Estimating oil recovery factor and CO2 storage capacity for CO2-EOR projects using Non-Linear Regression Analysis

Abstract

Carbon Dioxide Enhanced Oil Recovery (CO 2 -EOR) stands as a well-established technique involving the injection of carbon dioxide into oil reservoirs to increase oil production. This approach has garnered substantial attention owing to its dual advantage of augmenting oil recovery while concurrently providing the potential for carbon storage. As CO 2 permeates the reservoir upon injection, it effectively reduces the oil's viscosity, rendering it more mobile and facilitating its displacement from rock formations. Additionally, the ability of CO 2 to dissolve in oil induces swelling, thereby reducing the interfacial tension between CO 2 and oil. Furthermore, CO 2 injection contributes to sustaining or elevating reservoir pressure, counteracting the typical pressure decline during production and directing the oil towards production wells. Oil reservoirs present ideal storage sites for CO 2 due to their distinctive geological characteristics, including porosity, permeability, and trapping mechanisms. Various mechanisms exist for storing CO 2 once injected into oil reservoirs. For example, CO 2 can dissolve in the immobile oil and water phases in these reservoirs. In addition, CO 2 has the potential to react with the rock surfaces, establishing a physical bond that effectively traps the CO 2 . Furthermore, the reservoir can store CO 2 as a distinct phase in its pore spaces, with the caprock serving as a seal to prevent CO 2 escape. These trapping mechanisms, coupled with the stability of oil reservoirs, offer a secure and dependable storage solution for substantial volumes of CO 2 . This contributes significantly to mitigating greenhouse gas emissions and addressing the challenges of climate change. Estimating the oil recovery factor and the amount of CO 2 stored in CO 2 -EOR projects is of paramount importance. Assessing the oil recovery factor provides crucial insights into the project's economic viability and profitability whereas estimating the amount of CO 2 stored is vital for monitoring and verifying the environmental impact and effectiveness of carbon capture and storage. However, these tasks present several inherent challenges such as lengthy computational time and large input data requirements. The goal of this work is to develop quick and robust analytical equations to predict the performance of CO 2 -EOR projects in terms of CO 2 storability and oil recovery factor. This approach is based on using a well-known predictive model for CO 2 miscible flooding (MFPM) to generate an input-output dataset that takes into consideration the common practical reservoir and operational ranges of CO 2 -EOR projects. Then, Non-Linear Regression (NLR) is used to develop analytical models to estimate both oil recovery factor and CO 2 storage capacity. The developed analytical equations were validated against the MFPM predictive model and show excellent agreement. Moreover, the developed analytical equations were validated against published models in the literature. The developed analytical equations can be incorporated easily into the process of economical evaluation of CO 2 -EOR projects and/or can be used to rank and screen CO 2 -EOR opportunities.

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