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October 3, 2025SPE Journal

An Ensemble-Based Automated Workflow for Decline Curve Analysis and Probabilistic Estimated Ultimate Recovery Estimation in Shale Gas Wells

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This workflow improves estimated ultimate recovery in shale gas wells using an ensemble approach and noise removal.

The ensemble approach automates decline curve analysis, improving estimated ultimate recovery accuracy and efficiency.
Using advanced machine learning techniques, the workflow addresses uncertainties in fitting decline curve models.
Analysis of 666 shale gas wells achieved above 90% predictive accuracy, ensuring high reliability of estimated ultimate recovery.
The method's flexibility allows for scalable applications, providing valuable insights into fitting convergence and sensitivity across models.

Yehia et al. (2025) studied this question.

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October 3, 2025SPE Journal

View Full Paper

Ask AI

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Authors

This workflow improves estimated ultimate recovery in shale gas wells using an ensemble approach and noise removal.

The ensemble approach automates decline curve analysis, improving estimated ultimate recovery accuracy and efficiency.
Using advanced machine learning techniques, the workflow addresses uncertainties in fitting decline curve models.
Analysis of 666 shale gas wells achieved above 90% predictive accuracy, ensuring high reliability of estimated ultimate recovery.
The method's flexibility allows for scalable applications, providing valuable insights into fitting convergence and sensitivity across models.

Yehia et al. (2025) studied this question.

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