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Model Error Estimation Improves Forecasting

Ensemble-based methods have proved to be effective in calibrating multiple reservoir models with historical production data. However, because of the complex nature of hydrocarbon reservoirs, the model calibration is always a simplified version of reality with coarse representation and unmodeled physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when “perfect” model parameters are used as model input is known as model error. The results of the authors’ research showed a promising benefits from use of a systematic procedure of model diagnostics, model improvement, and model-error quantification during data assimilations.

Introduction

For reservoir history matching using ensemble-based methods, typically the model error is either ignored or treated by inflating the observation error covariance beyond the actual measurement errors. This simple inflation of measurement error, however, does not account for the correlated structure of the model error and can result in suboptimal analysis.

In this paper, the authors investigate and modify a work flow proposed previously in the literature for addressing model errors in assimilation of production data. The correlated total error for various time series of production data is estimated from the data residual after a standard history-matching process using the Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). Then the history-matching process is repeated using LM-EnRML with the estimated total error. To the best of the authors’ knowledge, this is the first real field application of quantifying model error through data residual for improved estimation and forecasting.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19142, “Improved Estimation and Forecast Through Model Error Estimation—Norne Field Example,” by Minjie Lu and Yan Chen, Total, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission.
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Model Error Estimation Improves Forecasting

01 April 2020

Volume: 72 | Issue: 4

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