Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm

Abstract

The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage. However, due to the serious nonlinear relationship between the logging curve response and high-quality reservoirs, the rapid identification of high-quality reservoirs has always been a problem of low accuracy. This study proposes a combination of the oversampling method and random forest algorithm to improve the identification accuracy of high-quality reservoirs based on logging data. The oversampling method is used to balance the number of samples of different types and the random forest algorithm is used to establish a high-precision and high-quality reservoir identification model. From the perspective of the prediction effect, the reservoir identification method that combines the oversampling method and the random forest algorithm has increased the accuracy of reservoir identification from the 44% seen in other machine learning algorithms to 78%, and the effect is significant. This research can improve the identifiability of high-quality marine shale gas reservoirs, guide the drilling of horizontal wells, and provide tangible help for the precise formulation of marine shale gas development plans.