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.
Last 5 scientific articles
Jensen, Erling Hugo; Hauge, Ragnar; Ulvmoen, Marit; Johansen, Tor Arne; Drottning, Åsmund. Rock.XML - Towards a library of rock physics models. Computers & Geosciences (ISSN 0098-3004). 93 pp 63-69. doi: 10.1016/j.cageo.2016.04.011. 2016.
Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Almendral-Vazquez, Ariel; Vigsnes, Maria. Surface prediction using rejection sampling to handle non-linear constraints. Bulletin of Canadian petroleum geology (ISSN 0007-4802). 63(4) pp 304-317. doi: http://dx.doi.org/10.2113/gscpgbull.63.4.304. 2015.
Lilleborge, Marie; Hauge, Ragnar; Eidsvik, Jo. Information Gathering in Bayesian Networks Applied to Petroleum Prospecting. Mathematical Geosciences (ISSN 1874-8961). 48(3) pp 233-257. doi: 10.1007/s11004-015-9616-8. 2015. Abstract
Hermansen, Gudmund Horn; Hjort, Nils Lid. Bernshteĭn-von Mises theorems for nonparametric function analysis via locally constant modelling: A unified approach. Journal of Statistical Planning and Inference (ISSN 0378-3758). 166 pp 138-157. doi: 10.1016/j.jspi.2015.02.007. 2015.