关键词: 煤层气, 随机森林算法, 多任务学习, 粒子群优化算法, 产能预测, 压裂参数优化

Abstract:

The coalbed methane (CBM) blocks in the southern Qinshui Basin exhibit strong reservoir heterogeneity, resulting in challenges for accurate productivity prediction of gas wells. Furthermore, the absence of tailored fracturing designs has caused substantial variations in post-fracturing production performance among adjacent wells. To address these issues, a predictive model for well production capacity was developed based on geological, well logging, fracturing, and production data from 187 vertical CBM wells in the southern Qinshui Basin. The model employs a random forest algorithm integrated with a multi-task learning strategy and utilizes a particle swarm optimization (PSO) algorithm to optimize fracturing parameters. A deep convolutional autoencoder-decoder was applied to unstructured data (e.g., well logs), and the integration of random forest with multi-task learning strategies effectively addressed limited sample sizes and poor generalization, ensuring high prediction accuracy under small-data conditions. The results indicate that well depth, fracturing fluid volume, and small-sized proppant dosage are the dominant factors affecting productivity. Geological conditions determine long-term productivity, whereas fracturing parameters predominantly affect peak production performance. The multi-task random forest algorithm achieved high accuracy on small datasets, with R² values of 0.883 for 30-day peak cumulative production and 0.887 for 5-year cumulative production in the test set. Furthermore, the R² for 5-year cumulative production predictions of six new wells reached 0.901, confirming the model’s robustness and reliability in field applications. The PSO-optimized fracturing parameters significantly improved the productivity classification and overall productivity levels of the gas wells. The optimized parameters increased single-well productivity by 153-188% compared to original designs, demonstrating substantial practical efficacy. The combined multi-task learning and PSO framework successfully resolves productivity prediction and fracturing optimization challenges under small-data constraints. The proposed model and fracturing parameter optimization algorithm provide theoretical support and practical references for efficient CBM development in the southern Qinshui Basin.

Key words: coalbed methane, random forest algorithm, multi-task learning, particle swarm optimization algorithm, production prediction, fracturing parameter optimization