Evaluation and Development of a Predictive Model for Geophysical Well Log Data Analysis and Reservoir Characterization: Machine Learning Applications to Lithology Prediction-Reference-Cited by-同舟云学术

Evaluation and Development of a Predictive Model for Geophysical Well Log Data Analysis and Reservoir Characterization: Machine Learning Applications to Lithology Prediction

Published:2022-10-13 Issue:6 Volume:31 Page:3195-3222
ISSN:1520-7439
Container-title:Natural Resources Research
language:en
Short-container-title:Nat Resour Res

Author:

Mishra Aditya^ORCID,Sharma Anjali^ORCID,Patidar Atul Kumar^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s11053-022-10121-z.pdf

Reference80 articles.

1. Abzalov, M. (2016). Exploratory data analysis. Modern Approaches in Solid Earth Sciences, 12, 207–219.

2. Ahmadi, M. A., & Chen, Z. (2019). Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs. Petroleum, 5(3), 271–284.

3. Al-Mudhafar, W. J., & Bondarenko, M. A. (2015). Integrating K-means clustering analysis and generalized additive model for efficient reservoir characterization. In 77th EAGE Conference and Exhibition 2015: Earth Science for Energy and Environment, June, 2301–2306. https://doi.org/10.3997/2214-4609.201413024.

4. Al-Mudhafar, W. J. (2017a). Integrating kernel support vector machines for efficient rock facies classification in the main pay of Zubair formation in South Rumaila oil field, Iraq. Modeling Earth Systems and Environment, 3(1), 1–8.

5. Al-Mudhafar, W. J. (2017b). Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. Journal of Petroleum Exploration and Production Technology, 7(4), 1023–1033.

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