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Delineation of bed boundaries of array induction logging curves using deep learning

Delineation of bed boundaries of array induction logging curves using deep learning Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data. Traditionally, the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference. However, measurement error or resolution mismatch may lead to misjudgment of the boundary. In view of the shortcomings of traditional methods, this paper presents an automatic layering algorithm of array induction logging curves based on deep learning. In this algorithm, a locally connected convolution neural network is used, and the generalization ability of the network is improved by enlarging the training set, optimizing the window length and threshold, and strengthening the layering effect. Simulation and field data show the effectiveness of the proposed algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Geophysics Springer Journals

Delineation of bed boundaries of array induction logging curves using deep learning

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References (27)

Publisher
Springer Journals
Copyright
Copyright © The Editorial Department of APPLIED GEOPHYSICS 2021
ISSN
1672-7975
eISSN
1993-0658
DOI
10.1007/s11770-021-0851-0
Publisher site
See Article on Publisher Site

Abstract

Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data. Traditionally, the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference. However, measurement error or resolution mismatch may lead to misjudgment of the boundary. In view of the shortcomings of traditional methods, this paper presents an automatic layering algorithm of array induction logging curves based on deep learning. In this algorithm, a locally connected convolution neural network is used, and the generalization ability of the network is improved by enlarging the training set, optimizing the window length and threshold, and strengthening the layering effect. Simulation and field data show the effectiveness of the proposed algorithm.

Journal

Applied Geophysics – Springer Journals

Published: Mar 1, 2021

Keywords: Deep learning; array induction logging; layering algorithm

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