高泽林,王佳琦,张启子. 智能化测井解释软件平台的架构研究[J]. 石油钻探技术,2024, 52(1):1-7. DOI: 10.11911/syztjs.2023119
引用本文: 高泽林,王佳琦,张启子. 智能化测井解释软件平台的架构研究[J]. 石油钻探技术,2024, 52(1):1-7. DOI: 10.11911/syztjs.2023119
GAO Zelin, WANG Jiaqi, ZHANG Qizi. Research on architecture of intelligent logging interpretation software platform [J]. Petroleum Drilling Techniques,2024, 52(1):1-7. 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(1):1-7. DOI: 10.11911/syztjs.2023119

智能化测井解释软件平台的架构研究

Research on Architecture of Intelligent Logging Interpretation Software Platform

  • 摘要: 智能化测井解释技术为油气开发提供了新的技术途径,但实际生产过程中由于受配置环境等因素影响算法运行效率低,为此开展了适用于智能化测井解释的分布式架构技术研究。从测井软件平台的应用开发角度出发,借鉴大数据相关技术在互联网行业应用的成功案例,采用分布式处理的技术思路,开展了系统设计与优化、人工智能支持模块开发及智能算法应用测试等工作,初步形成了基于测井软件平台的线上集群分布式处理机制,为智能算法与测井软件的高效融合提供了技术积累。算法测试结果表明,该机制能够减轻软件运行高迭代性智能算法时的内存、环境等压力,缩短大体量数据处理解释所需的时间。分布式架构可作为智能化测井解释软件的可行方案,也为智能化测井解释提供了技术支撑。

     

    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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