classified plane, the classified function is formally similar to a neural networks, the output is linear composition of intermediate nodes and each node corresponds to a supporting vector.The supporting vector computer can solve the classified problem and fitting problem,which is characterized by distinctive superiority in solving the small sample, non-linear and high-dimensional pattern recognition problems.Information merging is to use information source of multiple sensors in time and space, adopt mathematical methods and computer technology to automatically analyze,integrate and use the surveyed information under certain criterion,so that can obtain more optimal consistent information and description in comparison with surveyed object with single sensor and reduce the influence of environment on policy decision.Combining application of supporting vector computer with information merging can simultaneously reduce the uncertainty caused by multi-factors and improve oil/gas-predicted precision.Application of the method to real data obtained better prediction results
金龙, 陈小宏, 王守东. 基于支持向量机与信息融合的地震油气预测方法[J]. 石油地球物理勘探, 2006, 41(1): 76-80.
Jin Long, Chen Xiao-hong, Wang Shou-dong. Oil/gas-predicted method by seismology based on supporting vector computer and information merging. OGP, 2006, 41(1): 76-80.
