Machine Learning for Seismic Interpretation driven by the nature of the technical and business demands facing geoscientists as oil and gas activity is advancing a new phase of unconventional reservoir development in an economic environment that rewards efficiency and risk mitigation.
Permanent sensors both on land and on the seafloor are collecting a new stream of seismic data that can be used for repeated active seismic, microseismic analysis, and continuous passive monitoring.
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.
Using advanced attribute analysis to improve analysis for unconventional reservoirs and anomalies in subsurface datasets.