🤖 AI Summary
To address insufficient risk assessment for oil and gas field gathering and transportation pipelines, this study proposes a data-driven risk prediction framework integrating GIS spatial analysis and machine learning. Methodologically, it innovatively unifies GIS-derived geometric features—including slope, hydrology, and land use—with heterogeneous operational data (e.g., corrosion rates, pressure fluctuations, and inspection logs) to construct a dual-dimensional (spatial–operational) risk classification model. Furthermore, a PCA-enhanced ensemble classifier is introduced to improve both feature interpretability and classification robustness. Experimental results demonstrate significant performance gains over conventional approaches: the model achieves 92.3% accuracy in identifying high-risk pipeline segments, with spatial localization error ≤50 m. The framework establishes a reusable, scalable intelligent risk monitoring paradigm, providing technical support for enhancing environmental safety and mitigating personnel exposure risks.
📝 Abstract
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.