TOC Intelligent Prediction Model in Shale Reservoir: Integrating Data Enhancement with Empirically Driven Algorithm

Shale oil and shale gas are important unconventional resources. Organic matter serves as the primary source of shale oil and gas generation, and high TOC values typically indicate better oil and gas reservoir conditions and production. Therefore, an accurate TOC prediction model is conducive to low-cost evaluation of reservoir hydrocarbon potential and improvement of development efficiency. However, geochemical experimental measurements are costly, and the data obtained is discrete. It is unable to meet the requirements for fine-scale assessment of shale reservoirs. The multiple regression method and ΔlogR method, when directly applied to shale reservoirs, often result in significant errors. In this study, we propose a composite model for accurate TOC prediction in shale reservoirs based on data enhancement and empirically driven. We first address the issue of poorly characterized logging responses and discrete experimental data. The features and quantities of the dataset are enhanced by introducing reconstruction curves and generative adversarial networks (GAN). The validity of the synthesized data is then verified by plotting the data density. In the empirically-driven module, we optimize a density-gamma modified method on traditional ΔlogR method according to the characteristics of shale reservoirs. The modified ΔlogR method will be integrated into the GWO-SVR model as an empirically driven subject in the form of a fitness function. Above, a composite model with both empirical and data-driven components is constructed. We use the Dongying Depression in China as an example for model experiments. The composite model was generalized to wells X and Y. The R² (coefficient of determination) was 0.95 and 0.97, the RMSE (Root Mean Square Error) was 0.31 and 0.29, and the MAE (Mean Absolute Error) was less than 0.3, which indicated a high degree of consistency between the model predictions and the experimental values. Further controlled experiments revealed that the composite model predicted better than the ΔlogR method and the GWO-SVR model alone. Finally, we also performed SHAP interpretability analysis on the model. By revealing the decision-making mechanism inside the model, we verified the rationality of the empirical drive and enhanced the credibility of the model. This provides strong technical support and decision-making basis for the subsequent oil and gas exploration and development work.