Funder
National Science Foundation
Reference39 articles.
1. Generating unrepresented proportions of geological facies using generative adversarial networks;Abdellatif;Comput. Geosci.,2022
2. Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation;Bao;Math. Geosci.,2022
3. Coupling ensemble smoother and deep learning with generative adversarial networks to deal with non-gaissianity in flow and transport data assimilation;Bao;J. Hydrol.,2020
4. Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models;Caers;AAPG Mem.,2004
5. Canchumuni, S.A., Emerick, A.A., Pacheco, M.A., 2017. Integration of ensemble data assimilation and deep learning for history matching facies models. In: Proceedings of the Offshore Technology Conference. Rio de Janeiro, Brazil, 24–26 October, number OTC28015-MS.