Subject Area

Civil Engineering

Abstract

Natural gas (NG) production and usage as a fuel source have surged in recent years, becoming a critical component of the global energy system and requiring extensive belowground pipeline infrastructure. Leaks in belowground pipelines release fugitive methane (CH4) emissions, posing significant environmental, safety, and economic risks. Effective leak detection and quantification (LDAQ) methods are essential for identifying and measuring leaks to enable timely repairs and accurately address these threats. However, few studies have characterized the effectiveness of current and advanced LDAQ survey methods for belowground NG pipelines, with existing work often limited to technology-centric evaluations or studies of emission inventories lacking thorough controlled field experiments under diverse operating conditions. To bridge these knowledge gaps, this research study aims to characterize the effectiveness of LDAQ methods for pipeline applications under diverse operating conditions, including belowground, surface, and aboveground scenarios. Top of Form

The existing literature revealed that diverse subsurface conditions (gas composition, pipeline pressure, soil type, moisture, surface covers) and aboveground conditions (vegetation, topography, urban structures, traffic, weather) significantly impact gas transport and the detectability of NG pipeline leaks. Surveys and interviews with operators and research and development engineers, along the review of current academic literature, identified that most existing LDAQ methods have not been validated under diverse operating conditions, including different pipeline properties, subsurface environments, and above-ground scenarios, through controlled field experiments. Based on the review, an innovative approach to controlled field experiments was developed based on a method-centric protocol, utilizing knowledge gained from the literature and firsthand experience with operators. This study specifically selected ranges for survey parameters such as height, distance, and speed to deploy walking, driving and UAV surveys as they would be in real-world pipeline rights-of-ways at a controlled site.

Mixed natural gas composition was selected as the primary experimental variable for this study. Results indicate that the POD for walking, driving, and SUAV surveys is highly influenced by the hydrocarbon composition. For the composition simulating the higher vapor density DJ Basin, the POD for walking surveys is 2.5 times higher compared to the composition simulating lower vapor density distribution-grade composition. In contrast, driving and SUAV surveys show a 0.8 to 0.9 times lower POD compared composition simulating lower vapor density distribution-grade composition. For a higher vapor density NG composition like that of the Permian Basin, the POD for walking survey is 0.8 times higher, and for driving and SUAV surveys, it is 0.4 to 0.85 times lower compared to a lower vapor density Control composition. Results normalized for the flow rate of methane in the composition and the vapor density, found high R² (Walking: 0.62, Driving: 0.72, SUAV: 0.71) indicating a good fit to the experimental results. A 26% increase in the vapor density showed an average POD increase of 1.6 times for the walking surveys, but a decrease of 0.85 times for driving surveys, and a decrease of 0.46 times for SUAV surveys. Findings indicate that the selection of the leak detection method should account for gas compositions with high vapor density due to mixed hydrocarbon content. Basins or sections of the supply chain with lower vapor density compositions allow for greater flexibility in deployment methods and survey protocols. However, basins with higher vapor density than distribution-grade natural gas limit the selection of deployment methods and survey protocols.

Degree Date

Summer 2024

Document Type

Thesis

Degree Name

M.S.

Department

Civil and Enviornmental Engineering

Advisor

Prof. Kathleen M Smits

Number of Pages

124

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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Available for download on Sunday, August 02, 2026

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