Multiple-Point Statistics simulations (MPS) with Isatis.neo | Training course

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.

DAY 1

MORNING

• General introduction
  – Introduction to the geostatistical approach
  – The concept behind conditioning data and training images
  – General principles and introduction to the Direct Sampling algorithm
• Hands-on exercises
  – Isatis.neo fundamentals
  – A first simple application of DeeSse for a stationary categorical and continuous case

AFTERNOON

• From stationary to non-stationary simulations
  – Understanding DeeSse parameters
  – Why training images are needed: how to obtain them and what properties they should have
  – Handling non-stationarity in the simulation grid
  – Multivariate simulations
• Hands-on exercises
  – A simple practical case study: the Areuse delta
  – How to generate a training image and an orientation trend to control simulations
  – Joint simulation of two variables

DAY 2

MORNING

• Applying MPS to real data
  – How to handle non-stationarity using analog data
  – Discussion of examples involving secondary attributes: climate data, a bauxite mine in Australia, bedrock topography, and geophysics
  – Time-series simulation using the Direct Sampling technique
• Hands-on exercises
  – A practical 2D case study using secondary variables: the Herten aquifer (fluvioglacial deposit)
  – Filling gaps in satellite images using multivariate and multi-temporal techniques

AFTERNOON

• Modeling with elementary training images
  – Elementary training images and invariances
  – Application example for a mining site in South Africa
  – Multi-scale simulations based on Gaussian pyramids
• Hands-on exercises
  – Simple examples using elementary training images and invariances
  – Exploring pyramids
  – A first example with a 2D fluvioglacial facies model (Herten aquifer)

DAY 3

MORNING

• Hands-on exercises: modeling a fluvioglacial deposit
  – Building elementary training images
  – Introduction to Python programming for task automation
  – Building the stratigraphic model
  – Modeling the fluvioglacial aquifer from borehole data

AFTERNOON

• An overview of advanced methods
  – Cross-validation
  – Multi-scale simulations on unstructured grids
  – Inequalities and block conditioning
  – Connectivity conditioning

• 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.

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.

None.
A theoretical understanding of geostatistical approaches is an advantage.