Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images-Reference-Cited by-同舟云学术

Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images

Published:2020-02 Issue:2 Volume:125 Page:
ISSN:2169-9313
Container-title:Journal of Geophysical Research: Solid Earth
language:en
Short-container-title:J. Geophys. Res. Solid Earth

Author:

Jeong Jina¹^ORCID,Park Eungyu¹^ORCID,Emelyanova Irina²^ORCID,Pervukhina Marina²^ORCID,Esteban Lionel²^ORCID,Yun Seong‐Taek³^ORCID

Affiliation:

1. Department of GeologyKyungpook National University Daegu South Korea

2. Commonwealth Scientific and Industrial Research Organization (CSIRO) Energy Perth Western Australia Australia

3. Earth and Environmental SciencesKorea University Seoul South Korea

Funder

Korea Environmental Industry and Technology Institute

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB018204

Reference51 articles.

1. Abdulraheem A. E.Sabakhy M.Ahmed A.Vantala P. D.Raharja &G.Korvin(2007).Estimation of permeability from wireline logs in a Middle Eastern carbonate reservoir using fuzzy logic. Paper presented at the 15th Middle East Oil and Gas Show and Conference Bahrain.

2. Akkurt R. Conroy T. T. Psaila D. Paxton A. Low J. &Spaans P.(2018).Accelerating and enhancing petrophysical analysis with machine learning: a case study of an automated system for well log outlier detection and reconstruction Paper presented at the 59th Annual Logging Symposium London UK.

3. Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

4. Al‐Mudhafar W. J. andBondarenko M. A.(2015).IntegratingK‐means clustering analysis and generalized additive model for efficient reservoir characterization. Paper presented at the 77th EAGE Conference and Exhibition Incorporating SPE EUROPIC Madrid Spain. doi: 10.3997/2214‐4609.201413024.

5. Alusta G. E.Mackay J.Fennema &I.Collins(2011).EOR vs. infill well drilling: How to make the choice?SPE Enhanced Oil Recovery Conference 2011 Society of Petroleum Engineers doi:https://doi.org/10.2118/143300‐MS.

Cited by 11 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. Research on Gas Channeling Identification Using the Fuzzy Comprehensive Evaluation Method;Energies;2024-08-08

2. Assessment of the effectiveness of a convolutional autoencoder for digital image-based automated core logging;Geoenergy Science and Engineering;2023-08

3. A Combined Noisy Borehole Image Log Segmentation Method;2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP);2023-06-27

4. Ultradeep carbonate reservoir lithofacies classification based on a deep convolutional neural network — A case study in the Tarim Basin, China;Interpretation;2023-06-22

5. Implicit Seismic Full Waveform Inversion With Deep Neural Representation;Journal of Geophysical Research: Solid Earth;2023-02-27