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The dew point pressure (DPP) is a crucial thermodynamic property for gas reservoir performance evaluation, gas/condensate characterization, reservoir development and management, and downstream facility design. However, dew point pressure measurement is an expensive and time-consuming task; its estimation using the thermodynamic approaches has convergency problems, and available empirical correlations often provide high uncertainty levels. In this paper, the hybrid neuro-fuzzy connectionist paradigm is developed using 390 literature measurements. The adaptive neuro-fuzzy inference system (ANFIS) topology, including the training algorithm and cluster radius (radii), was determined by combining trial-and-error and statistical analyses. The hybrid optimization algorithm and radii = 0.675 are distinguished as the best characteristics for the ANFIS model. A high value of observed R2 = 0.97948 confirms the excellent performance of the designed approach for calculating the DPP of retrograde gas condensate reservoirs. Furthermore, visual inspections and statistical indices are employed to compare the ANFIS reliability and available empirical correlations. The results showed that the ANFIS model is more accurate than the well-known empirical correlations and previous intelligent paradigms in the literature. The designed ANFIS model, the best empirical correlation, and the most accurate intelligent paradigm in the literature present the absolute average relative deviation (AARD) of 1.60%, 11.25%, and 2.10%, respectively.
Seyed Mehdi Seyed Alizadeh; Ali Bagherzadeh; Soufia Bahmani; Amir Nikzad; Elnaz Aminzadehsarikhanbeglou; Subbotina Tatyana Yu. Retrograde Gas Condensate Reservoirs: Reliable Estimation of Dew Point Pressure by the Hybrid Neuro-Fuzzy Connectionist Paradigm. Journal of Energy Resources Technology 2021, 144, 1 -25.
AMA StyleSeyed Mehdi Seyed Alizadeh, Ali Bagherzadeh, Soufia Bahmani, Amir Nikzad, Elnaz Aminzadehsarikhanbeglou, Subbotina Tatyana Yu. Retrograde Gas Condensate Reservoirs: Reliable Estimation of Dew Point Pressure by the Hybrid Neuro-Fuzzy Connectionist Paradigm. Journal of Energy Resources Technology. 2021; 144 (6):1-25.
Chicago/Turabian StyleSeyed Mehdi Seyed Alizadeh; Ali Bagherzadeh; Soufia Bahmani; Amir Nikzad; Elnaz Aminzadehsarikhanbeglou; Subbotina Tatyana Yu. 2021. "Retrograde Gas Condensate Reservoirs: Reliable Estimation of Dew Point Pressure by the Hybrid Neuro-Fuzzy Connectionist Paradigm." Journal of Energy Resources Technology 144, no. 6: 1-25.
In this paper, three different scenarios were experimentally investigated to compare carbon dioxide based enhanced oil recovery methods. These methods are continuous carbon dioxide (immiscible injection), water alternating gas, and cyclic carbon dioxide injection were investigated. In scenario A, the maximum oil recovery factor for water flooding is about 19% when there is no oil production. The maximum oil recovery at miscibility stage is about 46%. The reason for this low value of oil recovery factor might correspond to the sufficient interaction time between oil and dissolved gas. In scenario B, the total oil recovery factor is about 60% when the water alternating gas injection was performed in the system. In scenario C, after cyclic carbon dioxide injection, final oil recovery factor reached to 62%. The maximum oil recovery after miscibility stage is about 78%. In scenario B and C, regarding the more oil volume production, there are more void spaces that can be a good place for carbon dioxide storage. However, for scenario B, as the injection pattern has been changed alternatively, the void spaced had been occupied by water and this is why the carbon storage capacity was being decreased for this scenario rather than other two scenarios.
Rahmad Syah; S.M. Alizadeh; Mahyuddin K.M. Nasution; Mohammad Nabi Ilani Kashkouli; Marischa Elveny; Afrasyab Khan. Carbon dioxide-based enhanced oil recovery methods to evaluate tight oil reservoirs productivity: A laboratory perspective coupled with geo-sequestration feature. Energy Reports 2021, 7, 4697 -4704.
