The Prediction of the Performance of an Oil Reservoir by Proxy Model: A Case Study

Abstract

Simulation model of an undeveloped oil reservoir is full of uncertainty. Assessing the effect of these parameters on the simulation results, is very important task in reservoir engineering. Making a proxy model is a method for forecast reservoir performance under different production scenarios. In this study, GMDH-type neural network is used as a proxy model and also Experimental Design theory is used to get the most information full data set which is applied to the input of the neural network. The traditional way needs to very large number of simulation but this method is very time consuming and costly. A sensitivity analysis was conducted to understand the most important initial parameters. In this study, proxy model is created to predict FOPR, Field Oil Production Rate, in a reservoir under immiscible gas injection scenario to 15 years.