Tanya Colwell, Product Manager
This webinar goes beyond conventional seismic reservoir characterization methods by harnessing the potential of deep learning approach through HampsonRussell's GeoAI product. Our focus lies on the West Tryal Rock field, a case study located at the Western margin of the Barrow Sub-basin in North Carnarvon Basin, Western Australia. We delve into the comprehensive analysis of its Middle to Late Jurassic shales, known as the primary source of hydrocarbons.
By overcoming data limitations through rock physics modeling and generating numerous synthetic wells with corresponding AVO responses, we train the convolutional neural networks to capture complex nonlinear relationships in the subsurface. As a result, CNN-predicted properties of P-impedance, Porosity, Vclay and Water Saturation exhibit enhanced spatial continuity and better match at blind well control, outperforming traditional prediction methods.
Our observations reveal that rock physics guided machine learning presents an effective means of simultaneously predicting elastic and reservoir properties, thereby providing additional dimension of data analysis for prospect identification and ranking in exploration type of scenarios.
Join us for this insightful webinar as we demonstrate the potential of GeoAI!
Presenter Bio
Tanya Colwell, Product Manager
Tanya is a Product Strategy Manager for GeoSoftware. She has been with HampsonRussell software for more than 20 years and holds Master Degrees in both Applied Mathematics and Computer Science. As a developer in HampsonRussell, she worked in various on AVO, Emerge, ProMC and other seismic characterization products. Now, she develops product strategies, oversees software development directions and studies market trends. With passion for science in traditional reservoir characterization and newer machine learning methods, she often interacts with clients and provides technical education for a world-wide audience.
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