页岩气田智能化生产辅助决策系统应用效果评价

doi:10.13809/j.cnki.cn32-1825/te.2021.04.019

摘要/Abstract

摘要：

页岩气的成功开发,有效推动了中国能源结构优化,但在页岩气井生产规律、合理生产制度、现场管理等方面,传统的人工+计算机模式已无法满足同步跟踪分析及精细气藏管理的需要,且国内外尚无成熟的理论、技术和经验供借鉴。为实现异常的快速反应、气藏的高效管理,在涪陵页岩气田的开发过程中,同步启动了生产辅助决策系统的开发与建设工作。从低压、积液、生产制度变化等方向开展了预警研究,并对单井的生产规律变化进行短期、中期、长期分析与预测,实现了数据变信息,信息促决策的智能化管理目标,有效支撑了页岩气田现场管理,并保障了涪陵页岩气田持续稳产。涪陵页岩气田率先探索的页岩气田智能化建设及应用,为我国页岩气精细化气藏管理奠定了良好基础,具有重要的参考价值和推广应用前景。

关键词: 智能化, 辅助决策, 决策系统, 精细化管理, 页岩气田

Abstract:

The successful development of shale gas has effectively promoted the optimization of China’s energy structure. However, in terms of shale gas well production rules, reasonable production systems and on-site management, the traditional manual and computer operating mode can no longer meet the needs of simultaneous tracking analysis and fine gas reservoir management. Meanwhile, there is no mature theory, technology and experience for reference at home and abroad. In order to achieve rapid response for abnormal situation and efficient management of gas reservoirs, the development of the production auxiliary decision-making system has been initiated simultaneously during the development of Fuling Shale Gas Field. The early warning research has been carried out from the direction of low pressure, liquid loading, production system changes, etc., and the short-term, mid-term and long-term analysis and the prediction of production decreasing trend of a single well have been carried out either. The management goal to obtain useful information by data mining for decision-making promotion is realized. This system effectively supports the on-site management of the shale gas field and ensured the continued stable production of the shale gas wells in Fuling Shale Gas Field. The intelligent construction and application of shale gas fields pioneered in Fuling Shale Gas Field has laid a good foundation for the refined management of shale gas reservoirs in China, and has important reference value and application prospects.

Key words: shale gas field, intelligence, auxiliary decision-making, decision-making system, refined management

中图分类号:

TE33

葛兰,蒲谢洋. 页岩气田智能化生产辅助决策系统应用效果评价[J]. 油气藏评价与开发, 2021, 11(4): 621-627.

Lan GE,Xieyang PU. Evaluation of application effect of an intelligent production assistant decision system for shale gas field[J]. Petroleum Reservoir Evaluation and Development, 2021, 11(4): 621-627.

