[CCoE Notice] Dissertation Announcement: Abdulrahman Abdulwarith, "Development of New Techniques for De-Risking the CO2 EOR and Sequestration"

[Greenwell, Stephen J]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Development of New Techniques for De-Risking the CO2 EOR and Sequestration
Abdulrahman Abdulwarith
April 28, 2025, 10 a.m. to 1 p.m. (CST)
Location: Petroleum Eng. Bldg. (ERP 9) Room 124,or Virtual Microsoft Teams Link<https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_NmRkYTQ4YzUtNzdhYS00NTFmLWE5OGMtNWVhYTYxYmM0ZGUz*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*22143c812b-0f77-4252-b039-9788badceee8*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!Fo8Cc1Ivc55C7WffgbI-K7Nqlpt3nyz8-l4y-7WVQ8wgGCQsqF2SIIPYITVZoolsxFAgqLApH0BKmP2Fw7Ix-rGUWRE$ >
Committee Chair:
Birol Dindoruk, Ph.D.
Committee Members:
Mohamed Y. Soliman, Ph.D. | Guan Qin, Ph.D. | Kyung Jae Lee, Ph.D. | Dengen Zhou, Ph.D.
Abstract
This dissertation explores strategies to reduce greenhouse gas emissions in the oil and gas industry by producing low-carbon oil through CO₂ Enhanced Oil Recovery (EOR) integrated with Carbon Capture, Utilization, and Storage (CCUS). As global efforts to mitigate climate change intensify, industry must adopt technologies that lower emissions while sustaining energy output. CO2-EOR, which involves injecting CO2 into mature oil fields to enhance oil recovery and store CO2, offers a promising approach. However, CO2-EOR faces technical, economic, and operational uncertainties that need to be resolved to ensure its success.
This research develops methodologies to de-risk CO₂-EOR operations by optimizing both oil recovery and CO₂ storage. It addresses key challenges such as accurately screening potential reservoirs, optimizing injection strategies, and managing interconnectivity between injection and production wells to minimize CO2 leakage. Advanced screening methods, proxy predictive models and fast reservoir simulation approach are introduced for rapid assessment of CO2-EOR potential, leveraging data from mature waterflooded reservoirs to identify suitable CO2 sequestration sites.
To enhance simulation efficiency, this study integrates physics-based models with machine learning, enabling faster and more cost-effective evaluations of complex geological and operational conditions compared to traditional approaches. This research also explores CO2-EOR in different reservoirs, such as Residual Oil Zones (ROZs) and shale formations, optimizing design parameters to maximize oil recovery and CO2 storage. By incorporating dynamic CO₂ PVT behavior into traditional simulations, this work offers an efficient alternative to full compositional models, supporting long-term planning and risk management.
These findings support CO₂ sequestration efforts by providing a framework to evaluate CO₂-EOR feasibility, enhance injection performance predictions, and accelerate simulations. These advancements enable the oil and gas industry to implement low-carbon technologies effectively, supporting global climate objectives while maximizing resource recovery. The proposed approaches can also be implemented in mature and/or marginal reservoirs, and even in transition zones, expanding their practical applicability. Overall, the findings lay a foundation for future research and practical applications in optimizing CO₂-EOR and CCUS projects, advancing both the engineering field and the energy transition process.
[Engineered For What's Next]


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