Optimization of Oil Field Development using a Surrogate Model: Case ofMiscible Gas Injection

摘要

The topic of the paper is an approach to find optimal regimes of miscible gas injection into the reservoirto maximize cumulative oil production using a surrogate model.The sector simulation model of the realreservoir with a gas cap,which is in the first stage of development,was used as a basic model for surrogatemodel training.As the variable (control) parameters of the surrogate model parameters of gas injection intoinjection wells and the limitation of the gas factor of production wells were chosen.The target variable isthe dynamics of oil production from the reservoir.A set of data has been created to train the surrogate modelwith various input parameters generated by the Latin hypercube.Several machine learning models were tested on the data set: ARMA,SARIMAX and Random Forest.The Random Forest model showed the best match with simulation results.Based on this model,the task ofgas injection optimization was solved in order to achieve maximum oil production for a given period.Theoptimization issue was solved by Monte Carlo method.The time to find the optimum based on the RandomForest model was 100 times shorter than it took to solve this problem using a simulator.The optimal solutionwas tested on a commercial simulator and it was found that the results between the surrogate model andthe simulator differed by less than 9%.