History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother

Abstract

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 164902, ’History Matching of the Norne Full-Field Model Using an Iterative Ensemble Smoother,’ by Yan Chen, SPE, International Research Institute of Stavanger, and Dean S. Oliver, SPE, Uni Center for Integrated Petroleum Research, prepared for the 2013 EAGE Annual Conference and Exhibition/SPE Europec, London, 10-13 June. The paper has been peer reviewed and is scheduled for publication in the SPE Reservoir Evaluation & Engineering journal. This paper describes the application of the iterative ensemble smoother to the history matching of the Norne field in the North Sea, which has a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of identification of important variables, generation of an initial ensemble, plausibility of results, and efficiency of minimization. Introduction The Norne field produces from an oil and gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and nine injectors in the field. Water-alternating- gas injection is used as the depletion strategy. Production rates of oil, gas, and water of the 22 producers from 1997 to 2006 and repeat formation tester (RFT) pressure from 14 wells are available for model calibration. The full-field simulation model has 22 layers, each with dimension of 46×112 cells. The total number of active cell is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother [Levenberg- Marquardt ensemble randomized maximum likelihood (LM-EnRML)] is used for history matching the Norne full-field model using production data and RFT pressure. The model parameters that are updated include permeability, porosity, and net-to-gross ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from the LM-EnRML. The LM-EnRML is able to achieve improved data match compared with the manually history matched model after three iterations. Updates from the LM-EnRML do not introduce artifacts in the property fields as are produced in the manually history matched model. The automated workflow is also much less labor intensive than manual history matching. Ensemble-based methods use information from an ensemble of reservoir models to compute directions of change to the model. The most popular of these methods, the ensemble Kalman filter (EnKF), has been successfully applied on synthetic test cases in which well-bywell data match required many degrees of freedom.