Can new mobile technologies enable fugitive methane reductions from the oil and gas industry?

Abstract

New mobile platforms such as vehicles, drones, aircraft, and satellites have emerged to help identify and reduce fugitive methane emissions from the oil and gas sector. When deployed as part of leak detection and repair (LDAR) programs, most of these technologies use multi-visit LDAR (MVL), which consists of four steps: (a) rapidly screen all facilities, (b) triage by emission rate, (c) follow-up with close-range methods at the highest-emitting sites, and (d) conduct repairs. The proposed value of MVL is to identify large leaks soon after they arise. Whether MVL offers an improvement over traditional single-visit LDAR (SVL), which relies on undirected close-range surveys, remains poorly understood. We use the Leak Detection and Repair Simulator (LDAR-Sim) to examine the performance and cost-effectiveness of MVL relative to SVL. Results suggest that facility-scale MVL programs can achieve fugitive emission reductions equivalent to SVL, but that improved cost-effectiveness is not guaranteed. Under a best-case scenario, we find that screening must cost < USD 100 per site for MVL to achieve 30% cost reductions relative to SVL. In scenarios with non-target vented emissions and screening quantification uncertainty, triaging errors force excessive close-range follow-up to achieve emissions reduction equivalence. The viability of MVL as a cost-effective alternative to SVL for reducing fugitive methane emissions hinges on accurate triaging after the screening phase.

Advances in Research on the Use of Drones in Production and Transportation Engineering in the Hydrocarbons Industry: A Bibliometric Analysis

“…In 2022, methane sensors were used to detect the emission rates at oil and gas production sites [21]. In 2021, Canada's methane sensors allowed the monitoring and inspection of gas leaks [51]. In addition, researchers from Brazil, Norway and Portugal collaborated in the development of a machine learning model, based on convolutional neural networks (CNN) and equipped with an RGB camera, installed on multirotor drones, for the inspection of unburied pipelines [52].…”

Complex multi-source emissions quantification results for the PoMELO vehicle measurement system, test results from the CSU METEC facility

“…The PoMELO system is designed for producing data about emissions sources on upstream pads with one visit, where all information necessary for emissions management is collected at one time. Modeling and practical deployments have indicated this is an efficient approach to emissions management (Fox et al, 2021). The system is designed to be easy to use to reduce training requirements and allow operators to follow-up on detections and quantifications immediately, providing vital information while on the pad.…”

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach

“…Methane leak detection and repair (LDAR) programs should also be optimized with quantification uncertainty in mind to give the best trade-off between cost and emissions reductions, as high quantification uncertainty can impair the cost-effectiveness of LDAR programs. 5 Furthermore, methane emissions measurements are used to develop broader jurisdiction-wide and global inventories, 6−8 which are needed to assess progress towards emissions reduction targets and to inform policies and regulations, but these decisions can only be made in the context of uncertainty. For example, the well-known "gap" between inventories compiled from "top-down" (aircraft or satellite-based modalities) and "bottom-up" techniques (handheld or in-situ technologies) is partially explained by the uncertainties attached to individual measurements.…”

“…Methane emission measurements can only be interpreted properly in the context of uncertainty. This aspect is particularly important in view of existing and emerging methane emissions regulations and reduction commitments, − e.g., to answer the question “with what probability is this facility compliant with a particular regulation?” Methane leak detection and repair (LDAR) programs should also be optimized with quantification uncertainty in mind to give the best trade-off between cost and emissions reductions, as high quantification uncertainty can impair the cost-effectiveness of LDAR programs . Furthermore, methane emissions measurements are used to develop broader jurisdiction-wide and global inventories, − which are needed to assess progress towards emissions reduction targets and to inform policies and regulations, but these decisions can only be made in the context of uncertainty.…”