Data-driven Drilling Optimization

Data-Driven Drilling Optimization is a systematic approach within drilling engineering that leverages empirical data and analytical models to iteratively refine drilling performance, distinguished by its reliance on measured operational data rather than purely theoretical or heuristic methods to optimize drilling parameters and outcomes.

55

Publications

1.9K

Citations

207

Authors

81

Institutions

Publications per year

2017–2026

32

Authors

207

  1. CH
    Chiranth Hegde

    The University of Texas at Austin, United States

    3 publications · 290 citations

    3

    h-index

  2. JY
    Jin Yang

    China University of Petroleum, Beijing, China

    3 publications · 97 citations

    3

    h-index

  3. SM
    Stéphane Menand

    Université Claude Bernard Lyon 1, France

    2 publications · 51 citations

    2

    h-index

  4. HD
    Hugh Daigle

    The University of Texas at Austin, United States

    2 publications · 129 citations

    2

    h-index

  5. XC
    Xuyue Chen

    China University of Petroleum, Beijing, China

    2 publications · 59 citations

    2

    h-index

1–5 of 207

Venues

  1. SPE Annual Technical Conference and Exhibition

    Conference · 5 publications · 118 citations

    5

    h-index

  2. Journal of Petroleum Science and Engineering

    5 publications · 191 citations

    5

    h-index

  3. IADC/SPE Drilling Conference and Exhibition

    Conference · 3 publications · 47 citations

    3

    h-index

  4. Journal of Natural Gas Science and Engineering

    2 publications · 197 citations

    2

    h-index

  5. SPE/IADC Middle East Drilling Technology Conference and Exhibition

    Conference · 2 publications · 44 citations

    2

    h-index

1–5