Accelerating Field Development Planning for Deepwater Greenfield with Multiple Realizations

Abstract Field development planning (FDP) is a time-consuming and manual process that takes a huge number of simulation runs to determine optimum development. The computation time and complexity increase even further with multiple realizations. In this research, a reduced physics-based model is presented that accelerates the ability to screen infill location optimization. In particular, the Fast Marching Method (FMM) is utilized to rank and optimize well locations and trajectories to maximize oil production for a given reservoir with multiple realizations. The proposed methodology combines the strengths of a reduced physics model, FMM, and reservoir simulations. FMM uses the concept of diffusive time of flight (DTOF), which is a representation of pressure and drainage propagation while accounting for reservoir heterogeneity and well location. After identifying drainage volume expansion for a specific well location using FMM, potential cumulative recovery can be determined within that area. Using potential recovery values associated with each well location, 3D Relative Opportunity Ranking (ROR) maps are determined for each realization separately. Finally, the ROR maps are coupled with a global optimizer to optimize well locations for each realization. We demonstrate the proposed model’s capabilities using an actual deepwater greenfield reservoir with more than 30 realizations. For the reservoir case studied, we demonstrate that the FMM-based ROR maps can accurately capture the relationship between the well trajectory and the actual recovery. More specifically, the correlation score between recovery prediction from FMM and actual simulation is more than 0.9. Finally, from 30 different realizations studied, well trajectories are optimized for the first and second infill wells, which are compared with base case prediction for risk analysis. We also show that the proposed workflow is up to 50 times computationally efficient when compared to traditional workflows. This research showcases the integration of reduced physics-based approaches to optimize infill well placement in greenfield reservoirs. The findings highlight the potential of this approach to significantly enhance the reservoir engineering workflow, reducing computational costs, and improving decision-making efficiency.