A data-driven smart proxy model for a comprehensive reservoir simulation

A data-driven smart proxy model for a comprehensive reservoir simulation

Artificial Intelligence

Computational modeling

Data Mining

Data models

Mathematical model

Numerical models

Numerical simulation

Proxy Modeling

Reservoir Simulation

Reservoirs

Training

One of the most important tools for studying fluid flow behavior in oil and gas reservoirs is reservoir simulation. It is constructed based on a comprehensive geological information. A comprehensive numerical reservoir model has tens of millions of grid blocks. Therefore, it becomes computationally expensive and time consuming to run the model for different reservoir simulation scenarios. There are many efforts have been made to reduce the computational size using the proxy models. Proxy models are the substitute to the complex numerical simulation by producing a meaningful representation of the complex system in a very short time. The conventional proxy models are either statistical or mathematical approaches. These conventional approaches are still limited to the complexity of the reservoir and the number of the numerical simulation runs needed to build the proxy model. In this study, a smart proxy model that is based on artificial intelligence and data mining is presented. A grid based smart proxy model is developed to reproduce the dynamic reservoir properties of a full- field numerical simulation in few seconds. A comprehensive spatio-temporal database is built using the conducted numerical simulation run. The data from the database is trained, calibrated, and verified throughout the development of the smart proxy model. Smart proxy model is able to produce pressure and saturation at each reservoir grid block accurately and with a significantly less computational time compared to the numerical reservoir simulation model.

1-6

F. Alenezi

S. Mohaghegh

2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT)

2016

conferencePaper

10.1109/KACSTIT.2016.7756063