Watch videos of Presentations from SEG 2017
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016

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Seismic attribute analysis can benefit from unsupervised neural network

By Tom Smith, Ph.D. and Deborah Sacrey, Geophysical Insights

Published with permission: Offshore Magazine
September 2011

Process identifies anomalies from original data without bias using Unsupervised Neural Networks in Greenfield Exploration.

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Full Article Text:

The primary task facing a seismic interpreter is to recognize and attribute a geologic significance to observable patterns in the seismic response. The most apparent patterns are found in seismic reflections. In recent years, the industry is using more subtle patterns and connecting them to such attributes as porosity, lithology, and fluid content, as well as underground structure. The separation of such patterns and their use as potential identifiers of subsurface characteristics comprises attribute analysis, a customary instrument in the geoscientist’s toolkit. Over the years, seismic data volumes have increased in terms of geographic area covered, depth of interest, and the number of attributes. New and potentially disruptive technologies have developed to take advantage of all the attributes available in the seismic data. One new technology, based on unsupervised neural networks (UNN), reveals deeper insights into the seismic response and thereby reduces exploration risk. Unsupervised neural network technology can help interpreters recognize seismic anomalies that may indicate the presence of hydrocarbons, often when conventional techniques fall short. This new technology may also find application in the prediction of lithologies and fluid properties, as well as in estimating the size of reservoirs. The self-organizing map (SOM), a form of UNN and a powerful pattern recognition method, was initially developed by Prof. Teuvo Kohonen of Finland during the 1970s-80s. Based on this approach, UNNs assist the interpreter in prospect evaluation by: • Enabling the rapid comparison of large sets of seismic attributes • Indentifying combinations of attributes that reveal seismic anomalies • Distilling the interpretation process to identify hydrocarbons with greater speed and accuracy. Supervised vs. unsupervised UNNs For several years there have been a few commercial tools in the upstream industry that are based on “supervised” neural networks. A supervised neural network (SNN) operates on data that has been classified, i.e., “ground truth” is known in specific locations, providing reference points for calibration of the network. In terms of seismic data, for example, a segment of a seismic survey at each logged well is employed to calibrate the SNN. Supervised neural networks unite the seismic data at the well to the known conditions from the well, while inferring geophysical properties away from wells. But what happens if there is no well log or other forms of ground truth available, such as in a greenfield exploration? This is the advantage of unsupervised neural networks, which do not need an “answer” beforehand and cannot, therefore, be biased. 


REDUCING RISK

Geophysical Insights’ seismic interpretation technology’s success lies in categorization and interpretation of all attributes of data—of which there may be six to 100— simultaneously. The company plans to implement its seismic data storage in the Professional Petroleum Data Management (PPDM) Association model. It has outlined a pattern of “populating” seismic survey meta-data to unambiguously store and access the data in a PPDM database through standard methods.

NEURAL NETWORK

Geophysical Insights applies unsupervised neural networks to the interpretation of 3-D seismic data. The new volume of data created by the process identifies specific areas of interest within a survey. A neuron “learns” by adjusting its position within the attribute space as it is drawn toward nearby data points. The winning neuron is the one that is closest to the selected data point.