Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

Abstract

Abstract The preferred common tool to estimate the performance of oil and gas fields under different production scenarios is numerical reservoir simulation. A comprehensive numerical reservoir model has tens of millions of grid blocks. The massive potential of the existing numerical reservoir simulation models go unrealized because they are computationally expensive and time-consuming [1]. Therefore, an effective alternative tool is required for fast and reliable decision making. To reduce the required computational time, proxy models are developed. Traditional proxy models are either statistical or reduced order models (ROM). They are developed to substitute the complex numerical simulation by producing a representation of the system at a lower computational cost. However, there are shortcomings associated with these approaches when applied to complex systems. In this study, a novel proxy model approach is presented. The smart proxy model presented in this article is based on artificial intelligence and data-mining techniques. A numerical simulation run is designed for the smart proxy objectives. The static and dynamic data from the simulation run are extracted. Selected data parameters are used to create a spatial-temporal database for the smart proxy model. The smart proxy is trained, calibrated, and validated using a series of neural networks for the targeted reservoir property. To validate the smart proxy model, it is deployed to replicate a blind numerical simulation run. The developed smart proxy model can replicate the simulation outcomes in a very short time (seconds) with an acceptable range of error.