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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

    Bárbara, Carla Patricia; Cabello, Patrícia; Bouche, Alexandre; Aarnes, Ingrid; Gordillo, Carlos; Ferrer, Oriol; Roma, Maria; Arbués, Pau. Quantifying the impact of the structural uncertainty on the gross rock volume in the Lubina and Montanazo oil fields (Western Mediterranean). Solid Earth (SE) (ISSN 1869-9510). 10(5) pp 1597-1619. doi: 10.5194/se-10-1597-2019. 2019.

    Abrahamsen, Petter; Kvernelv, Vegard Berg; Barker, Daniel Martin L. Simulation of Gaussian Random Fields Using the Fast Fourier Transform (FFT). In: ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery, 3–6 September 2018, Barcelona, Spain. (ISBN 978-94-6282-260-3). doi: 10.3997/2214-4609.201802134. 2018.

    Lilleborge, Marie; Hofvind, Solveig; Sebuødegård, Sofie; Hauge, Ragnar. Optimizing performance of BreastScreen Norway using value of information in graphical models. Statistics in Medicine (ISSN 0277-6715). 37(9) pp 1531-1549. doi: 10.1002/sim.7601. 2018.

    Kalfass, Daniel; Bertschik, Michael; Vrieler, Stefan; Hannay, Jo Erskine; Kvernelv, Vegard Berg. Proof of concept demonstrator of MSG-136 for using and providing simulation as a service within NATO environments. In: Proc. NATO Modelling and Simulation Group Symp. on M&S Technologies and Standards for Enabling Alliance Interoperability and Pervasive M&S Applications (STO-MP-MSG-149). (ISBN 978-92-837-2137-6). 2017.

    Hauge, Ragnar; Vigsnes, Maria; Fjellvoll, Bjørn; Vevle, Markus Lund; Skorstad, Arne. Object-Based Modeling with Dense Well Data. Quantitative Geology and Geostatistics (ISSN 0924-1973). 19 pp 557-572. doi: 10.1007/978-3-319-46819-8_37. 2017.

Publications in 2019, 2018, 2017, 2016, 2015, 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