Robust multi-objective optimization for water flooding under geological uncertainty

This study addresses the economic optimization of water flooding in oil reservoirs under significant geological uncertainty. By employing a multi-objective optimization (MOO) algorithm, we aim to minimize the sensitivity of objective functions to geological variability. The methodology clusters one hundred realizations of a synthetic “egg model” reservoir into ten groups using K-means, each defined by net present value (NPV) metrics. Robust optimization (RO) is then applied to representative realizations from each cluster, allowing comparison with nominal optimization (based on a single representative realization). Key innovations include incorporating investment costs into the NPV calculation and using the internal rate of return (IRR) as an objective, alongside multi-objective optimization based on capital (CAPEX) and operational expenditures (OPEX) under three oil price scenarios. Results indicate that RO improves objective stability and reduces sensitivity to geological uncertainty compared to nominal optimization, yielding optimal injection rates across eight wells. This research provides a robust approach to designing water flooding strategies, reducing decision-making costs, mitigating risks, and enhancing resilience to oil price fluctuations.