Solid Earth Physics and Computational Geoscience
The Solid Earth Physics and Computational Geoscience Group at the Niels Bohr Institute is engaged in inverse theory and algorithm development for the solution of complex problems in the study of the Earth's interior. Of particular interest to us is the integration of quantitative geophysical and geological models and the related computational challenges.
Our projects span from large-scale planetary studies to Earth resource exploration, including drinking water, geothermal energy and mineral resources. All these activities share common themes that continue to fascinate us: nonlinear inverse problems, the search for feasible solutions, probabilistic Earth models, and the complex flow of information from uncertain observations, through numerical modeling, to final interpretions and decisions.
Probabilistic approach for risk assessment of CO2 Storage
One of the possible post-production utilization of abandoned hydrocarbon fields in the North Sea fields is to use them as reservoirs for CO2 storage. There are several advantages in doing so: the reservoirs are already discovered and the wells needed to access them are in place.
However, CO2 storage requires a careful risk assessment, based on integrated data analysis where all uncertainties are correctly balanced. In an ideal approach, the analysis must evaluate risks related to capture, transport and storage, the associated CO2 migration mechanisms and pathways, as well as effects on atmosphere, soil, groundwater, and surface water. Each component in this chain of investigations must be based on all available data for the considered CO2 storage scenario, and the best possible probabilistic model for data uncertainties.
Current methods try to reach these ideal goals, but have severe limitations in the analysis of reservoir properties. Probabilistic analysis of geoscientific data and production data is not based on physically correct error propagation methods. Instead, statistical predictions about the reservoir are only based on (1) large geological data bases, or (2) subjective expert assessments of geophysical and geological data. Prediction models about the overburden are nearly non-existing. These limitations may have significant consequences for the appraisal of possible caprock failures, releases through induced or existing faults, displacement into non-target formations, or well leakage. Furthermore, a miscalculation of these factors may have a dramatic consequence on the evaluation of migration scenarios and their short and long-term consequences.
The aim of this pilot project is to investigate and describe how a risk analysis system based on a consistent, probabilistic approach to geophysical/geostatistical inversion and flow data analysis can be developed.
RESPROB [2018-2021] Probabilistic Geomodelling of Groundwater Resources
funded by Free Research Council (Technology and Production)
Groundwater mapping in Denmark is internationally acknowledged and regarded as a benchmark approach. Massive amounts of data (well logs, geophysical, geo- and hydrological data) have been collected. Today these data are combined in a deterministic sequential workflow, where, typically, a single final model represents all available information. While successful, this workflow has some limitations: There is no way to ensure the final model consistency with all information at hand, and there is no way to ensure correct uncertainty quantification. The main goal of RESPROB is to develop a probabilistic data integration workflow that allows consistent integration of well-log, geophysical and geological data. The resulting probabilistic geomodel should be efficient tool for end-users for informed, data-driven, decision making and for risk assessment.
A PhD position (located at Niels Bohr Institute) and a PostDoc position (GEUS/University of Aarhus) will be offered as part of the project.
Collaborators and contact:
Niels Bohr Institute, University of Copenhagen [contact: Thomas Mejer Hansen(firstname.lastname@example.org)]
GEUS [contact: Flemming Jørgensen (email@example.com)]
Dept. of Geosciences, Aarhus University [contact: Niels Bøie Christensen (firstname.lastname@example.org)]
University of Cagliary [contact: Giulio Vignoli (email@example.com)]
USGS, Denver, [contact: Burke J. Minsley (firstname.lastname@example.org)]
LOCRETA [2018-2020] Seismic modelling and optimal inversion
With this initiative we strive to improve seismic imaging of key features, which are determining for the reservoir properties of the lower Cretaceous sediment package, i.e. characteristic, alternating (cyclic?) lithologies as well as faults and fractures. We employ forward full-waveform modelling for improved understanding of origin and characterization of seismic arrivals occurring from such key features, and we build full-waveform inversion (FWI) schemes aimed particularly at resolution of such elements. The FWI schemes will be constrained by geological and rock physical prior knowledge (including results from other work packages of this proposal) and special emphasis will be on formulating the inversion schemes in a geostatistical framework, which provides realistic distributions and uncertainties of key seismic and/or rock physical parameters. We draw on existing collaboration with international leading experts in this research field.
