A Midland Basin Case Study - Predicting Reservoir Properties Directly from Seismic Gathers

Join us for an insightful webinar where we will explore how artificial intelligence (AI) is being employed to predict reservoir properties directly from seismic data in the Midland Basin. This case study demonstrates the training of a Convolutional Neural Network (CNN) using synthetic wells and seismic gathers to estimate key properties such as porosity, water saturation, brittleness, and mineral volumes. The model was tested on blind wells and displayed a strong alignment with real-world data, aiding in the identification of brittle, productive layers that are essential for successful fracking.brittle, productive layers that are essential for successful fracking.

If you're interested in how machine learning is reshaping subsurface analysis, this is a session you won’t want to miss!

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