Adaptive weight strategy for frequency-decomposed seismic attribute fusion in predicting of complex sand body distributionsOA
Adaptive weight strategy for frequency-decomposed seismic attribute fusion in predicting of complex sand body distributions
Hong-Li Wu;Xin-Ping Zhou;Sheng-He Wu;Zhen-Hua Xu;Ming-Cheng Liu;Bo Yang;De-Gang Wu;Zi-Shi Xie;Yu Tang;Xiao-Long Wan
National Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaResearch Institute of Exploration and Development,PetroChina Changqing Oilfield Company,Xi'an,710018,Shaanxi,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,China||CNOOC Hainan Branch Company,Haikou,570312,Hainan,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,ChinaNational Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing,102249,China||College of Geosciences,China University of Petroleum(Beijing),Beijing,102249,China||Research Institute of Exploration and Development,PetroChina Changqing Oilfield Company,Xi'an,710018,Shaanxi,China
Reservoir heterogeneitySand thickness predictionFrequency-decomposed attribute intelligent fusionAdaptive weightingDeep neural network
Reservoir heterogeneitySand thickness predictionFrequency-decomposed attribute intelligent fusionAdaptive weightingDeep neural network
《石油科学（英文版）》 2026 (5)
2367-2389,23
This study is funded by the Science Foundation of China Uni-versity of Petroleum(Beijing)(Grant No.2462025BJRC005),Stra-tegic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)and China University of Petroleum(Grant No.ZLZX2020-02),Major Science and Technology Project of Changqing Oilfield(Grant No.2023DZZ04),and China University of Petroleum(Grant No.2462023YJRC034),and the National Natural Science Foundation of China(Grant No.42202178,42272110).
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