Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China-Reference-Cited by-同舟云学术

Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China

Published:2021-08 Issue:4 Volume:32 Page:828-838
ISSN:1674-487X
Container-title:Journal of Earth Science
language:en
Short-container-title:J. Earth Sci.

Author:

Chen Lin^ORCID,Lin Weibing^ORCID,Chen Ping^ORCID,Jiang Shu,Liu Lu,Hu Haiyan

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12583-020-1396-5.pdf

Reference76 articles.

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2. Ahmadi, M. A., Chen, Z. X., 2019. Comparison of Machine Learning Methods for Estimating Permeability and Porosity of Oil Reservoirs via Petro-Physical Logs. Petroleum, 5(3): 271–284. https://doi.org/10.1016/j.petlm.2018.06.002

3. Ahmadi, M. A., Zendehboudi, S., Lohi, A., et al., 2013. Reservoir Permeability Prediction by Neural Networks Combined with Hybrid Genetic Algorithm and Particle Swarm Optimization. Geophysical Prospecting, 61(3): 582–598. https://doi.org/10.1111/j.1365-2478.2012.01080.x

4. Al-Anazi, A. F., Gates, I. D., 2012. Support Vector Regression to Predict Porosity and Permeability: Effect of Sample Size. Computers & Geosciences, 39(1): 64–76. https://doi.org/10.1016/j.cageo.2011.06.011

5. Asoodeh, M., Bagheripour, P., 2013. Core Porosity Estimation through Different Training Approaches for Neural Network: Back-Propagation Learning vs. Genetic Algorithm. International Journal of Computer Applications, 63(5): 11–15. https://doi.org/10.5120/10461-5172

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