Scholarlink

<p>Drilling fluid filtration (DFF) plays a critical role in oil and gas drilling. It directly affects both operational efficiency and downhole safety. There is a well-established link between filtration volume and structural damage to hydrocarbon formations. Recently, nanoparticles have emerged as game-changers in optimizing DFF behavior. However, the industry still lacks a robust and interpretable method to evaluate how different nanoparticles influence filtration performance. To address this, a novel machine learning (ML) approach is proposed, Ensemble Ant Colony Optimized Adaptive XGBoosting (EACO-AXGBoosting). This hybrid model combines the global search efficiency of Ant Colony Optimization with the predictive accuracy of Adaptive XGBoost. It accurately quantifies the influence of nanoparticle type and concentration on DFF efficiency. The model leverages correlations between filtration volume and key input parameters in water-based muds. These include temperature, pressure, RPM, time, and nanoparticle characteristics. The dataset is split with 80% for training and 20% for testing, ensuring reliability. Performance indicators—Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Relative Error (MRE)—confirm strong agreement between predictions and experimental data. This work not only introduces a powerful predictive framework but also reveals key insights into nanoparticle behavior in DFF. Among all variables, nanoparticle concentration emerges as the most influential factor affecting filtration volume. .</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

Enhancing Drilling Fluid Filtration with Novel Swarm-Intelligent Adaptive XGBoosting-Based Analysis of Nanoparticles

  • 期刊论文

  • 发表日期：

    2025年9月10日

查看全文
  • G. Shanmugasundar,
  • Prasun Dutta,
  • Jasgurpreet Singh Chohan,
  • B. Swarna,
  • Elangovan Muniyandy

摘要

Drilling fluid filtration (DFF) plays a critical role in oil and gas drilling. It directly affects both operational efficiency and downhole safety. There is a well-established link between filtration volume and structural damage to hydrocarbon formations. Recently, nanoparticles have emerged as game-changers in optimizing DFF behavior. However, the industry still lacks a robust and interpretable method to evaluate how different nanoparticles influence filtration performance. To address this, a novel machine learning (ML) approach is proposed, Ensemble Ant Colony Optimized Adaptive XGBoosting (EACO-AXGBoosting). This hybrid model combines the global search efficiency of Ant Colony Optimization with the predictive accuracy of Adaptive XGBoost. It accurately quantifies the influence of nanoparticle type and concentration on DFF efficiency. The model leverages correlations between filtration volume and key input parameters in water-based muds. These include temperature, pressure, RPM, time, and nanoparticle characteristics. The dataset is split with 80% for training and 20% for testing, ensuring reliability. Performance indicators—Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Relative Error (MRE)—confirm strong agreement between predictions and experimental data. This work not only introduces a powerful predictive framework but also reveals key insights into nanoparticle behavior in DFF. Among all variables, nanoparticle concentration emerges as the most influential factor affecting filtration volume. .