Training
Data analysis, mapping and subsurface property modeling with geostatistics
Objectives
- Enhanced data insight: Develop a deep understanding of your data through advanced geostatistical analysis, leading to more informed decision-making.
- Improved mapping quality: Learn to create high-quality maps by integrating various data types, ensuring comprehensive spatial representations.
- Uncertainty quantification: Master techniques to quantify uncertainties in your models, providing a clear assessment of confidence levels in your results.
- Technique selection: Understand the assumptions underlying different geostatistical methods to select the most suitable approach for your data
Target audience
This course is ideal for professionals working with spatial data across various fields, including:
- Geoscientists and reservoir engineers involved in geomodeling and reservoir characterization and looking for a practical, synthetic, and pragmatic introduction to geostatistical methods for reservoir characterization.
- Environmental consultants and engineers aiming to enhance data analysis and mapping capabilities.
- Academics and researchers.
- Agricultural engineers, air quality specialists, climatologists, epidemiologists, foresters, geotechnical engineers, soil scientists, and others interested in spatial data analysis.
Content
DAY 1: ANALYSING DATA AND THEIR VARIABILITY IN SPACE AND MAPPING
Introduction:
– Explore the added value of geostatistical methods over traditional deterministic interpolation techniques.
– Gain insights into usual deterministic interpolation methods (nearest neighbor, moving average, inverse distance, etc.) and their application limits.
Exploratory data analysis (EDA) and validation:
– Utilize statistical tools for data analysis and quality control, identifying outliers and understanding data distributions: mean, variance, histogram, correlation coefficients, linear regression, etc.
– Visualize data in 2D and 3D to comprehend spatial patterns.
Spatial variability assessment:
– Understand and quantify spatial variability through experimental variogram calculation, interpretation, and modeling.
– Learn to fit theoretical variogram models.
Interpolation by kriging:
– Grasp the principles and properties of kriging and address the smoothing effect.
– Define appropriate neighborhoods (single or moving, size, number of samples, etc.).
– Analyze kriging weights based on sample positions and variogram models.
DAY 2: REFINING THE MAPS
Cross-validation:
– Implement cross-validation to validate variogram models and ensure model reliability.
The different variants of kriging:
– Apply simple, ordinary, with variance in measurement error, etc.
Multivariate geostatistics:
– Analyze correlations between multiple data types, quantitative and semi-quantitative (e.g., remote sensing data, DEMs, soil occupation, physicochemical models, lithology, etc.), using scatter plots and correlation coefficients.
– Analyse the spatial relationships between variables by calculating and modeling multivariate variograms.
– Integrate secondary variables into interpolation through cokriging techniques, including collocated cokriging, to reduce interpolation uncertainty.
– Analyze the inputs of cokriging in comparison to kriging.
Non-stationary geostatistics:
– Address data showing trends or drifts using non-stationary geostatistical methods.
Simulations and risk analysis:
– Introduction to simulations for risk analysis, highlighting their added value with practical examples.
Practical exercises:
– Engage in hands-on exercises to apply learned concepts to real-life cases, reinforcing understanding and skill development.
What the course includes
- Balanced learning approach. Theory paired with practical applications, so you understand and apply the concepts.
- Hands-on software training. Computer exercises in Isatis.neo, on real datasets.
- Personalized feedback. Individual guidance from experienced trainers throughout the online sessions.
- Comprehensive resources. A temporary software license, course documentation, journal files, and datasets to keep and reuse after the course.
- Certificate of completion issued at the end of the session.
Prerequisites
No prior knowledge of geostatistics is required; however, a basic understanding of elementary statistics is recommended to facilitate comprehension of course material.
Register, or ask us first
This form handles all three: registration, a quote for yourself or your team, and questions about the content or the prerequisites.
Address
44 Avenue de Valvins, 77210 Avon, France