Interpreting estimates of whole site or area methane emissions estimates pose several technical challenges. First, technologies only estimate a subset of site-level emissions – emissions below the detection threshold are not captured. Second, some sources of methane such as exhaust slip or unburnt methane in flares may not be detected because of technological limitations. Third, uncertainty of each emissions estimate depends on factors including sample size, sensor performance, algorithmic limitations, and environmental conditions. The emissions estimate interpretation tool project will develop methods to obtain whole-site emissions estimates using methane emission estimates from multiple types of solutions and identify procedures to quantify emissions uncertainty. Initial focus will be on commonly deployed aerial systems, with expansion to other technologies in Year 2. Models and tools for developing measurement informed inventories (MII) and reconciliation initiated through Project 4 will be updated regularly in future years. The following tasks will be completed by Q4 2023.

Operator and Basin Emissions Reconciliation Tools

Interpreting estimates of whole site or area methane emissions estimates pose several technical challenges. First, technologies only estimate a subset of site-level emissions – emissions below the detection threshold are not captured. Second, some sources of methane such as exhaust slip or unburnt methane in flares may not be detected because of technological limitations. Third, uncertainty of each emissions estimate depends on factors including sample size, sensor performance, algorithmic limitations, and environmental conditions. The emissions estimate interpretation tool project will develop methods to obtain whole-site emissions estimates using methane emission estimates from multiple types of solutions and identify procedures to quantify emissions uncertainty. Initial focus will be on commonly deployed aerial systems, with expansion to other technologies in Year 2. Models and tools for developing measurement informed inventories (MII) and reconciliation initiated through Project 4 will be updated regularly in future years. The following tasks will be completed by Q4 2023.
Recent work by the EEMDL team led to preliminary work developing site-level emissions estimates from different measurement-based emission estimation solutions. The task will expand that work to formalize technology interpretation methods for commonly used aerial solutions. Initial work will focus on developing standardized methodology to supplement measurements with emissions estimates that are not detected (e.g., emissions below detection threshold) or not measured (e.g., exhaust emissions), and to develop a framework to understand how method uncertainty varies with environmental conditions or facility configuration. The methodology will be developed for upstream and midstream facilities in one representative basin (Marcellus). As part of this work, feedback will be requested from technology vendors, as appropriate, to ensure methods are consistent with technology developments. To facilitate this, EEMDL will establish a program to foster engagement with solution providers.
Recent work by EEMDL team members and others has developed robust emission quantification error estimates using controlled release tests for aerial technologies. This task will expand that work to develop methods for robust uncertainty analysis and error estimation for whole-site emissions estimates. These methods will account for both measurement error and sampling error using Monte-Carlo based approaches. Methods will be standardized to develop 95% confidence intervals for whole-site emissions estimates that can be compared across technologies.

Emission estimates for oil and gas facilities are typically undertaken using multiple technologies that vary in their detection thresholds, ability to detect dilute methane emissions, impact of environmental conditions, and quantification accuracy. Furthermore, high-volume, short-duration intermittent emission events make time of measurement a critical parameter in comparing data obtained through different technologies. The ability to compare and interpret measurements across technologies and facility types can improve accuracy of measurement informed inventory (MII) estimates. This project will develop methods to reconcile methane emissions measurements across technologies that enable cross-method comparisons. Initial work will focus on aerial technologies (aircraft-, drone-based measurements) and cross-technology reconciliation as applied to upstream production facilities. The following tasks will be completed by Q4 2023:
This task will focus on developing methods to compare measurements made by different aerial technologies. Initial work will focus on commonly deployed aerial systems at upstream production facilities and expand on the technology interpretation methods developed in Project 4a. Analysis will include consideration of differences in the detection threshold, detectability (i.e., detection of dilute sources of methane such as those found in exhaust streams), impact of environmental conditions, and effects of time of measurement as related to intermittent emission events. These methods will be updated periodically as more data become available.
This task will develop case studies that describe methodologies to reconcile methane emissions estimates across technologies at upstream production facilities. This will be accomplished through two subtasks. First, the team will develop a simulation tool to illustrate the impact of frequency and duration of intermittent emission events on observed emissions estimates by different technologies. Second, methods to compare emissions at the equipment-level will be developed that account for differences in technology performance and the role of intermittency in emissions estimates provided by each technology. A key focus for this task will be to understand the role of time of measurement in enabling reconciliation across technologies, identifying conditions necessary for effective reconciliation, and making recommendations on proper averaging intervals for estimation methods to improve estimation accuracy. Cross-technology reconciliation methods will be critical to the development of measurement informed inventory (MII) estimates in Project 4c.

