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Fathaddin MT, Irawan S, Setiati R, Rakhmanto PA, Prakoso S, Mardiana DA. Optimized artificial neural network application for estimating oil recovery factor of solution gas drive sandstone reservoirs. Heliyon 2024; 10:e33824. [PMID: 39027583 PMCID: PMC11255495 DOI: 10.1016/j.heliyon.2024.e33824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/02/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
The most crucial aspect in determining field development plans is the oil recovery factor (RF). However, RF has a complex relationship with the reservoir rock and fluid properties. The application of artificial neural networks is able to produce complex correlations between reservoir parameters that affect the recovery factor. This research provides a new approach to improve the accuracy of the ANN model in the form of steps including removing outlier data, selecting input parameters, selecting transferring functions, selecting the number of neurons, and determining hidden layers. By applying these steps, an ANN model was selected with nine input parameters consisting of oil viscosity, water saturation, initial oil formation volume factor, formation thickness, initial pressure, permeability, specific gravity of oil, porosity, and original oil in place. Furthermore, based on the correlation coefficient, a tangent sigmoid transferring function, 30 neurons, and two hidden layers were determined. The proposed ANN correlation gives the best accuracy compared to the previous correlations. This is proved by the highest correlation coefficient of 0.91657.
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Affiliation(s)
| | - Sonny Irawan
- Department of Petroleum Engineering, Nazarbayev University, 10000 Nur Sultan, Kazakhstan
| | - Rini Setiati
- Department of Petroleum Engineering, Universitas Trisakti, 11440 Jakarta, Indonesia
| | - Pri Agung Rakhmanto
- Department of Petroleum Engineering, Universitas Trisakti, 11440 Jakarta, Indonesia
| | - Suryo Prakoso
- Department of Petroleum Engineering, Universitas Trisakti, 11440 Jakarta, Indonesia
| | - Dwi Atty Mardiana
- Department of Petroleum Engineering, Universitas Trisakti, 11440 Jakarta, Indonesia
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Gomaa S, Abdalla M, Salem KG, Nasr K, Emara R, Wang Q, El-Hoshoudy AN. Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions. Sci Rep 2024; 14:15155. [PMID: 38956414 PMCID: PMC11219757 DOI: 10.1038/s41598-024-64752-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024] Open
Abstract
The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and - 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.
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Affiliation(s)
- Sayed Gomaa
- Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
- Department of Petroleum Engineering, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo, 11835, Egypt.
| | - Mohamed Abdalla
- Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX, USA
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Khalaf G Salem
- Department of Reservoir Engineering, South Valley Egyptian Petroleum Holding Company (GANOPE), Cairo, Egypt.
| | - Karim Nasr
- Petroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE), El Shorouk City, Cairo, Egypt
| | - Ramadan Emara
- Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
| | - Qingsheng Wang
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - A N El-Hoshoudy
- PVT Lab, Production Department, Egyptian Petroleum Research Institute, Cairo, 11727, Egypt.
- PVT Service Center, Egyptian Petroleum Research Institute, Cairo, 11727, Egypt.
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Salem KG, Tantawy MA, Gawish AA, Salem AM, Gomaa S, El-hoshoudy A. Key aspects of polymeric nanofluids as a new enhanced oil recovery approach: A comprehensive review. FUEL 2024; 368:131515. [DOI: 10.1016/j.fuel.2024.131515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
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Khattab H, Gawish AA, Gomaa S, Hamdy A, El-Hoshoudy AN. Assessment of modified chitosan composite in acidic reservoirs through pilot and field-scale simulation studies. Sci Rep 2024; 14:10634. [PMID: 38724544 PMCID: PMC11082220 DOI: 10.1038/s41598-024-60559-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
Chemical flooding through biopolymers acquires higher attention, especially in acidic reservoirs. This research focuses on the application of biopolymers in chemical flooding for enhanced oil recovery in acidic reservoirs, with a particular emphasis on modified chitosan. The modification process involved combining chitosan with vinyl/silane monomers via emulsion polymerization, followed by an assessment of its rheological behavior under simulated reservoir conditions, including salinity, temperature, pressure, and medium pH. Laboratory-scale flooding experiments were carried out using both the original and modified chitosan at conditions of 2200 psi, 135,000 ppm salinity, and 196° temperature. The study evaluated the impact of pressure on the rheological properties of both chitosan forms, finding that the modified composite was better suited to acidic environments, showing enhanced resistance to pressure effects with a significant increase in viscosity and an 11% improvement in oil recovery over the 5% achieved with the unmodified chitosan. Advanced modeling and simulation techniques, particularly using the tNavigator Simulator on the Bahariya formations in the Western Desert, were employed to further understand the polymer solution dynamics in reservoir contexts and to predict key petroleum engineering metrics. The simulation results underscored the effectiveness of the chitosan composite in increasing oil recovery rates, with the composite outperforming both its native counterpart and traditional water flooding, achieving a recovery factor of 48%, compared to 39% and 37% for native chitosan and water flooding, thereby demonstrating the potential benefits of chitosan composites in enhancing oil recovery operations.
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Affiliation(s)
- Hamid Khattab
- Petroleum Engineering Department, Faculty of Petroleum & Mining Engineering, Suez University, Cairo, Egypt
| | - Ahmed A Gawish
- Petroleum Engineering Department, Faculty of Petroleum & Mining Engineering, Suez University, Cairo, Egypt
| | - Sayed Gomaa
- Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
- Department of Petroleum Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, Egypt
| | - Abdelnaser Hamdy
- Reservoir Engineering Department, Khalda Petroleum Company, Cairo, Egypt
| | - A N El-Hoshoudy
- PVT lab, Production Department, Egyptian Petroleum Research Institute, Cairo, 11727, Egypt.
- PVT service center, Egyptian Petroleum Research Institute, Cairo, 11727, Egypt.
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Khattab H, Gawish AA, Hamdy A, Gomaa S, El-hoshoudy AN. Assessment of a Novel Xanthan Gum-Based Composite for Oil Recovery Improvement at Reservoir Conditions; Assisted with Simulation and Economic Studies. JOURNAL OF POLYMERS AND THE ENVIRONMENT 2024. [DOI: 10.1007/s10924-023-03153-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 07/05/2024]
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