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Harness the Power of Rock Physics and Machine Learning for Advanced Seismic Reservoir Insights

Rock Physics and Machine Learning for Quantitative Seismic Reservoir Characterization

Integrate rock physics and machine learning techniques to enhance seismic reservoir characterization for better decision-making in exploration and production.

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Course Schedule

Online Sessions:

Code	Date	Time	Duration	Location	Currency	Team of 10 Per Person	Team of 7 Per Person	Early Bird Fee Per Person	Normal Fee Per Person
PEAMLSG43	10 - 14 Nov 2025	9 AM CALIFORNIA TIME	4 Hours / Day	Zoom Online	USD	2150	2250	2500	2850

The Classes Will be Online Via Zoom from Mon to Fri With 4 Hours / Day.

Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.

Rock Physics and Machine Learning for Quantitative Seismic Reservoir Characterization

The objective of this course is to understand the fundamentals of Rock Physics ranging from basic laboratory and theoretical results to practical “recipes” that can be immediately applied in the field. We will present 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. We will present case studies and strategies for quantitative seismic interpretation and, suggestions for more effectively employing seismic-to-rock properties transforms in conjunction with data science and Bayesian machine learning for reservoir characterization and monitoring, with emphasis on seismic interpretation and uncertainty quantification for lithology and subsurface fluid detection and CO2 geosequestration. The course is recommended for all geophysicists, reservoir geologists, seismic interpreters, and engineers concerned with reservoir characterization, reservoir delineation, hydrocarbon detection, reservoir development, and CO2 sequestration and monitoring.

Description

In today’s oil and gas industry, understanding the subsurface with precision is critical to successful exploration and production. This course is designed to equip participants with advanced knowledge in rock physics principles and machine learning applications specifically tailored for quantitative seismic reservoir characterization. Through a blend of theory and practical examples, attendees will learn to interpret seismic data more effectively, linking rock properties with reservoir characteristics to improve forecasting and resource management.

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Course Description

Introduction

The integration of rock physics and machine learning is transforming seismic reservoir characterization. By connecting rock properties to seismic data, this course empowers professionals to improve reservoir predictions, optimize recovery, and reduce uncertainties in reservoir characterization. This training offers a hands-on approach to understanding and applying these powerful tools in real-world reservoir studies, enhancing the overall effectiveness of seismic data interpretation.

Objectives

Understand the fundamentals of rock physics and its application in reservoir characterization.

Learn to apply machine learning techniques to enhance seismic data interpretation.

Develop skills in integrating rock physics models with machine learning algorithms.

Improve prediction accuracy in reservoir properties using data-driven methods.

Gain practical experience in using tools and workflows for quantitative seismic reservoir characterization

Training Methodology

This course is delivered through a blend of interactive lectures, case studies, and hands-on exercises. Participants will engage with practical examples and real-world datasets, applying rock physics and machine learning methods in structured workshops, fostering both theoretical understanding and practical skills.

Organisational Impact

By participating in this course, organizations will benefit from improved reservoir forecasting accuracy and more efficient exploration and production operations. Enhanced seismic reservoir characterization translates to better risk management and cost-effective decision-making, fostering sustainable growth and resource optimization.

Personal Impact

Participants will gain a strong foundation in rock physics and machine learning, equipping them with the latest skills to advance in the field of seismic reservoir characterization. This knowledge will enable attendees to approach complex reservoir challenges with confidence, adding value to their roles and enhancing their career progression.

Who Should Attend?

This course is ideal for geoscientists, reservoir engineers, petrophysicists, data scientists, and other professionals involved in seismic interpretation, reservoir modeling, and subsurface data analysis. It is also valuable for those looking to integrate advanced data science techniques in their reservoir studies for improved accuracy and decision-making.

Course Outline

Day 1

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

Day 2

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

Day 3

Shaly sands and their seismic signatures

Granular media models, unconsolidated sand model, cemented sand model

Velocity dispersion and attenuation; Velocity Upscaling

Day 4

Rock Physics of AVO interpretation and Vp/Vs relations

Quantitative seismic interpretation and rock physics templates.

Rock physics for CO2 geosequestration

Example case studies using AVO and seismic impedance for quantitative reservoir characterization

Day 5

Selection of other topics including:

Fractures and anisotropy

Rock physics of organic-rich shales

Digital rock physics

Rock physics and Bayesian machine learning applications

Certificates

On successful completion of this training course, PEA Certificate will be awarded to the delegates

About The Trainer

Academic Leadership: Tapan Mukerji is a Professor (Research) and co-director at the Stanford Center for Earth Resources Forecasting, holding a Ph.D. in Geophysics from Stanford University.

Research Expertise: His research spans rock physics, geostatistics, wave propagation, and machine learning for reservoir characterization and monitoring.

Awards & Editorial Roles: Mukerji received the SEG’s Karcher Award (2000) and the ENI Award (2014) and serves as an associate editor for Geophysics and Computers and Geosciences.

Publications & Keynotes: He has co-authored influential books, including The Rock Physics Handbook, and frequently presents at international conferences and short courses on rock physics and geostatistics.