Volume 52 Issue 4
Aug.  2024
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GAO Zelin, WANG Jiaqi, ZHANG Qizi. Research on architecture of intelligent logging interpretation software platform [J]. Petroleum Drilling Techniques, 2024, 52(4)：128-134. DOI: 10.11911/syztjs.2023119
Citation: GAO Zelin, WANG Jiaqi, ZHANG Qizi. Research on architecture of intelligent logging interpretation software platform [J]. Petroleum Drilling Techniques, 2024, 52(4)：128-134. DOI: 10.11911/syztjs.2023119
shu

Research on Architecture of Intelligent Logging Interpretation Software Platform

  • 1.

    Sinopec Research Institute of Petroleum Engineering Co., Ltd., Beijing, 102206, China

  • 2.

    Beijing Science and Technology Innovation Research Center, Beijing, 100744, China

  • 3.

    Shengli Well Logging Company of Sinopec Matrix Co., Ltd., Dongying, Shandong, 257000, China

More Information
  • Received Date: August 08, 2022
  • Revised Date: October 11, 2023
  • Available Online: November 06, 2023
    Graphical Abstract
    • Abstract

  • Intelligent logging interpretation technology provides new technical method for oil and gas development. However, in the actual production process, the algorithm can be inefficient due to configuration environment and other factors. Therefore, the distributed architecture technology suitable for intelligent logging interpretation was studied. From the perspective of application and development of logging software platform, the successful application cases of big data-related technologies in the internet industry were used for reference, and the technical idea of distributed processing was adopted to carry out system design and optimization, artificial intelligence support module development, and intelligent algorithm application testing, etc. As a result, an online cluster distributed processing mechanism based on a logging software platform was initially formed. It provided technical accumulation for the efficient fusion of intelligent algorithms and logging software. The algorithm test results showed that this mechanism could effectively reduce the pressure of memory and environment when the software runs the highly iterative intelligent algorithm and effectively shorten the time required for processing and interpretation of large volume data. Distributed architecture can be used as a feasible solution for intelligent logging interpretation software, and the research results provide technical support for intelligent logging interpretation.

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