Going from measurement-based emission estimates, that are limited in their temporal coverage, to robust annual emissions inventory estimates remains a challenge. Methane emissions inventories represent site-level emissions that are typically intended to represent yearly emissions, while measurements of methane emissions through survey-type technologies typically provide snapshot estimates using seconds to minutes of observation time. This project will develop methods, models, and case studies to determine facility-level measurement-informed inventory (MII) estimates using measurement data, activity data, and other operational information. The MII will represent a reasonable estimate of emissions at multiple scales and be adapted for multiple mixes of measurement technologies. Large-scale development of MII across supply chains will eventually help update emissions factors in official greenhouse gas inventory approaches. Over time, this would help bridge the gap between top-down measurements and bottom-up inventory estimates. Initial work will focus on using aerial survey data to develop MII for upstream production facilities, with expansion to other facility types and technologies in Year-2 and beyond. The MII model will incorporate the methods and tools developed for the technology interpretation methods (Project 4a) and method intercomparison project (Project 4b). The following tasks will be completed by Q4 2023.
To support efforts in the other proposed projects, EEMDL will develop formats for reporting diverse data, including activity data, estimates from deployed detection and quantification methods, operational data, etc. These data will provide the basis for automation tools in other projects.
Methane emission estimates using multiple technologies need to be integrated with facility operational information to develop a robust MII. Prior work by EEMDL researchers developed case studies of integrating flux plane and imaging survey estimates at production and midstream facilities. This task will extend prior work to develop methods and models to develop MII estimates from a variety of snapshot measurements. This will include considerations of differences in detection thresholds, detectability (e.g., ability of technologies to detect unburnt methane in flares), impact of environmental conditions, and uncertainty. A key aspect of this task will be the development of methods for uncertainty analysis when integrating data from multiple technologies. Initial work will focus on integrating top-down measurements at production facilities, with Year-2 extending the model to other facility types and survey-type estimation technologies.
GHGRP-based methane emissions inventories have been shown to underestimate methane emissions across the oil and gas supply chain. This discrepancy between top-down estimates and bottom-up estimates can be attributed to several factors including outdated emissions factors, missing or inaccurate activity data, reporting gaps, and inadequate characterization of failure modes that lead to intermittent emissions. This task will begin work on developing a standardized framework to reconcile top-down estimates with GHGRP-based emission inventories. Year-1 will focus on identifying and attributing gaps between MII and inventory estimates. In addition, preliminary estimates will develop measurement-based emission factors for high or frequently emitting equipment at production facilities, such as tanks. As more data are collected, reconciliation models will help update these emissions factors and account for intermittent emission events.
EEMDL researchers have recently developed algorithms that detect, localize, and quantify methane emission events from point-source CMS data at upstream oil and gas facilities. In an additional case study, CMS data were used to develop preliminary distributions of the frequency and duration of emission events at small production sites. This task will develop standardized models to extract site- and equipment-specific emission events and duration information at upstream production facilities. This task will focus on developing models to incorporate CMS-based emission event information to develop MII. Initial work will use distributions of event frequencies and duration in a Monte Carlo simulation framework to scale aerial measurement data.