Research on predicting the productivity of fractured horizontal wells in shale reservoirs based on the tree regression method
The number of horizontal section volume fracturing sections in shale oil wells is large and the production capacity varies greatly,so conventional production capacity prediction and fracturing effect evaluation are difficult,and establishing a stable and efficient intelligent production capacity prediction method with the help of machine learning is an effective way to enhance the development of shale reservoirs.In this study,we used the geological parameters,engineering parameters,and production databases of 91 production wells in the Jimsar shale reservoirs,the data were validated based on thermodynamic plat and characteristic parameter correlation analysis,and determined the 6 best main control factors covering geological factors and construction factors from the 14 feature parameters.Three machine learning methods,decision tree(DT)with tree regression method,random forest(RF),and gradient boosting decision tree(GBDT)were used for yield prediction modeling,and the model performance was evaluated using root mean square error.The results showed that water content,oil saturation,sand addition,fracturing fluid dosage,the number of fracturing section clusters,and the number of fracturing stages were the main controlling factors affecting the production capacity of fractured horizontal wells.The random forest tree regression method had the best prediction effect,with 94％prediction accuracy and 0.934 root mean square error for the test set.The random forest model of the self-sampling method among the three tree regression methods outperformed the decision tree model and the gradient boosting decision tree model,which solved the problem of overfitting problem of other tree models.
万方数据
维普
