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

Petroleum reservoir models

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.

Structural geology

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.

Inversion of geophysical data

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.

Decision support and data analysis for the oil and gas industry

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.

History matching and dynamic data

The use of statistical methods for using historical production data to improver petroleum reservoir models.

CO2 storage

Storage of CO2 in saline aquifers is one important way mitigating climate change due to green housegases. We work on techniques for optimizing the storage process and new and better ways of monitoring the flow of CO2 within the saline aquifers.

Last 5 scientific articles

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.

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.

Publications in 2018, 2017, 2016, 2015, 2014, earlier years