Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques

Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques

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Arifur Rahman^a, George Daoud^b^,^c, Ezeddin Shirif^a^,^*, Mohamed El-Darieby^b, Mohamed El-Hendawi^c^,^d

Petroleum Research | 2026, 11(1) : 217 - 236

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Petroleum Research | 2026, 11(1): 217-236

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Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques

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Arifur Rahman^a, George Daoud^b^,^c, Ezeddin Shirif^a^,^*, Mohamed El-Darieby^b, Mohamed El-Hendawi^c^,^d

Affiliations

^aPetroleum Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, SK, Canada

^bFaculty of Engineering and Applied Science, Ontario Tech University, Oshawa, L1G 0C5, ON, Canada

^cElectrical Engineering Department, Assiut University, Assiut, 71515, Egypt

^dElectronic Systems Engineering, University of Regina, Regina, S4S 0A2, SK, Canada

Published: 2026-03-10 doi: 10.1016/j.ptlrs.2025.08.007

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Abstract

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Water saturation is one of the most critical yet often underappreciated petrophysical parameters in reservoir characterization. A wide range of petrophysical and reservoir engineering computations that lead to crucial field development decisions, including reserve estimation, waterflooding efficiency calculation, and capillary pressure deduction, rely on its accurate determination. This study demonstrates how machine learning techniques can forecast reservoirs' fluid saturation and pressure distribution. This study describes a deep learning-based proxy modeling technique for accurately predicting reservoir pressure distribution and fluid (oil, water, and gas) saturation during water flooding in single-layer heterogeneous reservoirs. This study used recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to build a proxy model. This work indicates that compared to simulation outcomes, computer-based machine learning algorithms can accurately predict fluid (oil, water, and gas) saturation and pressure distribution. The stated accuracy was evaluated numerically and graphically, and error analysis between various machine learning approaches and simulated results was utilized.

Key words

Fluid saturation / Pressure distribution / Feedforward neural network / Convolutional neural network / Deep neural network

Cite this Article

Arifur Rahman, George Daoud, Ezeddin Shirif, Mohamed El-Darieby, Mohamed El-Hendawi. Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques[J]. Petroleum Research, 2026 , 11 (1) : 217 -236 . DOI: 10.1016/j.ptlrs.2025.08.007

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Funding

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Petroleum Technology Research Center
Mitacs Center for Research

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Year 2026 volume 11 Issue 1

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Article Info

doi: 10.1016/j.ptlrs.2025.08.007

Receive Date：2024-01-25
Online Date：2026-10-14
Published：2026-03-10

Article Data

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History

Received：2024-01-25
Revised：2025-08-18
Accepted：2025-08-27

Funding

Petroleum Technology Research Center

Mitacs Center for Research

Affiliations

^aPetroleum Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, S4S 0A2, SK, Canada

^bFaculty of Engineering and Applied Science, Ontario Tech University, Oshawa, L1G 0C5, ON, Canada

^cElectrical Engineering Department, Assiut University, Assiut, 71515, Egypt

^dElectronic Systems Engineering, University of Regina, Regina, S4S 0A2, SK, Canada

Corresponding:

^* E-mail address: ezeddin.shirif@uregina.ca (E. Shirif).

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表12种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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