Enhanced prediction and uncertainty analysis for hydrogen production rate in depleted oil and gas reservoirs using advanced machine learning techniques

Climate change has driven a global shift from fossil fuels to renewable energy sources. However, the inherent variability of renewable energy, influenced by temporal and climatic factors, presents significant challenges. Underground hydrogen storage offers a promising solution for retaining surplus energy. The complexity and heterogeneity of geological formations are difficult to accurately quantify, leading to large uncertainties in storage assessment results, and computation of forward modeling for large-scale sites is often time-consuming. This study introduced a numerical modeling framework incorporating the complex geological structures into the uncertainty analysis of formation porosity and permeability. We developed surrogate models to predict the hydrogen storage process using three machine learning (ML) algorithms: Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR). The study utilized the Sobol algorithm to examine the impact of variations in porosity and permeability on model output. This study applied and analyzed the numerical modeling framework at Wangjiawan in China. The findings indicated that the average final stability of hydrogen injection mass approximates 1800 tons, with the average production mass of hydrogen reaching approximately 950 tons. The XGBoost model demonstrated excellent predictive performance (R2 = 0.9679 and RMSE = 0.0318). Hydrogen production mass and rate are primarily influenced by the permeability of the formations, including injection and production wells during stable periods, while the impact of formation porosity is relatively minor. This study quickly and accurately predicts hydrogen storage processes under different geological parameters by employing ML algorithms. It also evaluates the importance of various geological parameters, providing crucial insights for effectively designing underground hydrogen storage facilities.