Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.  Back