In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these methods are known to generate a high number of duplicated strategies in discrete problems. Duplicated strategies negatively impact the optimization process since they: (i) degrade the efficiency of recombination operators in evolutionary algorithms; (ii) slow the convergence speed as they require more iterations to find a well-distributed set of strategies; and (iii) perform unnecessary re-evaluations of previously seen strategies through reservoir simulation. To perform multi-objective well placement optimization while avoiding duplicated strategies, this paper investigates the application of a newly proposed algorithm named MOEA/D-NFTS, with a modified diversity preservation mechanism that incorporates prior knowledge of the problem, on a multi-objective well placement optimization problem. The proposed methodology is evaluated on the UNISIM-II-D benchmark case, a synthetic carbonate black-oil simulation model in a well placement optimization problem using a binary strategy representation, indicating the presence or absence of a given candidate well position in the final strategy. The objective functions are the maximization of the Net Present Value, the maximization of the Cumulative Oil Production, and the minimization of Cumulative Water Production. The modified MOEA/D-NFTS performance is compared with a baseline algorithm without diversity preservation, and the evidence shows that the MOEA/D-NFTS produces statistically significant superior results, and is suitable for binary multi-objective well placement optimization.
Tag: Production optimization
Methodology to optimize the WAG-CO2 injection strategy and injection well ICV control rules in light-oil carbonate reservoirs with pre-salt features
Reservoirs of the pre-salt contain a significate amount of CO2 that should not be emitted into the atmosphere. The WAG-CO2 injection process is an alternative to give an ecologically sustainable destination to the CO2 and can increase oil recovery in the pre-salt fields. The optimization of the WAG-CO2 injection scheme, such as cycle duration, can significantly affect its performance in terms of oil recovery and net present value (NPV), raising the need for good optimization methods. In the face of the high uncertainty that typically exists in these scenarios, Inflow Control Valves (ICV) provide operational flexibility to the production strategy, allowing to manage field injection/production more efficiently. This work proposes a methodology to optimize the injection well control variables since the early stages of field development that considers the condition of total gas reinjection (CO2 and natural gas). The methodology optimizes the
opening phase that each well will start injecting during the ramp-up period of the platform, the cycle duration and the phase, gas or water, that each well will inject in the first WAG-CO 2 bank, and the injection wells ICV control rules. The developed methodology was applied to a benchmark case called UNISIM-II-D, based on Brazilian pre-salt trends. Compared to a based injection strategy, the methodology proved capable of improving field management at minimum added cost, increasing oil recovery and the net present value.
A review on closed-loop field development and management
Closed-loop field development and management (CLFDM) is defined as a periodic update of an uncertain field model using the latest measurements (data assimilation), followed by production optimization aiming mainly at maximizing the field economic value. This paper provides a review of the concepts and methodologies in the CLFDM. We first discuss different types of uncertainty encountered in field development and management. Then, concepts, components, and elements of CLFDM are presented. We then discuss and compare different automated methodologies for data assimilation, followed by explaining a hierarchy of different decision variables for production optimization including design variables (G1), life-cycle control rules (G2L), short-term controls (G2S), and revitalization variables (G3). We continue with explanations for the use of closed-loop in both the development and management phases of a field project. We also discuss and compare different methodologies for production optimization. Afterwards, objective functions for production optimization are presented, followed by the description of concepts and different approaches for selecting representative models to speed up solutions. This paper also highlights the necessity of integrated modeling of reservoir and production systems in CLFDM, and also the need for a standardized stepwise approach to apply the CLFDM by discussing one method from the literature. Finally, we summarize all the previous CLFDM studies on the basis of aspects covered in this paper, and suggest open areas for future research to enhance the use of CLFDM.