AMA StyleRahmad Syah, S.M. Alizadeh, Mahyuddin K.M. Nasution, Mohammad Nabi Ilani Kashkouli, Marischa Elveny, Afrasyab Khan. Carbon dioxide-based enhanced oil recovery methods to evaluate tight oil reservoirs productivity: A laboratory perspective coupled with geo-sequestration feature. Energy Reports. 2021; 7 ():4697-4704.
Chicago/Turabian StyleRahmad Syah; S.M. Alizadeh; Mahyuddin K.M. Nasution; Mohammad Nabi Ilani Kashkouli; Marischa Elveny; Afrasyab Khan. 2021. "Carbon dioxide-based enhanced oil recovery methods to evaluate tight oil reservoirs productivity: A laboratory perspective coupled with geo-sequestration feature." Energy Reports 7, no. : 4697-4704.
The thermal conductivity of working fluids is among the most important thermophysical property in all heat transfer equipment. Accurate estimation of the nano-fluids thermal conductivity is a prerequisite for designing and optimizing the associated heat-based equipment. Therefore, the present study simulates the thermal conduction coefficients of water–alumina nano-suspensions using the least-squares support vector machines (LS-SVM). The best structure of this paradigm is determined using a combination of trial-and-error and statistical analyses. After that, it is validated by both available empirical correlations and intelligent models available in the open literature. Our LS-SVM paradigm predicted 282 experimental data samples available in fifteen references with the absolute average relative deviation (AARD) of 1.24%, mean squared errors (MSE) of 0.0007, root mean squared errors (RMSE) of 0.026, and regression coefficient (R2) of 0.9586. The leverage technique justifies that minor parts of experimental data are outliers (~ 6.03%) and have an insignificant negative effect on the derived LS-SVM generalization. The designed simulator shows that temperature and alumina concentration positively affect the nano-fluids thermal conductivity, and alumina size reduces the thermal behavior of water–alumina nano-suspensions.
Miralireza Nabavi; Vesal Nazarpour; Ali Hosin Alibak; Ali Bagherzadeh; Seyed Mehdi Alizadeh. Smart tracking of the influence of alumina nanoparticles on the thermal coefficient of nanosuspensions: application of LS-SVM methodology. Applied Nanoscience 2021, 11, 2113 -2128.
AMA StyleMiralireza Nabavi, Vesal Nazarpour, Ali Hosin Alibak, Ali Bagherzadeh, Seyed Mehdi Alizadeh. Smart tracking of the influence of alumina nanoparticles on the thermal coefficient of nanosuspensions: application of LS-SVM methodology. Applied Nanoscience. 2021; 11 (7):2113-2128.
Chicago/Turabian StyleMiralireza Nabavi; Vesal Nazarpour; Ali Hosin Alibak; Ali Bagherzadeh; Seyed Mehdi Alizadeh. 2021. "Smart tracking of the influence of alumina nanoparticles on the thermal coefficient of nanosuspensions: application of LS-SVM methodology." Applied Nanoscience 11, no. 7: 2113-2128.
Identification of reservoir interpretation model from pressure transient signals is a well-established technique in petroleum engineering. This technique aims to detect wellbore, reservoir, and boundary models employing an efficient matching process. The matching was first done manually; it then tried to be automated using artificial intelligence techniques. The level of uncertainty of matching outputs sharply increases, especially for noisy and incomplete signals. In this study, the pretrained GoogleNet (a novel combination of continuous wavelet transforms and deep convolutional neural networks) is used to decrease the uncertainty of matching results. Based on our best knowledge, it is the first application of GoogleNet to analyze transient signals in petroleum engineering. This technique is used to classify a relatively huge database, including synthetic, noisy, incomplete, and real-field signals. The GoogleNet can correctly discriminate among different reservoir interpretation classes with an overall classification accuracy of 98.36%. Moreover, it can successfully handle noisy, incomplete, and real-field pressure transient signals.
