A Physics-Based Proxy for Surface and Subsurface Coupled Simulation Models

Abstract

Summary Full-physics subsurface simulation models coupled with the surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run time of the coupled model to enable rapid decision-making for reservoir management. In the coupled model, the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves that are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the computationally intensive reservoir simulation is replaced with an IPR database constructed from a data pool of one or multiple simulation runs. The IPR database captures well performance that represents subsurface reservoir dynamics. The proxy model can then be used to predict the production performance of new scenarios—for example, new drilling sequence—by intelligently looking up the appropriate IPR curves for oil, gas, and water phases for each well and solving it with the surface network. All necessary operational events in the surface network and field management (FM) logic (such as facility constraints, well conditional shut-in, and group guide rate balancing) for the fully coupled model can be implemented and honored. In the proposed proxy model, the reservoir simulation component is eliminated for efficiency. The entirety of the surface network model is retained, which offers certain advantages. It is particularly suitable for investigating the impact of different surface operations, such as maintenance schedule and production routing changes, with the aim of minimizing production capacity offline because of maintenance. Replacing the computationally intensive subsurface simulation with the appropriate IPR significantly reduces the run time of the coupled model while preserving the essential physics of the reservoir. The accuracy depends on the difference between the scenarios that the proxy is trained on and the scenarios being evaluated. Initial testing with a complex reservoir with more than 300 wells showed the accuracy of the proxy model to be more than 95%. The computation speedup could be an order of magnitude, depending largely on the complexity of the surface network model. Prior work exists in the literature that uses decline curves to replicate the subsurface model performance. The use of the multiphase IPR database and the intelligent lookup mechanism in the proposed method allows it to be more accurate and flexible in handling complexities such as multiphase flow and interference in the surface network.