An open-source framework is presented for the development and evaluation of machine learning- (ML) assisted data-driven models of CO2 enhanced oil recovery (EOR) processes to predict oil production and CO2 retention. The main objective of the authors was to increase the speed, robustness, and accuracy of predicting oil recovery and CO2 retention using a complete open-source approach combining Python programming, reservoir simulation, and ML techniques.
Overview of the Open-Source Framework
The evaluation of the predictive models was performed using two CO2 water-alternating-gas (WAG) simulation cases, which were proposed using the SPE Comparative Solution Project (CSP) 5 simulation model as a reference. First, a reservoir simulation deck template and a configuration file, including variable inputs, were generated to create any number of simulation jobs for each case. The input-value range for the simulations was set in a Python script.
