Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

Abhinav Kumar; Paul Rodrigues; A K Kareem; Tingneyuc Sekac; Sherzod Abdullaev; Jasgurpreet Singh Chohan; R Manjunatha; Kumar Rethik; Shivakrishna Dasi; Mahmood Kiani

doi:10.1038/s41598-024-84556-0

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

Sci Rep. 2025 Jan 2;15(1):373. doi: 10.1038/s41598-024-84556-0.

Authors

Abhinav Kumar^{1

2

3}, Paul Rodrigues⁴, A K Kareem⁵, Tingneyuc Sekac⁶, Sherzod Abdullaev^{7

8

9}, Jasgurpreet Singh Chohan^{10

11}, R Manjunatha¹², Kumar Rethik¹³, Shivakrishna Dasi¹⁴, Mahmood Kiani¹⁵

Affiliations

¹ Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, Ekaterinburg, 620002, Russia.
² Department of Technical Sciences, Western Caspian University, Baku, Azerbaijan.
³ Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India.
⁴ Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Saudi Arabia.
⁵ Biomedical Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Hillah, 51001, Babil, Iraq.
⁶ Department of Surveying and Land Studies, Papua New Guinea University of Technology, Lae,, Morobe, Papua New Guinea.
⁷ Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan.
⁸ Scientific and Innovation Department, Tashkent State Pedagogical University, Tashkent, Uzbekistan.
⁹ Department of Oil Refining and Gas, Andijan Machine-Building Institute, Andijan, Uzbekistan.
¹⁰ School of Mechanical Engineering, Rayat Bahra University, Mohali, India.
¹¹ Faculty of Engineering, Sohar University, Sohar, Oman.
¹² Department of Data Analytics and Mathematical Sciences, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India.
¹³ Department of Computer Science and Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, India.
¹⁴ Department of Computing Science and Artificial Intelligence, NIMS Institute of Engineering & Technology, NIMS University Rajasthan, Jaipur, India.
¹⁵ Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran. [email protected].

Abstract

Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. Additionally, relevancy factor is estimated for each input parameter to assess the relative effects of input parameters on the saturation exponent. The sensitivity analysis indicates that resistivity index and true resistivity have direct correlation with the saturation exponent while porosity, absolute permeability and water saturation is inversely related with saturation exponent. In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).

Keywords: Data-driven intelligent modeling; Outlier detection; Saturation exponent; Sensitivity analysis.