Proxy Model Development for the Optimization of Water Alternating CO2 Gas for Enhanced Oil Recovery

Abstract

Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle lengths were assessed to maximize the oil recovery and CO2 stored in the reservoir. One model represents a simple geological model (the Egg Model), while the other represents a complex model (the Gullfaks Model). In this study, the good performance of the proxy models generated accurate results that could be improved by increasing the amount of sampling and segmenting the behavior of the reservoir model (depending on the complexity of the reservoir model). The developed proxies have an average error of less than 2% (compared with simulation results) and are concluded to be robust based on the blind test results. It has also been found that to reach the maximum oil recovery using CO2-WAG, the maximum gas injection rate with the minimum water injection rate is required. However, this configuration may result in a reduction in the total CO2 stored in the reservoir.

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery

“…To assess the reliability and performance of the developed models, several statistical indicators are commonly used. In this study, we employed three such indicators: average absolute relative deviation percentage (AARD%), determination coefficient ( ), and root mean square error (RMSE) 70 . These parameters are defined as follows, respectively: …”

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

“…The implementation of CO 2 – EOR can be done under several schemes, including the injection of miscible/immiscible CO 2 , water alternating CO 2 gas injection (WAG CO 2 ), polymer alternating gas CO 2 (PAG), simultaneous water CO 2 gas injection, foam assisted CO 2 , low salinity water–surfactant–CO 2 , and hybrid schemes with surfactant polymer (SP). − Most CO 2 – EOR processes are commonly confronted to satisfy two objectives consisting of maximizing ORF and minimizing the production of CO 2 (this latter means the increase in the storage performance). − However, it is worth mentioning that many published studies and experimental tests demonstrated the superiority of the miscible type (single–phase flow) of CO 2 – EOR processes in reaching higher ORF compared to the immiscible type (two–phase flow). , The suitable design of a CO 2 – EOR process depends greatly on the so-called minimum miscibility pressure (MMP) since this parameter allows the determination of the injection type, i.e., miscible or immiscible. MMP represents the cutoff pressure at reservoir temperature (T R ) above which miscibility between the reservoir fluid and the injected gas occurs after multiple contacts. − …”