Log-derived Permeability Models

Log-Derived Permeability Models is a computational method in well log interpretation that estimates formation permeability by establishing empirical, theoretical, or machine learning-based relationships between downhole log measurements and core-derived permeability data, distinguished from other petrophysical models by its specific reliance on log-derived inputs to predict fluid-flow properties in subsurface reservoirs.

58

Publications

3.5K

Citations

178

Authors

67

Institutions

Publications per year

2017–2026

11

Authors

178

  1. ZM
    Zhiqiang Mao

    China University of Petroleum, Beijing, China

    3 publications · 94 citations

    3

    h-index

  2. YS
    Yujiang Shi

    Research Institute of Petroleum Exploration and Development, China

    3 publications · 94 citations

    3

    h-index

  3. MA
    Mehmet Altunbay

    Baker Hughes (United States), United States

    2 publications · 1.5K citations

    2

    h-index

  4. DK
    Dare K. Keelan

    Core Laboratories (United States), United States

    2 publications · 1.5K citations

    2

    h-index

  5. SS
    Shamsuddin Shenawi

    Saudi Aramco (United States), United States

    2 publications · 29 citations

    2

    h-index

1–5 of 178

Venues

  1. Journal of Petroleum Science and Engineering

    5 publications · 321 citations

    5

    h-index

  2. SPE Annual Technical Conference and Exhibition

    Conference · 3 publications · 1.2K citations

    3

    h-index

  3. Proceedings of SPE Annual Technical Conference and Exhibition

    Conference · 3 publications · 487 citations

    3

    h-index

  4. Journal of Earth Science

    2 publications · 46 citations

    2

    h-index

  5. Water Resources Research

    2 publications · 87 citations

    2

    h-index

1–5