Abstract

The oil and gas industry is constantly seeking to improve environmental sustainability and reduce its carbon footprint. Methane, the second most abundant anthropogenic greenhouse gas, is more than 25 times as potent as carbon dioxide in trapping heat in the atmosphere. Leak detection and repair (LDAR) programs are required by regulators to monitor fugitive leak emissions across oil and gas infrastructure. In the United States, these regulations require use of optical gas imaging (OGI) cameras, and subsequently issued proposals include additional technologies to monitor for methane. However, the cost and effectiveness of different technologies at different survey frequencies are yet to be determined. Given the scale and distribution of oil and gas production sites across any basin, it is imperative to leverage numerical simulations to compare different LDAR programs, frequencies, technologies, and to provide guidance on optimal yet sustainable LDAR programs.

In this study, we performed numerical simulations using two industry recognized models-FEAST and LDAR-Sim-to quantify methane emissions reduction at oil and gas facilities and to compare the effectiveness of different LDAR programs. We also performed both qualitative and quantitative comparisons between the two models. The probabilistic emission simulations for the selected area are generalized using a stochastic approach. We performed a sensitivity analysis to identify the key factors that influence methane emissions and to determine appropriate configuration of the numerical simulations. We conducted history matching to calibrate the numerical simulation model. To compare the average methane emissions over a year, we simulate combinations of various technologies, such as OGI cameras and aerial surveys, with variable detection sensitivities and different survey frequencies.

From the sensitivity analysis, we noticed that the natural kill date of each leak (i.e., when a leak ceases) and the probability of a component leaking are two key factors that influence overall methane emissions respectively in LDAR-Sim and FEAST. However, these two variables can be uncertain and depend heavily on the addition and mitigation of emissions sources in the real world. The input emission rate distribution is also a critical input that can influence simulation outcomes significantly. FEAST and LDAR-Sim results in close simulation trends even if the magnitude differs slightly for some scenarios. In our observation, when compared to OGI monitoring, we found that the aerial surveys can provide equivalent or better methane emissions reduction because of its higher survey frequency, lower cost, and sustainable workforce requirement.

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