Multi-objective production strategy optimization methodology under uncertainties

Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs.Decision-making in this complex domain can rely on various objective functions.The increasing complexity of reservoir systems and the inherent uncertainties in geological and economic factors have necessitated the use of multi-objective optimization (MOO) to improve decision-making.The potential benefits of MOO include more robust decision-making that can account for trade-offs between different objectives.Additionally, it can better accommodate uncertainty, resulting in more resilient and adaptable production strategies.The main objective of this study is to design a robust multi-objective framework for optimizing production strategies considering reservoir and economic uncertainties, specifically targeting pre-salt oil fields applying the UNISIM-II-D benchmark reservoir.To address the challenges arising from the application of MOO, three key studies were conducted to develop and evaluate optimization frameworks that account for these complex objectives.The first study provides a comprehensive review of the evolution of MOO algorithms, highlighting their increasing relevance in field development optimization.It categorizes the algorithms into a priori and a posteriori method, while addressing challenges such as the resource-intensive nature of reservoir simulations under uncertainty.Further investigation is conducted into the behavior of various objective functions within the optimization of oilfield development, with a primary focus on expected monetary value (EMV) as the main criterion.This analysis aims to identify conflicting objectives, forming the foundation for the final stage of the study.The results highlight a strong correlation between EMV and cumulative oil production (COP), showing that optimizing EMV can drive improvements in COP and the recovery factor.Additionally, adopting strategies that prioritize environmental objectives (reduction of cumulative water and gas production), even at the cost of slightly lower EMV, can deliver substantial benefits, including reduced greenhouse gas emissions, lower water treatment costs, and an extended reservoir lifespan.Furthermore, it becomes evident that relying exclusively on EMV in oilfield development optimization carries inherent risks, as not all representative models (RMs) demonstrate an increase in net present value (NPV), specifically the most pessimistic case when EMV is maximized through singleobjective optimization.In the final phase of the study, the third stage was initiated after the conflicting objectives were identified in the second stage.The third stage was designed to systematically address and balance these conflicts in the field development planning.In this stage, MOO was conducted using the non-dominated sorting genetic algorithm II (NSGA-II) to simultaneously balance the EMV with the NPV under economic uncertainty (NPVeco) for the pessimistic RM, thereby generating a diverse set of Pareto-optimal solutions that effectively illustrate the trade-offs among different possible production strategies considering geological and economic uncertainties.This approach not only identifies a broader range of optimal strategies but also provides a more refined understanding of the trade-offs, improving the overall performance by capturing synergies between the objectives.By employing the proposed framework, a 3% improvement in EMV and a 28% enhancement in the NPVeco of the pessimistic RM is achieved compared to the single objective optimization of EMV, which highlights the strength and robustness of the framework.These insights and this framework foster informed, balanced decisions that guide reservoir development toward optimal economic and geological outcomes.

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