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About Estimating Reservoir Properties by Using Rock Physics Driven Machine Learning
This document discusses using machine learning techniques to estimate reservoir properties from seismic data. Specifically, it introduces GeoAI, which uses rock physics modeling and statistical simulations to generate synthetic well log and seismic data to train convolutional neural networks. The key aspects of the GeoAI workflow include using petrophysical analysis and rock physics modeling to create synthetic well logs spanning the expected geology. These synthetic logs are then used to generate a synthetic seismic catalog. A neural network is trained on the large synthetic dataset and applied to real seismic data to estimate multiple reservoir properties. This rock physics driven machine learning approach aims to overcome limitations of traditional machine learning like limited well data for training.
Detailed Information
| Author: | ['Rachma Yudha'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 42 |
| Language: | English |
| Format: | |
| Price: | FREE |
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