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Enhanced Reservoir Characterization Using Spectral Decomposition and Neural Network Inversion: A Carbonate Case History from the Chiapas of Southern Mexico

By
Michael D. Burnett
Michael D. Burnett
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Dr. John P. Castagna
Dr. John P. Castagna
Fusion Petroleum Technologies, Inc. 1818 W. Boyd St. Ste A105 Norman, Oklahoma 73069
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Camargo German
Camargo German
Fusion Petroleum Technologies, Inc. 1818 W. Boyd St. Ste A105 Norman, Oklahoma 73069
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Dr. He Chen
Dr. He Chen
Fusion Petroleum Technologies, Inc. 1818 W. Boyd St. Ste A105 Norman, Oklahoma 73069
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Julian Juarez Sanchez
Julian Juarez Sanchez
Petroleos Mexicanos C.P. 86030 Villahermosa Tabasco, Mexico
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Alberto Santana
Alberto Santana
Petroleos Mexicanos C.P. 86030 Villahermosa Tabasco, Mexico
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Efrain Mendez-Hernandez
Efrain Mendez-Hernandez
Petroleos Mexicanos C.P. 86030 Villahermosa Tabasco, Mexico
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Published:
December 01, 2006

Abstract

A reservoir study was conducted at Gaucho Field in the Chiapas of Southern Mexico, the primary objective of which was to determine porosity in the base of the upper Cretaceous carbonate in order to facilitate further field development. Conventional seismic impedance inversion alone did not adequately predict porosity nor did neural network predictions using conventional seismic attributes. Spectral decomposition and neural network inversion were integrated to produce an estimated porosity cube at the target level that provided excellent porosity indication in validation wells. The lateral variation of porosities within the area ranged from about 2% to more than 30%. Thus, the application of these techniques allowed final adjustment of drilling locations, in order to capture the maximum local porosity possible. Resulting porosity maps within the field area are shown to have important implications for field development and further exploration in this area.

For spectral decomposition, this study illustrates the relationship between porosity thickness and peak frequency and between the magnitude of the average effective zone porosity and peak amplitude. Additionally, the study demonstrates the importance of training a neural network properly with (1) appropriate input attributes and (2) utilization of wells which cover the spectrum of possible porosity encountered in the area. We show how such a methodology can be applied to similar carbonate reservoirs so as to distinguish locations having minimal to no effective porosity from areas having excellent porosity where additional development drilling can be fruitful.

The aim of this paper is to showcase an integrated workflow for this type of study, rather than to focus on results.

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GCSSEPM

Reservoir Characterization: Integrating Technology and Business Practices

Roger M. Slatt
Roger M. Slatt
Houston, Texas
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Norman c. Rosen
Norman c. Rosen
Houston, Texas
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Michael Bowman
Michael Bowman
Houston, Texas
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John Castagna
John Castagna
Houston, Texas
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Timothy Good
Timothy Good
Houston, Texas
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Robert Loucks
Robert Loucks
Houston, Texas
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Rebecca Latimer
Rebecca Latimer
Houston, Texas
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Mark Scheihing
Mark Scheihing
Houston, Texas
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Hu Smith
Hu Smith
Houston, Texas
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SEPM Society for Sedimentary Geology
Volume
26
ISBN electronic:
978-0-9836096-4-3
Publication date:
December 01, 2006

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