Scholarlink

<p>Shale gas reserves represent a significant source of natural gas, but unlocking their full potential depends on effective hydraulic fracturing. This research investigates the application of machine learning (ML) techniques to predict fracability index (FI), offering a faster and more cost-effective alternative to traditional experimental methods. Focusing on the Upper Ordovician Wufeng to Lower Silurian Longmaxi Formation in the Weiyuan shale gas field, Sichuan Basin, China, this study employed deep neural networks that integrate two metaheuristic algorithms—genetic algorithm (GA) and particle swarm optimization (PSO)—with the back-propagation technique. These combined algorithms—termed GABPNN and PSOBPNN—were utilized to predict the FI. Model performance was assessed using three metrics: R<sup>2</sup>, RMSE, and MAE. The GABPNN achieved R<sup>2</sup>, RMSE, and MAE of 0.97531, 0.024754, and 0.0042875, respectively, while the PSOBPNN yielded values of 0.97494, 0.024938, and 0.0048962, respectively. Notably, when predicting FI values for the test well, the PSOBPNN model attained a R<sup>2</sup> of 0.99848, and the GABPNN model achieved a R<sup>2</sup> of 0.9993, indicating exceptional predictive accuracy. Both models demonstrated nearly perfect prediction accuracy for FI in the testing dataset, underscored by their high R<sup>2</sup> values. Importantly, the GABPNN model exhibited superior capability in mitigating overfitting, a common challenge in ML applications. Overall, the GABPNN and PSOBPNN models offer effective alternatives for assessing the fracability of shale gas reservoirs. By facilitating the identification of sweet spots for fracturing, these ML-based approaches have the potential to optimize operations in shale gas reservoirs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of GA/PSO Metaheuristic Algorithms Coupled with Deep Neural Networks for Predicting the Fracability Index of Shale Gas Formations

  • 期刊论文

  • 发表日期：

    2025年4月29日

查看全文
  • Mbula Ngoy Nadege,
  • Biao Shu,
  • Meshac B. Ngungu,
  • Mutangala Emmanuel Arthur,
  • Kouassi Verena Dominique

摘要

Shale gas reserves represent a significant source of natural gas, but unlocking their full potential depends on effective hydraulic fracturing. This research investigates the application of machine learning (ML) techniques to predict fracability index (FI), offering a faster and more cost-effective alternative to traditional experimental methods. Focusing on the Upper Ordovician Wufeng to Lower Silurian Longmaxi Formation in the Weiyuan shale gas field, Sichuan Basin, China, this study employed deep neural networks that integrate two metaheuristic algorithms—genetic algorithm (GA) and particle swarm optimization (PSO)—with the back-propagation technique. These combined algorithms—termed GABPNN and PSOBPNN—were utilized to predict the FI. Model performance was assessed using three metrics: R2, RMSE, and MAE. The GABPNN achieved R2, RMSE, and MAE of 0.97531, 0.024754, and 0.0042875, respectively, while the PSOBPNN yielded values of 0.97494, 0.024938, and 0.0048962, respectively. Notably, when predicting FI values for the test well, the PSOBPNN model attained a R2 of 0.99848, and the GABPNN model achieved a R2 of 0.9993, indicating exceptional predictive accuracy. Both models demonstrated nearly perfect prediction accuracy for FI in the testing dataset, underscored by their high R2 values. Importantly, the GABPNN model exhibited superior capability in mitigating overfitting, a common challenge in ML applications. Overall, the GABPNN and PSOBPNN models offer effective alternatives for assessing the fracability of shale gas reservoirs. By facilitating the identification of sweet spots for fracturing, these ML-based approaches have the potential to optimize operations in shale gas reservoirs.