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
13
1–5 of 13
31
2
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1–5 of 31
16
European Central Bank
Frankfurt am Main, Germany
2 h-index
3 publications
355 citations
University of California, Davis
Davis, United States
1 h-index
1 publications
24 citations
Chevron (United States)
San Ramon, United States
1 h-index
2 publications
26 citations
University of Copenhagen
Copenhagen, Denmark
1 h-index
2 publications
106 citations

Lawrence Livermore National Laboratory
Livermore, United States
1 h-index
1 publications
24 citations
Bechtel (United States)
San Francisco, United States
1 h-index
2 publications
38 citations
University of Chile
Santiago, Chile
1 h-index
1 publications
20 citations
Stanford University
Stanford, United States
1 h-index
3 publications
39 citations
1–8 of 16
2 publications · 169 citations
2
h-index
Repository · 1 publications · 44 citations
1
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GW · 1 publications · 19 citations
1
h-index
1 publications · 61 citations
1
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1 publications · 267 citations
1
h-index
1–5