Research and Application of Complex Lithology Identification Method Based on CNN-GRU - ZHANG Xiaofeng; PANG Chunyang; HU Rui; ZHU Yunfeng; LI Hongxing (Cejing jishu)
In the exploration and development of oil and gas reservoirs, lithology identification is an important component of reservoir log evaluation. The Hailar basin is characterized by proximal sedimentation and transport deposition, with significant differences in rock composition and structure. The types of lithology are complex and diverse, mainly including fine sandy mudstone, siltstone, shale, tuffaceous sandstone, tuffaceous mudstone, oil-bearing coarse sandstone, oil-bearing fine sandstone, crystal detrital tuff conglomerate, sandstone conglomerate, and dense tuff conglomerate. Traditional identification methods have low accuracy in dealing with complex lithologies, which severely restricts the accuracy of reservoir logging interpretation. This study integrates convolutional neural networks with gated recurrent units (CNN-GRU) and selects six logging parameters, including sonic time difference, natural potential, natural gamma, density, and shallow and deep lateral resistivity, to train sample wells in the Hailar basin. A CNN-GRU model for identifying complex lithologies is constructed. The research results show that the average accuracy of the CNN-GRU model reaches 92.3%, with an improvement of 5.5%～10.0% compared to a single network. After applying this model to well A in the Hailar basin, the lithology identification conformity rate reaches 94.8%, which provides a reliable lithological basis for the accuracy of reservoir log interpretation.