pyELQ: python Emission Localization and Quantification

[bhakar7]

• Name
  pyELQ: python Emission Localization and Quantification

• Mission statement
  The python Emission Localization and Quantification (pyELQ) code aims to maximize effective use of existing measurement data, especially from continuous monitoring solutions. The code has been developed to detect, localize, and quantify methane emissions from concentration and wind measurements.

• Description (what it does, why it is valuable, origin and history)
  The algorithms in the pyELQ code are based on a Bayesian modelling framework. pyELQ can ingest long-term concentration and wind data, and invert to estimate the likely strengths and locations of persistent methane sources. The goal is to arrive at a plausible estimate of methane emissions from an area of interest that matches the measured data well. The estimates from pyELQ come with uncertainty ranges that are representative of probability density functions sampled by a Markov Chain Monte Carlo method. Time series of varying length can be processed by pyELQ: in general, the Bayesian inversion leads to a more constrained solution if more high-precision measurement data is available, from a larger range of wind directions. We have tested our code under controlled conditions as well as in operating oil and gas facilities. The information on the strength and the approximate location of methane emission sources provided by pyELQ can help operators with more efficient identification and quantification of (unexpected) methane sources, to start appropriate mitigating actions accordingly. The pyELQ code is being made available in an open-source environment, to support various assets in their quest to reduce methane emissions.

• Is this a new project/working group/special interest group or an existing one?
  This is an existing project within Shell. The project team working on the code consists of 5 people.

• Current lead(s)
  Bas van de Kerkhof, Matthew Jones, David Randell

• Sponsoring organization(s), along with any other key contributing individuals and/or organizations
  Shell

• Detail any existing community infrastructure, including:
  None

• GitHub/GitLab, or other location where the code is hosted
  https://github.com/sede-open/pyELQ

• Website and/or docs
  https://sede-open.github.io/pyELQ/

• Communication channels (such as Mailing lists, Slack, IRC)
  Internal

• Social Media Accounts
  None

• Are there any specific infrastructure needs or requests outside of what is provided normally by LF Energy (please refer to the lifecycle for project benefits)? If so, please detail them.
  No.

• Why would this be a good candidate for inclusion in LF Energy?
  Making the code generally available within the industry will allow vendors of sensing technology to incorporate it within their offerings, potentially improving the capabilities available to all companies within the energy sector. The code will hopefully also benefit from ideas and developments contributed by researchers in the community, improving the available capabilities for all.

• How would this benefit from inclusion in LF Energy?
  Adding pyELQ to LF Energy will help to increase awareness of the functionality outside of Shell, to encourage collaboration with others working in the industry, facilitating continuous improvement in the capabilities available for methane emissions quantification.

• Provide a statement on alignment with the mission in the LF Energy charter.
  Making the code available via LF Energy will help to create transparency around approaches to methane emissions quantification, and help to speed up development of new approaches.

• What specific need does this project/working group/special interest group address?
  The code provides an approach for locating and quantifying methane emissions, using measured concentration data and local atmospheric information. It aims to maximize the effective use of existing measured data, especially from continuous monitoring systems deployed to measure methane concentrations at production facilities. The code uses a Bayesian inversion algorithm to generate credible emissions estimates, with associated uncertainty ranges, which give a good match to the observed concentration data. The results generated by the code can help operators with more efficient identification of unknown sources, and more accurate quantification of emission rates from both known and unknown sources.

• Describe how this project/working group/special interest group impacts the energy industry.
  Methane is a potent greenhouse gas. When it is released into the atmosphere it has a much higher global warming impact than CO2. Reducing methane emissions is one of the most effective near-term actions to keep the more ambitious 1.5°C goal of the Paris Agreement within reach according to the United Nations Environment Programme (UNEP). Efforts to address climate change therefore require the industry to reduce both deliberate and unintended methane emissions from production to the final consumer. To help reduce likelihood of late detection of fugitive methane emissions, continuous, wide area monitoring of methane emissions is an option to provide early warning of leaks or prove their absence. Point sensor networks and beam sensor network technologies that continuously monitor methane emissions can present innovative approaches for detection, localization, and quantification of emissions.
  When combined with suitable measured concentration and atmospheric data, pyELQ allows operators in the energy industry to obtain better estimates of their methane emissions More accurate assessment of methane emissions from operating sites can drive better targeting of remediation actions, and thus more effective action to rapidly reduce the emissions associated with the oil & gas industry.

