Constrained CO₂EOR: optimization considering impurities, CO₂EOR type, volume, and oil recovery vs CO₂ storage

Pham, Quynh Chau (2025) Constrained CO₂EOR: optimization considering impurities, CO₂EOR type, volume, and oil recovery vs CO₂ storage. Masters thesis, Memorial University of Newfoundland.

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	[English] PDF (Appendix B - Phyton code developed for different Machine Learning techniques) - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (157kB)
	[English] PDF (Appendix C- CMG models) - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (854kB)
	[English] PDF (Appendix D- Running optimisation using CMOST AI module in CMG) - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (214kB)

Abstract

Optimizing CO₂ injection in offshore Enhanced Oil Recovery (EOR) operations aims to increase oil production while capturing CO₂, aligning with global carbon capture and storage (CCS) goals. CO₂ dissolves in oil, reducing its viscosity and making it more extractable, but offshore sites face unique challenges, such as limited CO₂ supply, high storage costs, and technical constraints. Various EOR methods can address these limitations: Carbonated Water Injection (CWI) dissolves CO₂ in water, reducing the total CO₂ required and enhancing oil recovery while maximizing carbon retention. Another approach, targeted CO₂ flooding in specific reservoir blocks, concentrates CO₂ where it's most effective, making efficient use of limited supplies. Water-Alternating-Gas (WAG) injection alternates CO₂ and water to manage gas mobility and improve the efficiency of oil displacement, allowing for strategic use of CO₂ without full-field application. This study analyzes the optimization of constrained volumes of varying CO2 concentrations and impurities considering different oil types and reservoir conditions. It examines how impurities impact CO₂ injection and retention, and how different oil types and reservoir characteristics respond to specific injection strategies. This approach enables offshore EOR to balance enhanced oil recovery with carbon storage objectives, optimizing both CO₂ usage efficiency and emission reductions to support sustainable energy goals. Current carbon capture technologies are imperfect, resulting in impurities within the CO₂ stream that affect the Minimum Miscibility Pressure (MMP) needed for effective oil recovery. This study investigates how these impurities influence the MMP in oil and gas mixtures using slimtube simulations across a range of CO₂ sources and capture technologies. While prior studies often focus on pure or low (<5 %) CO₂ concentrations, this research explores a broader range, examining CO₂ concentrations from 0 % to 100 % to fill an existing gap in the literature. The study reveals that impurities depend on the CO₂ source: for example, CH₄ is common in CO₂ from natural gas streams, while O₂ and N₂ are prevalent in CO₂ from flue gas. The results indicate that CO₂ mixed with natural gas effectively lowers MMP, enhancing miscibility, whereas impurities in flue gas (like O₂ and N₂) raise the MMP more significantly, as N₂ requires particularly high pressures to reach miscibility compared to CO₂. This work deepens understanding of the impacts of different CO₂ sources and impurity levels on MMP, contributing valuable insights for optimizing CO₂-based enhanced oil recovery processes. Understanding the Minimum Miscibility Pressure (MMP) between oil and gas mixtures is essential for accurately predicting reservoir performance, particularly in enhanced oil recovery (EOR) processes. However, no single Equation of State (EOS) consistently predicts fluid properties across all conditions. Machine Learning (ML) has become a valuable tool for estimating MMP, yet prior studies have often faced limitations due to small data sets and restricted ranges of CO₂ mole percentages. This study develops a Machine Learning model using Deep Learning and k-fold Cross Validation techniques, improving the size, accuracy, and range of the data, particularly for CO₂ concentrations. Additionally, a sensitivity analysis is performed to assess the influence of various input parameters, such as reservoir characteristics and oil and gas properties, on MMP. The study finds that key factors impacting MMP include reservoir temperature and the concentrations of CO₂ and methane (C₁) in the gas phase. Higher temperatures, heavier oils, a greater proportion of volatile and intermediate components in the oil, and higher concentrations of C₁ and N₂ in the gas phase all lead to higher MMP. In contrast, the presence of CO₂ and H₂S, especially CO₂, significantly lowers the MMP, aiding oil recovery. The study emphasizes how Deep Learning approaches can enhance the accuracy and range of MMP predictions, improving the optimization of EOR strategies by providing better insights into fluid dynamics. Previous studies in Enhanced Oil Recovery (EOR) and Carbon Capture, Utilization, and Storage (CCUS) have largely operated under the assumption of unlimited CO₂ supply, failing to adequately address the constraints associated with CO₂ availability, especially in offshore reservoirs. This oversight is significant, as the capacity for CO₂ storage and the ability to conduct effective EOR can be severely limited by the volume of CO₂ that can be feasibly captured and injected. Moreover, most EOR research tends to emphasize incremental oil recovery metrics while neglecting the financial impacts of carbon emissions, which can significantly influence project feasibility and sustainability. This study investigates the joint optimization of oil recovery and carbon storage by considering both the economic value of produced oil and the benefits of CO₂ tax credits, assigning equal weight to each factor with a 50:50 ratio. It examines various oil types (light, medium, and heavy) and reservoir conditions, including CO₂-EOR methods such as Water-Alternating-Gas (WAG), Carbonated Water Injection (CWI), and enriched-WAG, under different CO₂ constraints, impurities, and reservoir characteristics like stratification, crossflow, temperature, pressure, and permeability. The simulations use GMG, and optimization is performed using Multi-Objective Particle Swarm Optimization (MOPSO). The results show that CWI is the most effective method under CO₂ constraints for stratified reservoirs, whether crossflow is present or not. However, CO₂ storage is significantly lower in the CWI case. Among the factors influencing optimization, reservoir pressure has the most significant effect on the overall objectives, while permeability is the key factor in determining the oil recovery factor across all three CO₂-EOR methods. EOR studies typically focus on incremental oil recovery (without considering carbon pricing), whereas Carbon Capture, Utilisation and Storage (CCUS) prioritizes maximizing CO₂ storage (assuming an infinite CO₂ supply). The joint optimization of oil recovery factor and CO₂ storage varies based on phase behavior related to different oil types and conditions (EOR methods, the available amount and characteristics of injected gas, and reservoir properties), but also on economic factors such as the price of produced oil and the value of CO₂ tax credits. By incorporating all these factors into simulations and applying modern machine learning techniques, we can better optimize the balance between enhancing oil recovery and reducing carbon intensity during the energy transition era. Machine learning models can simulate and predict outcomes for various reservoir conditions and economic scenarios, enabling more informed decisions on the selection of the most appropriate EOR technique, the optimal amount of CO₂ to inject and also the precise conditions under which oil recovery and CO₂ storage can be balanced most effectively for a specific reservoir.

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