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About A Machine-Learning-Based Approach To Assistive Well-Log Correlation
This document summarizes a machine learning approach to assist with well log correlation. It trains a deep convolutional neural network (CNN) on over 5 million samples from expert well log correlations to perform pattern matching between wells. The CNN achieves 96.6% accuracy on a separate validation dataset. It is one component of a correlation tool that also uses algorithmic search logic and rules to determine optimal correlations while preserving stratigraphic order and structural trends. Examples are presented showing the tool can efficiently correlate markers on a dataset containing thousands of wells in a basin with structural and stratigraphic complexity.
Detailed Information
| Author: | ['Erick Morris'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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