Seyed Mehdi Seyed Alizadeh; Amin Khodabakhshi; Pouria Abaei Hassani; Behzad Vaferi. Smart Identification of Petroleum Reservoir Well Testing Models Using Deep Convolutional Neural Networks (GoogleNet). Journal of Energy Resources Technology 2021, 143, 1 -19.
AMA StyleSeyed Mehdi Seyed Alizadeh, Amin Khodabakhshi, Pouria Abaei Hassani, Behzad Vaferi. Smart Identification of Petroleum Reservoir Well Testing Models Using Deep Convolutional Neural Networks (GoogleNet). Journal of Energy Resources Technology. 2021; 143 (7):1-19.
Chicago/Turabian StyleSeyed Mehdi Seyed Alizadeh; Amin Khodabakhshi; Pouria Abaei Hassani; Behzad Vaferi. 2021. "Smart Identification of Petroleum Reservoir Well Testing Models Using Deep Convolutional Neural Networks (GoogleNet)." Journal of Energy Resources Technology 143, no. 7: 1-19.
In arid and semi-arid lands like Iran water is scarce, and not all the wastewater can be treated. Hence, groundwater remains the primary and the principal source of water supply for human consumption. Therefore, this study attempted to spatially assess the groundwater potential in an aquifer in a semi-arid region of Iran using geographic information systems (GIS)-based statistical modeling. To this end, 75 agricultural wells across the Marvdasht Plain were sampled, and the water samples’ electrical conductivity (EC) was measured. To model the groundwater quality, multiple linear regression (MLR) and principal component regression (PCR) coupled with elven environmental parameters (soil-topographical parameters) were employed. The results showed that that soil EC (SEC) with Beta = 0.78 was selected as the most influential factor affecting groundwater EC (GEC). CaCO3 of soil samples and length-steepness (LS factor) were the second and third effective parameters. SEC with r = 0.89 and CaCO3 with r = 0.79 and LS factor with r = 0.69 were also characterized for PC1. According to performance criteria, the MLR model with R2 = 0.94, root mean square error (RMSE) = 450 µScm−1 and mean error (ME) = 125 µScm−1 provided better results in predicting the GEC. The GEC map indicated that 16% of the Marvdasht groundwater was not suitable for agriculture. It was concluded that GIS, combined with statistical methods, could predict groundwater quality in the semi-arid regions.
Aliasghar Azma; Esmaeil Narreie; Abouzar Shojaaddini; Nima Kianfar; Ramin Kiyanfar; Seyed Seyed Alizadeh; Afshin Davarpanah. Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique. Sustainability 2021, 13, 3788 .
AMA StyleAliasghar Azma, Esmaeil Narreie, Abouzar Shojaaddini, Nima Kianfar, Ramin Kiyanfar, Seyed Seyed Alizadeh, Afshin Davarpanah. Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique. Sustainability. 2021; 13 (7):3788.
Chicago/Turabian StyleAliasghar Azma; Esmaeil Narreie; Abouzar Shojaaddini; Nima Kianfar; Ramin Kiyanfar; Seyed Seyed Alizadeh; Afshin Davarpanah. 2021. "Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique." Sustainability 13, no. 7: 3788.
The present study evaluates the drilling fluid density of oil fields at enhanced temperatures and pressures. The main objective of this work is to introduce a set of modeling and experimental techniques for forecasting the drilling fluid density via various intelligent models. Three models were assessed, including PSO-LSSVM, ICA-LSSVM, and GA-LSSVM. The PSO-LSSVM technique outperformed the other models in light of the smallest deviation factor, reflecting the responses of the largest accuracy. The experimental and modeled regression diagrams of the coefficient of determination (R2) were plotted. In the GA-LSSVM approach, R2 was calculated to be 0.998, 0.996 and 0.996 for the training, testing and validation datasets, respectively. R2 was obtained to be 0.999, 0.999 and 0.998 for the training, testing and validation datasets, respectively, in the ICA-LSSVM approach. Finally, it was found to be 0.999, 0.999 and 0.999 for the training, testing and validation datasets in the PSO-LSSVM method, respectively. In addition, a sensitivity analysis was performed to explore the impacts of several variables. It was observed that the initial density had the largest impact on the drilling fluid density, yielding a 0.98 relevancy factor.
