FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed

Today we close out our 2019 NeurIPS series with Mohamed Sidahmed, Machine Learning and Artificial Intelligence R&D Manager at Shell. Mohamed and his team submitted a few papers at the conference this year. We briefly touch on their paper Accelerating Least Squares Imaging Using Deep Learning Techniques, which details how researchers can computationally efficiently reconstruct imaging using a deep learning framework approach. We then transition into a discussion on the paper FaciesNet: Machine Learning Applications for Facies Classification in Well Logs, which Mohamed describes as "A novel way of designing a new architecture for how we use sequence modeling and recurrent networks to be able to break out of the benchmark for classifying the different types of rock." Enjoy!