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<p>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<sup>2</sup> of 0.9456, an RMSE of 0.7432, and a MAPE as low as 4.36%. Furthermore, paired t-test results indicate that the use of AutoFE significantly enhances the predictive performance of the model. Furthermore, to enhance the interpretability of the prediction results, the Shapley Additive Explanations (SHAP) technique was employed to conduct an explainable analysis of the machine learning models. This approach revealed the influence trends and magnitudes of reservoir parameters and based learners on the prediction outcomes. The results further indicate that lateral length is the primary factor affecting EUR, followed by proppant loading. This study accurately predicts shale gas EUR and identifies key factors influencing the prediction results, providing valuable insights for predicting shale gas reservoir parameters and optimizing development plans.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

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  • Min Pang,
  • Zheyuan Zhang,
  • Zhaoming Zhou,
  • Wendi Zhou,
  • Qiong Li

摘要

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 R2 of 0.9456, an RMSE of 0.7432, and a MAPE as low as 4.36%. Furthermore, paired t-test results indicate that the use of AutoFE significantly enhances the predictive performance of the model. Furthermore, to enhance the interpretability of the prediction results, the Shapley Additive Explanations (SHAP) technique was employed to conduct an explainable analysis of the machine learning models. This approach revealed the influence trends and magnitudes of reservoir parameters and based learners on the prediction outcomes. The results further indicate that lateral length is the primary factor affecting EUR, followed by proppant loading. This study accurately predicts shale gas EUR and identifies key factors influencing the prediction results, providing valuable insights for predicting shale gas reservoir parameters and optimizing development plans.