Advanced Reservoir-Management Workflow - EnKF-Based Assisted-History-Matching Method

Abstract

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 118906, ’Advanced Reservoir-Management Workflow Using an EnKF-Based Assisted-History-Matching Method,’ by A. Seiler, G. Evensen, J.-A. Skjervheim, J. Hove, and J.G. Vabo, StatoilHydro, prepared for the 2009 SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2-4 February. The ensemble Kalman filter (EnKF) is a tool to assist history matching. It uses sequential processing of measurements, is capable of handling large parameter sets, and it solves the combined state- and parameter-estimation problem. The proposed workflow was applied to a complex North Sea oil field. The EnKF provided an ensemble of history-matched reservoir models. The updated ensemble was used to predict the uncertainty in future production.  Introduction Reservoir modeling and history matching aim to deliver integrated reservoir models for reservoir-management purposes. These reservoir models must reproduce the historical field performance and must be consistent with all available static data (e.g., core, well-log, and seismic) and dynamic data (e.g., well-production, tracer-concentration, and 4D-seismic). Also, they should integrate current information about the reservoir and the associated uncertainty to enable real-time decisions. The EnKF was introduced for updating nonlinear ocean models and is a Monte Carlo approach in which errors are represented by an ensemble of realizations. Model parameters and state variables are updated sequentially as new measurements become available. The result is an updated ensemble of realizations, conditioned to all production data, that provides an improved estimate of the model parameters and state variables and their uncertainty. Previously, the focus was on estimating porosity and permeability fields in the simulation models. Here, parameters such as initial fluid contacts and fault- and vertical-transmissibility multipliers are included as additional uncertain parameters to be estimated. The full-length paper details an EnKF method for history matching reservoir-simulation models and discusses its relation to traditional methods. Assisted History Matching The EnKF method is an alternative to traditional assisted history matching. Conditioning reservoir stochastic realizations to production data is described generally as finding the optimal set of model parameters that minimizes the misfit between a set of measurements and the corresponding responses calculated on the realization of the stochastic model. However, it also can be formulated in a Bayesian framework as finding the posterior probability-density (PD) function of the parameters and the model state, given a set of measurements and a dynamic model with known uncertainties. The Bayesian formulation is the common starting point for traditional minimization methods and sequential-data-assimilation methods (detailed in the full-length paper). EnKF History-Matching Workflow The history-matching workflow, independent of the method used, involves three major steps. First, parameterization of the uncertain parameters, which are a characterization of the major uncertainty of the model solution, is identified. Then, a prior-error model is specified for the selected parameters on the basis of an initial uncertainty analysis. Finally, a solution method must be selected. All three steps may be equally important, and the selections and choices made will depend on the problem at hand.