Azadivash Ahmad
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Heliyon. 2024 Dec 9;11(1):e41059. doi: 10.1016/j.heliyon.2024.e41059. eCollection 2025 Jan 15.
Lost circulation is one of the important challenges in drilling operations and bears financial losses and operational risks. The prime causes of lost circulation are related to several geological parameters, especially in problem-prone formations. Herein, the approach of applying machine learning models to forecast the intensity of lost circulation using well-log data is presented in this work. It concerns a gas field in northern Iran and contains nine well logs with lost circulation incidents categorized into six intensity classes. After rigorous exploratory analysis and preprocessing of the data, seven machine learning methods are applied: Random Forest, Extra Trees, Decision Tree, XGBoost, k-Nearest Neighbors, Support Vector Machine, and Hard Voting. Random Forest, Extra Trees, and Hard Voting are the best-performing methods. These models attained the most robust results on both key performance metrics and, hence, can predict the intensity of lost circulation quite well. Models of Extra Trees and Hard Voting show very high predictive performance values. On the other hand, their limitations in some intensity classes suggest further refinement. In this regard, the ensemble methods are highly effective for managing the multivariate nature of the task. Hard Voting aggregates multiple classifiers, becoming superior to individual models like support vector machines. This paper offers new insight into integrating machine learning to well-log data toward enhancing lost circulation prediction by offering a dependable foundation for real-time drilling decision-making. These results show that the models have the potential to lower operational risks, improve drilling safety, and minimize nonproductive time; hence, they form a quantum leap in lost circulation control.
漏失是钻井作业中的重要挑战之一，会造成经济损失和作业风险。漏失的主要原因与多个地质参数有关，尤其是在易出现问题的地层中。本文提出了一种利用测井数据应用机器学习模型预测漏失强度的方法。该研究涉及伊朗北部的一个气田，包含9口发生过漏失事件的测井数据，这些漏失事件被分为六个强度等级。在对数据进行严格的探索性分析和预处理之后，应用了七种机器学习方法：随机森林、极端随机树、决策树、XGBoost、k近邻、支持向量机和硬投票。随机森林、极端随机树和硬投票是表现最佳的方法。这些模型在两个关键性能指标上都取得了最稳健的结果，因此能够很好地预测漏失强度。极端随机树和硬投票模型显示出非常高的预测性能值。另一方面，它们在某些强度等级上的局限性表明需要进一步改进。在这方面，集成方法对于处理该任务的多变量性质非常有效。硬投票聚合了多个分类器，比支持向量机等单个模型更具优势。本文通过为实时钻井决策提供可靠基础，为将机器学习与测井数据集成以增强漏失预测提供了新的见解。这些结果表明，这些模型有潜力降低作业风险、提高钻井安全性并最大限度地减少非生产时间；因此，它们在漏失控制方面实现了巨大飞跃。
