# Geostatistical Methods for Geological Modeling and Hydrogeology

Understand and master the use of geostatistical solutions for subsurface and hydrogeological property modeling.

## Learning objectives

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.

## Outlines

• 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.
• Individual work reviewed and corrected by the trainer during online courses.
• 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.

## Content

DAY 1: DATA VARIABILITY ANALYSIS AND MAPPING

• Introduction
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.

## Prerequisites

It is recommended that attendees have a reasonable knowledge of statistics, variography, and kriging. Having attended the course “Data Analysis and mapping with geostatistics” is a plus.