Tao Zhao unveils a fault detection workflow using deep learning and image processing technologies at the 2018 Annual SEG Meeting in Anaheim.
Using a new supervised learning technique, convolutional neural networks (CNN), interpreters are approaching seismic facies classification in a revolutionary way as explained by Tao Zhao at SEG Anaheim (2018).
Geoscientists Deborah Sacrey and Rocky Roden solve exploration problems using Paradise, machine learning software for seismic interpretation in the June 2018 issue of First Break.
Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics.
This paper sets out a unified mathematical framework for the process from seismic samples to geobodies.
Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.
Seismic interpretation of thin beds below tuning has always been a challenge in the oil and gas industry. A multi-attribute interpretation utilizes SOM to analyze numerous seismic attributes all at once to identify natural patterns in the data.
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.
Analyzing seismic data through geologic pattern recognition methods like Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in Paradise machine learning software.
Self-organizing maps are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data in an orderly fashion. The curvature and harvesting of the classification with low probability in a SOM are an indicator of multi-attribute anomalies for further investigation.