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