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Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in lower Brushy Canyon channeled sandstones, Southeast New Mexico

Daniel J. Leiphart and Bruce S. Hart
Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in lower Brushy Canyon channeled sandstones, Southeast New Mexico
Geophysics (October 2001) 66 (5): 1349-1358

Abstract

The Lower Brushy Canyon Formation of the Delaware Basin, New Mexico, consists of a series of overlying sand-filled channels and associated fans separated by laterally extensive organic siltstone and carbonate interbeds. This laterally and vertically complex geology creates the need for precise interwell estimation of reservoir properties. In this paper we integrate wireline log and 3-D seismic data to directly predict porosity in the area of an existing oil field in southeast New Mexico. The 3-D seismic data were used to interpret the location of major stratigraphic markers between wells, and these seismic horizons were used to constrain a time window for a volume-based attribute analysis. Stepwise regression and crossvalidation were used to combine seismic attributes to predict porosity in wells where the porosity was known from the well logs. The results of a linear regression porosity model showed good correlation (r (super 2) = 0.74) between seven seismic attributes and the observed porosity logs at 11 wells in the study area, but the porosity volume created from the regression model did not display the known geologic features. A probabilistic neural network was then trained to look for a nonlinear relationship between the input data (the seven attributes) and the observed porosity at the 11 wells. The correlation was better (r (super 2) = 0.82), but the biggest improvement over the linear regression model came in the more geologically realistic predicted porosity distribution.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 66
Serial Issue: 5
Title: Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in lower Brushy Canyon channeled sandstones, Southeast New Mexico
Affiliation: Anadarko Petrolem Corporation, Houston, TX, United States
Pages: 1349-1358
Published: 200110
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 26
Accession Number: 2002-003692
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 2 tables
N31°30'00" - N37°00'00", W109°04'60" - W103°00'00"
Secondary Affiliation: McGill University, CAN, Canada
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2017, American Geosciences Institute.
Update Code: 200202
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