用地理信息与机器学习分析油气管线风险，识别高危区域。
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach
- 结合GIS与多种机器学习算法，构建管线风险预测模型。
- 集成分类器配合主成分分析，显著提升预测准确率。
- 揭示空间与运营因素对风险的影响，适合安全评估人员参考。
本文采用地理信息系统（GIS）与机器学习（ML）方法，对油气行业的输送管线进行风险分析。输送管线作为从井口向地面设施输送油气水的关键通道，其风险评估常被低估，远低于输气管道。本研究利用科罗拉多能源与碳管理委员会（ECMC）的海量数据，通过空间匹配、特征工程和几何提取，构建稳健的预测模型。采用逻辑回归、支持向量机、梯度提升决策树及K均值聚类等算法评估与分类风险，其中集成分类器在结合主成分分析（PCA）降维后表现最优。数据分析揭示了影响风险的空间与运营因素，识别出需重点监控的高风险区域。研究展示了融合GIS与ML在管线风险管理中的变革潜力，提出依赖高质量数据与优化模型的数据驱动方法，以提升油气开采安全性。
原文摘要 · Abstract (English)
This paper presents a risk analysis of flowlines in the oil and gas sector using Geographic Information Systems (GIS) and machine learning (ML). Flowlines, vital conduits transporting oil, gas, and water from wellheads to surface facilities, often face under-assessment compared to transmission pipelines. This study addresses this gap using advanced tools to predict and mitigate failures, improving environmental safety and reducing human exposure. Extensive datasets from the Colorado Energy and Carbon Management Commission (ECMC) were processed through spatial matching, feature engineering, and geometric extraction to build robust predictive models. Various ML algorithms, including logistic regression, support vector machines, gradient boosting decision trees, and K-Means clustering, were used to assess and classify risks, with ensemble classifiers showing superior accuracy, especially when paired with Principal Component Analysis (PCA) for dimensionality reduction. Finally, a thorough data analysis highlighted spatial and operational factors influencing risks, identifying high-risk zones for focused monitoring. Overall, the study demonstrates the transformative potential of integrating GIS and ML in flowline risk management, proposing a data-driven approach that emphasizes the need for accurate data and refined models to improve safety in petroleum extraction.
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