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Book Chapter

History Matching of Reservoir Models by Ensemble Kalman Filtering: The State of the Art and a Sensitivity Study

By
Leila Heidari
Leila Heidari
IFP Energies nouvelles, Reservoir Engineering Department, Rueil Malmaison, France
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Véronique Gervais
Véronique Gervais
IFP Energies nouvelles, Reservoir Engineering Department, Rueil Malmaison, France
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Mickaële Le Ravalec
Mickaële Le Ravalec
IFP Energies nouvelles, Reservoir Engineering Department, Rueil Malmaison, France
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Hans Wackernagel
Hans Wackernagel
Geostatistics Group, Centre de Géosciences, MINES ParisTech, Fontainebleau, France
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Published:
January 01, 2011

Abstract

History matching is to integrate dynamic data in the reservoir model–building process. These data, acquired during the production life of a reservoir, can be production data, such as well pressures, oil production rates or water production rates, or four-dimensional seismic–related data. The ensemble Kalman filter (EnKF) is a sequential history-matching method that integrates the production data to the reservoir model as soon as they are acquired. Its ease of implementation and efficiency has resulted in various applications, such as history matching of production and seismic data.

We focus on the use of the EnKF for history match of a synthetic reservoir model. First, the method of ensemble Kalman filtering is reviewed. Then the geologic and reservoir characteristics of a case study are described. Several experiments are performed to investigate the benefits and limitations of the EnKF approach in building reservoir models that reproduce the production data. Last, special attention is paid to the sensitivity of the method to a set of parameters, including ensemble size, assimilation time interval, data uncertainty, and choice of initial ensemble.

A reservoir model relies on two sources of data: static data and dynamic data. Although static data (e.g., geologic observations, measurements on cores, logs, etc.) are constant through time, dynamic data change with time. They include production data measured at wells, such as pressures and oil production rates. As static data are too sparse to deterministically describe the spatial variation in transport properties (porosity and permeability) within the reservoir, they serve to characterize the parameters of a geostatistical model. Therefore, we refer to a stochastic framework in which reservoir models are viewed as realizations of a random function.

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Contents

AAPG Memoir

Uncertainty Analysis and Reservoir Modeling: Developing and Managing Assets in an Uncertain World

Y. Zee Ma
Y. Zee Ma
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Paul R. La Pointe
Paul R. La Pointe
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American Association of Petroleum Geologists
Volume
96
ISBN electronic:
9781629810102
Publication date:
January 01, 2011

GeoRef

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