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

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 x 104 to 159.6 x 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.