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Coalbed Methane Production Model Based on Random Forests Optimized by a Genetic Algorithm.

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Area of Science:

  • Petroleum Engineering: Focuses on optimizing coalbed methane (CBM) extraction and predicting production rates.
  • Machine Learning: Utilizes advanced algorithms for complex data analysis in energy resource management.
  • Computational Intelligence: Employs optimization techniques for enhancing predictive model performance.

Background:

  • Accurate coalbed methane (CBM) production evaluation is crucial for efficient CBM exploration and exploitation.
  • Traditional machine learning models for CBM prediction face challenges in hyperparameter tuning for diverse well types.
  • Understanding the complex flow dynamics of gas and water is vital for optimizing CBM recovery.

Purpose of the Study:

  • To develop an optimized machine learning model for accurate CBM production prediction.
  • To address the limitations of hyperparameter determination in existing Random Forest (RF) models for CBM wells.
  • To identify and rank the key drainage control parameters influencing CBM gas production.

Main Methods:

  • A hybrid Genetic Algorithm-Random Forest (GA-RF) model was developed to automatically optimize RF hyperparameters (n_tree, m_try).
  • Pearson method and RF were used for multicollinearity avoidance and variable importance analysis.
  • Analysis of drainage control parameters including casing pressure (Pc), bottom-hole pressure (Pb), and stroke frequency (fs).

Main Results:

  • The GA-RF model demonstrated superior performance, reducing mean-square-error by 40-60% compared to the standard RF model.
  • Casing pressure (Pc), bottom-hole pressure (Pb), and stroke frequency (fs) were identified as the most significant factors influencing CBM production.
  • High prediction accuracy achieved, with 93% of training errors <5% and 89% of prediction errors <10%.

Conclusions:

  • The GA-RF model effectively adapts to different CBM well types, offering robust production prediction.
  • The model provides prompt identification of key influencing factors, aiding in optimized CBM well management.
  • This hybrid approach significantly enhances the accuracy and efficiency of CBM production forecasting.