Soft Data Conditioning

Soft Data Conditioning is a geostatistical integration method that incorporates indirect, non-quantitative, or imprecise secondary information—such as seismic attributes, geological interpretations, or well-log derivatives—into reservoir property modeling by assigning probabilistic or categorical constraints that guide the spatial distribution of reservoir variables conditioned on both hard and soft data sources.

13

Publications

725

Citations

31

Authors

16

Institutions

Publications per year

2017–2026

2

Authors

31

  1. MB
    Marta Bańbura

    European Central Bank, Germany

    2 publications · 311 citations

    2

    h-index

  2. HC
    Helena C. Kraemer

    Stanford University, United States

    2 publications · 169 citations

    2

    h-index

  3. GR
    Gerhard Rünstler

    European Central Bank, Germany

    2 publications · 311 citations

    2

    h-index

  4. ST
    Sue Thiemann

    Stanford University, United States

    2 publications · 169 citations

    2

    h-index

  5. ZY
    Zhen Yin

    Stanford University, United States

    1 publications · 13 citations

    1

    h-index

1–5 of 31

Venues

  1. Journal of Consulting and Clinical Psychology

    2 publications · 169 citations

    2

    h-index

  2. SSRN Electronic Journal

    Repository · 1 publications · 44 citations

    1

    h-index

  3. Ground Water

    GW · 1 publications · 19 citations

    1

    h-index

  4. Geologica Acta

    1 publications · 61 citations

    1

    h-index

  5. International Journal of Forecasting

    1 publications · 267 citations

    1

    h-index

1–5