Abstract
Carbon capture, utilization, and storage (CCUS) is a crucial part of the energy industry nowadays, aiming to reduce the overall carbon emission into the environment. One solution to CCUS is via the means of \(\text {CO}_{2}\) enhanced recovery processes in a depleted oil reservoir. In such a case, life-cycle production optimization plays a crucial component, referring to optimizing a production-driven objective function via varying well controls during a reservoir’s lifetime. One challenge is to obtain the optimal cash flow while trying to maintain the maximum \(\text {CO}_{2}\) storage. Another challenge is the nonlinear constraints (such as field liquid production rate) which need to be honored due to the capacity of the processing facilities. This study presents an application of a stochastic gradient-based framework to solve the \(\text {CO}_{2}\) storage multi-objective optimization problem. Our study focuses on carbon capture and storage via the means of nonlinearly constrained production optimization workflow for a \(\text {CO}_{2}\) enhanced recovery process, in which we aim to bi-objectively maximize both the net-present-value (NPV) and the net present carbon tax credits (NPCTC). The main framework used in this work is line-search sequential quadratic programming (LS-SQP) with stochastic simplex approximated gradients (StoSAG). We demonstrate the performance and results of the algorithmic framework in a field-scale realistic problem. The case study being investigated is a multiphase flow Brugge model under \(\text {CO}_{2}\) injection, simulated using a commercial compositional reservoir simulator. Results show that the LS-SQP algorithm with StoSAG gradients performs computationally efficiently and effectively in handling nonlinear state constraints imposed onto the problem. The workflow successfully solves both the single-objective and the multi-objective optimization problems with minimal and acceptable constraint violations. Various numerical settings have been experimented with to estimate the Pareto front for the bi-objective optimization problem, showing the trade-off between the two objectives NPV and NPCTC. We have demonstrated an approach to the carbon capture, utilization, and storage (CCUS) in the context of multi-objective production optimization of a \(\text {CO}_{2}\) enhanced recovery process for a field-scale realistic reservoir model. The algorithmic framework used in this study has proven to be computationally effective on the problem and especially useful when utilized in conjunction with commercial flow simulators that lack the capability of computing adjoint-based gradients.
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Acknowledgements
The support of the member companies of Tulsa University Petroleum Reservoir Exploitation Projects (TUPREP) is gratefully acknowledged. The authors acknowledge the Computer Modeling Group (CMG) for making available multiple GEM licenses.
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Nguyen, Q.M., Onur, M. & Alpak, F.O. Multi-objective optimization of subsurface \(\text {CO}_{2}\) capture, utilization, and storage using sequential quadratic programming with stochastic gradients. Comput Geosci 28, 195–210 (2024). https://doi.org/10.1007/s10596-023-10213-6
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DOI: https://doi.org/10.1007/s10596-023-10213-6