Machine Learning Application for Well logging and Petrophysical Interpretation
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This webinar provides an overview of machine learning techniques applied to well logging and petrophysical data interpretation. Participants will learn how advanced algorithms can improve data analysis, automate interpretation workflows, and enhance reservoir understanding. The session emphasizes practical applications, case studies, and strategies to integrate ML tools into conventional petrophysical evaluation processes.
Webinar Objectives
• Understand the fundamentals of machine learning and its relevance to well logging.
• Learn techniques for automating petrophysical data analysis and interpretation.
• Explore practical applications and case studies in reservoir evaluation.
• Apply ML-driven insights to support more accurate and efficient reservoir characterization.
About the Presenter
The session is conducted by an experienced industry professional with a background in petrophysics and data-driven reservoir evaluation. The presenter combines technical expertise with practical experience to provide actionable guidance for professionals looking to leverage machine learning in well logging and interpretation workflows.
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Time : 9 PM Indian Time | Duration : 90 Minutes
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What Can PTA Tell You About Your Reservoir?
Pressure Transient Analysis (PTA) is a powerful tool for reservoir characterization — and it can tell you far more than permeability, reservoir contact, and skin factor. But to unlock its advanced capabilities, the analyst needs a solid grasp of the diagnostic features of pressure and derivative responses: how they relate to flow regimes, and how the static and dynamic features of a reservoir generate those regimes at different stages of depletion. Without that context, history matching pressure responses or fitting straight lines to portions of the data may return parameter estimates while missing the bigger picture entirely.This webinar makes the case for PTA as a reservoir characterization tool, shows its proper and improper use through worked examples, and highlights just how underused it remains in practice.Slides & Material — Copyright: All content in this session is copyrighted by Prof. Erdal Ozkan. Access to the recording and slides is provided exclusively through this platform. Any unauthorized use, reproduction, or distribution outside the portal is strictly prohibited and may result in legal action under applicable laws.Recordings and certificates will be issued within one business day of the event.