Current computing technology has allowed for the application of new machine learning techniques in analyzing seismic data through pattern recognition methods such as Self-Organizing Maps in Paradise.
How Self-Orgazining Maps (SOM) and Principal Componenrt Analysis (PCA) greatly enhances the interpretation process to identify geology in diffferent settings. Geophysicists interpret multiple attributes of seismic data using principal component analysis and self-organizing maps of machine learning.
Utilizing machine learning via Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) interpretation techniques to help identify sweet spots.
Using advanced attribute analysis to improve analysis for unconventional reservoirs and anomalies in subsurface datasets.
Risks and rewards are evenly poised in the hydrocarbons industry, and along with oil and gas companies, probably no one understands the nuances better than Tom Smith, founder and president of Geophysical Insights.