Collaborators and contact:
Niels Bohr Institute, University of Copenhagen, Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen. Geological Survey of Denmark And Greenland (GEUS), DTU Civil Engineering
[contact: Klaus Mosegaard (email@example.com)]
Outcrop Analog Studies of Chalk [2017-2020]
The goal is to build reservoir models spanning heterogeneities from millimeters to hundreds of meters, thereby providing a link between structural and depositional elements of widely different scales. In a cross-disciplinary effort, we combine geological, geophysical and geostatistical methods to identify different length-scale regimes. Statistical models will be developed for each regime and combined to one multiscale model to be compared with North Sea data sets. Geostatistical models provide not only spatial estimates of rock properties with uncertainties and correlations, they also provide a framework for consistent updating of existing reservoir models with new information, as well as the necessary information needed to upscale a reservoir model to coarser grids to facilitate reservoir simulations.
Collaborators and contact:
Niels Bohr Institute, University of Copenhagen, Natural History Museum of Denmark, University of Copenhagen, Department of Geosciences and Natural Resource Management (IGN), University of Copenhagen.
[contact: Klaus Mosegaard (firstname.lastname@example.org)]
|[pdf]||Hansen, T.M., Vu. L.T., Mosegaard, K., and Cordua, K. S. (2018) Multiple point statistical simulation using uncertain (soft) conditional data. Copmuters and Geosciences (114), May 2018, Pages 1-10. https://doi.org/10.1016/j.cageo.2018.01.017.|
|[pdf]||Madsen. R.B., and Hansen, T.M. (2018) Estimation and accounting for the modeling error in probabilistic linearized AVO inversion. Geophysics, 2018. 83(2), 601-606. doi:10.1190/geo2017-0404.1.|
|[pdf]||Efficient Monte Carlo sampling of inverse problems using a Neural Network based forward - applied to GPR crosshole traveltime inversion
Hansen, T.M. and Cordua, K.S.
Geophysical Journal International, 211(2), 2017, 1524--1533.. doi:10.1093/gji/ggx380.
|[pdf]||Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies
Høyer, A.S, Vignoli, G., Hansen, T.M., Vu, L.T., Keefer, D.A., and Jørgensen, F.
Hydrology and Earth System Sciences, 2017, 21(12), 6069--6089. doi:10.5194/hess-2016-567.
|[pdf]||On inferring the noise in probabilistic seismic AVO inversion using hierarchical Bayes.
Madsen. R.B., Zunino, A., and Hansen, T.M.
SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. 601-606. doi:10.1190/segam2017-17725822.1.
|[pdf]||Automatic mapping of base of aquifer – A case study from Morill Nebraska.
Gulbrandsen, M.L., Ball. L., Minsley, B., and Hansen, T.M., 2017
Interpretation 5(2), p. doi:10.1190/INT-2016-0195.1
|Smart Interpretation - Automatic geological interpretations based on supervised statistical models.
Gulbrandsen, M.L., Cordua. K.S., Bach, T., and Hansen, T.M., 2017
Computational Geosciences 21(3), pp 427-440. doi:10.1007/s10596-017-9621-8.
|[pdf]||Semi-Automatic Mapping of Permafrost in the Yukon Flats - Alaska.
Gulbrandsen, M.L., Minsley, B., Ball. L., and Hansen, T.M., 2016.
Geophysical Research Letters 43(13), pp 12131-12137. doi:10.1002/2016GL071334.
|[pdf]||Mixed-point geostatistical simulation: A combination of two- and multiple-point geostatistics.
Cordua. K.S., Hansen, T.M., Gulbrandsen, M.L., Barnes, C., and Mosegaard, K., 2016
Geophysical Research Letters 43(17), pp 9030-9037.. doi:10.1002/2016GL070348.
|Revealing multiple geological scenarios through unsupervised clustering of posterior realizations from reflection seismic inversion.
Gulbrandsen, M.L., Cordua. K.S., Hansen, T.M., and Mosegaard, K.
in Geostatistics Valencia 2016, Editors: Gómez-Hernández, J.J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M.E., Cassiraga, E., Vargas-Guzmán, J.A. (Eds.)
|[pdf,www]||MPSLIB: A C++ class for sequential simulation of multiple-point statistical models.
Hansen, T.M., Vu. L.T., and Bach, T.
in SoftwareX, doi:10.1016/j.softx.2016.07.001.
|||Probabilistic Integration of Geo-Information.
