In 2019 I was hired by the Energy Institute as a Research Scientist with the Zimmerle group, having previously completed a Ph.D. at King’s College London and worked as a postdoctoral researcher at Cornell, Cambridge, and Princeton Universities. At CSU, I lead research efforts related to detection of underground natural gas leaks, interface with other research groups using the METEC test facility, and lead field measurement campaigns to better quantify methane emissions from oil and gas operations.
As a research scholar at Princeton University, I measured methane emissions from oil and gas operations in West Virginia, Pennsylvania, and in the UK. With the aim of quantifying fugitive methane emissions from operational and abandoned oil and gas facilities, I was responsible for planning and conducting field campaigns and then analyzing the data to derive emission estimates. In working at Cambridge University, I estimated methane and nitrous oxide emissions from sources significant to national greenhouse gas inventories. This work resulted in papers that provide evidence to better understand the biogeochemistry of the carbon and nitrogen cycles. My work at Cornell University incorporated biogeochemical processes describing the formation of ammonia into the Community Earth System Model, and this work is currently being used to investigate the environmental effects of ammonia emissions from intensive agriculture in the US and China.
My research focuses on policy-relevant conservation science and, in particular, the formation, transport, and environmental effects of reactive nitrogen and greenhouse gases from natural and anthropogenic sources. I am interested in estimating the size, location, and effects of trace gas emissions, so that the largest emission sources can be identified and mitigation strategies can be developed. My ultimate aim is to balance the environmental costs of food and energy supply to the potentially catastrophic environmental effects of trace gas emissions.
A combined team from Colorado State University (prime, PI Zimmerle) and Pennsylvania State University (sub, co-PI, Davis) will conduct an intensive review of the disagreements between top-down (TD) and bottom-up (BU) methods for estimating emissions in an oil and gas production basin. Rather than construct ‘yet another’ BU model for comparison, the team proposes a deep dive into the uncertainties of both TD and BU approaches to better understand how to improve both the models and comparisons between them. The team expects to refine the understanding of what data is available, what data has the most impact on results, what is missing, and what approaches would likely be the most successful approaches to improvement.
The proposed project is designed to have key experts in BU and TD modeling consider these questions, supported by a graduate student for modeling experiments. The team is an intentional pairing of one of the leading BU modeling teams (CSU) with one of the leading TD modeling teams (PSU), to bring diverse viewpoints to the table. We disagree on some aspects … and that is exactly the point of combining these two teams. Additionally, we propose a project structure where CAMS’ technical representatives are integrated into working sessions to inject in-depth industry knowledge into the debate.
We are not proposing several aspects which have been part of similar studies performed by both teams. We will not build another ‘more complete’ model of a basin. Instead, we propose to do ‘just enough’ modeling to investigate key questions. To that end, we will hybridize data from the Permian basin with a very detailed data set from the Denver-Julesburg (DJ) basin. We will also not produce another ‘TD/BU disagree by X%’ result; that has been done N times before. Our goal is to understand why the disagreement is happening, through questioning and experiment, and then to outline what is needed to reduce the disagreement.
Deliverables from the project will include improved understanding of why & what, outlined above, and the co-learning of the team and participating technical representatives during working sessions. As requested, this will substantially develop a framework for better estimating emissions intensity, verified by TD/BU comparisons.
Working with a diverse set of BU and TD data (measurements, mapping data, industry data, etc.), the study team will:
- Analyze the usefulness and value of available input data sources through crafted simulations and comparisons.
- Evaluate assumptions in identified TD methods.
- Document one or more methods and quality control procedures to integrate each data set into the BU or TD model.
- Define required meta data, resolution, and coding of data needed to improve the usefulness of TD or BU estimates (both for future use). These data likely exist, but are not currently accessible. We will provide recommendations on which additional data (or data fields) are most likely to have a substantive impact on TD/BU reconciliation, considering technical, confidentiality and licensing concerns.
- Identify key methods and metrics to make high quality TD/BU comparisons, including quality control procedures. These methods and metrics will include both what is possible with today’s data and what could be possible with expected future data.
In summary, we propose a thorough and inter-disciplinary approach to improve TD/BU comparisons, providing a roadmap to comparisons that are sufficiently robust to guide policy decisions at company, state, and federal levels.
The proposed work is to demonstrate that (a) high frequency sampling can be used to create inventory emissions estimates that accurately represent emissions in a basin; and (b) the proposed method can be replicated in other basins. The result will provide the methodological underpinning required by current and contemplated certified gas programs (e.g. Veritas™, MiQ™, etc.) and key field processes required for any of the Integrated Methane Monitoring Platforms which may be developed as a result of AOI4 under this FOA.
The team is proposing to complete the method development and validation in the Denver-Julesburg Basin (DJ), in northeastern Colorado. In comparison with other basins in the USA, the DJ has several unique characteristics which make it an ideal location for top-down/bottom-up (TD/BU) reconciliation of emissions. These include: (a) prior work in the basin has developed an excellent starting point for BU inventories; (b) operators in the basin have been active in emissions measurement, emissions reduction, and innovative reporting; and (c) the State of Colorado has a strong interest in DJ emissions which contribute to an ozone non-attainment zone. The DJ is complex, intermixing oil and gas operations with agriculture, waste, urban, and peri-urban emission sources. The combination makes the DJ basin an excellent location to demonstrate that TD and BU emissions estimates can be reconciled in a complex basin.
While method development will focus on the DJ, the team will also add a smaller reconciliation project in a second basin – the Upper Green River (UGR) basin in Wyoming as a demonstration that the methods developed in the DJ will be applicable elsewhere. The Jonah UGR also represents a unique opportunity to demonstrate reconciliation.
- Develop a dynamic BU inventory model in the Methane Emissions Estimation Tool (MEET) that captures both average emissions and emissions variability.
- Demonstrate methods to tune BU inventories using high-frequency top-down sampling by a well-characterized sampling method.
- Compare results from the tuned BU model to all-in basin emissions estimates from a network of tower-based sensors to check BU model accuracy.
- Demonstrate that the method utilized in the DJ basin can be applied in another basin, potentially using a different top-down sampling method, to produce regionally specific emissions models.
- Capture the learnings and process from these objectives as a replicable method for any production basin.
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