Ahmed T (2006) Reservoir engineering handbook. Elsevier/Gulf Professional, Oxford OX2 8DP, UK
Elsharkwy AM, Gharbi RBC (2001) Comparing classical and neural regression techniques in modeling crude oil viscosity. Adv Eng Softw 32(3):215–224
Article
Google Scholar
Moharam H, Al-Mehaideb R, Fahim M (1995) New correlation for predicting the viscosity of heavy petroleum fractions. Fuel 74(12):1776–1779
Article
CAS
Google Scholar
Labedi R (1992) Improved correlations for predicting the viscosity of light crudes. J Pet Sci Eng 8(3):221–234
Article
CAS
Google Scholar
Salimi H, Sieders B, Rostami J (2022) Non-isothermal compositional simulation study for determining an optimum EOR strategy for a middle-east offshore heavy-oil reservoir with compositional variations with depth. In: SPE. https://doi.org/10.2118/200274-ms
Abedini A, Abedini R (2012) Investigation of splitting and lumping of oil composition on the simulation of asphaltene precipitation. Pet Sci Technol 30(1):1–8
Article
Google Scholar
Standing MB (1947) A pressure-volume-temperature correlation for mixtures of california oils and gases. In: Drilling and production practice. OnePetro
Lasater J (1958) Bubble point pressure correlation. J Petrol Technol 10(05):65–67
Article
CAS
Google Scholar
Chew J-N, Connally CA (1959) A viscosity correlation for gas-saturated crude oils. Trans AIME 216(01):23–25
Article
Google Scholar
Beggs HD, Robinson JR (1975) Estimating the viscosity of crude oil systems. J Petrol Technol 27(09):1140–1141
Article
Google Scholar
Glaso O (1980) Generalized pressure-volume-temperature correlations. J Petrol Technol 32(05):785–795
Article
Google Scholar
Vazquez M, Beggs HD (1977) Correlations for fluid physical property prediction. In: SPE annual fall technical conference and exhibition. OnePetro
Petrosky G, Farshad F (1993) Pressure-volume-temperature correlations for gulf of mexico crude oils. In: SPE annual technical conference and exhibition. OnePetro
Dindoruk B, Christman PG (2004) Pvt properties and viscosity correlations for Gulf of Mexico oils. SPE Reserv Eval Eng 7(06):427–437
Article
CAS
Google Scholar
Abd Talib MQ, Al-Jawad MS (2022) Assessment of the common PVT correlations in Iraqi Oil Fields. J Pet Res Stud 12(1):68–87
Google Scholar
Hadavimoghaddam F, Ostadhassan M, Heidaryan E, Sadri MA, Chapanova I, Popov E, Cheremisin A, Rafieepour S (2021) Prediction of dead oil viscosity: machine learning vs. classical correlations. Energies 14(4):930. https://doi.org/10.3390/en14040930
Article
CAS
Google Scholar
Ahmed T (2018) Reservoir engineering handbook. Gulf Professional Publishing, Oxford OX2 8DP, UK
Ahrabi F, Ashcroft S, Shearn R (1987) High pressure volumetric, phase composition and viscosity data for a north sea crude oil and Ngl. Chem. Eng. Res. Des. (United Kingdom) 65(1):329–334
Beal C (1946) The viscosity of air, water, natural gas, crude oil and its associated gases at oil field temperatures and pressures. Trans AIME 165(01):94–115
Article
Google Scholar
Beggs HD, Robinson JR (1975) Estimating the viscosity of crude oil systems. J Petrol Technol 27(09):1140–1141
Article
Google Scholar
Chew J-N, Connally CA (1959) A viscosity correlation for gas-saturated crude oils. Trans AIME 216(01):23–25
Article
Google Scholar
Egbogah EO, Ng JT (1990) An improved temperature-viscosity correlation for crude oil systems. J Petrol Sci Eng 4(3):197–200
Article
Google Scholar
Elsharkawy A, Alikhan A (1999) Models for predicting the viscosity of middle east crude oils. Fuel 78(8):891–903
Article
CAS
Google Scholar
Rice P, Teja AS (1982) A generalized corresponding-states method for the prediction of surface tension of pure liquids and liquid mixtures. J Colloid Interface Sci 86(1):158–163
Article
CAS
Google Scholar
Vazquez M, Beggs H (1980) Correlations for fluid physical property prediction. Ipt 32(6):968–970. https://doi.org/10.2118/6719-PA
Article
Google Scholar
Little J, Kennedy H (1968) A correlation of the viscosity of hydrocarbon systems with pressure, temperature and composition. Soc Petrol Eng J 8(02):157–162
Article
CAS
Google Scholar
