用概率机器学习预测油田钻井液漏失，提升安全与效率
Explainable Probabilistic Machine Learning for Predicting Drilling Fluid Loss of Circulation in Marun Oil Field
- 基于高斯过程回归建模钻井参数非线性关系并量化不确定性
- 结合LBFGS优化与LIME解释，实现高精度且可解释的预测
- 适合油田钻井风险预警与堵漏材料设计人员参考
钻井过程中漏失循环仍是重大且成本高昂的挑战，常导致井壁失稳、卡钻及非生产时间延长。准确预测钻井液损失对提升钻井安全与效率至关重要。本研究提出一种基于高斯过程回归（GPR）的概率机器学习框架，用于预测复杂地层中的钻井液漏失。该模型捕捉钻井参数间的非线性依赖关系，并量化预测不确定性，为高风险决策提供更高可靠性。模型超参数通过有限记忆拟牛顿法（LBFGS）优化，以确保数值稳定性和鲁棒泛化能力。为提升可解释性，采用局部可解释模型无关解释（LIME）揭示各特征对预测的影响。结果表明，可解释的概率学习在主动识别漏失风险、优化堵漏材料（LCM）设计以及降低钻井操作不确定性方面具有显著潜力。
原文摘要 · Abstract (English)
Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate prediction of fluid loss is therefore essential for improving drilling safety and efficiency. This study presents a probabilistic machine learning framework based on Gaussian Process Regression (GPR) for predicting drilling fluid loss in complex formations. The GPR model captures nonlinear dependencies among drilling parameters while quantifying predictive uncertainty, offering enhanced reliability for high-risk decision-making. Model hyperparameters are optimized using the Limited memory Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm to ensure numerical stability and robust generalization. To improve interpretability, Local Interpretable Model agnostic Explanations (LIME) are employed to elucidate how individual features influence model predictions. The results highlight the potential of explainable probabilistic learning for proactive identification of lost-circulation risks, optimized design of lost circulation materials (LCM), and reduction of operational uncertainties in drilling applications.
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