Use of Emulator Methodology for Uncertainty-Reduction Quantification

Abstract

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169405, “Use of Emulator Methodology for Uncertainty-Reduction Quantification,” by C. Ferreira, Universidade Estadual de Campinas; I. Vernon, Durham University; D.J. Schiozer, SPE, Universidade Estadual de Campinas; and M. Goldstein, Durham University, prepared for the 2014 SPE Latin American and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, 21–23 May. The paper has not been peer reviewed. Most simulation models go through a series of iterations before being judged as giving an adequate representation of the physical system. This can be difficult because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time. Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space. Introduction Reservoir simulators are important and widely used in reservoir management. They are used in reservoir-performance prediction and for decision making. These simulators are computer implementations of high-dimensional mathematical models for reservoirs, where the model inputs are physical parameters and the outputs are observable characteristics such as well-pressure measurements and fluid production. Uncertainties are always present in the reservoir-characterization process; thus, input parameters are usually uncertain and so is the simulator output. The procedure to calibrate the reservoir-simulation model is called history matching. On the basis of observed data, a set of possible input choices for the reservoir model is identified. Two different procedures can be used to perform the history matching: deterministic and probabilistic approaches. The deterministic approach involves running the initial simulation model with different input values to obtain one simulation model between many probable matches to the field data. In a probabilistic approach, in which several reservoir-model scenarios are considered, the uncertainty analysis procedure is used. Identifying the input parameters for which the simulation outputs match the observed data can be a difficult task because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time.