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Partial Automation of The Seismic To Well Tie With Deep Learning and Bayesian Optimization by mojtaba bavand is a document available to read on EtoBox.
This research paper presents a method for automating the seismic to well tie process using deep learning and Bayesian optimization. A variational convolutional neural network is developed to extract wavelets from seismic data, while a Bayesian optimizer tunes key parameters to enhance the quality of the tie. The proposed approach is validated on real datasets, demonstrating its effectiveness in improving accuracy and reducing manual effort in well tying.
- Author
- mojtaba bavand
- Language
- EN