Application of Combined Streamline Based Reduced-Physics Surrogate and Response Surface Method for Field Development Optimization

Model-based field development optimization typically requires a large number of simulations. Consequently, this process may face challenges as model size and complexities increase. The objective of this paper is to apply a reduced-physics model with response surface approach for replacing full field simulation runs to reduce the time and resources required during a search for optimal solutions. The streamline technique is used to develop a reduced-physics model in this study. There are number of previous studies that demonstrated the use of streamlines for production optimization (e.g. well placement and/or rate allocation optimization). In a recent work (SPE 187298), an approximate equation was formulated to estimate the expected economic value based on streamlines and was applied into rate allocation optimization of a given well pattern. Our approach is to use this formulation to improve the efficiency of field development optimization by potentially screening out poor performing development designs without performing full simulations. The streamline-based surrogate model workflow was first applied and validated using a synthetic SPE-10 case study. The workflow was then applied to the Olympus field waterflood study. The study goal is to maximize the economic value by optimizing the well count, injector and producer locations, and completion design. The validation performed using random field development designs provided a rank correlation coefficient of 0.92 between the NPV values of full field simulations and streamline-based approximation from the Olympus field application. The streamline-surrogate model was then adopted with an optimization workflow (Genetic Algorithm) and response surface method with 2-stage approach. First, Genetic Algorithm (GA) optimization was performed using the streamline-surrogate as an initial stage to screen out suboptimal field development design. Then, a second GA optimization was performed using full simulations coupled with the response surface method, starting with results from the first stage. Response surfaces that were developed using samples through GA improved the process of screening poor economic cases at later stages, as the predictability of solution improved with more training. We demonstrated that the streamline-based surrogate formulation combined with the response surface approach will improve the optimization process of field development scenarios. The applicability of using the response surface approach by itself is limited for field applications due to the large number of simulations required for training and the risk of convergence at local minima. Multiple tests from the Olympus field development application demonstrated that the sequential combination of streamline-based surrogate formulation with response surface method had the best performance.