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ROCK PHYSICS FOR QUANTITATIVE SEISMIC RESERVOIR CHARACTERIZATION (GEO 122)

COURSE OUTLINE

5 Days

Day 1:

o Introduction to SGeMS

o Introduction to Rock Physics, motivation, introductory examples

o Parameters that influence seismic velocities

o Conceptual Overview

o Effects of fluids, stress, pore pressure, temperature, porosity, fractures

Day 2:

o Bounding methods for robust modeling of seismic velocities

o Effective media models for elastic properties of rocks

Day 3:

o Gassmann Fluid substitution

o Partial saturation and the relation velocities to reservoir processes

o The importance of saturation scales

Day 4:

o Shaly sands and their seismic signatures

o Granular media models, unconsolidated/ cemented sand model

o Velocity dispersion and attenuation

Day 5:

o Rock Physics of AVO interpretation and Vp/Vs relations

o Quantitative seismic interpretation, uncertainty, and rock physics templates

Example case studies using AVO and seismic impedance

COURSE OVERVIEW

This five-day comprehensive course is designed to provide in-depth knowledge and practical skills in well test analysis and interpretation for petroleum engineers. It covers key concepts, methodologies, and advanced techniques required to evaluate reservoir performance, characterize reservoir properties, and optimize production. The course combines theoretical instruction with hands-on exercises, enabling participants to interpret real-world well test data and apply their knowledge to complex reservoir scenarios.

In addition, the course covers the fundamentals of rock physics, ranging from basic laboratory measurements and theoretical foundations to practical workflows that can be directly applied in the field. Participants will be introduced to quantitative tools for understanding and predicting diagnostic seismic signatures related to deposition, diagenesis, lithology, pore fluid saturation, stress, pore pressure, temperature, and fractures.

The program includes case studies and practical strategies for quantitative seismic interpretation, along with guidance on effectively applying seismic-to-rock property transforms in reservoir characterization and monitoring. Particular emphasis is placed on seismic interpretation and uncertainty quantification for lithology prediction and subsurface fluid detection.

COURSE SCHEDULE

Code Date Location price (€)*
GEO 122 14 – 18 Sep 2026 Online 3300
GEO 122 2 – 6 Nov 2026 Online 3300
GEO 122 17 – 21 Aug 2026 Stavanger 4400
GEO 122 7 – 11 Dec 2026 Abu Dhabi 4400

INSTRUCTOR

Professor Tapan Mukerji

Professor Tapan Mukerji is a Professor (Research) at Stanford University where he got his Ph.D. (1995) in  Geophysics. Tapan co-directs the Stanford Center for Earth Resources Forecasting (SCERF), the Stanford Rock Physics and Borehole Geophysics (SRB) and Basin and Petroleum System Modeling (BPSM) projects at Stanford University. Tapan combines experience in conducting leading edge research, teaching, and directing graduate student research. He was awarded the Karcher Award in 2000 by the Society of Exploration Geophysicists, and received the ENI award in 2014. He has been an associate editor for Geophysics, journal of the Society of Exploration Geophysicists, and Computers and Geosciences. In addition to numerous journal publications, Tapan has co-authored The Rock Physics Handbook, Quantitative Seismic Interpretation, and The Value of Information in the Earth Sciences, all  published by Cambridge University Press. He has been an invited keynote speaker and instructor for numerous short courses on rock physics and geostatistics, in North and South America, Europe, Africa, Australia and Asia.

FAQ

DESIGNED FOR

 

Geophysicists, Reservoir Geologists, Seismic  interpreters, and Engineers concerned with  seismic characterization using rock physics.

COURSE LEVEL

 

o Intermediate to Advance

LEARNING OBJECTIVES

 

After completing this course, participants will be familiar with the following:

o Use rock physics models for quantitative seismic interpretation.

o Model the effects of fluids on seismic  amplitudes and AVO.

o Use statistical rock physics and Bayesian machine learning for lithofacies  prediction.

REGISTER

 

Registration is now OPEN!

Ph.D. students, group and early bird registrants are eligible to DISCOUNT!

For more details and registration please send email to: register@petro-teach.com

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