Coal Geology & Exploration


The accurate evaluation of reservoir fracability is an essential prerequisite for the fracturing design and post-fracturing productivity evaluation of reservoirs. Rock mechanical parameters have been applied to the fracability evaluation of shales presently, exhibiting great field application performance. Accordingly, it is crucial to obtain accurate rock mechanical parameters. This study developed a physics-informed neural network (PINN) model. Driven by data and physical information, the PINN model can accurately predict rock mechanical parameters using only a small amount of data. To verify its performance, the PINN model was compared with the artificial neural network, random forest, and XGBoost models. The comparison results show that the PINN model yielded an average accuracy greater than 95%, outperforming other models. Using the PINN model, this study obtained four rock mechanical parameters, namely modulus of elasticity, Poisson's ratio, tensile strength, and fracture toughness. Given the influence of rock mechanical parameters on reservoir fracability, this study developed an evaluation method for reservoir fracability based on the brittleness index and mechanical parameters. This fracability evaluation method was applied to reservoirs in the K2 member in the Cangdong sag of the Bohai Bay Basin. The evaluation results indicate generally high fracability of the study area. Specifically, lamellar mixed shales showed a fracability index of higher than 0.7, indicating high fracability, while lamellar felsic shales and thickly and thinly laminated shales comprising calcareous and dolomitic rocks of equal amounts exhibited fracability indices of 0.4‒0.7, indicating moderate fracability. The comparison between the evaluation results and the daily oil production of various reservoirs at the construction site verified the reliability of the smart fracability evaluation method developed in this study. Therefore, this fracability evaluation method can be applied to the fracability evaluation of shale reservoirs.


shale oil researvoirs, rock mechanical parameter, fracability, machine learning, physics-informed


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