Advanced Leak Detection Methods for Belowground Natural Gas Pipeline Leaks: Evaluation under Diverse Environmental...

Area of Science:

• Environmental Science
• Geophysics
• Engineering

Background:

• Advanced leak detection (ALD) methods are crucial for natural gas (NG) infrastructure safety and reducing methane (CH4) emissions.
• Current ALD technologies are effective for aboveground leaks but limited for belowground leaks due to complex subsurface gas migration.

Purpose of the Study:

• To experimentally evaluate the performance of ALD methods under controlled conditions.
• To assess the impact of various environmental and operational factors on leak detection probability of detection (POD).

Main Methods:

• Conducted comprehensive experiments using walking, driving, and simulated unmanned aerial vehicle (UAVsim)-based surveys.
• Tested at leak rates of 0.5, 5, and 10 slpm under diverse conditions: gas composition, soil properties, urban geometry, slopes, and snow cover.
• Utilized Probability of Detection (POD) as the primary performance metric over two years at METEC and FERL.

Main Results:

• Walking surveys consistently achieved high POD (>90%) across most conditions, including snow, moist soils, and slopes.
• Driving and UAVsim surveys showed high POD in ideal conditions but significant reductions (>80 pp) in snow and urban environments.
• Gas composition, soil permeability, and moisture significantly impacted POD, with notable reductions for driving and UAVsim surveys.

Conclusions:

• ALD method performance is highly dependent on environmental and operational variables.
• Walking surveys offer superior reliability for detecting natural gas leaks in complex real-world scenarios.
• Findings provide critical insights for optimizing ALD protocols and survey strategies.