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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

    Stordal, Frode; Svensen, Henrik; Aarnes, Ingrid; Roscher, Marco. Global temperature response to century-scale degassing from the Siberian Traps Large Igneous Province. Palaeogeography, Palaeoclimatology, Palaeoecology (ISSN 0031-0182). 471 pp 96-107. doi: 10.1016/j.palaeo.2017.01.045. 2017.

    Vigsnes, Maria; Kolbjørnsen, Odd; Hauge, Vera Louise; Dahle, Pål; Abrahamsen, Petter. Fast and Accurate Approximation to Kriging Using Common Data Neighborhoods. Mathematical Geosciences (ISSN 1874-8961). doi: DOI 10.1007/s11004-016-9665-7. 2017.

    Røe, Per; Almendral Vazquez, Ariel; Hanea, Remus Gabriel. Distinguishing Signal from Noise in History Matching - Analysis of Ensemble Collapse on a Synthetic Data Set. In: ECMOR XV - Proceedings of 15th European Conference on the Mathematics of Oil Recovery, Amsterdam, Netherlands, 29 August - 1 September, 2016. (ISBN 978-94-6282-193-4). doi: 10.3997/2214-4609.201601819. 2016.

    Kolbjørnsen, Odd; Buland, Arild; Hauge, Ragnar; Røe, Per; Jullum, Martin; Metcalfe, Richard William; Skjæveland, Øyvind. Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model. The Leading Edge (ISSN 1070-485X). 35(5) pp 431-436. doi: 10.1190/tle35050431.1. 2016.

    Fachri, Muhammad; Tveranger, Jan; Braathen, Alvar; Røe, Per. Volumetric faults in field-sized reservoir simulation models: A first case study. American Association of Petroleum Geologists Bulletin (ISSN 0149-1423). 100(5) pp 795-817. doi: 10.1306/02011614118. 2016. Full-text 

Publications in 2017, 2016, 2015, 2014, 2013, 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