Quantitative Seismic Inversion & Artificial Intelligence
| 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 |
|---|---|---|---|---|---|---|---|---|---|
| PEA-QSI26 | 26 - 30 Oct 2026 | 7 PM Indian Time |
4 Hours Per Day
|
Zoom Online
|
USD
|
2000
|
2250
|
2500
|
3000
|
The classes will be conducted online via Zoom from Monday to Friday, for 4 hours per day excluding breaks.
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Quantitative Seismic Inversion & Artificial Intelligence
This course explores the latest developments in quantitative seismic inversion and artificial intelligence applications for reservoir characterization. Participants will learn how to integrate AI-driven analytics with inversion workflows to enhance seismic interpretation, lithology prediction, and reservoir property estimation.
Description
Modern exploration and development projects demand precise subsurface models built from reliable seismic and well data. The Quantitative Seismic Inversion & Artificial Intelligence course introduces participants to advanced seismic inversion techniques and how AI and machine learning can transform seismic data analysis into powerful predictive tools.
The course covers deterministic, stochastic, and geostatistical inversion methods, focusing on how they extract rock and fluid properties from seismic amplitudes. It also demonstrates how AI algorithms—such as neural networks, pattern recognition, and regression models—can be applied to classify lithofacies, predict petrophysical parameters, and automate interpretation tasks.
Participants will gain practical experience in data integration, workflow design, and validation methods to ensure accurate, data-driven reservoir characterization.
Quantitative seismic inversion has long been a cornerstone of seismic reservoir characterization. Today, Artificial Intelligence is reshaping this field—bringing automation, pattern discovery, and predictive modeling capabilities to seismic interpretation.
This course provides participants with an in-depth understanding of how AI complements traditional inversion workflows. It bridges theory and practice, guiding professionals through end-to-end workflows—from data preprocessing and inversion modeling to machine learning–based property prediction. Emphasis is placed on practical applications that deliver real improvements in accuracy, speed, and decision-making.
Understand the fundamentals of quantitative seismic inversion and its role in reservoir characterization.
Apply deterministic, stochastic, and geostatistical inversion techniques.
Integrate seismic inversion results with well and petrophysical data.
Utilize artificial intelligence and machine learning for seismic interpretation and property prediction.
Build and train AI models to automate facies and lithology classification.
Assess uncertainty and validate inversion and AI results with real data.
Design integrated workflows combining geophysics, data science, and reservoir engineering.
The course combines lectures, practical demonstrations, and hands-on exercises using real datasets. Participants will work through case studies illustrating AI-assisted inversion, data-driven classification, and reservoir prediction. Interactive discussions and software-based examples will ensure a strong link between theoretical understanding and practical application.
Improved seismic interpretation accuracy through AI-enhanced workflows.
Faster and more consistent reservoir characterization.
Strengthened data integration between geophysics, petrophysics, and data science teams.
Reduced uncertainty and improved decision-making in field development.
Enhanced technical capability in advanced digital subsurface technologies.
Gain expertise in both traditional inversion and modern AI-driven interpretation.
Develop practical skills in applying machine learning to geophysical data.
Strengthen multidisciplinary knowledge bridging geoscience and data analytics.
Improve problem-solving and data interpretation efficiency.
Build professional credibility in digital geoscience and intelligent reservoir characterization.
Geophysicists and Geologists
Reservoir Engineers and Petrophysicists
Data Scientists and Geoscience Analysts
Seismic Interpreters and Modelers
Professionals involved in reservoir characterization, seismic inversion
Day 1: Inversion Theory & Post-Stack Workflows
The program opens with a comprehensive look at post-stack seismic inversion theory. Attendees explore various algorithms, including local (deterministic/stochastic) and direct (coloured/recursive) inversion. The day focuses on the essential workflow: preconditioning seismic and log data, performing well-to-seismic ties, and generating synthetics.
Day 2: Practical Analysis & Seismic Attributes
Day 2 is heavily software-focused, featuring practical sessions for log interpretation and pre-inversion analysis. Participants perform forward modeling and quality control (QC) on inversion results while learning to detect thin beds. The afternoon introduces seismic attributes, covering their mathematical meaning and the theory of multi-attribute analysis.
Day 3: AVO Theory & Pre-Stack Inversion
This session transitions into Amplitude Versus Offset (AVO) and Rock Physics theory. You will work through AVO conditioning and modeling, specifically analyzing factors like intercept, gradient, and fluid factors. The day concludes with an introduction to pre-stack inversion and geostatistical methods for predicting facies and geomechanical properties.
Day 4: AI Foundations & Supervised Learning
Day 4 introduces the "why" and "how" of Artificial Intelligence (AI) in petroleum exploration. The curriculum covers the mathematical foundations of AI and focuses on Supervised Learning techniques such as Support Vector Machines (SVM) and Artificial Neural Networks. Practical applications include using AI to predict missing logs, identify lithology, and assess reservoir quality.
Day 5: Unsupervised Learning & Reservoir Prediction
The final day applies AI to complex reservoir challenges through Unsupervised Learning methods like K-means and Kohonen Self-Organizing Maps. In practical sessions, participants generate seismic attributes and use neural networks to predict reservoir properties and facies. The course wraps up with case studies on predicting porosity, water saturation, and using Bayesian inversion for Direct Hydrocarbon Indicators (DHI).
On successful completion of this training course, PEA Certificate will be awarded to the delegates.
Your expert course leader is a distinguished authority in the field of petrophysics and rock physics, recognized for their technical excellence in supporting quantitative seismic interpretation. With extensive experience in constructing and applying models for seismic amplitude interpretation, they possess specialized expertise in AVO modeling, seismic inversion, and the application of Gassmann’s equation for fluid substitution. Their contribution to the industry's body of knowledge is highlighted by their authored research, including a notable technical paper for the London Petrophysical Society regarding the pitfalls of Gassmann assumptions in shaly sand rocks. Throughout their career, they have mastered the integration of petrophysical log data with seismic scales, providing critical insights into prospect risking and reservoir characterization. As an educator, they bridge the gap between complex geophysical theory and practical application, drawing from real-world case histories across various global basins to deliver high-level training for experienced geoscientists
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
