Subsurface Lithology Classification Using Well Log Data, an Application of Supervised Machine Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1620-7_18
Reference21 articles.
1. Zhang J, He Y, Zhang Y, Li W, Zhang J (2022) Well-logging-based lithology classification using machine learning methods for high-quality reservoir identification: a case study of baikouquan formation in Mahu Area of Junggar Basin. NW China. Energies 15(10):3675. https://doi.org/10.3390/en15103675
2. Li W, Hu W, Abubakar A (2020) Machine learning and data analytics for geoscience applications-Introduction. Geophysics 85(4). https://doi.org/10.1190/geo2020-0518-spseintro.1
3. Mahmoud, A.A., Elkatatny, S., Al-AbdulJabbar, A. (2021) Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters. Journal of Petroleum Science and Engineering, 203,108574, DOI: https://doi.org/10.1016/j.petrol.2021.108574
4. Liu H, Wu Y, Cao Y, Lv W, Han H, Li Z, Chang J (2020) Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method. Sensors 20(13):3643. https://doi.org/10.3390/s20133643
5. Joshi D, Patidar AK, Mishra A et al (2021) Prediction of sonic log and correlation of lithology by comparing geophysical well log data using machine learning principles. GeoJournal. https://doi.org/10.1007/s10708-021-10502-6
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1. Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling;ACS Omega;2023-10-30
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