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Deep Learning for Carbon Storage History Matching by edrcer is a document available to read on EtoBox.

What is Deep Learning for Carbon Storage History Matching about?

This study presents a deep-learning-based data-space inversion (DSI) framework for history matching in geological carbon storage, enabling efficient uncertainty reduction in pressure and CO2 saturation predictions. The framework employs an adversarial autoencoder and convolutional long short-term memory network for spatio-temporal parameterization, allowing for rapid posterior predictions without extensive flow simulations. Results demonstrate significant uncertainty reduction across various geological scen

Author

edrcer

Language

EN