Petroleum Science and Technology

Papers
(The H4-Index of Petroleum Science and Technology is 14. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-03-01 to 2024-03-01.)
ArticleCitations
Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA29
Kinetics of thermal decomposition of the polyester nanocomposites23
Heavy oil aquathermolysis in the presence of rock-forming minerals and iron oxide (II, III) nanoparticles22
Effects of SARA fractions on pyrolysis behavior and kinetics of heavy crude oil20
Proposing a modified mechanism for determination of hydrocarbons dynamic viscosity, using artificial neural network18
Effect of water cut on the performance of an asphaltene inhibitor package: experimental and modeling analysis18
Effect of pour point depressant (PPD) and the nanoparticles on the wax deposition, viscosity and shear stress for Malaysian crude oil18
Potassium carbonate based deep eutectic solvent (DES) as a potential drilling fluid additive in deep water drilling applications16
Physicochemical and rheological effects of the incorporation of micronized polyethylene terephthalate in asphalt binder16
A new experimental method for a fast and reliable quantification of saturates, aromatics, resins, and asphaltenes in crude oils16
Effects of aromatics, resins, and asphaltenes on oxidation behavior and kinetics of heavy crude oil15
Hydrogen production by Co-based bimetallic nano-catalysts and their performance in methane steam reforming15
A review on applications of nanoparticles in the enhanced oil recovery in carbonate reservoirs14
Enhancing coal bed methane recovery: using injection of nitrogen and carbon dioxide mixture14
Fault and fracture study by incorporating borehole image logs and supervised neural network applied to the 3D seismic attributes: a case study of pre-salt carbonate reservoir, Santos Basin, Brazil14
Prediction of oil well production based on the time series model of optimized recursive neural network14
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