48: Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach

Music City Center 

This paper presents a comprehensive risk analysis of flowlines in the oil and gas sector using Geographic Information Systems (GIS) and machine learning. Flowlines, vital conduits transporting oil, gas, and water from wellheads to surface facilities, often face under-assessment compared to pipelines. This study addresses this gap using advanced tools to predict and mitigate failures, enhancing environmental safety and reducing casualties. 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 machine learning algorithms were used to assess risks, with ensemble classifiers showing superior accuracy, especially when paired with Principal Component Analysis (PCA) for dimensionality reduction. Exploratory Data Analysis (EDA) highlighted spatial and operational factors influencing risks, identifying high-risk zones for focused monitoring. The study demonstrates the potential of integrating GIS and machine learning in flowline risk
management, proposing a data-driven approach to enhance safety in petroleum extraction.

Risk Analysis

Flowlines

Machine Learning

GIS 

Section on Statistics and the Environment