Improving the oil production greenhouse gas emissions estimator (OPGEE) : validation, modeling, and system design
Abstract/Contents
- Abstract
- The oil and gas industry is responsible for significant upstream greenhouse gas (GHG) emissions, amounting to approximately 5.1 billion tonnes. This research, employing the Oil Production Greenhouse gas Emission Estimator (OPGEE), seeks to comprehend the intricate landscape of these emissions. One key discovery is the vast discrepancy in Carbon Intensity (CI) across different countries and operations. Interestingly, high CI countries can have emissions quadruple those of lower CI countries, emphasizing the importance of this metric. A critical examination of OPGEE's output versus actual emissions shows a disparity. This arises from OPGEE's reliance on default literature values rather than specific field data. In a detailed exploration of this issue, it's found that nearly half of the discrepancies could be attributed to this reliance. Additionally, variations in system boundaries and accounting methods account for another 30% of the differences. This investigation paves the way for refining emission estimations, an effort augmented by the study of potential mitigation paths like the integration of solar photovoltaics into oil and gas operations. Further enriching this research is the introduction of a novel "Pattern-Based Modelling" technique. This model provides a mechanism to history match and project future emissions for vast thermal enhanced oil recovery operations. Two in-depth case studies - the well-documented Kern River oilfield in California and the emerging Mukhaizna oilfield in Oman - showcase this model's utility. A significant advancement in this study is the evolution of OPGEE from an Excel-based platform to a more dynamic Python-based system, termed OPGEE v4. This transformation addresses computational limitations and introduces a suite of new features, increasing efficiency and precision. Comparative studies between OPGEE v3 and v4 highlight the stark improvements made in the latest version, emphasizing its readiness to handle complex global oil field datasets. In essence, this dissertation offers a three-fold contribution: a rigorous validation of OPGEE against actual field data, the pioneering of "Pattern-Based Modelling" for predicting entire field lifespan emissions, and the innovative transformation of OPGEE to a Python-centric platform, enhancing its capabilities.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Long, Wennan |
|---|---|
| Degree supervisor | Brandt, Adam |
| Thesis advisor | Brandt, Adam |
| Thesis advisor | Azevedo, Ines |
| Thesis advisor | Kovscek, Anthony |
| Degree committee member | Azevedo, Ines |
| Degree committee member | Kovscek, Anthony |
| Associated with | Stanford Doerr School of Sustainability |
| Associated with | Stanford University, Department of Energy Resources Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Wennan Long. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/qt694nn1439 |
Access conditions
- Copyright
- © 2023 by Wennan Long
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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