Open sourcing code to improve methane emission localisation and quantification

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.

Shell has a target to maintain methane emissions intensity below 0.2% and to near zero methane emissions by 2030 and we are enhancing our reporting of methane emissions through the implementation of the Oil and Gas Methane Partnership (OGMP) 2.0 reporting framework in our operated and non-operated assets. Continuous, wide area monitoring systems are studied as an option to provide early warning of leaks or prove absence of emissions. In house technology development is strengthened by selective external collaboration to produce a deployable solution that simplifies the effort for assets to pinpoint their emissions to address them.

The Python Emission Localisation and Quantification (pyELQ) code aims to maximise effective use of existing measurement data, especially from continuous monitoring solutions. The code has been developed to detect, localise, and quantify methane emissions from concentration and wind measurements. It can be used in combination with point or beam sensors that are placed strategically on an area of interest.

The algorithms in the pyELQ code are based a Bayesian statistics framework. pyELQ can ingest long-term concentration and wind data, and it performs an inversion to predict the likely intensity and location of persistent methane sources.

The goal is to achieve a credible quantification estimate of the methane emissions from an area of interest that matches the measured concentration data. The predictions 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.

We have tested our code under controlled conditions as well as in operating oil and gas facilities. The information on the intensity and the approximate location of methane emission sources provided by pyELQ can help operators with more efficient identification and quantification of unexpected methane sources, in order to reduce emissions accordingly. The pyELQ code is being made available in an open-source environment, to support various assets in their quest to reduce methane emissions.

IJzermans, R., Jones, M., Weidmann, D. et al. "Long-term continuous monitoring of methane emissions at an oil and gas facility using a multi-open-path laser dispersion spectrometer." Sci Rep 14, 623 (2024). (https://doi.org/10.1038/s41598-023-50081-9

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Weidmann, D., Hirst, B. et al. "Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach." ACS Earth and Space Chemistry 2022 6 (9), 2190-2198 (https://doi.org/10.1021/acsearthspacechem.2c00093

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Open source also offers a way to collaborate and support integration across value chains supporting rapid innovation. Increased interoperability helps companies easily co-create solutions for frictionless flow of ideas and data. Furthermore, by enabling market access for smaller entities, open source can support rapid innovation. Shell is already involved in several open-source communities focused on various industry sectors. For example, we contributed code from the data ingestion element of our sensor intelligence platform called RTDIP. As customers and suppliers in the energy sector work ever more closely together, Shell sees a clear need to foster this collaboration to support the Energy Transition.

The pyELQ code has been made available in an open-source environment, to support industrial facilities to reduce methane emissions. This enables vendors to reach out and test their solution in combination with pyELQ, and Shell is open to collaborate on implementation of pyELQ into their offerings.

This code contribution marks another step in Shell’s and the energy industries’ journey towards shared, open-source development of tools that help enable better data management.

GitHub - sede-open/pyELQ: 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

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User guide: pyELQ Python Emission Localization and Quantification (sede-open.github.io

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