Dynamic simulation of double-cased perforation in deepwater high temperature and high-pressure oil and gas wells

被引:0
|
作者
Bi, Gang [1 ,2 ]
Han, Fei [2 ]
Wu, Jie-Min [2 ]
Yuan, Pei-Jie [2 ]
Fu, Shuai-Shuai [2 ]
Ma, Ying [2 ]
机构
[1] Xian Shiyou Univ, Shaanxi Key Lab Well Stabil & Fluid & Rock Mech Oi, Xian 710065, Shaanxi, Peoples R China
[2] Xian Shiyou Univ, Coll Petr Engn, Xian 710065, Shaanxi, Peoples R China
关键词
Deepwater HTHP; Double-cased perforation; Optimization of perforation parameters; Dynamic simulation; Full-scale perforation simulation; MODEL;
D O I
10.1016/j.petsci.2024.05.008
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
In order to ensure the penetrability of double-cased perforation in offshore oil and gas fields and to maximize the capacity of perforation completion, This study establishes a dynamic model of doublecased perforation using ANSYS/LS-DYNA simulation technology. The combination of critical perforation parameters for double casing is obtained by studying the influencing factors of the jet-forming process, perforation depth, diameter, and stress changes of the inner and outer casing. The single-target perforation experiments under high-temperature and high-pressure (HTHP) conditions and ground full-scale ring target perforation tests are designed to verify the accuracy of numerical simulation results. The reduced factor is adopted as the quantitative measure of perforation depth and diameter for different types of perforation charge under different conditions. The results show that the perforation depth reduction increases with temperature and pressure, and the reduced factor is between 0.67 and 0.87 under HTHP conditions of 130 degrees C/4 4 MPa and 137 degrees C/60 MPa. Comparing the results of the numerical simulation and the full-scale test correction, the maximum error is less than 8.91%, and this numerical simulation has strong reliability. This research provides a basis for a reasonable range of double-cased perforation parameters and their optimal selection. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:3482 / 3495
页数:14
相关论文
共 50 条
  • [41] STUDY ON INFLUX RISK EVALUATION OF HORIZONTAL GAS WELLS WITH HIGH-PRESSURE HIGH-TEMPERATURE IN THE SOUTH CHINA SEA
    Luo, Ming
    Gao, Deli
    Zhao, Xin
    Chen, Yuan
    Yang, Yupeng
    Li, Zhong
    Zhang, Wandong
    Zhou, Xu
    PROCEEDINGS OF THE ASME 39TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, OMAE2020, VOL 11, 2020,
  • [42] HIGH-PRESSURE GAS-DYNAMIC LASER INVESTIGATION
    ANDRAUD, M
    CARREGA, AF
    TARAN, JPE
    IEEE JOURNAL OF QUANTUM ELECTRONICS, 1972, QE 8 (06) : 619 - &
  • [43] SIMULATION OF DYNAMIC BEHAVIOR OF HIGH-PRESSURE DISCHARGE LAMPS
    HEERING, W
    APPLIED PHYSICS, 1977, 12 (04): : 321 - 325
  • [44] Dynamic gas disengagement in a high-pressure bubble column
    Jordan, U
    Saxena, AK
    Schumpe, A
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2003, 81 (3-4): : 491 - 498
  • [45] Design of High-temperature and High-pressure Environment Simulation Device
    Du, Jian
    Wei, Linshan
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015), 2015, 117 : 1714 - 1719
  • [46] DYNAMIC CALORIMETRY IN HIGH-PRESSURE, HIGH-TEMPERATURE THERMODYNAMICS OF LIQUIDS
    RANDZIO, SL
    THERMOCHIMICA ACTA, 1987, 121 : 463 - 471
  • [47] HOW EPNG PRODUCES DEEP HIGH-PRESSURE GAS WELLS.
    Gandy, Gary R.
    Oil and Gas Journal, 1978, 76 (26): : 147 - 149
  • [48] HOW EPNG PRODUCES DEEP HIGH-PRESSURE GAS-WELLS
    GANDY, GR
    OIL & GAS JOURNAL, 1978, 76 (26) : 147 - 149
  • [49] Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells
    Agwu, Okorie E.
    Akpabio, Julius U.
    Dosunmu, Adewale
    JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY, 2020, 10 (03) : 1081 - 1095
  • [50] Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells
    Okorie E. Agwu
    Julius U. Akpabio
    Adewale Dosunmu
    Journal of Petroleum Exploration and Production Technology, 2020, 10 : 1081 - 1095