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Statistical Analysis of Natural Resource Data - SAND

The SAND department was established in 1984. It is a significant international contributor to research and services within reservoir description, stochastic modeling and geostatistics for the oil industry. Our primary goal is to use statistical methods to reduce and quantify risk and uncertainty. The main area is stochastic modeling of the geology in petroleum reservoirs including upscaling and history matching. There is also a significant activity on all kinds of risk quantification, primarily within the energy sector.

The staff has a background in statistics, mathematics, physics, numerical analysis and computer science. To ensure that we work with interesting and relevant problems for the petroleum industry, we encourage close cooperation with professionals within the geo-science whenever this is relevant for the project. Oil companies, software vendors within the oil industry and research project sponsored by the European Commission and The Research Council of Norway, finance most projects.

Research areas

Last 5 scientific articles

    Oakley, David Owen Smith; Cardozo, Nestor; Almendral Vazquez, Ariel; Røe, Per. Structural geologic modeling and restoration using ensemble Kalman inversion. Journal of Structural Geology (ISSN 0191-8141). 171 doi: 10.1016/j.jsg.2023.104868. 2023.

    Sektnan, Audun; Almendral Vazquez, Ariel; Hauge, Ragnar; Aarnes, Ingrid; Skauvold, Jacob; Vevle, Markus Lund. A Tree Representation of Plurigaussian Truncation Rules. In: Proceedings of the European Conference on the Mathematics of Geological Reservoirs (ECMOR 2022). (ISBN 0-000-00001-9). doi: 10.3997/2214-4609.202244066. 2022.

    Almendral Vazquez, Ariel; Dahle, Pål; Abrahamsen, Petter; Sektnan, Audun. Conditioning geological surfaces to horizontal wells. Computational Geosciences (ISSN 1420-0597). doi: 10.1007/s10596-022-10154-6. 2022.

    Nesvold, Erik; Mukerji, Tapan. Simulation of Fluvial Patterns With GANs Trained on a Data Set of Satellite Imagery. Water Resources Research (ISSN 0043-1397). 57(5) doi: 10.1029/2019WR025787. 2021.

    Aker, Eyvind; Kjønsberg, Heidi; Fawad, Manzar; Mondol, Nazmul Haque. Estimation of Thickness and Layering of Johansen and Cook Sandstones at the Potential Co2 Storage Site Aurora. In: TCCS–11. CO2 Capture, Transport and Storage. Trondheim 22nd–23rd June 2021. Short Papers from the 11th International Trondheim CCS Conference. (ISBN 978-82-536-1714-5). pp 19-26. 2021.

Publications in 2023, 2022, 2021, 2020, 2019, earlier years
Postal address:
Norsk Regnesentral/
Norwegian Computing Center
P.O. Box 114 Blindern
NO-0314 Oslo
Visit address:
Norsk Regnesentral
Gaustadalleen 23a
Kristen Nygaards hus
NO-0373 Oslo.
(+47) 22 85 25 00
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Postal address: Norsk Regnesentral/Norwegian Computing Center, P.O. Box 114 Blindern, NO-0314 Oslo, Norway
Visit address: Norsk Regnesentral, Gaustadalleen 23a, Kristen Nygaards hus, NO-0373 Oslo.
Phone: (+47) 22 85 25 00
AddressHow to get to NR