Multi-objective optimization for multi-agent injection strategies in subsurface CO$_2$ storage

TL;DR

The paper addresses optimization of CO$_2$ injection strategies at basin scale with multiple independent agents who can form coalitions. It couples a multi-agent coalition model with constrained multi-objective optimization, using both the weighted-sum approach and a multitasking constrained multiobjective optimizer to generate Pareto fronts, demonstrated on the Bjarmeland formation. Key findings show inter-agent pressure coupling effects, yielding similar total injections across several coalition structures for selected Pareto solutions, while highlighting trade-offs in coalition formation and the need for scalable evaluation strategies. The framework enables decision-makers to explore coalition-driven strategies under physical constraints and provides a pathway for staged, low- to high-fidelity optimization in large-scale CO$_2$ storage planning.

Abstract

We propose a novel framework for optimizing injection strategies in large-scale CO$_2$ storage combining multi-agent models with multi-objective optimization, and reservoir simulation. We investigate whether agents should form coalitions for collaboration to maximize the outcome of their storage activities. In multi-agent systems, it is typically assumed that the optimal strategy for any given coalition structure is already known, and it remains to identify which coalition structure is optimal according to some predefined criterion. For any coalition structure in this work, the optimal CO$_2$ injection strategy is not a priori known, and needs to be found by a combination of reservoir simulation and a multi-objective optimization problem. The multi-objective optimization problems all come with the numerical challenges of repeated evaluations of complex-physics models. We use versatile evolutionary algorithms to solve the multi-objective optimization problems, where the solution is a set of values, e.g., a Pareto front. The Pareto fronts are first computed using the so-called weighted sum method that transforms the multi-objective optimization problem into a set of single-objective optimization problems. Results based on two different Pareto front selection criteria are presented. Then a truly multi-objective optimization method is used to obtain the Pareto fronts, and compared to the previous weighted sum method. We demonstrate the proposed framework on the Bjarmeland formation, a pressure-limited prospective storage site in the Barents Sea. The problem is constrained by the maximum sustainable pressure buildup and a supply of CO$_2$ that can vary over time. In addition to identifying the optimal coalitions, the methodology shows how distinct suboptimal coalitions perform in comparison to the optimum.

Figures (10)

• Figure 1: Pareto fronts for the coalition structures with at least two coalitions, WSM and SOO.
• Figure 2: Annual injection rates for all time intervals (in years), plume extension, and relative pressure buildup for the Pareto solution maximizing the total, obtained using WSM and SOO. A total of 44 Mt CO$_2$ is injected over 15 years. The color bar for the plume migration represents the mass of CO$_2$ in tons per lateral square meter, and the red contour represents 0.3 tons per m$^2$.
• Figure 3: Injection rates for the Pareto solution maximizing the injections of W1 for different coalition structures (CS), obtained using WSM and SOO.
• Figure 4: Relative pressure with respect to overburden pressure at the end of the injection period. Pareto solution maximizing the injections of W1 for different coalition structures, obtained using WSM and SOO.
• Figure 5: CO$_2$ plume at the end of the injection period. Pareto solution maximizing the injections of W1, obtained using WSM and SOO. The color bar for the plume migration represents mass of CO$_2$ in tons per lateral square meter, and the red contour represents 0.3 tons per m$^2$.