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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

    Hauge, Vera Louise; Kolbjørnsen, Odd. Bayesian inversion of gravimetric data and assessment of CO2 dissolution in the Utsira Formation. Interpretation (ISSN 2324-8858). 3(2) pp SP1. doi: 10.1190/INT-2014-0193.1. 2015.

    Røe, Per; Hauge, Ragnar. A volume-conserving representation of cell faces in corner point grids. Computational Geosciences (ISSN 1420-0597). doi: 10.1007/s10596-015-9500-0. 2015.

    Qu, Dongfang; Røe, Per; Tveranger, Jan. A method for generating volumetric fault zone grids for pillar gridded reservoir models. Computers & Geosciences (ISSN 0098-3004). 81 pp 28-37. doi: 10.1016/j.cageo.2015.04.009. 2015.

    Vonnet, Julie; Hermansen, Gudmund Horn. Using predictive analytics to unlock unconventional plays. First Break (ISSN 0263-5046). 33(2) pp 87-92. 2015. Full-text 

    Martinelli, Gabriele; Eidsvik, Jo; Hokstad, Ketil; Hauge, Ragnar. Strategies for Petroleum Exploration on the Basis of Bayesian Networks: A Case Study. SPE Journal (ISSN 1086-055X). 19(4) pp 564-575. doi: 10.2118/159722-PA. 2014.

Publications in 2015, 2014, 2013, 2012, 2011, 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