Shale gas reservoirs are currently a focus in exploration and development in China. However, they exhibit pronounced vertical heterogeneity, are influenced by numerous geological and engineering parameters, and present significant challenges for “sweet spot” identification. Traditional sweet spot identification methods mainly rely on geologists’ experience and judgment regarding individual influencing parameters, which inevitably introduces subjectivity and uncertainty. The rapid development of artificial intelligence technology offers an opportunity to address this issue. This study adopts a geology–engineering integration approach and, based on data integration and a multi-algorithm prediction ensemble model with deep learning, proposes a predictive model built on actual data from the Nanchuan Block of the Sichuan Basin. The model integrates the Tetrahedral Topology Optimization (TBO) algorithm, Extreme Gradient Boosting (XGBoost), and Geological Attribute Feature Mapping (GAFM), aiming to improve the accuracy of shale gas reservoir sweet spot identification more effectively. The results show that sweet spots are jointly influenced by geological, rock-mechanical, and hydraulic fracturing parameters. The primary reservoir property factors controlling post-fracture productivity include TOC, permeability, porosity, and gas saturation, while the main rock-mechanical controlling factors are Poisson’s ratio, Young’s modulus, brittleness index, and Bursting Pressure. Based on the analysis of these productivity-controlling factors, the proposed integrated AI learning model achieved a sweet spot identification accuracy of 88.5%, enabling precise identification of single-well sweet spot distribution.
Fang et al. (Fri,) studied this question.