Sutton RP, Farshad F (1990) Evaluation of empirically derived PVT properties for Gulf of Mexico crude oils. SPE Reserv Eng 5(01):79–86
Article
CAS
Google Scholar
Dexheimer D, Jackson CM, Barrufet MA (2001) A modification of Pedersen’s model for saturated crude oil viscosities using standard black oil Pvt data. Fluid Phase Equilib 183:247–257
Article
Google Scholar
Taghizadeh M, Eftekhari M (2014) Improved correlations for prediction of viscosity of Iranian crude oils. Chin J Chem Eng 22(3):346–354
Article
CAS
Google Scholar
Shokir EME-M, Ibrahim AE-SB (2022) Undersaturated oil viscosity based on multi-gene genetic programming. J Energy Resour Technol. https://doi.org/10.1115/1.4055396
Article
Google Scholar
Moghadam EM, Naseri A, Riahi MA (2021) Further model development for prediction of reservoir oil viscosity. Pet Sci Technol 40(3):310–321. https://doi.org/10.1080/10916466.2021.1993914
Article
CAS
Google Scholar
Sinha U, Dindoruk B, Soliman MY (2022) Physics augmented correlations and machine learning methods to accurately calculate dead oil viscosity based on the available inputs. SPE J 27(05):3240–3253. https://doi.org/10.2118/209610-pa
Article
CAS
Google Scholar
Kartoatmodjo T, Schmidt Z (1991) New correlations for crude oil physical properties. paper SPE 23556
Obanijesu E, Omidiora E (2009) The artificial neural network’s prediction of crude oil viscosity for pipeline safety. Pet Sci Technol 27(4):412–426
Article
CAS
Google Scholar
Gao X, Dong P, Cui J, Gao Q (2022) Prediction model for the viscosity of heavy oil diluted with light oil using machine learning techniques. Energies 15(6):2297. https://doi.org/10.3390/en15062297
Article
Google Scholar
Bhat SS, Selvam V, Ansari GA, Ansari MD, Rahman MH (2022) Prevalence and early prediction of diabetes using machine learning in North Kashmir: a case study of district bandipora. Comput Intell Neurosci 2022:1–12. https://doi.org/10.1155/2022/2789760
Article
Google Scholar
Kannan R, Halim HAA, Ramakrishnan K, Ismail S, Wijaya DR (2022) Machine learning approach for predicting production delays: a quarry company case study. J Big Data. https://doi.org/10.1186/s40537-022-00644-w
Article
Google Scholar
Zhang Z, Yang L, Han W, Wu Y, Zhang L, Gao C, Jiang K, Liu Y, Wu H (2022) Machine learning prediction models for gestational diabetes mellitus: meta-analysis. J Med Internet Res 24(3):26634. https://doi.org/10.2196/26634
Article
Google Scholar
Dhiman P, Ma J, Navarro CLA, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Calster BV, Moons KGM, Collins GS (2022) Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol. https://doi.org/10.1186/s12874-022-01577-x
Article
Google Scholar
Tercan H, Meisen T (2022) Machine learning and deep learning based predictive quality in manufacturing: a systematic review. J Intell Manuf 33(7):1879–1905. https://doi.org/10.1007/s10845-022-01963-8
Article
Google Scholar
Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE (2022) Machine learning-based research for COVID-19 detection, diagnosis, and prediction: a survey. SN Comput Sci. https://doi.org/10.1007/s42979-022-01184-z
Article
Google Scholar
Tao H, Hameed MM, Marhoon HA, Zounemat-Kermani M, Heddam S, Kim S, Sulaiman SO, Tan ML, Sa’adi Z, Mehr AD, Allawi MF, Abba SI, Zain JM, Falah MW, Jamei M, Bokde ND, Bayatvarkeshi M, Al-Mukhtar M, Bhagat SK, Tiyasha T, Khedher KM, Al-Ansari N, Shahid S, Yaseen ZM (2022) Groundwater level prediction using machine learning models: a comprehensive review. Neurocomputing 489:271–308. https://doi.org/10.1016/j.neucom.2022.03.014
Article
Google Scholar
Zhou Y, Han F, Shi X-L, Zhang J-X, Li G-Y, Yuan C-C, Lu G-T, Hu L-H, Pan J-J, Xiao W-M, Yao G-H (2022) Prediction of the severity of acute pancreatitis using machine learning models. Postgrad Med 134(7):703–710. https://doi.org/10.1080/00325481.2022.2099193
Article
Google Scholar
Vallim Filho AR, Moraes DF, de Aguiar Vallim MVB, da Silva LS, da Silva LA (2022) A machine learning modeling framework for predictive maintenance based on equipment load cycle: an application in a real world case. Energies 15(10):3724. https://doi.org/10.3390/en15103724
Article
Google Scholar
