Understanding Resolution Requirements for Sensing Technologies and Emission Scenarios: Toward Effective Emission Detection and Mitigation in the Permian Basin

Localizing methane emissions from oil and gas facilities is a complex and challenging task,
especially when those emissions are intermittent, vary in intensity, and are highly contingent on
wind and weather patterns.

In order to effectively detect emissions when multiple oil and gas operators are active and
present — as is true in the Permian basin — multiple sensing systems, like ground sensor
networks, aircrafts, and satellites, should effectively work together. However, these technologies
each have their own advantages, limitations, and requirement for optimal operation.
In this study, we strive to understand the impact of wind and model resolution, emission
scenarios, sensing technologies, and detection and response time. Due to the complexity of our
models, halving the resolution can increase the computational run time by an order of magnitude,
making it imperative that we identify a critical time period for our work given our limited
resources. Following an extensive literature review, we choose to find a representative week for
each quarter of the year by constructing a joint frequency distribution (in space, through time) for
each component of our high-resolution wind data. Ultimately, we verify our methods with
divergence metrics and simple averaging as a means for comparison, and robust visualizations
that demonstrate the similarity between our chosen week and the overall distribution for the
quarter.

Next, we run our simulations on this week of wind for each quarter, and model a series of
emission scenarios (10, 100, and 1000 kg/hr), which are representative of medium to super
emitters. We draw conclusions on the optimal grid resolution to model various emission
scenarios, and observe the impact of height, which roughly corresponds to the nature and
precision of different sensing technologies, from our simulations. We demonstrate when grid
resolution is between 500 m to 1.5 km, the evolution of the plume at the ground level is well-
captured. Further work is needed to understand how satellites and aircrafts “see” methane, and to
accurately model their sensing profiles.

Modeling methane dispersion from oil and gas sources using the appropriate resolution and
timescale is crucial for accurate, reliable, and timely detection of abnormal emissions, leading to
effective mitigation strategies.