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Reservoir Characterization and Uncertainty Assessment Using the Ensemble Kalman Filter: Application to Reservoir Development

By
Deepak Devegowda
Deepak Devegowda
University of Oklahoma, Norman, Oklahoma, U.S.A.
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Chao Gao
Chao Gao
University of Oklahoma, Norman, Oklahoma, U.S.A.
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Published:
January 01, 2011

Abstract

This chapter describes the ensemble Kalman filter for the purpose of reservoir characterization and uncertainty assessment through the assimilation of dynamic data. The key advantages of the ensemble Kalman filter approach are its ability to handle diverse measurements efficiently and in real time, thereby exploiting the continuous stream of data from operational well sensors and field monitoring systems. Two examples, including a field-level study, are presented to illustrate these advantages. Finally, we detail some of the difficulties associated with the ensemble Kalman filter formulation and describe some recent developments that address these challenges.

The economic impact of inaccurate predictions of future petroleum reservoir performance is substantial, making proper characterization of the reservoir and uncertainty analyses in production forecasts a crucial aspect of any reservoir development strategy. In the petroleum industry, management decisions and business projections are guided by findings from reservoir simulation studies, with the ultimate goal being production optimization to improve reservoir performance and productivity. The validity of the simulation study and thus the reliability of reservoir management decisions depend on the accuracy with which the geology of the reservoir, the structural and stratigraphic compartmentalization, and the associated complexities are understood, characterized, and modeled. In building the geologic model, however, the available data are commonly restricted to a sparse set of static information, such as measurements derived from a limited number of wells, the interpretation of the depositional environment and subsequent geologic events, outcrop studies, geologic controls on reservoir quality, and the processing of any available seismic surveys (Deutsch and Journel, 1992). The geostatistical modeling of the relevant reservoir properties anchored on these observations enables the construction of the subsurface geologic model (Chiles and Delfiner, 1999; Caers and Zhang, 2004; Ma et al., 2009). Reservoir simulation models are typically scaled-up derivatives of the corresponding geologic models and are commonly referred to as static models that are expected to be geologically consistent and physically meaningful.

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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

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