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 existing numerical reservoir simulation models have gone unrealized because they are computationally expensive and time-consuming. Therefore, an effective alternative tool is required for fast and reliable decision making. To reduce the required computational time, proxy models have been developed. Traditional proxy models are either statistical or reduced-order models (ROM). They were developed to substitute complex numerical simulation with 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 in order to overcome the computational size and the traditional proxy-model challenges. The smart proxy model presented is based on artificial intelligence and data-mining techniques. The objective of this study was to develop two types of smart proxy models at each grid block. The first smart proxy model was generated to identify dynamic reservoir properties (pressure and saturations). The other proxy model was created to determine the production profile of a well. The two smart proxy models can be coupled in order to examine field production performance under different operational and geological realization. The field of study in this work is the SACROC unit. It is a depleted oil field located in Scurry County, Texas. The production history of this field began back in the late 1940s. Based on the long period of production and the different drive mechanisms employed throughout the fields exploitation, its performance history was divided into three phases in this study. Each phase was investigated and smart proxy models were applied to each. ful children, Jana, Naif and Reelam, who provide unending inspiration. Finally, I extend my thanks and gratitude to Saudi Aramco for giving me the opportunity and financial support to pursue a PhD degree.

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.