Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field
One of the most prevalent problems in drilling industry is lost circulation which causes
intense increase in drilling expenditure as well as operational obstacles such as well …
intense increase in drilling expenditure as well as operational obstacles such as well …
An ANN model to predict oil recovery from a 5-spot waterflood of a heterogeneous reservoir
Waterflooding is a secondary oil recovery technique in which water is injected into an
underground oil reservoir to maintain the reservoir pressure and boost oil recovery. The …
underground oil reservoir to maintain the reservoir pressure and boost oil recovery. The …
Prediction of dead oil viscosity: Machine learning vs. classical correlations
F Hadavimoghaddam, M Ostadhassan, E Heidaryan… - Energies, 2021 - mdpi.com
Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems
and one of the most unreliable properties to predict with classical black oil correlations …
and one of the most unreliable properties to predict with classical black oil correlations …
[HTML][HTML] Neural network supported flow characteristics analysis of heavy sour crude oil emulsified by ecofriendly bio-surfactant utilized as a replacement of sweet …
The present study was carried out to assess the replacement of the sweet crude oil (SWCO)
with the natural additive surfactant (NAS) namely Madhuca latifolia for the emulsification of …
with the natural additive surfactant (NAS) namely Madhuca latifolia for the emulsification of …
Enhanced CO2/CH4 separation properties of asymmetric mixed matrix membrane by incorporating nano-porous ZSM-5 and MIL-53 particles into Matrimid® 5218
Asymmetric Matrimid/fillers mixed matrix membranes, composed of a metal organic
framework (MIL-53; with CO 2 sorption properties) and a zeolite (ZSM-5; with size selective …
framework (MIL-53; with CO 2 sorption properties) and a zeolite (ZSM-5; with size selective …
Navigating viscosity of GO-SiO2/HPAM composite using response surface methodology and supervised machine learning models
This paper presents the use of response surface methodology (RSM) and robust supervised
machine learning approaches to model nano-polymeric viscosity. In the absence of studies …
machine learning approaches to model nano-polymeric viscosity. In the absence of studies …
New correlations to predict oil viscosity using data mining techniques
E Bahonar, M Chahardowli, Y Ghalenoei… - Journal of Petroleum …, 2022 - Elsevier
Oil viscosity is used in any fluid transport calculation in both subsurface and surface
conditions. It is possible to determine oil viscosity from laboratory measurements or …
conditions. It is possible to determine oil viscosity from laboratory measurements or …
Petroleum viscosity modeling using least squares and ANN methods
Abstract 274 crude oils pertaining to the groups of extra light (gas condensates), light,
medium, heavy, and extra heavy crude oils were characterized by true boiling point …
medium, heavy, and extra heavy crude oils were characterized by true boiling point …
Development of a robust model for prediction of under-saturated reservoir oil viscosity
S Hajirezaie, A Pajouhandeh… - Journal of Molecular …, 2017 - Elsevier
Fluid viscosity is considered as one of the most important parameters for reservoir
simulation, performance evaluation, designing production facilities, etc. In this …
simulation, performance evaluation, designing production facilities, etc. In this …
Viscosity prediction of lubricants by a general feed-forward neural network
Modern industrial lubricants are often blended with an assortment of chemical additives to
improve the performance of the base stock. Machine learning-based predictive models allow …
improve the performance of the base stock. Machine learning-based predictive models allow …