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Porosity inversion by Caianiello neural networks with Levenberg-Marquardt optimization

Cyril D. Boateng, Fu Liyun, Yu Wu and Xizhu Guan
Porosity inversion by Caianiello neural networks with Levenberg-Marquardt optimization
Interpretation (Tulsa) (May 2017) 5 (3): SL33-SL42

Abstract

Caianiello neural networks (CNNs) incorporated with the Robinson seismic convolutional model are modified by the Levenberg-Marquardt algorithm to improve convergence. CNNs are extended to the multiattribute domain for reservoir property inversion, with time-varying signal processing by a frequency-domain block implementation using fast Fourier transforms. Optimal inversion can be achieved by applying the Levenberg-Marquardt optimization to multiattribute domain CNNs for convergency improvement due to its ability to swing between the steepest-descent and Gauss-Newton algorithms. The methodology is applied to porosity estimation in an oilfield with six wells in the Bohai Basin of China. Cross-validation results indicate significant correlation between actual porosity logs and predicted porosity logs. Compared with a traditional method, our technique is robust.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 5
Serial Issue: 3
Title: Porosity inversion by Caianiello neural networks with Levenberg-Marquardt optimization
Affiliation: Chinese Academy of Sciences, Institute of Geology and Geophysics, Key Laboratory of Petroleum Resource Research, Beijing, China
Pages: SL33-SL42
Published: 201705
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 56
Accession Number: 2017-074499
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table, sects.
N38°00'00" - N40°00'00", E117°00'00" - E119°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: 201739
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