Estimating uncertainty bounds in field production using ensemble-based methods

Estimating uncertainty bounds in field production using ensemble-based methods Ensemble-based methods have been successfully applied in reservoir history-matching problems in the last decade. Among the advantages normally attributed to these methods, the fact that they generate multiple realizations of the model is one of the most important. By simulating these realizations, one can estimate the uncertainty in the production forecast for the field. However, because of limitations related to the use of relatively small ensembles, these methods often underestimate the posterior variance in the reservoir model parameters. Consequently, they tend to underestimate uncertainty in production forecasts. This paper introduces a simple procedure to evaluate the uncertainty bounds in the field production using ensemble-based data assimilation. The implementation of the proposed method is straightforward requiring very few modifications in a standard data assimilation code. The method was tested against the PUNQ-S3 case and a real field problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Petroleum Science and Engineering Elsevier

Estimating uncertainty bounds in field production using ensemble-based methods

Estimating uncertainty bounds in field production using ensemble-based methods

Journal of Petroleum Science and Engineering 145 (2016) 648–656 Contents lists available at ScienceDirect Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol Estimating uncertainty bounds in field production using ensemble-based methods Alexandre A. Emerick Petrobras Research and Development Center – CENPES, Av. Horácio de Macedo 950, Cidade Universitária, Rio de Janeiro, RJ 21941-915, Brazil a r ti cle in fo abstract Article history: Ensemble-based methods have been successfully applied in reservoir history-matching problems in the Received 24 January 2016 last decade. Among the advantages normally attributed to these methods, the fact that they generate Received in revised form multiple realizations of the model is one of the most important. By simulating these realizations, one can 31 May 2016 estimate the uncertainty in the production forecast for the field. However, because of limitations related Accepted 22 June 2016 to the use of relatively small ensembles, these methods often underestimate the posterior variance in the Available online 25 June 2016 reservoir model parameters. Consequently, they tend to underestimate uncertainty in production fore- Keywords: casts. This paper introduces a simple procedure to evaluate the uncertainty bounds in the field pro- Uncertainty bounbs duction using ensemble-based data assimilation. The implementation of the proposed method is History matching straightforward requiring very few modifications in a standard data assimilation code. The method was Ensemble-based methods tested against the PUNQ-S3 case and a real field problem. Ensemble smoother with multiple data & 2016 Elsevier B.V. All rights reserved. assimilation 1. Introduction Carlo (Hastings, 1970; Tierney, 1994) are computationally too de- manding for large-scale field applications. Therefore, less compu- Reservoir simulation plays an important role in the develop- tationally demanding...
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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0920-4105
eISSN
1873-4715
D.O.I.
10.1016/j.petrol.2016.06.037
Publisher site
See Article on Publisher Site

Abstract

Ensemble-based methods have been successfully applied in reservoir history-matching problems in the last decade. Among the advantages normally attributed to these methods, the fact that they generate multiple realizations of the model is one of the most important. By simulating these realizations, one can estimate the uncertainty in the production forecast for the field. However, because of limitations related to the use of relatively small ensembles, these methods often underestimate the posterior variance in the reservoir model parameters. Consequently, they tend to underestimate uncertainty in production forecasts. This paper introduces a simple procedure to evaluate the uncertainty bounds in the field production using ensemble-based data assimilation. The implementation of the proposed method is straightforward requiring very few modifications in a standard data assimilation code. The method was tested against the PUNQ-S3 case and a real field problem.

Journal

Journal of Petroleum Science and EngineeringElsevier

Published: Sep 1, 2016

References

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