Open AccessModeling of Oil Viscosity for Southern Iraqi Reservoirs using Neural Network Method
Author(s) -
Doaa Mohammed Salih,
Sameera M. Hamdalla,
Mohammed H. Al-Kabi
Publication year - 2020
Publication title -
mağallaẗ al-buḥūṯ wa-al-dirāsāt al-nafṭiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2710-1096
pISSN - 2220-5381
DOI - 10.52716/jprs.v10i1.514
Subject(s) - viscosity , oil viscosity , thermodynamics , artificial neural network , equation of state , range (aeronautics) , enhanced oil recovery , materials science , petroleum engineering , mathematics , geology , computer science , physics , composite material , artificial intelligence
The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperature andpressure extends from the reservoir to surface conditions.The generated viscosity data is utilized in the neural network tool (NN) to get fittingmodel correlates the viscosity of oil with composition, pressure and temperature. Theresulted error and the correlation coefficient of the model constructed are close to 0and 1 respectively. The NN model is also tested with data that are not used in set upthe model. The results proved the validity of the model. Moreover, the model’soutcomes demonstrate its superiority to selected empirical correlations.