2020
DOI: 10.1109/access.2020.3034053
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Analysis of Multi-Phase Flow Through Porous Media for Imbibition Phenomena by Using the LeNN-WOA-NM Algorithm

Abstract: The flow of fluids in multi-phase porous media results due to many interesting natural phenomena. The counter-current water imbibition phenomena, that occur during oil extraction through a cylindrical well is an interesting problem in petroleum engineering. During the secondary oil recovery process, water is injected into a porous media having heterogenous and homogenous characteristics. Due to the difference in viscosities of fluids in oil wells, the counter-current imbibition phenomenon occurs. At that momen… Show more

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Cited by 31 publications
(20 citation statements)
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“…In this section, the performance of the design scheme for solving different problems of heat transfer is studied by incorporating performance indicators in terms of mean absolute error (MAE), Theil's inequality coefficient (TIC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and error in Nash-Sutcliffe efficiency (ENSE). Mathematical formulation of these indicators are given as [51].…”
Section: Performance Indicesmentioning
confidence: 99%
See 1 more Smart Citation
“…In this section, the performance of the design scheme for solving different problems of heat transfer is studied by incorporating performance indicators in terms of mean absolute error (MAE), Theil's inequality coefficient (TIC), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and error in Nash-Sutcliffe efficiency (ENSE). Mathematical formulation of these indicators are given as [51].…”
Section: Performance Indicesmentioning
confidence: 99%
“…In recent times, intelligence-based nature-inspired meta heuristic algorithms have gained the attention of researchers. Some recent applications of soft computing techniques include the saturation process of water and oil through a porous medium during secondary oil recovery [51], heat transfer prediction of supercritical water [52], physics-informed neural networks [53], temperature profiles in longitudinal fin designs [54], wire coating dynamics [55,56], data-driven modeling for boiling heat [57], prediction of turbulent heat transfer [58], the corneal model for eye surgery [59], fuzzy systems [60], infrared, boiling heat transfer investigations [61], neuro-fuzzy modeling is used to predict the summer precipitation in targeted metrological sites [62], and prediction of heat transfer rates for shell-and-tube heat exchangers [63], beam-column designs [64], and nonlinear dusty plasma systems are analyzed with the help of NAR-RBFs neural networks [65]. The plant prorogation algorithm (PPA) and improved PPA are developed to solve a number of constrained and unconstrained engineering optimization problems [66,67].…”
Section: Introductionmentioning
confidence: 99%
“…In recent times, NM algorithm is used to find numerical solution of the dynamical model of Li-ion batteries for electric vehicle [32], nonlinear Muskingum models [33], economic load dispatch problem with valve point loading effect [34], optimization of TIG welding parameters [35], optimization of noisy CNLS problems [36], and parameter identification of chaotic systems [37]. Implementation of NM algorithm is based on four basic operators [38]. Below, we present details of these operators:…”
Section: Nelder-mead Algorithmmentioning
confidence: 99%
“…In recent times, stochastic computing paradigms based on artificial intelligence have been used extensively to find numerical solutions for different problems arising in various fields, such as fuzzy systems [ 16 , 17 , 18 ], petroleum engineering [ 19 ], carbon capture process [ 20 , 21 , 22 ], wire coating dynamics [ 23 ], biological systems [ 24 , 25 ], civil engineering [ 26 , 27 ], coal-fired power plant retrofitted [ 28 ], and electrical and thermal engineering [ 29 , 30 , 31 ]. These contributions motivated the authors to investigate the absorption of carbon dioxide (CO ) into solutions of phenyl glycidyl ether (PGE) by strengthening the computational ability of neural networks.…”
Section: Introductionmentioning
confidence: 99%