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
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). NATO Science and Technology Organisation. (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.
Olsen, Håvard Goodwin; Hermansen, Gudmund Horn. Recent Advancements to Nonparametric Modeling of Interactions Between Reservoir Parameters. Quantitative Geology and Geostatistics (ISSN 0924-1973). 19 pp 653-669. doi: 10.1007/978-3-319-46819-8_44. 2017.
Hauge, Vera Louise; Hermansen, Gudmund Horn. Machine Learning Methods for Sweet Spot Detection: A Case Study. Quantitative Geology and Geostatistics (ISSN 0924-1973). 19 pp 573-588. doi: 10.1007/978-3-319-46819-8_38. 2017.
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.