Jef Caers : Research

Geostatistics

• New concepts and philosophies in geostatistics: Can we free geostatistics from the variogram ?
• Multiple-point Geostatistics
• Theory and Practice of Stochastic Simulation
• Theory of Stochastic Inversion
• Conditioning stochastic simulation to non-linear data
• Integration of data at various scales and precision
• Geostatistics and pattern recognition
• Applications of geostatistics to reservoir characterizations, mining and environmental sciences
• Discrete Geostatistics, point process models
• Valuation of diamond deposits

Reservoir modeling

• The role of geostatistics in reservoir modeling
• Modeling geological continuity
• Use of seismic data in reservoir characterization
• Probabilistic data integration
• History matching
• Incoporating 4D seismic
• Joint static and dynamic modeling of fractured reservoirs
• Dealing with multi-scale data and other scale issues

Statistical Pattern recognition

• The statistical side and interpretation of Neural Networks
• Pattern recognition and feature extraction
• Image analysis: what is the essence of an image ?
• The concept of Data Generalization
• Classification
• Supervised versus unsupervised learning

Inverse Problems

• Dealing with non-Gaussian priors
• Inversion of seismic data into reservoir properties
• Alternative Bayesian approaches to inverse modeling
• History matching: inversion of flow and pressure data

Applied statistics in general

• Extreme value theory
• Extreme value statistics
• Spatial statistics
• Graphical representation of statistical data
• How can we communicate statistics to the layman ?
• Central limit theorem or not ?
• Maximum likelihood versus least squares methods
• Bayesian versus frequentist approaches
• Valuation of diamond deposits

Back to Front of Page
Back to Home