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Research on classification and discrimination of polyhalite with drilling and logging data by BP neural network
CHEN Kegui1, LIU Li2,3, CHEN Yuanyuan4, WEI Hang5, WANG Gang6
(1.School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;2.School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China;3.SINOPEC Zhongyuan Oilfield Company, Puyang 457001, China;4.Geophysical Exploration Company, Chuanqing Drilling Engineering Company Limited, Chengdu 610213, China;5.Shixi Oilfield Operating Area, PetroChina Xinjiang Oilfield Company, Karamay 834000, China;6. Research Institute of Development, PetroChina Xinjiang Oilfield Company, Karamay 834000, China)
Abstract:
Based on the theory of back propagation neural network and logging interpretation methods, a neural network model with logging curves as input was built, and applied to the polyhalite reservoirs in the lower-middle Triassic strata. The discrimination results were compared with logging data. The accuracy rate of the model reaches 86.3%, and achieves 97% if changing the constraint conditions, suggesting that the discrimination ability of the new model is good. The new model shows the accuracy rate reaches 82.51% to classify the polyhalite reservoirs. The model can efficiently discriminate pure polyhalite reservoirs, gypsiferous polyhalite reservoirs and polyhalite-gypsum reservoirs, thus is more advanced than regular logging interpretation methods. This study demonstrates the great potential applying the BP neural network in potash exploration.
Key words:  polyhalite  BP neural network model  classification and discrimination  logging response