Abstract
This paper presents a Pareto-based multi-objective optimization for operating CO2 sequestration with a multi-well system under geological uncertainty; the optimal well allocation, i.e., the optimal allocation of CO2 rates at injection wells, is obtained when there is minimum operation pressure as well as maximum sequestration efficiency. The distance-based generalized sensitivity analysis evaluates the influence of geological uncertainty on the amount of CO2 sequestration through four injection wells at 3D heterogeneous saline aquifers. The spatial properties significantly influencing the trapping volume, in descending order of influence, are mean sandstone porosity, mean sandstone permeability, shale volume ratio, and the Dykstra–Parsons coefficient of permeability. This confirms the importance of storable capacity and heterogeneity in quantitatively analyzing the trapping mechanisms. Multi-objective optimization involves the use of two aquifer models relevant to heterogeneity; one is highly heterogeneous and the other is less so. The optimal well allocations converge to non-dominated solutions and result in a large injection through one specific well, which generates the wide spread of a highly mobile CO2 plume. As the aquifer becomes heterogeneous with a large shale volume and a high Dykstra–Parsons coefficient, the trapping performances of the combined structural and residual sequestration plateau relatively early. The results discuss the effects of spatial heterogeneity on achieving CO2 geological storage, and they provide an operation strategy including multi-objective optimization.
Highlights
A challenging problem in engineering analytics has been the existence of many objectives
Evolutionary multi-objective optimization (EMO) algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA; NSGA-II; NSGA-III), strength Pareto evolutionary algorithm (SPEA; SPEA-II), Pareto envelope-based selection algorithm (PESA; PESA-II), and multi-objective evolutionary algorithm based on decomposition (MOEA/D), have continuously improved fitness assignment and diversity control [1,2,3,4,5,6,7,8,9,10]
This paper discussed the multi-objective problem related to CO2 sequestration under geological uncertainty
Summary
A challenging problem in engineering analytics has been the existence of many objectives. The Pareto front, i.e., a set of Pareto solutions, illustrates the trade-offs for which algorithms should secure solution diversity as well as make comparative evaluations among the potential solutions [4,5,6,7,8]. Evolutionary multi-objective optimization (EMO) algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA; NSGA-II; NSGA-III), strength Pareto evolutionary algorithm (SPEA; SPEA-II), Pareto envelope-based selection algorithm (PESA; PESA-II), and multi-objective evolutionary algorithm based on decomposition (MOEA/D), have continuously improved fitness assignment and diversity control [1,2,3,4,5,6,7,8,9,10]. NSGA is a well-known scheme designed to preserve non-dominated points in objective space that has a wide solution-searching capability with a genetic algorithm. Its strength is that it can provide non-dominated trade-offs in the comparison of objective functions, while its weaknesses are the fact that it requires a large amount of computing power and its decreasing convergence with an increasing number of objective functions, i.e., ‘the curse of dimensionality’ [5,6,7,8,9,10]
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