Gulyani BB, Kumar BP, Fathima A (2017) Bagging ensemble model for prediction of dead oil viscosity. Int J Chem Eng Appl 8(2):102
CAS
Google Scholar
Zhou ZH (2009) Ensemble learning. In: Li SZ, Jain A (eds) Encyclopedia of biometrics. Springer, Boston, MA, pp
270–273
Zheng Z, Padmanabhan B (2007) Constructing ensembles from data envelopment analysis. INFORMS J Comput 19(4):486–496
Article
Google Scholar
Polikar R (2009) Ensemble learning. Scholarpedia 4(1):2776
Article
Google Scholar
Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer, Berlin, Heidelberg, pp 1–15
Santos R, Vellasco MM, Artola F, Da Fontoura S (2003) Neural net ensembles for lithology recognition. In: International workshop on multiple classifier systems, pp. 246–255. Springer
Gifford CM, Agah A (2010) Collaborative multi-agent rock facies classification from wireline well log data. Eng Appl Artif Intell 23(7):1158–1172
Article
Google Scholar
Masoudi P, Tokhmechi B, Bashari A, Jafari MA (2012) Identifying productive zones of the Sarvak formation by integrating outputs of different classification methods. J Geophys Eng 9(3):282–290
Article
Google Scholar
Davronova R, Adilovab F (2020) A comparative analysis of the ensemble methods for drug design
Smirani LK, Yamani HA, Menzli LJ, Boulahia JA (2022) Using ensemble learning algorithms to predict student failure and enabling customized educational paths. Sci Program 2022:1–15. https://doi.org/10.1155/2022/3805235
Article
Google Scholar
Whitaker T, Whitley D (2022) Prune and tune ensembles: low-cost ensemble learning with sparse independent subnetworks. https://doi.org/10.48550/ARXIV.2202.11782. arXiv arxiv:2202.11782
Marwah GPK, Jain A (2022) A hybrid optimization with ensemble learning to ensure VANET network stability based on performance analysis. Sci Rep 12:1. https://doi.org/10.1038/s41598-022-14255-1
Article
CAS
Google Scholar
Banerjee S, Sinclair SR, Tambe M, Xu L, Yu CL (2022) Artificial replay: a meta-algorithm for harnessing historical data in Bandits. https://doi.org/10.48550/ARXIV.2210.00025. arXiv arxiv:2210.00025
Longo L, Riccaboni M, Rungi A (2022) A neural network ensemble approach for GDP forecasting. J Econ Dyn Control 134:104278. https://doi.org/10.1016/j.jedc.2021.104278
Article
Google Scholar
Flennerhag S, Schroecker Y, Zahavy T, van Hasselt H, Silver D, Singh S (2021) Bootstrapped meta-learning. arXiv arxiv:2109.04504
Liu H, Du Y, Wu Z (2022) Generalized ambiguity decomposition for ranking ensemble learning. J Mach Learn Res 23(88):1–36
Google Scholar
Ganaie MA, Hu M, Malik AK, Tanveer M, Suganthan PN (2022) Ensemble deep learning: a review. Eng Appl Artif Intell 115:105151. https://doi.org/10.1016/j.engappai.2022.105151
Article
Google Scholar
Anifowose F, Labadin J, Abdulraheem A (2015) Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. Appl Soft Comput 26:483–496
Article
Google Scholar
Anifowose FA, Labadin J, Abdulraheem A (2017) Ensemble machine learning: an untapped modeling paradigm for petroleum reservoir characterization. J Petrol Sci Eng 151:480–487
Article
CAS
Google Scholar
Bestagini P, Lipari V, Tubaro S (2017) A machine learning approach to facies classification using well logs. In: Seg technical program expanded abstracts 2017. Society of Exploration Geophysicists, pp 2137–2142
Xie Y, Zhu C, Zhou W, Li Z, Liu X, Tu M (2018) Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances. J Petrol Sci Eng 160:182–193
Article
CAS
Google Scholar
Tewari S, Dwivedi U (2019) Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs. Comput Ind Eng 128:937–947
Article
Google Scholar
Tewari S, Dwivedi U, et al (2018) A novel automatic detection and diagnosis module for quantitative lithofacies modeling. In: Abu Dhabi international petroleum exhibition & conference. Society of Petroleum Engineers
Bhattacharya S, Carr TR, Pal M (2016) Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: case studies from the bakken and mahantango-marcellus shale, usa. J Nat Gas Sci Eng 33:1119–1133
Article
CAS
Google Scholar
