GeoAI is a HampsonRussell product, encompassing a novel seismic reservoir characterization technology for projects with limited well control. Use Rock Physics theory and statistical simulations to model various geological scenarios. Train convolutional neural networks (CNNs) using simulated synthetic data and transfer learning to apply to real seismic gathers, estimating multiple rock and elastic reservoir properties in a simplified machine learning approach.
This course covers all aspects of GeoAI, starting with an introduction to the practical use of deep neural networks (DNNs) to predict elastic and rock properties, as well as an overview of relevant machine learning theory. In this supervised learning workflow, the relationships mapping the pre-stack seismic to the properties of interest are learned from the data itself. Key to deriving robust operators are big data sets of training data. GeoAI allows the generation and incorporation of synthetic data in the machine learning workflow and utilization of the synthetic data in the CNNs to get more reliable volume estimates.
Duration: 2 days (all day)
Software used: HampsonRussell
Course Format: Instructor-led, workflow-based, virtual training.
Who should attend: Geophysicists, geologists, engineers, and technical staff who would like to understand the theory behind essential Seismic Reservoir Characterization Workflow.
*Virtual classroom capacity is a minimum of 4 and a maximum of 10 attendees for this course; group discount may apply