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.
Tag: Field development
Analysis of different objective functions in petroleum field development optimization
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, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.
Optimization of design variables and control rules in field development under uncertainty: A case of intelligent wells and CO2 water alternating gas injection
This paper focuses on life-cycle optimization of oil field development plan under uncertainty. The optimization problem included a wide range of design variables and control rules related to wells and platform in a realistic benchmark case (UNISIM–II–D) with a known ground truth reservoir model, UNISIM–II–R, that resembles Brazilian pre-salt fractured carbonate reservoirs in their early development stage. The design variables were the number and location of wells, the fluid processing capacity of the platform, and the location of internal control valves (ICVs), whereas the control rules were the setting of ICVs, production and injection rates, and the duration of the water-alternating-gas (WAG-CO2) cycle. An iterative sequential optimization framework was developed to deal with the massive search space and complex parameterization. The optimization further took into consideration the subsurface, operational and economic uncertainties, and used iterative discrete Latin hypercube method as the search algorithm. The robust optimization was carried out on a subset of representative models derived from the reduction of a large ensemble of data-assimilated models. As a low-fidelity representation of the compositional fluid model, a black-oil model was used to reduce simulation runtime. To validate our optimization framework, we applied the optimal development strategy to the ensemble of compositional simulation models, as well as the ground truth model. The true model’s responses were within the ranges predicted by the compositional ensemble, confirming the optimization framework’s reliability. The general methodology developed in this study, as well as our findings, can be used to optimize other similar real-world complex and high-risk field development projects, and are especially useful in closed-loop field development and management practices.
Binary well placement optimization using a decomposition-based multi-objective evolutionary algorithm with diversity preservation
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.
Iterative sequential robust optimization of quantity and location of wells in field development under subsurface, operational and economic uncertainty
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.
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.