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Advanced Seismic Analysis: Application of Unsupervised Neural Networks and Principal Component Analysis in Conventional and Unconventional Geologic Settings

This paper presents four case studies on the application of Self-Organizing Maps (SOM's), a form of unsupervised neural networks, and Principal Component Analysis (PCA) as applied to seismic attributes in various geologic settings, including onshore - conventional and unconventional - and offshore. The case studies tangibly demonstrate how exploration and field development risk are reduced using these advanced pattern-recognition methods, which are supported in Paradiseā„¢, the advanced geoscience analysis platform by Geophysical Insights.

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