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ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Georeservoirs
Volume 12 - 2024 |
doi: 10.3389/feart.2024.1434820
Prediction of Saturation Exponent for Subsurface Oil and Gas Reservoirs Using Soft Computing Methods
Provisionally accepted
Anupam Yadav
1*
Saeed Hameed
2*
Farag M. Altalbawy
3*
Raja Praveen
4*
M Janaki Ramudu
5*
Nizomiddin Juraev
6*
Hameed Hassan Khalaf
7*
Bassam Farman Bassam
8*
Nada Qasim Mohammed
9*
Dunya J. Kassid
10*
Ahmed Elawady
11*
Mohammad Sina
12*- 1 Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh, India
- 2 College of Administrative Sciences, Applied Science University, Manama, Capital Governorate, Bahrain
- 3 Department of Chemistry, College of Science, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia
- 4 Department of Computer Science and Engineering, Faculty of Engineering and Technology, Jain University, Banglaore, Karnataka, India
- 5 Department of Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, Arunachal Pradesh, India
- 6 Department of Chemical and Materials Engineering, School of Engineering, New Uzbekistan University, Tashkent, Tashkent, Uzbekistan
- 7 Department of Medical Engineering, Al-Manara College For Medical Sciences, Maysan, Maysan, Iraq
- 8 College of Engineering, Al-Esraa University College, Baghdad, Baghdad, Iraq
- 9 Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Baghdad, Iraq
- 10 Mazaya University College, Dhi-Qar, Iraq
- 11 College of Technical Engineering, The Islamic University, Najaf, Iraq, najaf, Iraq
- 12 Islamic Azad University, Omidiyeh, Omidiyeh, Iran
The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean square error, root mean square error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous.
Keywords: Soft computing methods, sensitivity analysis, Archie equation, Saturation exponent, Oil & gas reservoir
Received: 19 May 2024; Accepted: 09 Jul 2024.
Copyright: © 2024 Yadav, Hameed, Altalbawy, Praveen, Ramudu, Juraev, Khalaf, Bassam, Qasim Mohammed, Kassid, Elawady and Sina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Anupam Yadav, Department of Computer Engineering & Applications, GLA University, Mathura, 281 406, Uttar Pradesh, India
Saeed Hameed, College of Administrative Sciences, Applied Science University, Manama, Capital Governorate, Bahrain
Farag M. Altalbawy, Department of Chemistry, College of Science, University College of Duba, University of Tabuk, Tabuk, 71491, Saudi Arabia
Raja Praveen, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Jain University, Banglaore, 560069, Karnataka, India
M Janaki Ramudu, Department of Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, 531162, Arunachal Pradesh, India
Nizomiddin Juraev, Department of Chemical and Materials Engineering, School of Engineering, New Uzbekistan University, Tashkent, Tashkent, Uzbekistan
Hameed Hassan Khalaf, Department of Medical Engineering, Al-Manara College For Medical Sciences, Maysan, Maysan, Iraq
Bassam Farman Bassam, College of Engineering, Al-Esraa University College, Baghdad, Baghdad, Iraq
Nada Qasim Mohammed, Department of Medical Laboratories Technology, AL-Nisour University College, Baghdad, 6770, Baghdad, Iraq
Dunya J. Kassid, Mazaya University College, Dhi-Qar, Iraq
Ahmed Elawady, College of Technical Engineering, The Islamic University, Najaf, Iraq, najaf, Iraq
Mohammad Sina, Islamic Azad University, Omidiyeh, Omidiyeh, Iran
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