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参考文献 17

[1]	艾军, 张金成, 臧艳彬, 等. 涪陵页岩气田钻井关键技术[J]. 石油钻探技术, 2014, 42(5):9-15.
	AI Jun, ZHANG Jincheng, ZANG Yanbin, et al. The key drilling technologies in Fuling Shale Gas Field[J]. Petroieum Drilling Techniques, 2014, 42(5):9-15.
[2]	王志刚. 涪陵焦石坝地区页岩气水平井压裂改造实践与认识[J]. 石油与天然气地质, 2014, 35(3):425-430.
	WANG Zhigang. Practice and cognition of shale gas horizontal well fracturing stimulation in Jiaoshiba of Fuling Area[J]. Oil & Gas Geology, 2014, 35(3):425-430.
[3]	汪宏金, 邢克, 夏钦锋. 涪陵页岩气田无人值守集气站建设[J]. 油气田地面工程, 2019, 38(3):69-74.
	WANG Hongjin, XING Ke, XIA Qinfeng. Construction of the unattended gas gathering stations in Fuling Shale Gas Field[J]. Oil-Gasfield Surface Engineering, 2019, 38(3):69-74.
[4]	李海, 任惠琴, 夏维嘉, 等. 数据集成助力页岩气勘探开发[J]. 中国管理信息化, 2019, 22(3):72-74.
	LI Hai, REN Huiqin, XIA Weijia, et al. Data integration helps shale gas exploration and development[J]. China Management Informationization, 2019, 22(3):72-74.
[5]	王洪峰, 王胜军, 朱松柏, 等. “互联网+”时代智慧油气田建设的构想与探索[J]. 油气田地面工程, 2018, 37(8):1-5.
	WANG Hongfeng, WANG Shengjun, ZHU Songbai, et al. Conception and exploration of the smart oil and gas field construction in "Internet+" Era[J]. Oil-Gasfield Surface Engineering, 2018, 37(8):1-5.
[6]	李阳, 廉培庆, 薛兆杰, 等. 大数据及人工智能在油气田开发中的应用现状及展望[J]. 中国石油大学学报(自然科学版), 2020, 44(4):1-11.
	LI Yang, LIAN Peiqing, XUE Zhaojie, et al. Application status and prospect of big data and artificial intelligence in oil and gas field development[J]. Journal of China University of Petroleum, 2020, 44(4):1-11.
[7]	胡耀义. 一种智能化油气田建设的解决方案[J]. 信息系统工程, 2020(2):148-149.
	HU Yaoyi. A Solution for intelligent oil and gas field construction[J]. Information System Engineering, 2020(2):148-149.
[8]	NOSHI C I, ASSEM A I, SCHUBERT J J. The role of big data analytics in exploration and production: A review of benefits and applications [C]// Paper SPE-193776-MS presented at the SPE International Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, December 2018.
[9]	MOHAGHEGH S D. Shale descriptive analytics: Which parameters are controlling production in shale [C]// Paper SPE-196226-MS presented at the SPE Annual Technical Conference and Exhibition, 2019.
[10]	王强, 叶梦旎, 李宁, 等. 页岩气藏数值模拟模型研究进展[J]. 中国地质, 2019, 46(6):1284-1299.
	WANG Qiang, YE Mengni, LI Ning, et al. Research progress of numerical simulation models for shale gas reservoirs[J]. Geology in China, 2019, 46(6):1284-1299.
[11]	沈金才, 刘尧文. 涪陵焦石坝区块页岩气井产量递减典型曲线应用研究[J]. 石油钻探技术, 2016, 44(4):88-95.
	SHEN Jincai, LIU Yaowen. Application study on typical production decline curves of shale gas wells in the Fuling Jiaoshiba Block[J]. Petroleum Driiiing Techniques, 2016, 44(4):88-95.
[12]	郭建林, 贾爱林, 贾成业, 等. 页岩气水平井生产规律[J]. 天然气工业, 2019, 39(10):53-58.
	GUO Jianlin, JIA Ailin, JIA Chengye, et al. Production laws of shale-gas horizontal wells[J]. Natural Gas Industry, 2019, 39(10):53-58.
[13]	蔡勋育, 赵培荣, 高波, 等. 中国石化页岩气“十三五”发展成果与展望[J]. 石油与天然气地质, 2021, 42(1):16-27.
	CAI Xunyu, ZHAO Peirong, GAO Bo, et al. Sinopec’s Shale Gas Development Achievements during the "Thirteenth Five-Year Plan” Period and Outlook for the Future[J]. Oil & Gas Geology, 2021, 42(1):16-27.
[14]	LEIVA L A, VIDAL E. Warped K-Means: An algorithm to cluster sequentially-distributed data[J]. Information Sciences, 2013, 237:196-210. doi: 10.1016/j.ins.2013.02.042
[15]	MEOR HASHIM, MEOR M, YUSOFF M, et al. Utilizing artificial neural network for real-time prediction of differential sticking symptoms[C]. IPTC21221, 2021.
[16]	DIAZ S P, VILAR J A. Comparing several parametric and nonparametric approaches to time series clustering: A simulation study. Journal of Classification. 2010, 27(3):333-362. doi: 10.1007/s00357-010-9064-6
[17]	XU R, ZHOU M L. Elman neural network-based identification of Krasnosel’skii-Pokrovskii model for magnetic shape memory alloys actuator[J]. IEEE Transactions on Magnetics, 2017: 1-1.