Hansen, T.M., Cordua. K.S., Zunino, A., and Mosegaard, K.
in Integrated Imaging of the Earth: Theory and Applications, pp 93-116. ISBN:978-1-118-92905-6.
|||Inverse Methods: Problem Formulation and Probabilistic Solutions.
Mosegaard, K. and Hansen, T.M.
in Integrated Imaging of the Earth: Theory and Applications, pp 9-28 ISBN:978-1-118-92905-6.
|||Constitution and Structure of Earth’s Mantle: Insights from Mineral Physics and Seismology.
Zunino, A., Khan, A., Cupillard, P., and Mosegaard, K.
in Integrated Imaging of the Earth: Theory and Applications, pp 219-244 ISBN:978-1-118-92905-6.
|[pdf]||A general probabilistic approach for inference of Gaussian model parameters from noisy data of point and volume support.
Hansen, T.M., Cordua. K.S., and Mosegaard, K.
Mathematical Geosciences 47(7), pp 843-865. published online 09-2014. doi:10.1007/s11004-014-9567-5
An example application using the SIPPI Matlab toolbox
|[pdf]||Monte Carlo reservoir analysis combining seismic reflection data and informed priors.
Zunino, A., Mosegaard, K., Lange, K., Melnikova, Y., and Hansen, T.M.
Geophysics 80(1), pp R31–R41, 2014. doi:10.1190/geo2014-0052.1
|[pdf]||History Matching through a Smooth Formulation of Multiple-Point Statistics.
Melnikova,. Y., Zunino, A., Lange, K., Cordua, K. S., and Mosegaard, K.
Mathematical Geosciences, May, 2014. doi:10.1007/s11004-014-9537-y
|[pdf]||Improving the pattern reproducibility of multiple-point-based prior models using frequency matching.
Cordua, K. S., Hansen, T.M., and Mosegaard, K.
Mathematical Geosciences, April 2014. doi:10.1007/s11004-014-9531-4
|[pdf]||Accounting for imperfect forward modeling in geophysical inverse problems - exemplified for cross hole tomography.
Hansen, T.M., Cordua, K. S., Jacobsen, B. J., and Mosegaard, K.
Geophsyics, 79(3) H1-H21, 2014. doi:10.1190/geo2013-0215.1
|[pdf,code]||SIPPI : A Matlab toolbox for Sampling the solution to Inverse Problems with complex Prior Information: Part 1 - Methodology.
Hansen, T.M., Cordua, K. S., Looms. M.C., and Mosegaard, K.
Computers & Geosciences, 52, 470-480, 2013. doi:10.1016/j.cageo.2012.09.004.
SIPPI Matlab toolbox
|[pdf, code]||SIPPI : A Matlab toolbox for Sampling the solution to Inverse Problems with complex Prior Information: Part 2 - Application to cross hole GPR tomography.
T.M., Cordua, K. S., Looms. M.C., and Mosegaard, K.
Computers & Geosciences, 52, 481-492-480, 2013. doi:10.1016/j.cageo.2012.09.001.
SIPPI Matlab toolbox
|Modeling and detection of oil in sea water.
Xenaki, A., Gerstoft, P., and Mosegaard, K.
Journal of the Acoustical Society of America, 134(4), pp 2790-2798, 2013. doi:10.1121/1.4818897.
|[pdf]||A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information.
Lange, K., Frydendall, J., Cordua, K. S., Hansen, T.M., Melnikova, Y., and Mosegaard, K.
Mathematical Geosciences, 44(7), 783-803, 2012. doi:10.1007/s11004-012-9417-2.
|[pdf]||Monte Carlo Full Waveform Inversion of Crosshol GPR Data Using Multiple-point Geostatistical a Priori Information. Cordua, K. S., Hansen, T. M., and Mosegaard, K.,
Geophysics, 77(2), pp H19-H31, 2012. doi:10.1190/geo2011-0170.1.
|[pdf ]||Inverse problems with non-trivial priors - Efficient solution through Sequential Gibbs Sampling.
Hansen, T. M., Cordua, K. S., and Mosegaard, K.
