Staged fracturing in horizontal wells is a key technology for the efficient development of unconventional oil and gas reservoirs.Accurate productivity prediction of fractured horizontal wells is crucial for the selection of well locations and the optimization of fracturing parameters.With the continuous accumulation of historical exploitation data and the rapid development of artificial intelligence technologies,data-driven artificial intelligence methods have provided new channels for the productivity prediction of fractured horizontal wells.Based on the physical production process of fractured horizontal wells,this paper analyzes the physical constraints of intelligent productivity prediction and proposes an intelligent productivity prediction framework matching the physical process.In addition,case verification is carried out based on the shale gas exploitation data in the southern Sichuan Basin.The following research results are obtained.First,intelligent productivity prediction of fractured horizontal wells requires feature fusion with fracturing stage as a unit.Single well productivity is the cumulative result from the initial stage to the final stage,and there is a sequential relationship between different stages.There are differences in the factor input dimensions of different wells.Second,the recurrent neural network(RNN)can fully match the sequential relationship and aggregation effect among fracturing stages,and the Mask mechanism can solve the contradiction of inconsistent stage number in different wells.In conclusion,this intelligent prediction model can learn the complex mapping relationship between input sequence and productivity of each well,demonstrating an excellent predictive performance.Its relative errors of training set and testing set are 0.098 and 0.117,respectively,which are 37.6％and 37.0％lower than those of RNN model and 77.3％and 77.4％lower than those of multi-layer perceptron(MLP)model.What's more,the research results can provide new ideas and references for the technological progress and rapid development of the productivity prediction of fractured horizontal wells in unconventional oil and gas reservoirs.
维普
万方数据