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Open Access

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

Institute of Computer Application Technology, PetroChina Research Institute of Petroleum Exploration and Development (RIPED), Beijing 100083, China.
Department of Well Logging & Remote Sensing Technology of RIPED, Beijing 100083, China.
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Abstract

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata. The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors. As a result, manual interpretation accuracy is low. Therefore, it is highly useful to develop effective automatic imaging logging interpretation by machine learning. Resistivity imaging logging is the most widely used technology for imaging logging. In this paper, we propose an automatic extraction procedure for the geological features in resistivity imaging logging images. This procedure is based on machine learning and achieves good results in practical applications. Acknowledging that the existence of valueless data significantly affects the recognition effect, we propose three strategies for the identification of valueless data based on binary classification. We compare the effect of the three strategies both on an experimental dataset and in a production environment, and find that the merging method is the best performing of the three strategies. It effectively identifies the valueless data in the well logging images, thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

Keywords

machine learningbinary classificationmulticlass classificationoutlier detectionimaging logging

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Tsinghua Science and Technology
Volume 25 Issue 2,
April 2020
Pages 281-293
DOI: 10.26599/TST.2019.9010020
Cite this article:
Ren Y, Gong R, Feng Z, et al. Valuable Data Extraction for Resistivity Imaging Logging Interpretation. Tsinghua Science and Technology, 2020, 25(2): 281-293. https://doi.org/10.26599/TST.2019.9010020

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Received: 22 December 2018
Revised: 17 April 2019
Accepted: 14 May 2019
Published: 02 September 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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