Tewari S, Dwivedi U, Shiblee M et al. (2019) Assessment of big data analytics based ensemble estimator module for the real-time prediction of reservoir recovery factor. In: SPE middle east oil and gas show and conference. Society of Petroleum Engineers
Tewari S, Dwivedi UD (2020) A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies. J Pet Explor Product Technol 10(5):1849–1868
Article
Google Scholar
Touati R, Elngar AA (2022) Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines. Beni-Suef Univ J Basic Appl Sci 1:11. https://doi.org/10.1186/s43088-022-00216-0
Article
Google Scholar
Mahdy AMS (2022) A numerical method for solving the nonlinear equations of Emden-Fowler models. J Ocean Eng Sci. https://doi.org/10.1016/j.joes.2022.04.019
Article
Google Scholar
Ke W, Liu Y, Zhao X, Yu G, Wang J (2022) Study on the effect of threshold pressure gradient on remaining oil distribution in heavy oil reservoirs. ACS Omega 7(5):3949–3962. https://doi.org/10.1021/acsomega.1c04537
Article
CAS
Google Scholar
Othman K (2022) Prediction of the hot asphalt mix properties using deep neural networks. Beni-Suef Univ J Basic Appl Sci 1:11. https://doi.org/10.1186/s43088-022-00221-3
Article
Google Scholar
Ahmad A, Sulaiman M, Aljohani AJ, Alhindi A, Alrabaiah H (2021) Design of an efficient algorithm for solution of Bratu differential equations. Ain Shams Eng J 12(2):2211–2225. https://doi.org/10.1016/j.asej.2020.11.007
Article
Google Scholar
Noeiaghdam S, Araghi MAF, Abbasbandy S (2020) Valid implementation of sinc-collocation method to solve the fuzzy Fredholm integral equation. J Comput Appl Math 370:112632. https://doi.org/10.1016/j.cam.2019.112632
Article
Google Scholar
Khan MM, Sohrab MG, Yousuf MA (2020) Customer gender prediction system on hierarchical e-commerce data. Beni-Suef Univ J Basic Appl Sci 9:1. https://doi.org/10.1186/s43088-020-0035-7
Article
Google Scholar
Gumah G, Naser MFM, Al-Smadi M, Al-Omari SKQ, Baleanu D (2020) Numerical solutions of hybrid fuzzy differential equations in a Hilbert space. Appl Numer Math 151:402–412. https://doi.org/10.1016/j.apnum.2020.01.008
Article
Google Scholar
Arqub OA (2015) Adaptation of reproducing Kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610. https://doi.org/10.1007/s00521-015-2110-x
Article
Google Scholar
Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415. https://doi.org/10.1016/j.ins.2014.03.128
Article
Google Scholar
Abo-Hammour Z, Arqub OA, Alsmadi O, Momani S, Alsaedi A (2014) An optimization algorithm for solving systems of singular boundary value problems. Appl Math Inf Sci 8(6):2809–2821. https://doi.org/10.12785/amis/080617
Article
Google Scholar
Abo-Hammour Z, Arqub OA, Momani S, Shawagfeh N (2014) Optimization solution of Troesch’s and Bratu’s problems of ordinary type using novel continuous genetic algorithm. Discret Dyn Nat Soc 2014:1–15. https://doi.org/10.1155/2014/401696
Article
Google Scholar
Lopez-Franco C, Hernandez-Barragan J, Alanis AY, Arana-Daniel N (2018) A soft computing approach for inverse kinematics of robot manipulators. Eng Appl Artif Intell 74:104–120. https://doi.org/10.1016/j.engappai.2018.06.001
Article
Google Scholar
Dereli S, Köker R (2019) Simulation based calculation of the inverse kinematics solution of 7-DOF robot manipulator using artificial bee colony algorithm. SN Appl Sci. https://doi.org/10.1007/s42452-019-1791-7
Article
Google Scholar
Obot NI, Humphrey I, Chendo MAC, Udo SO (2019) Deep learning and regression modelling of cloudless downward longwave radiation. Beni-Suef Univ J Basic Appl Sci. https://doi.org/10.1186/s43088-019-0018-8
Article
Google Scholar
Pintelas P, Livieris IE (2020) Special issue on ensemble learning and applications. Algorithms 13(6):140
Article
Google Scholar
Liu Y (2016) Error awareness by lower and upper bounds in ensemble learning. Int J Pattern Recognit Artif Intell 30(09):1660003
Article
Google Scholar
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Google Scholar
Al-Khafaji AH, Abdul-Majeed GH, Hassoon SF et al (1987) Viscosity correlation for dead, live and undersaturated crude oils. J Pet Res 6(2):1–16
Google Scholar
Bennison T (1998) Prediction of heavy oil viscosity. In: IBC Heavy oil field development conference, vol 2, p 4