Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods.
Saved in:
| Title: | Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods. |
|---|---|
| Authors: | Kumar A; Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, Ekaterinburg, 620002, Russia.; Department of Technical Sciences, Western Caspian University, Baku, Azerbaijan.; Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India., Rodrigues P; Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Saudi Arabia., Kareem AK; Biomedical Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Hillah, 51001, Babil, Iraq., Sekac T; Department of Surveying and Land Studies, Papua New Guinea University of Technology, Lae,, Morobe, Papua New Guinea., Abdullaev S; Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan.; Scientific and Innovation Department, Tashkent State Pedagogical University, Tashkent, Uzbekistan.; Department of Oil Refining and Gas, Andijan Machine-Building Institute, Andijan, Uzbekistan., Chohan JS; School of Mechanical Engineering, Rayat Bahra University, Mohali, India.; Faculty of Engineering, Sohar University, Sohar, Oman., Manjunatha R; Department of Data Analytics and Mathematical Sciences, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India., Rethik K; Department of Computer Science and Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, India., Dasi S; Department of Computing Science and Artificial Intelligence, NIMS Institute of Engineering & Technology, NIMS University Rajasthan, Jaipur, India., Kiani M; Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran. mahmoodkiani373@gmail.com. |
| Source: | Scientific reports [Sci Rep] 2025 Jan 02; Vol. 15 (1), pp. 373. Date of Electronic Publication: 2025 Jan 02. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 2045-2322 |
|---|---|
| DOI: | 10.1038/s41598-024-84556-0 |
