Modern Reservoir Characterization : Integrating Artificial Intelligence (AI) with Traditional Methods
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During this workshop, participants will develop a more comprehensive grasp of the geophysical theory of seismic inversion and explore advanced seismic reservoir characterization techniques. Discover the essential steps in the seismic reservoir characterization workflow using machine learning, covering data processing to inversion analysis. Acquire valuable knowledge applicable to your everyday projects.
In classical reservoir characterization, it is commonly assumed that the reservoir exhibits both elastic and hydraulic isotropy. However, real reservoirs often exhibit anisotropic characteristics and spatial heterogeneity on multiple scales. Relying solely on core and log data may not accurately represent the larger reservoir volume. Therefore, the most effective way to accurately analyze the physical properties of most actual reservoirs, across their entire volume, involves utilizing advanced machine learning methods for acquired and interpreted seismic data.
Workshop Objectives
This workshop aims to offer Exploration & Development professionals the chance to gain practical experience in a range of Seismic Reservoir Characterization techniques. This comprehensive workshop will provide a complete overview of seismic approaches, including the underlying theoretical principles and practical techniques used in real-world scenarios.
About the Presenter
The webinar will be delivered by a seasoned industry professional with extensive experience in reservoir engineering, geoscience, and data-driven technologies. The presenter combines strong technical expertise with practical field knowledge, offering participants both strategic insights and actionable applications relevant to today’s oil and gas challenges.
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Machine Learning for Drilling Engineering with Python
Drilling generates huge amounts of data — but most of it sits unused. This hands-on workshop shows you how to turn that data into better decisions using Python and machine learning.Over the sessions, you'll work with real drilling datasets to build models that predict problems before they happen — stuck pipe, ROP optimization, bit wear, formation changes, and more. No heavy math theory. You write code, run it on actual data, and see the results.By the end, you'll have working Python scripts you can adapt to your own field data, plus a clear understanding of where ML adds value in drilling and where it doesn't.Built for drilling engineers, not data scientists. If you can read a drilling report, you can follow this course.