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Self-adaptively inpainting gaps between electrical image pads according to the image fabric

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Abstract

Images created from measurements made by wireline microresistivity imaging tools have longitudinal gaps when the well circumference exceeds the total width of the pad-mounted electrode arrays. The gap size depends on the tool design and borehole size, and the null data in these gaps negatively affect the quantitative evaluation of reservoirs. Images with linear and texture features obtained from microresistivity image logs have distinct dual fabric features because of logging principles and various geological phenomena. Linear image features usually include phenomena such as fractures, bedding, and unconformities. Contrarily, texture-based image features usually indicate phenomena such as vugs and rock matrices. According to the characteristics of this fabric-based binary image structure and guided by the practice of geological interpretation, an adaptive inpainting method for the blank gaps in microresistivity image logs is proposed. For images with linear features, a sinusoidal tracking inpainting algorithm based on an evaluation of the validity and continuity of pixel sets is used. Contrarily, the most similar target transplantation algorithm is applied to texture-based images. The results obtained for measured electrical imaging data showed that the full borehole image obtained by the proposed method, whether it was a linear structural image reflecting fracture and bedding or texture-based image reflecting the matrix and pore of rock, had substantially good inpainting quality with enhanced visual connectivity. The proposed method was effective for inpainting electrical image logs with large gaps and high angle fractures with high heterogeneity. Moreover, ladder and block artifacts were rare, and the inpainting marks were not obvious. In addition, detailed full borehole images obtained by the proposed method will provide an essential basis for interpreting geological phenomena and reservoir parameters.

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Acknowledgments

The authors are grateful to Researcher Huo Shoudong, Professor Liu Hua-qing, and Li Xiang-bo for their suggestions. Thanks to the comments and suggestions of the reviewers and the extensive help provided by the editorial staff .

Author information

Authors and Affiliations

  1. School of Geology and Mining Engineering, Xinjiang University, Urumqi, 830046, China

    Zhao-hui Zhang

  2. Exploration and Development Research Institute of Changqing Oilfield Company, PetroChina, Xi’an, 710018, China

    Jing Deng

  3. Xinjiang branch of CNPC Logging, Karamay, 834000, China

    Hua-yong Chen & Hong-guo Yu

Authors
  1. Zhao-hui Zhang
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  2. Jing Deng
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  3. Hua-yong Chen
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  4. Hong-guo Yu
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Corresponding author

Correspondence to Zhao-hui Zhang.

Additional information

This work was supported by Initial Scientific Research Fund for Doctor of Xinjiang University (No. 620321016), Gansu Provincial Natural Science Foundation of China (No. 17JR5RA313), Key Laboratory of Petroleum Resource Research of Chinese Academy of Science Foundation (No. KFJJ2016-02).

Zhang Zhao-hui, assistant professor, graduated from Yangtze University with a bachelor’s degree in 2005, graduated from Yangtze University with a master’s degree in 2008, received a Ph.D. in Mineralogy, Petrology & Mineral Deposits from the University of Chinese Academy of Sciences in 2020. He is currently working at School of Geology and Mining Engineering Xinjiang University, Urumqi, China. His research interests include well logging processing and interpretation methods for unconventional reservoirs.

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Zhang, Zh., Deng, J., Chen, Hy. et al. Self-adaptively inpainting gaps between electrical image pads according to the image fabric. Appl. Geophys. 17, 823–833 (2020). https://doi.org/10.1007/s11770-020-0878-7

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  • DOI: https://doi.org/10.1007/s11770-020-0878-7

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