Skip to Main Content
Book Chapter

Nonparametric Transformations for Data Correlation and Integration: From Theory to Practice

By
Akhil Datta-Gupta
Akhil Datta-Gupta
Department of Petroleum Engineering Texas A&M University College StationTexas, U.S.A.
Search for other works by this author on:
Guoping Xue
Guoping Xue
Department of Petroleum Engineering Texas A&M University College StationTexas, U.S.A.
Search for other works by this author on:
Sang Heon Lee
Sang Heon Lee
Department of Petroleum Engineering Texas A&M University College StationTexas, U.S.A.
Search for other works by this author on:
Published:
January 01, 1999

Abstract

The purpose of this paper is two-fold. First, we introduce the use of non-parametric transformations for correlating petrophysical data during reservoir characterization. Such transformations are completely data driven and do not require an a priori functional relationship between response and predictor variables, which is the case with traditional multiple regression. The transformations are very general, computationally efficient, and can easily handle mixed data types; for example, continuous variables such as porosity, and permeability, and categorical variables such as rock type and lithofacies. The power of the nonparametric transformation techniques for data correlation has been illustrated through synthetic and field examples. Second, we use these transformations to propose a two-stage approach for data integration during heterogeneity characterization. The principal advantages of our approach over traditional cokriging or cosimulation methods are: (1) it does not require a linear relationship between primary and secondary data, (2) it exploits the secondary information to its full potential by maximizing the correlation between the primary and secondary data, (3) it can be easily applied to cases where several types of secondary or soft data are involved, and (4) it significantly reduces variance function calculations and thus greatly facilitates non-Gaussian cosimulation. We demonstrate the data integration procedure using synthetic and field examples. The field example involves estimation of pore-footage distribution using well data and multiple seismic attributes

You do not currently have access to this article.
Don't already have an account? Register

Figures & Tables

Contents

AAPG Memoir

Reservoir Characterization—Recent Advances

Richard A. Schatzinger
Richard A. Schatzinger
Search for other works by this author on:
John F. Jordan
John F. Jordan
Search for other works by this author on:
American Association of Petroleum Geologists
Volume
71
ISBN electronic:
9781629810720
Publication date:
January 01, 1999

GeoRef

References

Related

A comprehensive resource of eBooks for researchers in the Earth Sciences

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

View Article Abstract & Purchase Options

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Subscribe Now