Research and practice on an artificial intelligence seismic interpretation mode based on the E&P Dream Cloud

doi:10.3969/j.issn.1672-7703.2020.05.012

Abstract

Abstract: A new seismic data interpretation mode has been proposed, which is based on the E&P Dream Cloud and deep learning methods. This new approach explores the combination of cloud computing and artificial intelligence technology in seismic data interpretation, taking advantage of their collaborative capabilities. It is based on practical R&D experience with deep-learning seismic interpretation software, which includes: (1) Data Lake and Computing Center of the E&P Dream Cloud provides a large volume of high-quality labeled data and a highly elastic computing platform on which to run the deep learning algorithm. (2) Deep-learning technology and traditional geophysical methods complement each other to establish a new process for seismic and geological interpretation. (3) The deep-learning module in the E&P Dream Cloud platform provides effective information support from seismic results for business decision-making within the E&P Dream Cloud. This mode considerably reduces the number of manual operations required and achieves higher prediction accuracy, laying a solid foundation for high-efficiency and high-precision oil and gas exploration. Experience in practice with real data has already produced encouraging results.

Key words: E&, P Dream Cloud, deep learning, artificial intelligence, seismic data interpretation

CLC Number:

Yang Ping, Zhan Shifan, Li Ming, Li Lei, Guo Rui, Shang Minqiang, Tao Chunfeng. Research and practice on an artificial intelligence seismic interpretation mode based on the E&P Dream Cloud[J]. China Petroleum Exploration, 2020, 25(5): 89-96.

References

[1] 杨金华，李晓光，孙乃达，等．未来10年极具发展潜力的20项油气勘探开发新技术 [J].石油科技论坛,2019,38(1):38-48. Yang Jinhua, Li Xiaoguang, Sun Naida, et al . Twenty items of new technology for oil and gas exploration and development in next decade[J]. Oil Forum, 2019,38(1):38-48.
[2] 王宏琳．通向智能勘探与生产之路[J].石油工业计算机应用，2016, 24(4):7-20. Wang Honglin. The road to smart E&P[J]. Computer Applications of Petroleum, 2016,24(4):7-20.
[3] 高志亮，石玉江，王娟，等．数字油田在中国及其发展[J].石油科技论坛，2015,34(3):33-38. Gao Zhiliang, Shi Yujiang, Wang Juan, et al . Development of digital oilfields in China[J]. Oil Forum, 2015,34(3):33-38.
[4] 李斌，刘伟，毕永斌，等．智慧油田建设与发展[J].石油科技论坛， 2018,37(3):47-52. Li Bin, Liu Wei, Bi Yongbin, et al . Construction and development of intelligent oilfields[J]. Oil Forum, 2018,37(3): 47-52.
[5] 杜金虎，时付更，张仲宏，等．中国石油勘探开发梦想云研究与实践[J].中国石油勘探，2020,25(1):58-66. Du Jinhu, Shi Fugeng, Zhang Zhonghong, et al . Research and practice of Dream Cloud for exploration and development of PetroChina[J]. China Petroleum Exploration, 2020,25(1):58-66.
[6] 张荣，李伟平，莫同．深度学习研究综述[J].信息与控制，2018, 47(4):5-17. Zhang Rong，Li Weiping，Mo Tong. Review of deep learning[J]. Information and Control, 2018,47(4):5-17.
[7] 郭珺，邹鑫，周子康，等．深度学习在油气地震勘探中的应用[J].中国石油和化工标准与质量，2019,39(20):86-90. Guo Jun, Zou Xin, Zhou Zikang, et al . Application of deep learning in oil and gas seismic exploration[J]. China Petroleum and Chemical Standard and Quality, 2019, 39(20):86-90.
[8] 赵改善．石油物探智能化发展之路：从自动化到智能化[J].石油物探， 2019,58(6):791-810. Zhao Gaishan. Road to intelligent petroleum geophysical exploration: from automatic to intelligent[J]. Geophysical Prospecting for Petroleum, 2019,58(6):791-810.
[9] 许子明，田杨锋．云计算的发展历史及其应用[J].信息记录材料， 2018,19(8):66-67. Xu Ziming, Tian Yangfeng. Development history and application of cloud computing[J]. Information Recording Material, 2018,19(8):66-67.
[10] 罗晓慧．浅谈云计算的发展[J].电子世界，2019(8):104. Luo Xiaohui. On the development of cloud computing[J]. Electronics World, 2019(8):104.
[11] 王雄．云计算的历史和优势[J].计算机与网络，2019,45(2):44. Wang Xiong. The history and advantages of cloud computing[J]. Computer & Network, 2019,45(2):44.
[12] 蒋万胜，李冰洁．论人工智能技术对人类社会发展的影响[J].西安财经学院学报，2020,33(1):23-29. Jiang Wansheng, Li Bingjie. On the revolutionary influence of artificial intelligence technology on the development of human society[J]. Journal of Xi’an University of Finance & Economics, 2020,33(1):23-29.
[13] 金弟，庄锡进，王启迪，等．一种地震资料解释系统[J].计算机系统应用，2014,23(8):63-67. Jin Di, Zhuang Xijin, Wang Qidi, et al . A seismic data interpretation system[J]. Computer Systems & Applications, 2014,23(8):63-67.