Grant: Alliance Grants
University of Alberta
$185,200.00
Assessment of Methane Emission Source Characteristics from Oil and Gas Operations Using Satellite, Weather, Environmental, and Operational Data
FY 2023-24
Summary
The fossil fuel energy sector has contributed approximately 1/3 of the contemporary anthropogenic methane emissions worldwide. However, reliable estimating of methane emissions from oil/gas (O/G) operations is complicated for several reasons: (1) continual detailed field sampling data over an extensive geographical area (e.g., a producing basin with >1000 wells) is unavailable; (2) identifying and quantifying emission sources from large-scale monitoring (e.g., satellite) data is challenging because there is a lack of robust analysis techniques for this ill-posed inverse problem. In fact, there is increasing evidence that bottom-up approaches involving local measurements would often lead to systematic underestimation, as compared to other top-down approaches based on atmospheric measurements. This project aims to develop new techniques for utilizing satellite retrievals of mole fractions of atmospheric methane (XCH4) to quantify methane emission rate, identify emission sources, and assess their characteristics. Maps of time-averaged XCH4, together with other environmental, meteorological, and O/G operational/production data (e.g., well activity and production volumes), are used to infer potential emission sources. Information about other major emission sources in surrounding areas will also be analyzed (e.g., landfills, coal mines, and urban dwellings). Previous studies focused on tracking emissions from one or a few specific emission sources, and those techniques are not suitable for examining emissions from large O/G basins with many sources. This project aims to develop data-driven modelling frameworks for quantifying methane emission sources from O/G operations and assessing the associated characteristics by (1) building machine- or deep-learning models for correlating production data and well activity to methane emission rates; and (2) understanding the relationships between O/G activities and emission source characteristics. The outcomes, including codes, procedures, and specific case studies of major Canadian-producing basins, would be useful for assessing the environmental performance of existing operations and identifying further monitoring opportunities.
Program
Alliance Grants
Recipient
Location
Alberta, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
577118-2022
Name nom
Leung, Juliana J
Department département
Civil and Environmental Engineering
Organization id
9
Institution établissement
University of Alberta
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
185200
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
401
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Climate and atmosphere
Area of application fr
Climat et atmosphère
Research subject code
1503
Research subject group en
Environmental engineering
Research subject group fr
Air and noise pollution
Research subject en
Génie de l'environnement
Research subject fr
Pollution de l'air et pollution par le bruit
Application title
Assessment of Methane Emission Source Characteristics from Oil and Gas Operations Using Satellite, Weather, Environmental, and Operational Data
Keyword
data analytics; deep learning; domain knowledge; GHG Tracking; large-scale monitoring; machine learning; methane emissions; oil and gas operations; satellite data; source characteristics
Application summary
The fossil fuel energy sector has contributed approximately 1/3 of the contemporary anthropogenic methane emissions worldwide. However, reliable estimating of methane emissions from oil/gas (O/G) operations is complicated for several reasons: (1) continual detailed field sampling data over an extensive geographical area (e.g., a producing basin with >1000 wells) is unavailable; (2) identifying and quantifying emission sources from large-scale monitoring (e.g., satellite) data is challenging because there is a lack of robust analysis techniques for this ill-posed inverse problem. In fact, there is increasing evidence that bottom-up approaches involving local measurements would often lead to systematic underestimation, as compared to other top-down approaches based on atmospheric measurements. This project aims to develop new techniques for utilizing satellite retrievals of mole fractions of atmospheric methane (XCH4) to quantify methane emission rate, identify emission sources, and assess their characteristics. Maps of time-averaged XCH4, together with other environmental, meteorological, and O/G operational/production data (e.g., well activity and production volumes), are used to infer potential emission sources. Information about other major emission sources in surrounding areas will also be analyzed (e.g., landfills, coal mines, and urban dwellings). Previous studies focused on tracking emissions from one or a few specific emission sources, and those techniques are not suitable for examining emissions from large O/G basins with many sources. This project aims to develop data-driven modelling frameworks for quantifying methane emission sources from O/G operations and assessing the associated characteristics by (1) building machine- or deep-learning models for correlating production data and well activity to methane emission rates; and (2) understanding the relationships between O/G activities and emission source characteristics. The outcomes, including codes, procedures, and specific case studies of major Canadian-producing basins, would be useful for assessing the environmental performance of existing operations and identifying further monitoring opportunities.
