Reservoir Uncertainty Assessment Using Machine Learning Techniques

Petroleum exploration and production are associated with great risk because of the uncertainty on subsurface conditions. Understanding the impact of those uncertainties on the production performance is a crucial part in the decision making process.Traditionally, uncertainty assessment is performed using experimental design and response surface method, in which a number of training points are selected to run reservoir simulations on and a proxy model is built to give prediction for the whole design space. The quality of the response surface strongly depends on the method used to construct them. In this paper we evaluate the use of thin plate spline (TPS), artificial neural network (ANN) and support vector regression (SVR) for this application. Results show that when properly tuned ANN and SVR provide superior performance to TPS.