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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

    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 

    Ghione, Federica; Köhler, Andreas; Dichiarante, Anna Maria; Aarnes, Ingrid; Oye, Volker. Vs30 and depth to bedrock estimates from integrating HVSR measurements and geology-slope approach in the Oslo area, Norway. Frontiers in Earth Science (ISSN 2296-6463). 11 doi: 10.3389/feart.2023.1242679. 2023.

    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.

    Kjønsberg, Heidi; Hauge, Ragnar; Ndingwan, Abel Onana. Time-lapse Bayesian AVO inversion applied to the Edvard Grieg field in the North Sea. SEG technical program expanded abstracts (ISSN 1949-4645). doi: 10.1190/image2022-3745774.1. 2022.

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