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A new method for high-precision fluid identification in bidirectional long short-term memory network
ZHOU Xueqing1,2, ZHANG Zhansong1,2, ZHU Linqi3,4, ZHANG Chaomo1,2
(1.Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan 430100,China;2.Hubei Cooperative Innovation Center of Unconventional Oil and Gas (Yangtze University), Wuhan 430100, China;3.Institute of Deep-sea Science and Engineering, Chinese Academy of Science, Sanya 572000, China;4.Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China)
Abstract:
Due to the diversity of space types and complex reservoir properties of carbonate reservoir, the logging response of fluid is affected by strong heterogeneity, which causes great difficulties in fluid identification. Aiming at this problem of fluid identification in complex carbonate reservoir, the bidirectional long short-term memory network (Bi-LSTM) fluid identification model based on logging sequence information was proposed. From the analysis of the differences of logging response characteristics and the similarity analysis of characteristic curves, the sensitivity curve was determined. Combined with the input requirements of the Bi-LSTM, the fluid identification sample database was established, and the fluid identification model was obtained. This method was applied to identify Majiagou Formation in Ordos Basin. Compared with the predictions of the LSTM model and other three types of machine learning algorithms, the accuracy of fluid identification increased from 82.7% to 91.5%. The model can not only make full use of the logging response value of the corresponding depth, but also take into account the changing trend and correlation of the logging curve with depth in ways that avoid the influence of vertical heterogeneity of reservoir and therefore improve fluid identification ability.
Key words:  fluid identification  bidirectional long short-term memory network  carbonate rock  logging response sequences