EN 
BR Iterative sequential robust optimization of quantity and location of wells in field development under subsurface, operational and economic uncertainty
Abstract
Determination of optimal quantity and location of wells is a crucial step in any field development project. Multiple challenges exist in the real-world projects, such as gigantic multidimensional search space and complex parameterization of the problem. To address these challenges, we present an iterative sequential optimization framework that consists of multiple stages for optimizing number and location of producers and injectors. Once the initial guesses have been established, the first optimization stage begins in order to determine the optimal number of producers and injectors. Next, a two-stage process is used to determine the best location for each well. By optimizing the number and location of wells, well type is optimized implicitly and does not need to be included in the optimization process as an individual decision variable. To consider the dependency and coupling between parameters, the entire optimization process is repeated iteratively up to a stopping point. The proposed approach is highly flexible and can be extended to include other potential decision variables of field development projects. We test the proposed methodology on UNISIM–II–D, a realistic benchmark case that represents pre-salt reservoirs in deep offshore Brazil, which are light-oil fractured carbonates with large volumes of CO2-rich associated gas. In our application, all producers and injectors are equipped with internal control valves (ICVs), all produced gas is reinjected into the reservoir, and water-alternating-gas with CO2 (WAG-CO2) is used to increase oil recovery and ensure that the produced CO2, as a harmful greenhouse gas, can be safely discarded under- ground. We use the iterative discrete Latin hypercube (IDLHC) optimization algorithm. To achieve a robust decision, we consider subsurface, operational and economic uncertainties by considering nine representative reservoir models and three economic scenarios. Based on the results, the proposed framework can provide an appropriate solution containing optimal quantity of wells, their locations and types in realistic cases with large search spaces and complex parameterizations.
EPIC Authors
