Rock Physics for Quantitative Seismic Reservoir Characterization
By: Tapan Mukerji
Instructor
Prof. Tapan Mukerji
(Stanford University)
Duration
22–24 June 2021:
6:00PM-9:00PM CEST
3 hours/day
Disciplines
Reservoir Characterization – Rock Physics
Course Format
The EAGE Interactive Online Short Courses bring carefully selected courses of experienced instructors from industry and academia online to give participants the possibility to follow the latest education in geoscience and engineering remotely. The courses are designed to be easily digested over the course of two or three days. Participants will have the possibility to interact live with the instructor and ask questions.
Education Packages
To help you save on registration fees and better organize your learning path, we are offering Education Packages for all EAGE online courses. The packages are valid for a period of 12 months and give you credits (3, 5 or 10) to spend on courses of your choice.
Course Description
The purpose of the course is to give an overview of rock physics observations and models relating reservoir properties such as saturation, lithology, clay content, and pore pressure and their seismic signatures. Understanding this relation can help to improve quantitative seismic interpretation. The course covers fundamentals of Rock Physics ranging from basic laboratory and theoretical results to practical “recipes” that can be immediately applied in the field. Application of quantitative tools for understanding and predicting the effects of lithology, pore fluid types and saturation, saturation scales, stress, pore pressure and temperature, and fractures on seismic velocity. Use of rock physics models requires understanding the assumptions and pitfalls of each model and the uncertainties associated with the interpretations using these models. Analysis of case studies and strategies for quantitative seismic interpretation using statistical rock physics work flows, and suggestions for more effectively employing seismic-to-rock properties transforms in Bayesian machine learning for reservoir characterization and monitoring, with emphasis on seismic interpretation and uncertainty quantification for lithology and subsurface fluid detection
Course Objectives
On completion of the course, participants will be able to:
- Use rock physics models with a better understanding of assumptions and pitfalls;
- Combine statistical rock physics in quantitative seismic interpretation workflows;
- Select appropriate rock physics models for reservoir characterization;
- Use rock physics models to build appropriate training sets for Bayesian machine learning applications in quantitative seismic interpretation.
Course Outline
- Introduction to Rock Physics, motivation, introductory examples
- Parameters that influence seismic velocities - conceptual overview
- Effects of fluids, stress, pore pressure, temperature, porosity, fractures
- Bounding methods for robust modeling of seismic velocities
- Effective media models for elastic properties of rocks
- Gassmann Fluid substitution – uses, abuses, and pitfalls
- Derivation, recipe and examples, useful approximations
- Partial saturation and the relation of velocities to reservoir processes
- The importance of saturation scales and their effect on seismic velocity
- Shaly sands and their seismic signatures
- Granular media models, unconsolidated sand model, cemented sand model
- Velocity dispersion and attenuation
- Velocity Upscaling
- Rock Physics of AVO interpretation and Vp/Vs relations
- Quantitative seismic interpretation and rock physics templates
- Statistical rock physics, Bayesian machine learning and uncertainty quantification
- Rock physics modeling to augment deep learning training data
- Example case studies using AVO and seismic impedance for quantitative reservoir characterization
Participants' Profile
The course is recommended for all geophysicists, reservoir geologists, seismic interpreters, and engineers concerned with reservoir characterization, reservoir delineation, hydrocarbon detection, reservoir development and recovery monitoring.
Prerequisites
No specific prerequisites needed.
About the Instructor
Tapan Mukerji
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), Stanford Rock Physics and Borehole Geophysics (SRB) and the Basin and Petroleum System Modeling (BPSM) projects at Stanford University. His research interests include rock physics, spatial statistics, wave propagation, and stochastic methods for quantitative reservoir characterization and time-lapse reservoir monitoring. 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 is 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.