Estimated ultimate recovery prediction of shale gas wells based on stacked integrated learning algorithm.

Accurately predicting the estimated ultimate recovery (EUR) of shale gas from a single well is challenging due to geological, engineering, and production factors. Conventional methods often lack sufficient transparency and clarity in the calculation process. As a result, machine learning (ML) algorithms have proven to be an effective alternative. Still, single algorithms are susceptible to outliers or feature selection in the data, leading to unstable predictions. Based on the concept of ensemble learning, this study proposes an intelligent method utilizing automated feature engineering (AutoFE) and stacking ensemble techniques. The method employs Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) as base learners, with Logistic Regression (LR) as the meta-learner. Furthermore, the model is optimized using the Tree-structured Parzen Estimator (TPE) algorithm. The proposed stacking ensemble learning model was validated using a publicly available dataset comprising 506 groups of EUR data of shale gas. The results demonstrate that the proposed stacking ensemble model outperforms individual machine learning models, achieving an R