S. M. Alizadeh; Issam Alruyemi; Reza Daneshfar; Mohammad Mohammadi-Khanaposhtani; Maryam Naseri. An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies. Scientific Reports 2021, 11, 1 -14.
AMA StyleS. M. Alizadeh, Issam Alruyemi, Reza Daneshfar, Mohammad Mohammadi-Khanaposhtani, Maryam Naseri. An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies. Scientific Reports. 2021; 11 (1):1-14.
Chicago/Turabian StyleS. M. Alizadeh; Issam Alruyemi; Reza Daneshfar; Mohammad Mohammadi-Khanaposhtani; Maryam Naseri. 2021. "An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies." Scientific Reports 11, no. 1: 1-14.
Fluoride ions present in drinking water are beneficial to human health when at proper concentration levels (0.5–1.5 mg L−1), but an excess intake of fluoride (>1.5 mg L−1) may pose several health problems. In this context, reducing high fluoride concentrations in water is a major worldwide challenge. The World Health Organization has recommended setting a permissible limit of 1.5 mg L−1. The application of electrocoagulation (EC) processes has received widespread and increasing attention as a promising treatment technology and a competitive treatment for fluoride control. EC technology has been favourably applied due to its economic effectiveness, environmental versatility, amenability of automation, and low sludge production. This review provides more detailed information on fluoride removal from water by the EC process, including operating parameters, removal mechanisms, energy consumption, and operating costs. Additionally, it also focuses attention on future trends related to improve defluoridation efficiency.
Milad Mousazadeh; S. Alizadeh; Zacharias Frontistis; Işık Kabdaşlı; Elnaz Karamati Niaragh; Zakaria Al Qodah; Zohreh Naghdali; Alaa Mahmoud; Miguel Sandoval; Erick Butler; Mohammad Emamjomeh. Electrocoagulation as a Promising Defluoridation Technology from Water: A Review of State of the Art of Removal Mechanisms and Performance Trends. Water 2021, 13, 656 .
AMA StyleMilad Mousazadeh, S. Alizadeh, Zacharias Frontistis, Işık Kabdaşlı, Elnaz Karamati Niaragh, Zakaria Al Qodah, Zohreh Naghdali, Alaa Mahmoud, Miguel Sandoval, Erick Butler, Mohammad Emamjomeh. Electrocoagulation as a Promising Defluoridation Technology from Water: A Review of State of the Art of Removal Mechanisms and Performance Trends. Water. 2021; 13 (5):656.
Chicago/Turabian StyleMilad Mousazadeh; S. Alizadeh; Zacharias Frontistis; Işık Kabdaşlı; Elnaz Karamati Niaragh; Zakaria Al Qodah; Zohreh Naghdali; Alaa Mahmoud; Miguel Sandoval; Erick Butler; Mohammad Emamjomeh. 2021. "Electrocoagulation as a Promising Defluoridation Technology from Water: A Review of State of the Art of Removal Mechanisms and Performance Trends." Water 13, no. 5: 656.
Chemically enhanced oil recovery techniques have been considered efficient tertiary methods to improve the oil production from oil reservoirs regarding their compatibility with the reservoir characteristics. Moreover, gravity segregation and viscous fingering during carbon dioxide flooding would be the main problems of oil recovery techniques. In this paper, a hybrid chemical flooding that contained the subsequent flooding of surfactant–polymer (henceforth; SP) coupled with supercritical carbon dioxide was performed to enhance the oil recovery factor. The foaming agent that is used in this experiment is solely formed by CO2. It is concluded that SP-foam flooding had witnessed the highest blockage, which is caused to have the maximum cumulative oil recovery factor (about 78%) due to the more mobilization oil in low permeable pores and cracks. Furthermore, direct foam flooding has the second-highest oil recovery factor (nearly 70%), which is considered as the preferable techniques to SP-CO2 flooding (65%). On the contrary, SP-foam flooding has provided the highest pressure drop after the minimum miscible pressure circumstances, which is about 0.27 MPa at the end of the process.
