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.
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.
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.
Dr. Thomas A. Smith founded Seismic Micro-Technology (SMT) in 1984 and led the development of the widely adopted Kingdom Suite software suite for seismic interpretation. He went on to found Geophysical Insights in 2008, which launched Paradise™ at SEG 2013.
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.