Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

Abstract

Quantifying and analyzing the geological uncertainties associated with the geostatistical models is critical to design a reliable production strategy. A large number, hundreds, even thousands, of model realizations should be run in a reservoir simulator to quantify the uncertainties in terms of a target flow response. However, this becomes a time consuming and computationally demanding process. In practice, different methods are used to select only a few representative models for further analyses. This study aims to take a fresh look at uncertainty quantification solutions by means of a proxy model based on artificial neural networks (ANN). The idea is replacing the flow simulator with an ANN-based proxy model by which the target responses of all realizations are calculated much faster and with a lower computational demand. The ability of computing the combined effect of several uncertain parameters along with successful experiences of utilizing ANNs for reservoir simulation, motivated us to propose our ANN-based methodology as a solution for uncertainty quantification problem. We apply our methodology on a realistic 3D synthetic model which is a channelized model with uncertainties in porosity, permeability, and facies properties. The results are compared with those of two widely used methods, i.e., the traditional ranking and distance-based clustering techniques. It is shown that the ANN-based proxy is able to accurately quantify the geological uncertainties in a much shorter time. Hence, it can be used as a reliable tool for uncertainty quantification and representative models selection. • A new solution for quantifying the geological uncertainties of reservoir models is presented. • A data-Driven proxy model based on Artificial Neural Networks is built to replace the comprehensive reservoir simulator. • The Data-Driven proxy model calculates the desired target response much faster. • The proposed solution outperforms the widely-used uncertainty quantification techniques.