Single point bar interpretation in meandering belt with extreme learning machine driven multiple seismic attributes fusion
ZHANG Xianguo1, WU Xiaoxiao2, HUANG Derong1, LIN Chengyan1
1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China;
2. Shanghai Branch of CNOOC Ltd., Shanghai 200335, China
Abstract:The identification of single point bars in high-curvature meandering rivers is of great significance for understanding the evolution and characteristics of meandering rivers and guiding the oilfield deve-lopment. With the meandering river of Guantao Formation in Gudong oilfield as an example, a fusion technology of multiple seismic attributes with the extreme learning machine algorithm is constructed to recognize single point bars in complex meandering belts. The technology integrates the clustering analysis of seismic attributes, seismic attributes fusion by the extreme learning machine algorithm, and seismic forward modeling. Through the drilling and dynamic verification, the following results can be obtained. 1) The method can improve the prediction accuracy of sandstone thickness with the coincidence rate of a single well reaching 93.3% which is higher than that of SVM and BP neural network. 2) Three combination models for the point bar in the meandering belt are studied, including the migration pattern of point bars in the opposite direction, the migration patterns with and without abandoned channels in the same direction. The three differ in reflection continuity and amplitude. 3) There are five single point bars in the study area and the abandoned channels between adjacent point bars form seepage barriers influencing the remaining oil development. The methods and results provide direct geological support for oil development.
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