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<p>The redevelopment of ultra-deep, low-porosity fractured gas reservoirs poses significant challenges due to complex geological structures, data limitations, and the need for precise evaluation methods. This study addresses these challenges by proposing a data-driven approach that integrates Pearson correlation with an Improved Copula-Based Feature Selection (ICBFS) method to identify and prioritize the main factors influencing redevelopment success. Using these selected factors, a Reservoir Evaluation Score (RES) is calculated for each well, offering a quantitative assessment of redevelopment potential. Wells are classified into three potential categories (Class I, Class II, and Class III) using K-means++ clustering, allowing for targeted optimization and process recommendations for each category. Applied to Well A3, this methodology demonstrated significant improvements, increasing the unrestricted flow rate from 6 × 10<sup>4</sup> to 159.6 × 10<sup>4</sup> m<sup>3</sup>/d, thus confirming the approach’s effectiveness. This method is computationally efficient, suitable for cases with limited data, and supports on-site application. By combining advanced feature selection and correlation techniques, this study offers a structured framework for assessing redevelopment potential in complex reservoirs, moving beyond previous evaluation methods to achieve a more accurate and actionable approach.</p>

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Research and application of redevelopment potential evaluation methods for ultra-deep low-porosity fractured gas reservoirs based on data mining

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月12日

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  • Dengfeng Ren,
  • Haojiang Xi,
  • Longcang Huang,
  • Yuzhen Li,
  • Ju Liu,
  • Zhifeng Luo

摘要

The redevelopment of ultra-deep, low-porosity fractured gas reservoirs poses significant challenges due to complex geological structures, data limitations, and the need for precise evaluation methods. This study addresses these challenges by proposing a data-driven approach that integrates Pearson correlation with an Improved Copula-Based Feature Selection (ICBFS) method to identify and prioritize the main factors influencing redevelopment success. Using these selected factors, a Reservoir Evaluation Score (RES) is calculated for each well, offering a quantitative assessment of redevelopment potential. Wells are classified into three potential categories (Class I, Class II, and Class III) using K-means++ clustering, allowing for targeted optimization and process recommendations for each category. Applied to Well A3, this methodology demonstrated significant improvements, increasing the unrestricted flow rate from 6 × 104 to 159.6 × 104 m3/d, thus confirming the approach’s effectiveness. This method is computationally efficient, suitable for cases with limited data, and supports on-site application. By combining advanced feature selection and correlation techniques, this study offers a structured framework for assessing redevelopment potential in complex reservoirs, moving beyond previous evaluation methods to achieve a more accurate and actionable approach.