Concepedia

Machine Learning For Kick Detection

Machine Learning for Kick Detection is a computational methodology that employs data-driven algorithms to automatically identify and predict early indicators of wellbore influxes during drilling operations by learning patterns from sensor measurements and operational parameters.

15

Publications

473

Citations

75

Authors

21

Institutions

Publications per year

2017–2026

12

Parents

Publications

15

A new pattern recognition model for gas kick diagnosis in deepwater drilling

Xiaohui Sun, Baojiang Sun, Shuai Zhang et al. · Journal of Petroleum Science and Engineering · 2018 · 63 citations

, , +16

Supervised data-driven approach to early kick detection during drilling operation

Somadina Muojeke, R. Venkatesan, Faisal Khan · Journal of Petroleum Science and Engineering · 2020 · 60 citations

, , +16

Downhole quantitative evaluation of gas kick during deepwater drilling with deep learning using pilot-scale rig data

Qishuai Yin, Jin Yang, Mayank Tyagi et al. · Journal of Petroleum Science and Engineering · 2021 · 57 citations · Full text

, , +16

Drilling Through Data: Automated Kick Detection Using Data Mining

Raed Alouhali, Mohammad Aljubran, Salem Gharbi et al. · SPE International Heavy Oil Conference and Exhibition · 2018 · 39 citations

, , +17

Enhanced Kick Detection with Low-Cost Rig Sensors Through Automated Pattern Recognition and Real-Time Sensor Calibration

Parham Pournazari, Pradeepkumar Ashok, Eric van Oort et al. · 2015 · 39 citations

, , +15

1–5 of 15

Authors

75

  1. JY
    Jin Yang

    China University of Petroleum, Beijing, China

    2 publications · 76 citations

    2

    h-index

  2. ZW
    Zizhen Wang

    China University of Petroleum, East China, China

    1 publications · 26 citations

    1

    h-index

  3. SZ
    Shuai Zhang

    China University of Mining and Technology, China

    1 publications · 63 citations

    1

    h-index

  4. FJ
    Fu Jianhong

    Southwest Petroleum University, China

    1 publications · 19 citations

    1

    h-index

  5. PA
    Pradeepkumar Ashok

    The University of Texas at Austin, United States

    1 publications · 39 citations

    1

    h-index

1–5 of 75

Institutions

21

  1. China University of Petroleum, East China

    Qingdao, China

    3 h-index

    16 publications

    614 citations

  2. NORCE Research AS

    Bergen, Norway

    2 h-index

    2 publications

    42 citations

  3. China University of Petroleum, Beijing

    Beijing, China

    2 h-index

    5 publications

    171 citations

  4. Brigham Young University

    Provo, United States

    1 h-index

    4 publications

    76 citations

  5. Petroleum University of Technology

    Tehran, Iran

    1 h-index

    4 publications

    132 citations

  6. Memorial University of Newfoundland

    St. John's, Canada

    1 h-index

    3 publications

    180 citations

  7. The University of Texas at Austin

    Austin, United States

    1 h-index

    3 publications

    117 citations

  8. University of Stavanger

    Stavanger, Norway

    1 h-index

    2 publications

    20 citations

1–8 of 21

Venues

  1. Journal of Petroleum Science and Engineering

    3 publications · 180 citations

    3

    h-index

  2. Process Safety and Environmental Protection

    2 publications · 45 citations

    2

    h-index

  3. SPE Drilling & Completion

    1 publications · 23 citations

    1

    h-index

  4. Nigeria Annual International Conference and Exhibition

    Conference · 1 publications · 33 citations

    1

    h-index

  5. Geoenergy Science and Engineering

    1 publications · 22 citations

    1

    h-index

1–5