Exploring the predictive performance of deep learning for fracturing fluid flowback and shale gas production.

ABSTRACT: The big data system has been developed to optimally combine numerical-model predictions with actual measurements from the gas shale play to create the best estimates of current shale gas conditions and their uncertainties, improving our ability to forecast and understand the shale gas production variations. However, considering the hydraulic fracturing fluid flowback in predicting shale gas production introduces new challenges. For example, complexities in the flow of gas-water two-phase fluid defy traditional numerical simulation, while flowback are complicated by strong environmental disruptions, and shale gas production encounters substantial noise interference. Here, we developed CNN-Transformer, a production and fluid flowback predicted system using deep learning, by integrating a con

SUBMITTER: Sun S 

PROVIDER: S-EPMC12663573 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature