An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

Abstract

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters. Therefore, to quantitatively evaluate the relative importance of model parameters on the production forecasting performance, sensitivity analysis of parameters is required. The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design. A data-driven global sensitivity analysis (GSA) method using convolutional neural networks (CNN) is proposed to identify the influencing parameters in shale gas production. The CNN is trained on a large dataset, validated against numerical simulations, and utilized as a surrogate model for efficient sensitivity analysis. Our approach integrates CNN with the Sobol' global sensitivity analysis method, presenting three key scenarios for sensitivity analysis: analysis of the production stage as a whole, analysis by fixed time intervals, and analysis by declining rate. The findings underscore the predominant influence of reservoir thickness and well length on shale gas production. Furthermore, the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.