Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence

Summary Reservoir characterization is critical to the oil and gas industry, influencing field development, production optimization, hydraulic fracturing, and reserves estimation decisions. Accurately estimating porosity is crucial for reservoir characterization, well planning, and production optimization in the oil and gas industry. Traditional porosity determination methods, such as porosimetry, geostatistical, and core analysis, often involve complex geological and geophysical models, which are expensive and time-consuming. This study used the integrated machine learning and optimization model of differential evolution (DE) with group method of data handling (GMDH-DE) to estimate the porosity using integrated well log and core data from the Mpyo oil field, Uganda. The GMDH-DE demonstrates superior performance compared with conventional GMDH, support vector regression (SVR), and random forest (RF), achieving a coefficient of determination (R2) of 0.9925 and a root mean square error (RMSE) of 0.0017 during training, an R² of 0.9845 with an RMSE of 0.0121 during testing, and when validated the R2 was 0.9825 with RMSE of 0.00018. A key novelty of this work is the integration of Shapley additive explanations (SHAP), which provides an interpretable analysis of the model’s input features. SHAP reveals that bulk density (RHOB) and neutron porosity (NPHI) are the most critical parameters for porosity estimation, offering valuable insight into features importance. The proposed GMDH-DE model and SHAP analysis represent a novel and independent approach for accurate porosity estimation and interpretability, significantly enhancing the efficiency and reliability of hydrocarbon exploration and development.