An efficient transformer-based surrogate model with end-to-end training strategies for automatic history matching-Reference-Cited by-同舟云学术

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An efficient transformer-based surrogate model with end-to-end training strategies for automatic history matching

Published:2024-09 Issue: Volume:240 Page:212994
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Zhang Jinding^ORCID,Kang Jinzheng,Zhang Kai^ORCID,Zhang Liming,Liu Piyang^ORCID,Liu Xingyu,Sun Weijia,Wang Guangyao

Publisher

Elsevier BV

Reference61 articles.

1. A spatio-temporal Transformer for 3D human motion prediction;Aksan,2021

2. Understanding dropout;Baldi;Adv. Neural Inf. Process. Syst.,2013

3. On the inductive bias of neural tangent kernels;Bietti;Adv. Neural Inf. Process. Syst.,2019

4. Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters;Chaudhuri;Adv. Water Resour.,2018

5. Application of proxy-based MCMC and EDFM to history match a Vaca Muerta shale oil well;Dachanuwattana;Fuel,2018

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1. Advanced attention-based spatial-temporal neural networks for enhanced CO2 water-alternating-gas performance prediction and history matching;Physics of Fluids;2024-09-01

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