测井人工智能应用场景及实现

doi:10.3969/j.issn.1002-302X.2024.06.004

摘要/Abstract

摘要： 测井作为油气勘探开发过程中的重要一环，面临勘探开发领域日趋复杂、数据问题突出、应用接口不统一、模型泛化性差、多维多尺度数据整合能力弱等挑战。中油测井在数智转型过程中，以建设“数智中油测井”为目标，围绕仪器装备智能研发全生命周期管理、生产作业智能操控全流程管控、测井数据智能解释一体化集成应用3条主线，加强人工智能与测井技术深度融合，打造支撑当前、引领未来的数智化能力，为油气勘探开发提供更可靠的技术支持。目前已在多个场景中实现人工智能应用且成效显著，未来将从仪器装备数字孪生应用、井下物联网、多维多尺度数据融合等方面持续着力发展人工智能技术。

关键词: 测井, 人工智能, 智能制造, 数字孪生, 智能解释

Abstract: Logging is an important link in oil an gas exploration and development. It faces a series of challenges, such as increasingly complicated exploration and development area, serious data problems, incompatible application interfaces, poor generalization of models and weak ability for integration of multi-dimension and multi-size data. In the intelligent and digital transition, China National Logging Corporation aimed at construction of “intelligent and digital CNLC” and focused on three main areas--instrument and equipment intelligent R&D life-cycle management, production and service intelligent control full-process management and integration of intelligent interpretation with logging data. The company enhanced in-depth integration of artificial intelligence with logging technology and strove to establish the intelligent and digital ability to support its business both at the present time and in the future,thus providing reliable technological support for oil and gas exploration and development. Currently, AI application has been realized in a number of scenarios with good results achieved. In the future, the efforts for sustainable development of AI technology will be concentrated on instrument and equipment digital twin application, downhole IOT and integration of multi-dimension and multi-size data.

Key words: logging, artificial intelligence, intelligent manufacture, digital twin, intelligent interpretation

中图分类号:

P631.81
TP18

石玉江, 周军, 李雄伟, 张娟, 陈义祥, 李鹏飞, 崔式涛. 测井人工智能应用场景及实现[J]. 石油科技论坛, 2024, 43(6): 28-37.

Shi Yujiang, Zhou Jun, Li Xiongwei, Zhang Juan, Chen Yixiang, Li Pengfei, Cui Shitao. Scenario and Realization of Logging Artificial Intelligence Application[J]. Petroleum Science and Technology Forum, 2024, 43(6): 28-37.

