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Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation

Liu Xingye, Li Jingye, Chen Xiaohong, Zhou Lin and Guo Kangkang
Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation
Interpretation (Tulsa) (May 2017) 5 (2): SE1-SE10

Abstract

The accurate identification of lithofacies is indispensable for reservoir parameter prediction. In recent years, the application of multivariate statistical methods has gained more and more attention in petroleum geology. In terms of the identification for lithofacies, the commonly used multivariate statistical methods include discriminant analysis and cluster analysis. Fisher and Bayesian discriminant analyses are two different discriminant analysis methods, which include intrinsic advantages and disadvantages. Given the discriminant efficiency of different methods, calculation cost, difficulty in the degree of determining the parameters, and the ability to analyze statistical characteristics of data, we put forward a new method combined with seismic information to classify reservoir lithologies and pore fluids. This method integrates the advantages of Fisher discrimination, the kernel function, and Bayesian discrimination. First, we analyze training data and search a projection direction. Then, data are transformed through Fisher transformation according to this direction and different kinds of facies can be distinguished more efficiently by exploiting transformed data than by using primitive data. Subsequently, using the kernel function estimates the conditional probability density function of the transformed variable. A classifier is constructed based on Bayesian theory. Then, the pending data are input to the classifier and the solution whose posteriori probability reaches the maximum is extracted as the predicted result at each grid node. An a posteriori probability distribution of predicted lithofacies can be acquired as well, from which interpreters can evaluate the uncertainty of the results. The ultimate goal of this study is to provide a novel and efficient lithofacies discriminant method. Tests on model and field data indicate that our method can obtain more accurate identification results with less uncertainty compared with conventional Fisher approaches and Bayesian methods.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 5
Serial Issue: 2
Title: Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation
Affiliation: China University of Petroleum, National Engineering Laboratory for Offshore Oil Exploration, Beijing, China
Pages: SE1-SE10
Published: 201705
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 25
Accession Number: 2017-069974
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 2 tables, sects.
N20°00'00" - N53°00'00", E74°00'00" - E135°00'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2017, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201737
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