Uncertainty Quantification in Predicting Physical Property of Porous Medium With Bayesian Evidential Learning-Reference-Cited by-同舟云学术

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Uncertainty Quantification in Predicting Physical Property of Porous Medium With Bayesian Evidential Learning

Published:2024 Issue: Volume:62 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Fan Zhenzhen¹^ORCID,Zuo Chen²^ORCID,Guo Chen¹^ORCID,Yang Zhifang³,Yan Xinfei³,Li Xiaoming³

Affiliation:

1. School of Information Engineering, Chang’an University, Xi’an, China

2. Department of Big Data Management and Applications, Chang’an University, Xi’an, China

3. Key Laboratory of Geophysics and the Research Institute of Petroleum Exploration and Development, PetroChina, Beijing, China

Funder

National Science Foundation of China

Natural Science Foundation of Shaanxi Province

Open Foundation of Research Institute of Petroleum Exploration and Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10599321.pdf?arnumber=10599321

Reference63 articles.

1. Inversion method of elastic and fracture parameters of shale reservoir with a set of inclined fractures

2. Electrical conductivity models in saturated porous media: A review

3. The critical factors for permeability-formation factor relation in reservoir rocks: Pore-throat ratio, tortuosity and connectivity

4. Compressional and shear wave velocities relationship in anisotropic organic shales

5. 1. Digital Images and Computer Modeling

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