Early warning and prediction of kicks and lost circulation accident during rescue drilling of mine

被引：0
|
作者：
Chen W. [1 ,2 ,3 ]
Wang J. [1 ]
Fan D. [4 ]
Hao S. [4 ]
Zhao J. [4 ]
Qiu Y. [1 ]
机构：
[1] Faculty of Engineering, China University of Geosciences, Wuhan
[2] Institute for Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences, Wuhan
[3] Shaanxi Key Laboratory of Prevention and Control Technology for Coal Mine Water Hazard, Xi'an
[4] CCTEG Xi'an Research Institute (Group) Co., Ltd., Xi'an
来源：
Meitiandizhi Yu Kantan/Coal Geology and Exploration | 2024年 / 52卷 / 03期
关键词：
drilling process; early warning and prediction; kick; lost circulation; machine learning; mine accident rescue; PSO-LSTM; XGBoost;
D O I：
10.12363/issn.1001-1986.23.07.0449
中图分类号：
学科分类号：
摘要：
In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circulation accidents during emergency rescue drilling of mine, a machine learning-based early for warning and prediction model of drilling process was established. Firstly, the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed. Secondly, the accident characterization parameters were cleaned and processed. On this basis, XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents. Then, the PSO-LSTM accident development prediction model was established to predict the development trend of downhole pressure parameters after the accident, so as to understand the development status of drilling accidents in advance. Finally, the effectiveness of the early warning prediction model was verified by the actual drilling data. The results show that XGBoost and early warning model could quickly and accurately diagnose the kick and loss circulation accidents in the drilling process according to the abnormal changes of four drilling parameters, including the total tank volume, riser pressure, inflow-outflow differential, and power head load. The PSO-LSTM accident development status prediction model could fully learn the development law of downhole pressure parameters. With full consideration to the four error evaluation indicators, such as EMAP, EMA, ERMS and R2, the prediction performance of the PSO-LSTM models is the best compared with BP, RNN and SVM, capable of accurately predicting the development trend of the downhole pressure after the accident, thereby knowing about the severity and development situation of kick and lost circulation accidents. Generally, the research results enrich the early warning and prediction methods of kicks and lost circulation accidents in the drilling process, improve the reliability of surface rescue in mine accident, and have a reference and guiding effect on accident control during the emergency rescue drilling of mine. © 2024 Science Press. All rights reserved.
引用
收藏
页码：144 / 152
页数：8
相关论文
共 28 条
  • [1] ZHAO Jiangpeng, HAO Shijun, Application of pneumatic DTH hammer in drilling rescue of mine accidents[J], Coal Geology & Exploration, 50, 11, (2022)
  • [2] ZHENG Xuezhao, SUN Ziyu, GUO Jun, Et al., Research and application of multi-source information detection technology for drilling rescue of mine[J], Coal Geology & Exploration, 50, 11, (2022)
  • [3] WANG Lei, ZHU Yuqin, ZHANG Weina, Et al., Research on key technology of pipe jacking drilling equipment for large-diameter rescue borehole of coal mine[J], Coal Geology & Exploration, 50, 11, (2022)
  • [4] TIAN Hongliang, ZOU Zujie, HAO Shijun, Et al., Key technologies and equipment of quickly and safely building life support and rescue channel in mine disaster[J], Coal Geology & Exploration, 50, 11, (2022)
  • [5] ZHANG Huili, Research on cause analysis and countermeasures of welling-loss accident in drilling site[J], West-China Exploration Engineering, 34, 9, (2022)
  • [6] CHEN Kaifeng, YANG Xuewen, SONG Xianzhi, Et al., An intelligent diagnosis method for lost circulation based on engineering logging data[J], China Petroleum Machinery, 50, 11, (2022)
  • [7] WAN Kang, MA Zhichao, GUO Qingsong, Et al., Application of artificial intelligence on petroleum drilling engineering accident early warning[J], Mud Logging Engineering, 33, 2, (2022)
  • [8] GALDINO J F, OLIVEIRA G M, FRANCO A T, Et al., Gas kick detection and pressure transmission in thixotropic, compressible drilling fluids[J], Journal of Petroleum Science and Engineering, 180, (2019)
  • [9] SUN Wenfeng, Maoran LI, LIU Chunyang, Et al., Experimental research on lost circulation and overflow monitoring system for drilling engineering based on the requirements of safe and efficient oil and gas drilling[J], Fresenius Environmental Bulletin, 30, 2, (2021)
  • [10] KANG Yili, MA Chenglin, XU Chengyuan, Et al., Prediction of drilling fluid lost-circulation zone based on deep learning[J], Energy, 276, (2023)
123