Evaluation Method of Reservoir Heterogeneity Based on Neural Network Technology

Abstract

Abstract Reservoir is the underground storage and accumulation place of oil and natural gas. The accuracy of reservoir heterogeneity evaluation has great economic value for correctly guiding the production and development of oil and natural gas. The high-order neural network method is used to comprehensively evaluate the heterogeneity of the reservoir. This method was applied to the evaluation of reservoir heterogeneity in the PK area. The results show that the heterogeneity of sandy clastic flow sand bodies is the weakest, the sandy landslide sand bodies are medium, and the turbidity current sand bodies are strongest. The evaluation method of reservoir heterogeneity based on high-order neural network technology effectively solves the problem of inconsistent conclusions of single-parameter evaluation of heterogeneity in conventional methods, and can quantitatively characterize the degree of reservoir heterogeneity.