The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333
Dec 27, 2019
Join Mohamed Sidahmed, R&D Manager at Shell, as he discusses groundbreaking advancements in machine learning and AI at NeurIPS. He dives into the innovative FaciesNet architecture, which transforms geological data into spectrograms for improved rock facies classification. Learn how these techniques revolutionize seismic imaging and enhance predictive capabilities, ultimately boosting hydrocarbon exploration confidence. Sidahmed also highlights the vital role of collaboration between academia and industry in driving energy-related AI innovations.
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Least Squares Imaging
- Least squares imaging offers a computationally efficient way to reconstruct images using deep learning.
- It relies on sequence models for image construction and image recognition for classification.
Shell's NeurIPS Papers
- Shell has two papers at NeurIPS.
- One is on least squares imaging, and the other, FaciesNet, focuses on facies classification in well logs.
FaciesNet Architecture
- FaciesNet is a novel architecture for classifying rock facies using well logs.
- It combines sequence models and recurrent neural networks to improve benchmark performance.