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SAND

SAND

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

    Almendral Vazquez, Ariel; Dahle, Pål; Abrahamsen, Petter; Sektnan, Audun. Consistent prediction of well paths and geological surfaces. Computational Geosciences (ISSN 1420-0597). doi: 10.1007/s10596-024-10310-0. 2024.

    Ovanger, Oscar; Eidsvik, Jo; Skauvold, Jacob; Hauge, Ragnar; Aarnes, Ingrid. Addressing Configuration Uncertainty in Well Conditioning for a Rule-Based Model. Mathematical Geosciences (ISSN 1874-8961). doi: 10.1007/s11004-024-10144-7. 2024.

    Lilleborge, Marie; Hauge, Ragnar; Fjellvoll, Bjørn; Abrahamsen, Petter. Using Pattern Counts to Quantify the Difference Between a Pair of Three-Dimensional Realizations. Mathematical Geosciences (ISSN 1874-8961). doi: 10.1007/s11004-024-10145-6. 2024.

    Lee, Daesoo; Ovanger, Oscar; Eidsvik, Jo; Aune, Erlend; Skauvold, Jacob; Hauge, Ragnar. Latent Diffusion Model for Conditional Reservoir Facies Generation. arXiv 2023.

    Sanchis, Charlotte Juliette Semin; Kolbjørnsen, Odd. Sampling-Free Bayesian Inference for Local Refinement in Linear Inversion Problems with a Latent Target Property. IEEE Transactions on Geoscience and Remote Sensing (ISSN 0196-2892). 61 doi: 10.1109/TGRS.2023.3301717. 2023. Institutional archive 

Publications in 2024, 2023, 2022, 2021, 2020, earlier years
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
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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