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
Efficient depletion of oil and gas reservoirs require an accurate description of geometric shapes and the petrophysical properties of the sedimentary rocks. We use statistical methods to integrate all available data and stochastic models to describe the remaining uncertainty.
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NR has long experience in stochastic modeling faults and surfaces. This is done to integrate seismic and well data with geological knowledge, and to study the uncertainty both in reservoir volume and in flow within the reservoir.
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We use Bayesian approach for inversion of geophysical data. This provides a common framework when integrating rock physics and structural geology with geophysical data. We do work on seismic and electromagnetic data.
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The oil and gas industry has lot of data from different sources. These data can give valuable information to decision-makers about uncertainty. Statistical methods are valuable tools to estimate uncertainty and thereby give important decision support.
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The use of statistical methods for using historical production data to improve petroleum reservoir models.
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Last 5 scientific articles
Sanchis, Charlotte Juliette Semin; Kolbjørnsen, Odd. Sampling-Free Bayesian Inference for Local Refinement in Linear Inversion Problems With a Latent Target Property. IEEE Transactions on Geoscience and Remote Sensing (ISSN 0196-2892). doi: 10.1109/TGRS.2023.3301717. 2023.
Ghione, Federica; Köhler, Andreas; Dichiarante, Anna Maria; Aarnes, Ingrid; Oye, Volker. Vs30 and depth to bedrock estimates from integrating HVSR measurements and geology-slope approach in the Oslo area, Norway. Frontiers in Earth Sciences (ISSN 1863-4621). 11 doi: 10.3389/feart.2023.1242679. 2023.
Oakley, David Owen Smith; Cardozo, Nestor; Almendral Vazquez, Ariel; Røe, Per. Structural geologic modeling and restoration using ensemble Kalman inversion. Journal of Structural Geology (ISSN 0191-8141). 171 doi: 10.1016/j.jsg.2023.104868. 2023.
Sektnan, Audun; Almendral Vazquez, Ariel; Hauge, Ragnar; Aarnes, Ingrid; Skauvold, Jacob; Vevle, Markus Lund. A Tree Representation of Plurigaussian Truncation Rules. In: Proceedings of the European Conference on the Mathematics of Geological Reservoirs (ECMOR 2022). (ISBN 0-000-00001-9). doi: 10.3997/2214-4609.202244066. 2022.
Almendral Vazquez, Ariel; Dahle, Pål; Abrahamsen, Petter; Sektnan, Audun. Conditioning geological surfaces to horizontal wells. Computational Geosciences (ISSN 1420-0597). doi: 10.1007/s10596-022-10154-6. 2022.
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