This study employs an adjusted version of the multi-objective particle swarm optimization (MOPSO) algorithm to plan an optimized reservoir's injection/production strategy. Three case studies, including two water-flooding benchmark models and one gas-condensate problem, are exercised as subjected problems to validate the MOPSO approach. The contradicting values of objectives, long-term net present value (LNPV) versus short-term net present value (SNPV), are obtained so that relying on a Pareto front improves decision-making. In one water-flooded case, inflow control valves of smart wells are considered to be adjusted within the optimization, while in the second case, the optimized well injection rates are control variables. The obtained results for water-flooded reservoirs are shown to optimize the competitive objective functions more than the previous efforts in the literature. Moreover, 10 different permeability maps of the second case are implemented to obtain the optimum injection rates to perform optimization under uncertainty. The MOPSO robustly optimized the production/injection strategy in the presence of model uncertainty. In the gas-condensate problem, the optimal gas injection rate in the SPE-3 benchmark model is determined. The gas-condensate case's outputs yield a decreased oil saturation result in the reservoir compared to non-optimized production scenarios. Results illustrate that for all cases, MOPSO can provide optimal injection/production scenarios. Therefore, the proposed scheme gives the advantage of deciding between the set of results into a decision-maker to optimize the production program by trading-off within different strategies. Besides the Pareto fronts with adequate variety and steadiness, a great converging rate is the main advantage of this method.

中文翻译：

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基于模型的多目标粒子群生产优化，用于有效的注入/生产计划，以提高储层采收率

本研究采用多目标粒子群优化 (MOPSO) 算法的调整版本来规划优化的油藏注入/生产策略。三个案例研究，包括两个注水基准模型和一个凝析气问题，被用作验证 MOPSO 方法的主题问题。获得了相互矛盾的目标值，即长期净现值 (LNPV) 与短期净现值 (SNPV)，因此依靠帕累托前沿可以改善决策。在一种注水情况下，智能井的流入控制阀被认为是在优化范围内进行调整，而在第二种情况下，优化后的井注入速度是控制变量。与文献中先前的努力相比，所获得的水淹油藏结果显示出更优化了竞争目标函数。此外，实施了第二种情况的 10 个不同的渗透率图，以获得在不确定性下进行优化的最佳注入速率。在模型不确定的情况下，MOPSO 稳健地优化了生产/注入策略。在凝析气问题中，确定了 SPE-3 基准模型中的最佳注气速率。与未优化的生产情景相比，凝析油案例的产出导致油藏中含油饱和度降低。结果表明，对于所有情况，MOPSO 都可以提供最佳注入/生产方案。所以，提议的方案具有在一组结果之间做出决定的优势，决策者可以通过在不同策略之间进行权衡来优化生产计划。除了帕累托前沿具有足够的多样性和稳定性外，收敛速度快是该方法的主要优点。