Application of artificial neural networks in a history matching process

Reservoir simulation is an important tool for reservoir studies because it enables the testing of production strategies and to perform forecasts. To obtain a reliable production prediction, the reservoir model must reproduce results similar to the observed data. This is accomplished through a history matching process, which basically consists of modifying the reservoir parameters until this condition is reached. Usually the process is complex, demanding great time and computational effort and, thus, has been the object of several studies, such as the use of proxy models to substitute the flow simulator in some stages of the history matching process to reduce the number of simulations required to achieve an acceptable match. In this work, the application of proxy models generated through Artificial Neural Networks (ANN) as a substitute for the flow simulator in the history matching process was assessed, showing that the ANN can efficiently capture the nonlinearities of the problems. A synthetic reservoir with real characteristics was used to test the methodology. The results showed that the application of the ANN as a proxy model is promising and that a good match can be achieved with fewer simulations.