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Book Chapter

How Incorporating More Data Reduces Uncertainty in Recovery Predictions

By
Fernando P. Campozana
Fernando P. Campozana
Petrobras Rio de Janeiro, Brazil
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Larry W. Lake
Larry W. Lake
Center for Petroleum and Geosystems Engineering The University of Texas at AustinAustin, Texas, U.S.A.
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Kamy Sepehrnoori
Kamy Sepehrnoori
Center for Petroleum and Geosystems Engineering The University of Texas at AustinAustin, Texas, U.S.A.
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Published:
January 01, 1999

Abstract

From the discovery to the abandonment of a petroleum reservoir, there are many decisions that involve economic risks because of uncertainty in the production forecast. This uncertainty may be quantified by performing stochastic reservoir modeling (SRM); however, it is not practical to apply SRM to account for new data every time the model is updated.

This paper suggests a novel procedure to estimate reservoir uncertainty (and its reduction) as a function of the amount and type of data used in the reservoir modeling. Two types of data are analyzed: conditioning data and well-test data; however, the same procedure can be applied to other data types.

SRM is typically performed for the following stages: discovery, primary production, secondary production, and infill drilling. From those results, a set of curves is generated that can be used to estimate (1) the uncertainty for any other situation and (2) the uncertainty reduction caused by the introduction of new wells (with and without well-test data) into the description.

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Figures & Tables

Contents

AAPG Memoir

Reservoir Characterization—Recent Advances

Richard A. Schatzinger
Richard A. Schatzinger
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John F. Jordan
John F. Jordan
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American Association of Petroleum Geologists
Volume
71
ISBN electronic:
9781629810720
Publication date:
January 01, 1999

GeoRef

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