Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.
Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce terabytes of data. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM.
Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2
Results of a Self-Organizing Map (SOM) of many instantaneous attributes to reveal different types of facies and shale that apply machine learning to improve resolution and reveal facies.
Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps
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.