Celine Scheidt
Sr Res Engineer
Energy Science & Engineering
Bio
Céline Scheidt has worked extensively in uncertainty modeling, sensitivity analysis, geostatistics and in the use of distance-based methods in reservoir modeling. She obtained her PhD at Strasbourg University and the IFP (France) in applied mathematics, with a focus on the use of experimental design and geostatistical methods to model response surfaces.
Professional Education
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Ph.D, ULP Strasbourg and IFP (France), Applied Mathematics (2006)
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MS, ULP, Strasbourg (France), Mathematics for Industry – Specialty in Quality/Reliability (2003)
All Publications
- Quantifying uncertainty in subsurface systems Washington, D.C. : American Geophysical Union ; Hoboken, NJ : John Wiley and Sons, Inc., 2018.. 2018
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Assessing and visualizing uncertainty of 3D geological surfaces using level sets with stochastic motion
COMPUTERS & GEOSCIENCES
2019; 122: 54–67
View details for DOI 10.1016/j.cageo.2018.10.006
View details for Web of Science ID 000453338800006
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Exploring viable geologic interpretations of gravity models using distance-based global sensitivity analysis and kernel methods
GEOPHYSICS
2018; 83 (5): G79–G92
View details for DOI 10.1190/GEO2017-0742.1
View details for Web of Science ID 000453050000047
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Quantifying Uncertainty in Subsurface Systems PREFACE
QUANTIFYING UNCERTAINTY IN SUBSURFACE SYSTEMS
2018; 236: VII-IX
View details for Web of Science ID 000481474600001
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Direct forecasting of reservoir performance using production data without history matching
COMPUTATIONAL GEOSCIENCES
2017; 21 (2): 315-333
View details for DOI 10.1007/s10596-017-9614-7
View details for Web of Science ID 000398928300009
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DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments
COMPUTERS & GEOSCIENCES
2016; 97: 15-29
View details for DOI 10.1016/j.cageo.2016.08.021
View details for Web of Science ID 000387521800002
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Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model
JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
2016; 121 (10)
View details for DOI 10.1002/2016JF003922
View details for Web of Science ID 000392830200009
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Probabilistic falsification of prior geologic uncertainty with seismic amplitude data: Application to a turbidite reservoir case
GEOPHYSICS
2015; 80 (5): M89-M100
View details for DOI 10.1190/GEO2015-0084.1
View details for Web of Science ID 000361665500021
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Updating joint uncertainty in trend and depositional scenario for reservoir exploration and early appraisal
COMPUTATIONAL GEOSCIENCES
2015; 19 (4): 805-820
View details for DOI 10.1007/s10596-015-9491-x
View details for Web of Science ID 000361462400008
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Prediction-Focused Subsurface Modeling: Investigating the Need for Accuracy in Flow-Based Inverse Modeling
MATHEMATICAL GEOSCIENCES
2015; 47 (2): 173-191
View details for DOI 10.1007/s11004-014-9521-6
View details for Web of Science ID 000348376900003
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Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir Modeling
MATHEMATICAL GEOSCIENCES
2014; 46 (4): 493-511
View details for DOI 10.1007/s11004-014-9530-5
View details for Web of Science ID 000335676800006
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History matching and uncertainty quantification of facies models with multiple geological interpretations
COMPUTATIONAL GEOSCIENCES
2013; 17 (4): 609-621
View details for DOI 10.1007/s10596-013-9343-5
View details for Web of Science ID 000321637000001
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A multi-resolution workflow to generate high-resolution models constrained to dynamic data
COMPUTATIONAL GEOSCIENCES
2011; 15 (3): 545-563
View details for DOI 10.1007/s10596-011-9223-9
View details for Web of Science ID 000291058000012
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Bootstrap confidence intervals for reservoir model selection techniques
COMPUTATIONAL GEOSCIENCES
2010; 14 (2): 369-382
View details for DOI 10.1007/s10596-009-9156-8
View details for Web of Science ID 000274455900012
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Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods-Application to a West Africa Deepwater Turbidite Reservoir
SPE JOURNAL
2009; 14 (4): 680-692
View details for Web of Science ID 000272852500013
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Representing Spatial Uncertainty Using Distances and Kernels
MATHEMATICAL GEOSCIENCES
2009; 41 (4): 397-419
View details for DOI 10.1007/s11004-008-9186-0
View details for Web of Science ID 000265442200003
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Toward a reliable quantification of uncertainty on production forecasts: Adaptive experimental design
IFP International Conference on Quantitative Methods for Reservoir Characterization
EDITIONS TECHNIP. 2007: 207–24
View details for DOI 10.2516/ogst:2007018
View details for Web of Science ID 000246558900009