Here is a list of classes that I have taught at Stanford and there are currently still being offered by other faculty or myself. Please consult the proper Stanford resources to verify when these classes are being offered.

Modeling Uncertainty in the Earth Sciences (Energy 160/260)

Whether Earth Science modeling is performed on a local, regional or global scale, for scientific or engineering purposes, uncertainty is inherently present due to lack of data and lack of understanding of the underlying phenomena. This course highlights the various issues, techniques and practical modeling tools available for modeling uncertainty of complex 3D/4D Earth systems. The course focuses on a practical breath rather than theoretical depth. Topics covered are: the process of building models, sources of uncertainty, probabilistic techniques, spatial data analysis and geostatistics, grid and scale, spatio-temporal uncertainty, visualizing uncertainty in large dimensions, Monte Carlo simulation, reducing uncertainty with data, value of information. Applications to both local (reservoir, aquifer) and global (climate) are covered through literature study.

Optimization and Inverse Problems (Energy 284)

Treatment of deterministic and stochastic optimization, gradient-based optimization, polytopy method, generalized least squares, non-linear least squares and confidence intervals by numerical methods and bootstrap. Adjoint method for gradient calculation. Genetic algorithms and simulated annealing. Application of optimization methods to solving non-linear inverse problems. Bayesian methods, rejection sampling, metropolis sampling, uncertainty quantification. Development of priors for spatial problems. Parametrization of high-dimensional problems through various expansion techniques. Development of proxy functions using regression techniques, neural networks and kernel techniques. Examples of various Earth sciences inverse problems including flow and wave equations.

Practice of geostatistics and seismic data integration (Energy 241)

Practical methods for quantitative characterization and uncertainty assessment of subsurface reservoir models integrating well-log and seismic data. Multidisciplinary combination of rock-physics, seismic attributes, sedimentological information and spatial statistical modeling techniques. Student teams build reservoir models using limited well data and seismic attributes typically available in practice, comparing alternative approaches.

Fundamentals of Petroleum Engineering (Energy 120)

Lectures, problems, field trip. Engineering topics in petroleum recovery; origin, discovery, and development of oil and gas. Chemical, physical, and thermodynamic properties of oil and natural gas. Material balance equations and reserve estimates using volumetric calculations. Gas laws. Single phase and multiphase flow through porous media.

This is the first course of the Geostatistics series. Probabilistic modeling of spatial and/or time dependent phenomena. Kriging and cokriging for gridding and spatial interpolation. Integration of heterogeneous sources of information. Stochastic imaging of reservoir/field heterogeneities. Introduction to the SGEMS software. Case studies from the oil and mining industry and environmental sciences.