Go beyond variograms. Learn how to simulate complex geological patterns and subsurface properties using state-of-the-art MPS techniques.
A 4 half-day course – December 7-10, 2026 – Level: Intermediate
Objectives
This course introduces you to Multiple-Point Statistics (MPS), a powerful simulation technique for modeling complex spatial variability using training images. Developed in collaboration with the University of Neuchâtel, the course combines theoretical foundations with hands-on practice using Isatis.neo and its integrated DeeSse engine. You’ll learn to select suitable training images, prepare your data, and generate realistic subsurface models, whether categorical or continuous. Ideal for applications in mining, hydrogeology, remote sensing, and reservoir modeling, MPS equips you to assess uncertainty and model features driven by geological morphology, such as channelized permeability or ore grades in vein deposits.
Course content
Theory:
- Overview of the MPS approach
- Exploration of the various tools to manage non-stationarity (rotation, scaling, proportions, and trends)
- Advanced neighborhood parameters optimization
- Continuous MPS simulation
- Multivariate MPS simulation
- Multi-resolution MPS simulation
- MPS simulations for gap-filling
Practice in Isatis.neo:
- First application using a simple and historical case study
- Simulating various geometric patterns to gain a deeper understanding of MPS behavior
- Simulating horizon depth as a continuous variable.
- Upscaling permeability to work with a multivariate dataset
- Simulating a fracture network at multiple resolutions
- Simulating sedimentary deposits in the French Roussillon plain
- Filling in undefined areas with geophysical properties
Outlines
- Balanced learning approach: The course combines theory with practical applications, ensuring concepts are understood and applied effectively.
- Hands-on software training: Engage in computer-based exercises using Isatis.neo software, reinforcing learning through real-world data scenarios.
- Personalized feedback: Receive individualized guidance and feedback from experienced trainers during online sessions to support your learning journey.
- Comprehensive resources: Access detailed course materials, including documentation, journal files, and datasets, to reinforce learning and facilitate application post-training.
Who should attend
This course is tailored for professionals and researchers involved in spatial modeling who want to enhance their ability to simulate complex geological structures and facies distributions using Multiple-Point Statistics (MPS). Ideal participants include:
- Geologists & geomodellers
Working in mining, oil & gas, or hydrogeology who need to model intricate geological patterns – such as channels, fractures, or stratigraphy – that are difficult to capture with traditional variogram-based approaches.
- Reservoir engineers
Focused on building realistic facies or property models that improve reservoir characterization and flow simulations.
- Environmental & hydrogeological scientists
Needing to simulate spatial heterogeneities in aquifer systems with geological realism.
- Geostatisticians and data scientists
Looking to deepen their expertise in MPS and apply advanced simulation techniques using training images and high-resolution geological analogs.
- Consultants and technical advisors
Supporting clients with subsurface modeling projects who want to stay at the forefront of geostatistical innovation.
- Researchers and academics
Engaged in spatial data analysis, stochastic simulation, or geoscientific modeling who want to explore MPS in practical workflows.
Explore Geovariances’ training offerings
Geovariances – Datamine France provides training for mining professionals seeking to strengthen their geostatistics expertise. Our courses blend theory with hands-on practice.
Flexible delivery formats, including online, hybrid, and face-to-face, are complemented by on-demand training, available for both in-company and public sessions, and tailored to your specific needs.
Develop skills in key areas such as:
– Local and recoverable resource estimation
– Uncertainty and risk analysis
– Resource classification
– Geological domain modeling
– Drill Hole Spacing Analysis
– Machine Learning
On-demand hands-on sessions on Isatis.neo and Isatis.py allow you to directly apply geostatistical methods in industry-standard software.
