11th-March-2021
• Oil well operators can use GPU accelerator analytics to visualize and analyze large volumes of production and sensor data, such as pump pressure, flow, and temperature. This gives you deeper insight into costly issues, such as predicting equipment at risk of failure and how those failures will affect a wider range of systems.
• For example, converting large amounts of seismic data images into 3D maps could improve reservoir prediction accuracy. More generally, using deep learning to train models can also predict and improve the efficiency, reliability, and safety of costly drilling and production operations.
AI Models vs. Physical Models
Models are useful tools to understand the behaviors and processes in the real world, and to make inferences about the future. In science, there are essentially two modeling approaches:
Data Driven Models: The data driven models build relationships between input and output data, without worrying too much about the underlying processes, using statistical/machine learning techniques. A linear regression model is an example of a data driven model that, for example, builds a relationship between a dependent variable and a set of independent variables.
Physical models: Physical models are driven by certain processes. These processes can usually be described by a set of mathematical equations. For example, Navier-Stokes (N-S) equations govern the motion of fluids and can be seen as Newton’s second law of motion for fluids.
Commentaires