• Describe how this project/working group/special interest group intersects with other LF Energy projects/working groups/special interest groups.
  The pyELQ project is aligned with LFE's mission statement, which is to 'Support society and the planet'. It would be a right fit for the Emission Landscape, primarily 'Emission Observation and Modelling' section, other similar projects which are a part this landscape are Methane Source Finder by NASA JPL & Methane detection from hyperspectral imagery.

• Who are the potential benefactors of this project/working group/special interest group?
  Operators of facilities that have a need to continuously monitor their (methane) emissions, e.g. oil & gas producers, renewable natural gas plants etc. Also, sensor vendors and data analytics companies who are looking to develop solutions for the industry or adapt the inversion engine for application in other fields.

• What other organizations in the world should be interested in this project/working group/special interest group?
  Sensor vendors, oil and gas companies.

• Plan for growing in maturity if accepted within LF Energy
  Shell plans to further test the technology in combination with sensor vendor solutions. Vendors of sensor solutions are encouraged to reach out and test their solution in combination with pyELQ. Inclusion in LFE could grow the community by encouraging technology improvements and focus on high code standards for open-sourced code.

Questions for Technical Projects ONLY

• Project license
  Apache 2.0

• Is the project's code available now?
  If so, provide a link to the code location. https://github.com/sede-open/pyELQ

• Does this project have ongoing public (or private) technical meetings?
  Yes (private technical meetings), but ad-hoc.

• Do this project's community venues have a code of conduct?
  If so, what is it? Shell internal code of conduct.

• Describe the project's leadership team and decision-making process.
  3 admin members, 2 additional contributors, ad-hoc meetings, consensus-based decision-making.

• Does this project have public governance (more than just one organization)?
  No

• Does this project have a development schedule and/or release schedule?
  No, updated ad-hoc when required, or when a new feature is implemented.

• Does this project have dependencies on other open-source projects? Which ones?
  Python packages described in the pyproject.toml file. Mkdocs for documentation generation.

• Describe the project's documentation.
  Documentation has been built and deployed using Material for Mkdocs. This can be found at this link https://sede-open.github.io/pyELQ/ Additionally, the repo is maintained to contain the required files (LICENSE, CONTRIBUTING, etc.) with developer guidelines on how to get started with the project.

• Describe any trademarks associated with the project.
  None.

• Do you have a project roadmap? please attach [Are this project's roadmap and meeting minutes public posted?]
  There is no specific roadmap currently associated with the project.

• Does this project have a legal entity and/or registered trademarks?
  No.

• Has this project been announced or promoted in any press?
  https://www.shell.com/what-we-do/digitalisation/collaboration-and-open-innovation/open-sourcing-code-to-improve-methane-emission-localisation-and-quantification.html

• Does this project compete with other open-source projects or commercial products?
  No.

[jmertic]

Hi @bhakar7 - I see this issue had the title changed and description removed. Can you confirm if this was intentional or an accident? Thanks!

[bhakar7]

Hi @jmertic it was intentional. I have corrected the description now. Can we add this to upcoming TAC?

[jmertic]

No worries - we will start LF Onboarding process and once done get it added to the next TAC meeting. Thanks!

[bhakar7]

Hi @jmertic has this project been added to the upcoming TAC on 9th Oct?

[jmertic]

Hi @bhakar7 - as per the project lifecycle requirements for the Sandbox stage ( https://tac.lfenergy.org/process/lifecycle.html#requirements ), we will need to complete the LF Onboarding process which includes the technical charter and transferring accounts to the LF before we can bring it to the TAC. @yarille I believe is working with you on these next steps. Thanks!

[yarille]

Approved via TAC vote on 11/19.