Mohammad Hossein Ahmadi; S.M. Alizadeh; Dmitry Tananykhin; Saba Karbalaei Hadi; Pavel Iliushin; Aleksandr Lekomtsev. Laboratory evaluation of hybrid chemical enhanced oil recovery methods coupled with carbon dioxide. Energy Reports 2021, 7, 960 -967.
AMA StyleMohammad Hossein Ahmadi, S.M. Alizadeh, Dmitry Tananykhin, Saba Karbalaei Hadi, Pavel Iliushin, Aleksandr Lekomtsev. Laboratory evaluation of hybrid chemical enhanced oil recovery methods coupled with carbon dioxide. Energy Reports. 2021; 7 ():960-967.
Chicago/Turabian StyleMohammad Hossein Ahmadi; S.M. Alizadeh; Dmitry Tananykhin; Saba Karbalaei Hadi; Pavel Iliushin; Aleksandr Lekomtsev. 2021. "Laboratory evaluation of hybrid chemical enhanced oil recovery methods coupled with carbon dioxide." Energy Reports 7, no. : 960-967.
Due to the high amount of natural gas resources in Iran, the gas cycle as one of the main important power production system is used to produce electricity. The gas cycle has some disadvantages such as power consumption of air compressors, which is a major part of gas turbine electrical production and a considerable reduction in electrical power production by increasing the environment temperature due to a reduction in air density and constant volumetric airflow through a gas cycle. To overcome these weaknesses, several methods are applied such as cooling the inlet air of the system by different methods and integration heat recovery steam generator (HRSG) with the gas cycle. In this paper, using a heliostat solar receiver (HSR) in gas and combined cycles are investigated by energy, exergy, and economic analyses in Tehran city. The heliostat solar receiver is used to heat the pressurized exhaust air from the air compressor in gas and combined cycles. The key parameter of the three mentioned analyses was calculated and compared by writing computer code in MATLAB software. Results showed the use of HSR in gas and combined cycles increase the annual average energy efficiency from 28.4% and 48.5% to 44% and 76.5%, respectively. Additionally, for exergy efficiency, these increases are from 29.2% and 49.8% to 45.2% and 78.5%, respectively. However, from an economic point of view, adding the HRSG increases the payback period (PP) and it decreases the net present value (NPV) and internal rate of return (IRR).
S. M. Alizadeh; Arezoo Ghazanfari; Mehdi Aliehyaei; Abolfazal Ahmadi; D. H. Jamali; Navid Nedaei; Afshin Davarpanah. Investigation the Integration of Heliostat Solar Receiver to Gas and Combined Cycles by Energy, Exergy, and Economic Point of Views. Applied Sciences 2020, 10, 5307 .
AMA StyleS. M. Alizadeh, Arezoo Ghazanfari, Mehdi Aliehyaei, Abolfazal Ahmadi, D. H. Jamali, Navid Nedaei, Afshin Davarpanah. Investigation the Integration of Heliostat Solar Receiver to Gas and Combined Cycles by Energy, Exergy, and Economic Point of Views. Applied Sciences. 2020; 10 (15):5307.
Chicago/Turabian StyleS. M. Alizadeh; Arezoo Ghazanfari; Mehdi Aliehyaei; Abolfazal Ahmadi; D. H. Jamali; Navid Nedaei; Afshin Davarpanah. 2020. "Investigation the Integration of Heliostat Solar Receiver to Gas and Combined Cycles by Energy, Exergy, and Economic Point of Views." Applied Sciences 10, no. 15: 5307.