Rapid Detection of Methane Super-Emitters Through Advanced Interpretation

Abstract

Abstract Large methane emissions occur from a wide variety of oil and gas industry sites with no discernable patterns thus requiring methodologies to frequently monitor for these releases throughout the entire production chain. To cost-effectively monitor widely dispersed well pads, we describe a continuous monitoring system based on the Internet of Things (IoT) to leverage cost-optimized methane concentration sensors permanently deployed at facilities and connected to a cloud-based interpretation platform. A key component of this system is the interpretation methodology linking measurements to the desired answers regarding existence of a methane emission and, if one exists, its location and rate. This paper describes the methodology we have developed and its key improvements. Testing at controlled methane release facilities enabled the validation of the fidelity of the atmospheric dispersion modeling underlying our interpretation along with the interpretation performance in detecting, localizing, and quantifying methane releases.