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Improving the Oil Production Greenhouse Gas Emissions Estimator (Opgee) : = Validation, Modeling, and System Design.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving the Oil Production Greenhouse Gas Emissions Estimator (Opgee) :/
其他題名:	Validation, Modeling, and System Design.
作者:	Long, Wennan.
面頁冊數:	1 online resource (227 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
標題:	Water resources management. -
電子資源:	click for full text (PQDT)
ISBN:	9798381019964

Improving the Oil Production Greenhouse Gas Emissions Estimator (Opgee) : = Validation, Modeling, and System Design.
Long, Wennan.

Improving the Oil Production Greenhouse Gas Emissions Estimator (Opgee) :Validation, Modeling, and System Design. - 1 online resource (227 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

The global upstream greenhouse gas (GHG) emissions from the oil and gas industry, encompassing the extraction, processing, and delivery of crude petroleum to the refinery gates, account for about 15% of the total energy-related emissions. This represents a distressing 5.1 billion tonnes of GHG emissions [49]. These emissions, however, exhibit significant disparities across countries and operations due to varying Carbon Intensity (CI). It's observed that a country with high CI can produce emissions up to four times higher than those of a low CI country. Furthermore, operations involving significant flaring and onsite combustion for producing heavy oils or oil sands can show emissions up to ten times greater than fields without such practices. This study uses an in-house simulator, the Oil Production Greenhouse gas Emission Estimator (OPGEE), to analyze these emissions.Endorsed by regulators like the California Air Resources Board's Low-Carbon Fuel Standard (LCFS), operators such as Chevron, consulting firms like McKinsey Energy Insights, financial institutes like S&P Global Platts, international organizations like the International Energy Agency (IEA), and NGOs like Rocky Mountain Institute's OCI+ and The Archie Initiative, OPGEE has proven its worth. This engineering-based, bottom-up lifecycle assessment (LCA) tool was designed to simulate upstream GHG emissions across four LCA stages: Exploration & Development, Production, Surface Processing, and Transportation [15]. It encompasses various production methods, including waterflooding, gasflooding, steamflooding, and gas lifting, as well as common surface operations like venting, flaring, and water & gas reinjection or disposal. This variety allows for comprehensive and precise modeling of various scenarios, enhancing our understanding of the upstream GHG emissions landscape.Each of these production methods necessitates substantial data input. While OPGEE provides default values for most required variables, these inputs and default values are primarily sourced from published literature. An inherent limitation of this approach is the potential disparity between the estimated carbon intensity (CI) and the actual emissions measured by the operators. To address this, Chapter 2 conducts a rigorous validation of OPGEE's steamflooding and waterflooding functionalities, as well as the associated processes. This validation is facilitated by the collection of detailed input data in collaboration with an operator, enabling a comparison of these production methods with selected fields operating in California's San Joaquin Valley. The results reveal approximately a 10% CI difference between OPGEE estimates and actual emissions. Notably, nearly 50% of the initial discrepancies can be traced back to OPGEE's usage of default literature values instead of actual field data. It is further discovered that differences in system boundaries and accounting methods account for about 30% of the variance. Additionally, the potential for mitigation pathways, such as incorporating solar photovoltaics into oil and gas operations, is examined, demonstrating a significant impact on CI.Upon model validation, the next step is to predict future emissions based on the current operation. Chapter 3 introduces a semi-conceptual model known as "Pattern-Based Modelling". This model allows history matching and future prediction for large-scale thermal enhanced oil recovery (TEOR) operations. Then, OPGEE generates a comprehensive CI profile of field production history. This chapter presents two case studies: the well-documented Kern River oilfield in California, U.S., and the relatively new Mukhaizna oilfield in Oman. The overall CI for Kern River varies between 10 to 40 gCO2eq./MJ over its operational lifespan, and the projected weighted CI until 2030 stays around 23 gCO2eq./MJ. The Mukhaizna field, however, exhibits a more narrow emission spectrum, ranging from 6 to 16 gCO2eq./MJ, with a future weighted CI until 2035 of approximately 14 gCO2eq./MJ.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381019964Subjects--Topical Terms:

1178858
Water resources management.
Index Terms--Genre/Form:

554714
Electronic books.

Improving the Oil Production Greenhouse Gas Emissions Estimator (Opgee) : = Validation, Modeling, and System Design.
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520 $a The global upstream greenhouse gas (GHG) emissions from the oil and gas industry, encompassing the extraction, processing, and delivery of crude petroleum to the refinery gates, account for about 15% of the total energy-related emissions. This represents a distressing 5.1 billion tonnes of GHG emissions [49]. These emissions, however, exhibit significant disparities across countries and operations due to varying Carbon Intensity (CI). It's observed that a country with high CI can produce emissions up to four times higher than those of a low CI country. Furthermore, operations involving significant flaring and onsite combustion for producing heavy oils or oil sands can show emissions up to ten times greater than fields without such practices. This study uses an in-house simulator, the Oil Production Greenhouse gas Emission Estimator (OPGEE), to analyze these emissions.Endorsed by regulators like the California Air Resources Board's Low-Carbon Fuel Standard (LCFS), operators such as Chevron, consulting firms like McKinsey Energy Insights, financial institutes like S&P Global Platts, international organizations like the International Energy Agency (IEA), and NGOs like Rocky Mountain Institute's OCI+ and The Archie Initiative, OPGEE has proven its worth. This engineering-based, bottom-up lifecycle assessment (LCA) tool was designed to simulate upstream GHG emissions across four LCA stages: Exploration & Development, Production, Surface Processing, and Transportation [15]. It encompasses various production methods, including waterflooding, gasflooding, steamflooding, and gas lifting, as well as common surface operations like venting, flaring, and water & gas reinjection or disposal. This variety allows for comprehensive and precise modeling of various scenarios, enhancing our understanding of the upstream GHG emissions landscape.Each of these production methods necessitates substantial data input. While OPGEE provides default values for most required variables, these inputs and default values are primarily sourced from published literature. An inherent limitation of this approach is the potential disparity between the estimated carbon intensity (CI) and the actual emissions measured by the operators. To address this, Chapter 2 conducts a rigorous validation of OPGEE's steamflooding and waterflooding functionalities, as well as the associated processes. This validation is facilitated by the collection of detailed input data in collaboration with an operator, enabling a comparison of these production methods with selected fields operating in California's San Joaquin Valley. The results reveal approximately a 10% CI difference between OPGEE estimates and actual emissions. Notably, nearly 50% of the initial discrepancies can be traced back to OPGEE's usage of default literature values instead of actual field data. It is further discovered that differences in system boundaries and accounting methods account for about 30% of the variance. Additionally, the potential for mitigation pathways, such as incorporating solar photovoltaics into oil and gas operations, is examined, demonstrating a significant impact on CI.Upon model validation, the next step is to predict future emissions based on the current operation. Chapter 3 introduces a semi-conceptual model known as "Pattern-Based Modelling". This model allows history matching and future prediction for large-scale thermal enhanced oil recovery (TEOR) operations. Then, OPGEE generates a comprehensive CI profile of field production history. This chapter presents two case studies: the well-documented Kern River oilfield in California, U.S., and the relatively new Mukhaizna oilfield in Oman. The overall CI for Kern River varies between 10 to 40 gCO2eq./MJ over its operational lifespan, and the projected weighted CI until 2030 stays around 23 gCO2eq./MJ. The Mukhaizna field, however, exhibits a more narrow emission spectrum, ranging from 6 to 16 gCO2eq./MJ, with a future weighted CI until 2035 of approximately 14 gCO2eq./MJ.
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