Data analysis, mapping and subsurface property modeling with geostatistics

• 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

• 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.

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.

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.

No prior knowledge of geostatistics is required; however, a basic understanding of elementary statistics is recommended to facilitate comprehension of course material.