Identification of lithofacies from well log data in the upper Assam basin using machine learning techniques
Author:
Funder
Science and Engineering Board India
Publisher
Springer Science and Business Media LLC
Subject
Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s11600-023-01229-8.pdf
Reference48 articles.
1. Adoghe LI, Aniekwe OS, Nwosu C (2011) Improving electrofacies modeling using multivariate analysis techniques: a deepwater turbidite case study. Presented at the Nigeria Annual International Conference and Exhibition, p. SPE-150776-MS. https://doi.org/10.2118/150776-MS
2. Allen DR (1975) Chapter 7 identification of sediments—their depositional environment and degree of compaction—from well logs. In: Developments in sedimentology. Elsevier, pp 349–401. https://doi.org/10.1016/S0070-4571(08)71089-6
3. Al-Mudhafar W (2017a) Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. J Petrol Explor Prod Technol 7:1023–1033. https://doi.org/10.1007/s13202-017-0360-0
4. Al-Mudhafar WJ (2015) Integrating component analysis & classification techniques for comparative prediction of continuous & discrete lithofacies distributions. Presented at the Offshore Technology Conference, p OTC-25806-MS. https://doi.org/10.4043/25806-MS
5. Al-Mudhafar W (2017b) Integrating kernel support vector machines for efficient rock facies classification in the main pay of Zubair formation in South Rumaila oil field, Iraq. Model Earth Syst Environ 3:12. https://doi.org/10.1007/s40808-017-0277-0
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