Geostatistical Methods for Geological Modeling and Hydrogeology| Training course
Understand and master the use of geostatistical solutions for subsurface and hydrogeological property modeling.
In many domains, like geotechnics, natural risks evaluation or optimization of acquisition networks in hydrogeology, it is of primary importance to base calculations on a realistic and accurate description of subsurface properties, accounting for their spatial heterogeneity. This is the strength of geostatistics.
By attending the course, you will learn how to:
- Characterize aquifers and build robust numerical static models of subsurface using appropriate techniques.
- Model the spatial distribution of continuous variables (top/bottom of geological layers, petrophysical properties, mechanical or hydrogeological properties) and discrete ones (facies data)
- Map soil properties in heterogeneous environments.
- Assess map uncertainty.
- Half of the course is devoted to theoretical and methodological presentations, the second half to practical exercises on real-life cases to deepen the understanding of concepts. The focus is on illustrations and practical contributions of the covered concepts.
- Computer exercises with Isatis.neo and real-life datasets.
- Course material provided (documentation, journal files, training data, worked examples) for re-use in your workplace.
Who should attend
Engineers, geologists, and hydrogeologists involved in subsoil and hydrogeological property modeling.
DAY 1: DATA VARIABILITY ANALYSIS AND MAPPING
– Current practices in geological modeling and hydrogeology.
– Heterogeneity and uncertainty characterization.
- Understanding and estimation of the spatial heterogeneity of the studied phenomenon
– A practical introduction to the concepts of heterogeneity and spatial variability.
– Quantification of the spatial variability: calculation, interpretation, and modeling of the variogram.
- Mapping of a continuous variable (e.g. geological layer top, hydraulic head)
– Review of classic deterministic interpolation methods.
– Kriging (2D / 3D): principles and properties. Map uncertainty.
– Integration of one or several secondary (i.e. Digital Elevation) or fuzzy data in the interpolation. Analysis of the correlations between variables. Multivariate variogram. Co-kriging.
DAY 2: UNCERTAINTY ANALYSIS
- Uncertainty quantification
– Introduction to the conditional simulations for uncertainty quantification.
– Difference between kriging and conditional simulations.
- Modeling of the geological heterogeneity
– Introduction to simulation methods for discrete variables (e.g. facies), in particular, the plurigaussian and multipoint (MPS) simulations.
– Post-processing of the results.
- Controlling permeable pathways distribution
– Probabilistic maps.
– Risk analysis.