Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing

被引:40
作者
Bin, Yuan [1 ]
Mingze, Zhao [1 ]
Siwei, Meng [2 ]
Wei, Zhang [1 ]
He, Zheng [1 ]
机构
[1] China Univ Petr East China, Sch Petr Engn, Qingdao 266580, Peoples R China
[2] Petrochina Res Inst Petr Explorat & Dev, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
horizontal well fracturing; fracturing events; intelligent identification; real-time warning; deep learning;
D O I
10.1016/S1876-3804(24)60482-9
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the AttBiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.
引用
收藏
页码:1487 / 1496
页数:10
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