盐下油藏开发中平衡冲突目标:一个稳健的多目标优化框架

IF 4.6 Unconventional Resources Pub Date : 2025-01-01 Epub Date: 2024-11-26 DOI:10.1016/j.uncres.2024.100130
Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer
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引用次数: 0

摘要

优化油气田的生产策略是石油工程中的一个关键挑战,因为它涉及到平衡多个经常相互冲突的目标,例如,提高产量,降低运营成本,减轻累积水或气生产对环境的影响。该研究旨在为UNISIM-II-D油藏开发并应用一个强大的多目标优化框架,该油藏代表巴西盐下油田的九个代表性模型(rm),以解决地质不确定性,同时考虑三种经济情景。研究的重点是最大化预期货币价值(EMV)和RM4的净现值考虑经济不确定性(RM4的NPVeco),在RM4中最悲观的情景。优化变量为井位、井型(注、产)和井数,采用非支配排序遗传算法II (NSGA-II)进行多目标优化。研究表明,优先考虑主要目标函数EMV并不一定会使RM4的NPVeco达到最优或接近最优值。然而,通过采用所提出的框架,与EMV的单目标优化相比,EMV提高了3%,RM4的NPVeco提高了28%,这突出了框架的强度和鲁棒性。
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Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework
Optimizing production strategies for gas and oil fields is a critical challenge in petroleum engineering as it involves balancing multiple and often conflicting objectives, for instance, enhancing production rates, reducing operational costs, and mitigating the environmental effects of cumulative water or gas production. This study aims to develop and apply a robust multi-objective optimization framework to the UNISIM-II-D reservoir, which represents Brazilian pre-salt fields on nine representative models (RMs) to address geological uncertainties while considering three economic scenarios. The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. The study indicates that prioritizing EMV, the primary objective function, does not inevitably result in the NPVeco of RM4 achieving its optimal or near-optimal value. However, by employing the proposed framework, a 3 % improvement in EMV and a 28 % enhancement in the NPVeco of RM4 is achieved compared to the single objective optimization of EMV, which highlights the strength and robustness of the framework.
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