Comparing Natural Gas Leakage Detection Technologies Using an Open-Source "Virtual Gas Field" Simulator.

Area of Science:

• Environmental science
• Energy systems analysis
• Risk management

Background:

• Methane (CH4) emissions from natural gas infrastructure pose significant environmental and economic challenges.
• Effective leak detection and repair (LDAR) programs are crucial for mitigating these emissions.
• Evaluating the performance of diverse LDAR strategies and technologies requires robust modeling tools.

Purpose of the Study:

• To introduce a novel modeling tool for assessing the performance of methane LDAR programs.
• To compare the effectiveness of different methane detection technologies and mitigation policies.
• To provide insights into optimizing LDAR strategies for economic and environmental benefits.

Main Methods:

• Development of a two-state Markov model to simulate methane leakage dynamics in a natural gas field.
• Stochastic simulation of leak occurrence based on known frequency and size distributions.
• Integration of economic and environmental metrics for technology and policy comparison.
• Simulation of four distinct detection technologies: flame ionization detection, infrared cameras, drones, and distributed sensors.

Main Results:

• Over 80% of simulated methane leakage can be mitigated with a positive net present value.
• Selective targeting of larger leaks maximizes economic and environmental benefits.
• Low-cost LDAR programs can effectively utilize high-cost technologies by prioritizing rapid detection of significant leaks.

Conclusions:

• The developed tool provides a robust framework for evaluating methane LDAR program performance.
• Optimizing LDAR strategies requires careful consideration of technology costs versus program implementation costs.
• Prioritizing the detection of large methane leaks is key to maximizing the benefits of LDAR programs.