参考文献

[1] 匡立春, 刘合, 任义丽, 等. 人工智能在石油勘探开发领域的应用现状与发展趋势[J]. 石油勘探与开发, 2021, 48(1): 1-11.
Kuang Lichun, Liu He, Ren Yili, et al. Application and development trend of artificial intelligence in petroleum exploration and development[J]. Petroleum Exploration and Development, 2021, 48(1): 1-11.
[2] 刘合. 油气勘探开发数字化转型人工智能应用大势所趋[J]. 石油科技论坛, 2023, 42(3): 1-9, 47.
Liu He. Digital transformation of oil and gas exploration and development; unstoppable artificial intelligence application[J]. Petroleum Science and Technology Forum, 2023, 42 (3): 1-9, 47.
[3] 唐大麟. 人工智能赋能油气行业向高质量跃升: 访中国工程院院士刘合[J]. 中国石油企业, 2021(4): 24-27, 111.
Tang Dalin. Artificial intelligence empowers the oil and gas industry to develop towards high quality: Interview with Liu He, academician of China Engineering Academician[J]. China Petroleum Enterprise, 2021(4): 24-27, 111.
[4] 曾涛, 袁园. 国际油服公司数字化转型和智能化发展策略分析[J]. 国际石油经济, 2022, 30(7): 36-43.
Zeng Tao, Yuan Yuan. Analysis on digital transition and intelligent development of international oilfield service companies[J].International Petroleum Economics, 2022, 30(7): 36-43.
[5] 陈溯, 安鹏, 吴刚, 等. 海上智能油田建设研究[J]. 石油科技论坛,2020, 39(5): 16-23.
Chen Su, An Peng, Wu Gang, et al. Research on offshore intelligent oilfield construction[J]. Petroleum Science and Technology Forum,2020, 39(5): 16-23.
[6] 石玉江, 刘国强, 钟吉彬, 等. 基于大数据的测井智能解释系统开发与应用[J]. 中国石油勘探, 2021, 26(2): 113-126.
Shi Yujiang, Liu Guoqiang, Zhong Jibin, et al. Development and application of intelligent logging interpretation system based on big data[J]. China Petroleum Exploration, 2021, 26(2): 113-126.
[7] 谢晓辉, 安鹏. 中国海油人工智能建设探索与实践[J]. 石油科技论坛, 2023, 42(3): 22-29.
Xie Xiaohui, An Peng. Research and practice of CNOOC AI application[J]. Petroleum Science and Technology Forum, 2023,42(3):22-29.
[8] 黄玉峰, 马磊, 岳永军, 等. 浅谈智能机器人在石油勘探领域中的应用[J]. 物探装备, 2018, 28(5): 300-303.
Huang Yufeng, Ma Lei, Yue Yongjun, et al. The application of intelligent robot in oil and gas survey[J]. Equipment for Geophysical Prospecting, 2018, 28(5): 300-303.
[9] 邹文波. 人工智能研究现状及其在测井领域的应用[J]. 测井技术,2020, 44(4): 323-328.
Zou Wenbo. Artificial intelligence research status and applications in well logging[J]. Well Logging Technology, 2020, 44(4): 323-328.
[10] 王华, 张雨顺. 测井资料人工智能处理解释的现状及展望[J]. 测井技术, 2021, 45(4): 345-356.
Wang Hua, Zhang Yushun. Research status and prospect of artificial intelligence in logging data processing and interpretation[J]. Well Logging Technology, 2021, 45(4): 345-356.
[11] 张野, 李明超, 韩帅. 基于岩石图像深度学习的岩性自动识别与分类方法[J]. 岩石学报, 2018, 34(2): 333-342.
Zhang Ye, Li Mingchao, Han Shuai. Automatic identification and classification in lithology based on deep learning in rock images[J].Acta Petrologica Sinica, 2018,34(2): 333-342.
[12] 段友祥, 李根田, 孙歧峰. 卷积神经网络在储层预测中的应用研究[J]. 通信学报, 2016, 37(S1): 1-9.
Duan Youxiang, Li Gentian, Sun Qifeng. Research on convolutional neural network for reservoir parameter prediction [J]. Journal of Communications, 2016, 37(S1): 1-9.
[13] 郑宇哲, 叶朝辉, 刘西恩, 等. 基于深度学习的储层物性参数预测方法研究[J]. 电子世界, 2018(4): 23-26.
Zheng Yuzhe, Ye Chaohui, Liu Xi’en, et al. Research on prediction method of reservoir physical properties based on deep learning[J]. Electronics World, 2018(4): 23-26.
[14] 孙龙祥, 韩宏伟, 冯德永, 等. 基于人工智能的测井地层划分方法研究现状与展望[J]. 油气地质与采收率, 2023, 30(3): 49-58.
Sun Longxiang, Han Hongwei, Feng Deyong, et al. Research status and outlook of logging stratigraphic division methods based on artificial intelligence[J]. Petroleum Geology and Recovery Efficiency,2023, 30 (3): 49-58.
[15] 吴奇, 梁兴, 鲜成钢, 等. 地质—工程一体化高效开发中国南方海相页岩气[J]. 中国石油勘探, 2015, 20(4): 1-23.
Wu Qi, Liang Xing, Xian Chenggang, et al. Geoscience-to-production integration ensures effective and efficient south China marine shale gas development[J]. China Petroleum Exploration, 2015, 20(4): 1-23.
[16] 曹宇. 人工智能在石油勘探中的应用[J]. 信息系统工程, 2022(5):56-59.
Cao Yu. Application of artificial intelligence in petroleum exploration[J]. China CIO News, 2022(5): 56-59.
[17] 王云波, 李铁. 智能制造发展过程的阶段及其特征[J]. 冶金自动化,2020, 44(5): 1-7, 55.
Wang Yunbo, Li Tie. Stages and characteristics of intelligent manufacturing development process[J]. Metallurgical Industry Automation, 2020, 44(5): 1-7, 55.
[18] 陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.
Tao Fei, Zhang He, Qi Qinglin, et al. Theory of digital twin modeling and its application[J]. Computer Integrated Manufacturing Systems,2021, 27(1): 1-15.