Computational Geosciences, 16(3), pp 593-611, 2012. doi:10.1007/s10596-011-9271-1
We thank Ian Lynam for allowing us to use one of his Neojaponsime patterns.
|||Mosegaard, K., 2011. Quest for consistency, symmetry and simplicity : The Legacy of Albert Tarantola, Geophysics, 76, pp. W51-W61. doi:10.1190/geo2010-0328.1|
|Mosegaard, K., 2010. Albert Tarantola Memorial, The Leading Edge (), pp 874-875.|
|||Looms, M. C., Hansen, T. M., Cordua, K. S., Nielsen, L., Jensen, K. H., Binley, A., 2010. Geostatistical inference using crosshole ground-penetrating radar : Geostatistical inference using GPR, Geophysics, 75(6), pp J29-J41. doi:10.1190/1.3496001|
|||Nielsen, L., Looms, M. C., Hansen, T. M., Cordua, K. S., Stemmerik, L., 2010. Estimation of Chalk Heterogeneity from Stochastic Modeling Conditioned by Crosshole GPR Traveltimes and Log Data, in Advances in Near-Surface Seismology and Ground-Penetrating Radar, eds. Miller, R., Bradford, J., and Holliger, K., Society of Exploration Geophysicists, Tulsa, Oklahoma, ISBN: 978-1-56080-224-2 , pp 379-396. 10.1190/1.9781560802259.ch23|
|||Hansen, T. M., Mosegaard, K., and Schiøtt, C. R., 2010. Kriging interpolation in seismic attribute space applied to the South Arne Field, North Sea. Geophysics, 75(6), pp 31-41.doi:10.1190/1.3494280|
|||Hansen, T. M., Mosegaard, K., Pedersen-Tatalovic, R., Uldall, A., and Jacobsen, N. L., 2008. Attribute guided well log interpolation. Geophysics, 73(6), pp R83-R95.doi:10.1190/1.2996302|
|||Pedersen-Tatalovic, R., Uldall, A., Jacobsen, N.L., Hansen, T.M., and Mosegaard, K., 2008. Event Based Low Frequency Impedance Modelling using Well Logs and Seismic Attributes. The Leading Edge 27(5), pp 592-603.|
|||Hansen, T. M., Looms, M. C., and Nielsen, L., 2008. Infering the sub-surface structural covariance model using corss bore-borehole ground penetrating radar tomography. Vadose Zone Journal, special issue on Ground Penetrating Radar in Hydrogeophysics 7(1), pp 249-262. doi:10.2136/vzj2006.0144|
|||Hansen, T. M. and Mosegaard, K., 2008. VISIM : Sequential simulation for linear inverse problems, Computers and Geosciences 34(1), pp 53-76, doi:10.1016/j.cageo.2007.02.003.|
|||Hansen T. M., Journel A. G, Tarantola A., and Mosegaard, K., 2006. Linear Inverse Gaussian Theory and Geostatistics, Geophysics 71(6), pp R101-R111. doi:10.1190/1.2345195|
Software developed by Solid Earth Physics and Geostatistics
A Matlab toolbox for Sampling the solution to inverse problems with complex a priori models.
A C++ class for Multiple-Point based sequential Simulation.
See Hansen et al. 2016. (https://doi.org/10.1016/j.softx.2016.07.001)
Developed in collaboration with I-GIS.
The use of phase equilibrium calculations to compute physical properties of rocks has become commonplace in geophysical modeling. Typically, the phase equilibrium calculations are used to construct two-dimensional tables of rock properties as a function of pressure and temperature. Phemgp is a Fortran program that can be used to assemble a three-dimensional table that accounts for compositional variations from two-dimensional tables.
Source Code: https://github.com/inverseproblem/Phemgp
See Zunino et al., 2011 (https://doi.org/10.1029/2010GC003304)
Geostatistical toolbox for Matlab : Contains native Matlab functions and Matlab interfaces to gstat, snesim and sgems.
Source Code and documentation: https://github.com/cultpenguin/mgstat
A Matlab toolbox for reading, writing and editing SEGY formatted files.
A FOTRAN 77 code for sequential Simulation conditioned to noisy data of mixed support.
See Hansen and Mosegaard, 2008. (https://dx.doi.org/10.1016/j.cageo.2007.02.003)
2D elastic finite difference waveform modeling. Especially efficent when simulation the elastic response from a target zone.
Source code: https://github.com/cultpenguin/mpm
See Hansen and Jacobsen, 2008 (https://doi.org/10.1016/S0098-3004(01)00113-3)
|Jacob Henriksen||MSc. Student|
|Klaus Ortving Lindholmer||MSc. Student|
|Lana Zupancic||MSc. Student|
|Pedro Martinez||MSc. Student|
|Peter Bagnegaard||MSc. Student|
|Tomasso Ferrari||MSc. Student|