Physics-informed deep learning for rock physical inversion and its uncertainty analysis-Reference-Cited by-同舟云学术

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Physics-informed deep learning for rock physical inversion and its uncertainty analysis

Published:2023-11 Issue: Volume:230 Page:212229
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Feng Runhai

Publisher

Elsevier BV

Reference38 articles.

1. A review of developments in carbon dioxide storage;Aminu;Appl. Energy,2017

2. Ensemble machine learning: an untapped modeling paradigm for petroleum reservoir characterization;Anifowose;J. Petrol. Sci. Eng.,2017

3. AVO inversion in exploration—key learnings from a Norwegian Sea prospect;Avseth;Lead. Edge,2016

4. Rock-physics diagnostics of depositional texture, diagenetic, alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks — a review of selected models and suggested work flows;Avseth;Geophysics,2010

5. Joint estimation of porosity and saturation using stochastic rock- physics modeling;Bachrach;Geophysics,2006

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning;Earth Science Informatics;2024-02-08

2. Joint Inversion Method of Rock Physics Based on Hunger Games Search Correction and Bidirectional Long-Short-Term Memory Network;IEEE Transactions on Geoscience and Remote Sensing;2024

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