A mathematical programming model for the optimal utilization of deep saline aquifers for CO2 storage

Abstract

This work presents a novel nonlinear programming (NLP) formulation aimed at maximizing the overall amount of CO₂ stored into deep saline aquifers in the long term. The goal is to optimally determine CO₂ injection rates into vertical wells while properly managing bottom-hole pressures over time. The reservoir may comprise several layers with heterogeneous physical properties. The injection plan should meet the subsurface engineering policies for safe operations along with existing technical constraints. The major challenge is to track the CO₂ migration across the reservoir to ensure containment during the injection periods and also in the long term. The NLP formulation is based on a discrete space and time representation of the reservoir, comprising pressure propagation and mass balance equations between every pair of adjacent blocks in the grid. Results for several illustrative case studies in two dimensions show the potential of the model to find optimal solutions in few seconds. Injection plans suggested by the optimization model are efficient and have been validated by accurate simulation runs. Based on these findings, the model has the potential to be extended to three dimensions and adapted to real-world cases.