Announcement Details

The Collaboratory to Advance Methane Science (CAMS) is seeking responses to a funding opportunity to create a transparent, freely available, vendor agnostic model to quantify and localize methane emissions at oil and gas production sites. The work will help to improve our understanding of trends in emissions and our ability to predict emissions in the future. Improving the accuracy of inverse dispersion modeling to quantify emission rates and localize emission sources gives industry regulators, as well as operators, a powerful tool for evaluating bottom-up emissions. Importantly, this will allow policymakers and regulators to have higher confidence in emissions estimates using modeling techniques that can be continuously field tested and improved.

Responding entities should submit full proposals for projects by December 09, 2022. It is anticipated that one award will be made with funding ranging from $450,000-$500,000.

Primary Study Objectives

The primary objective of the funded work will be to develop a non-proprietary, open-source algorithm that utilizes methane concentration data detected by continuous monitoring point sensors. Training and validation of the model can be performed using a combination of simulated “data” and/or field data collected at controlled release testing sites.

This research will allow CAMS to build on past successes and continue to advance the science around methane emissions.

This will be accomplished by sponsoring and engaging in research targeted at identifying, understanding, and supporting the most viable technologies to ultimately reduce overall methane emissions.

Specific study objectives include to build, train and apply an inverse dispersion model to:

• Localize an emissions source at the equipment category level (e.g. storage tank, separator, compressor) with 90%+ confidence;
• Determine whether and under what conditions low-cost sensors can achieve reasonable quantification.
• Improve emission rate quantification with uncertainty comparable to the best available technology today, including specified boundary conditions in specified test environments and operating parameters (i.e. ±30% or better¹);
• Determine emissions category (e.g. intermittent, continuous, routine, fugitive, etc.).

The study should be repeatable, publishable and allow opportunities for future work, preferably through publication of the model as open source. The model should perform with a high degree of computational efficiency / usability (should be able to report back on near real-time basis using minimal computational resources).

value chain. Point concentration sensors at fixed ground locations will provide continuous monitoring capabilities of emissions for the purposes of training, validation, and application of the model. Actual sensors used in the study will be provided by CAMS subject to availability and cost.

The successful project should build on previous analyses of emission estimates based on dispersion modeling downwind. AI / ML approaches have been shown to achieve higher accuracy and efficiency than other inverse modeling approaches in studies at larger scales and at small spatial scales for efficient source localization of indoor airborne contaminants.² This should create a context to understand high quantification uncertainties and improve previously reported error bounds.³

Project proposals should include a discussion of the intended approach, criteria for sensor selection, and a strategy for data acquisition. The results of this effort will benefit:

• Regulators and policymakers concerned with methane emissions;
• Measurement technology vendors seeking to improve proprietary algorithms; and
• Operators and environmental organizations seeking to understand the accuracy and limitations of their data, take advantage of rapidly emerging sensor technologies, and democratize access to technology options outside of proprietary algorithms.

Background

Inverse dispersion modeling is an approach that allows operator to estimate total methane emissions from point and area sources and temporal trends when measurements are made continuously. Inverse dispersion models look for answers to the question: What would source emission rates have to be to cause the observed increase in downwind concentrations?

The approach involves measurement of downwind methane concentrations with estimated or measured meteorological parameters (e.g. wind speed, wind direction and air turbulence) to back-calculate emissions rates using atmospheric dispersion models. An emissions rate is estimated by using meteorological parameters and atmospheric models to calculate how a plume would disperse downwind to achieve the measured concentration.

Methods combining wind information with an inverse plume model have been demonstrated on aircraft (Hirst et al., 2013), vehicles (Yacovitch et al., 2015, Rella et al., 2015), and unmanned aerial systems (Nathan et al., 2015), with data often collected at some distance downwind.

As sensor technologies have evolved, some continuous monitoring technologies perform well in detection (as demonstrated through METEC, EDF Methane Detectors Challenge, and Astra). Nevertheless, these technologies have fallen short of the goals for measurement and attribution, as demonstrated by the results seen in flux rates compared with a controlled release and, to a lesser extent, in attribution.

While ARPA-E MONITOR has focused more on the development of cheap, high-accuracy sensor technologies, there has been less of a focus on the development and improvement of inverse flux algorithms. As a result, emission estimates obtained using inverse dispersion modeling demonstrate uncertainty. Previous analysis of emission estimates based on dispersion modeling downwind reports error bounds of +117/−46 percent (Robertson et al., 2017). Another recent study by CIRES that implemented a HYSPLIT inverse dispersion model to estimate emissions found that uncertainties for inverse dispersion modeling in the range of 30-40% (Angevine 2020).

One explanation for the relatively high quantification uncertainties (Golston et al., 2018) associated with continuous methane monitoring during field campaigns is the complexity of the inverse flux algorithms employed by technology providers. Technology vendors employ proprietary plume inversion models or inverse flux algorithms based on site-specific data collected from operators and estimate the size of the leak.

Deliverables

• An open and transparent model to detect, localize and quantify emissions for short-range dispersion effects.
  • Model(s) should include localization and emission rate quantification algorithms that use a machine learning approach.
  • Source code should be made available for public use.
• A peer-reviewed manuscript describing the study design, modeling approach, methods and results.
• A report that summarizes findings and provides a gap assessment and next steps for improving accuracy quantification (e.g. data limitations, hardware / sensing limitations, physical limitations).<sup?4

Cost Sharing

Funding awarded under this announcement may be used by the project team to meet cost share requirements, where eligible, for Area of Interest 4 (AOI 4) in DOE-FOA-0002616.

Project proposals should indicate if the primary applicant intends to apply for funding under AOI 4 of the DOE FOA.

For applicants who plan to apply for funding under AOI 4 of the DOE FOA and are seeking to allocate funding awarded under this announcement toward cost share commitments required under AOI 4 of the DOE FOA, proposals must be accompanied by as much information about the cost sharing budget and resources as is necessary for internal evaluation of CAMS’ ability to fulfill the cost sharing obligation.

Eligibility Criteria

The solicitation is open to public and private entities. The study objectives can be accomplished with an assembled team that can include academics, national laboratories, environmental organizations, technology vendors, etc. A multi-disciplinary research team comprising two or more entities is strongly preferred. The ideal team will combine skills, experience and demonstrated expertise in:

• Working with a range of instrumentation including methane sensors and 3-D sonic anemometers
• AI/ML model development and application
• Atmospheric modeling, dispersion processes and inverse methods
• Field measurement campaigns

RFP Release Date: 09/27/2022
Deadline to submit proposals: 12/06/2022
Anticipated Award Date: 01/30/2023
Anticipated Project Start Date: 03/20/2023
Anticipated Period of Performance: 12 months

Submit proposals to: cams@gti.energy

Available Funding: $450,000 to $500,000, no matching funds are required

Questions on the funding opportunity should be directed to:

Keily Bouchentouf
CAMS Program Administrator
cams@gti.energy

Eligibility

The solicitation is open to public and private entities. The successful contracting group should consist of a team of experts with data analysis and statistical experience and field design and execution experience at oil and gas operations.