Petroleum Science and Technology

Papers
(The H4-Index of Petroleum Science and Technology is 13. 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 2020-11-01 to 2024-11-01.)
ArticleCitations
Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA39
Kinetics of thermal decomposition of the polyester nanocomposites27
Research progress and development trend of heavy oil emulsifying viscosity reducer: a review24
Potassium carbonate based deep eutectic solvent (DES) as a potential drilling fluid additive in deep water drilling applications21
Physicochemical and rheological effects of the incorporation of micronized polyethylene terephthalate in asphalt binder21
Effect of water cut on the performance of an asphaltene inhibitor package: experimental and modeling analysis20
On application of machine learning method for history matching and forecasting of times series data from hydrocarbon recovery process using water flooding17
A review on applications of nanoparticles in the enhanced oil recovery in carbonate reservoirs16
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, Brazil16
Prediction of oil well production based on the time series model of optimized recursive neural network16
Impact of a novel HPAM/GO-SiO2 nanocomposite on interfacial tension: Application for enhanced oil recovery15
Thermodynamic performance and emission prediction of CI engine fueled with diesel and Vachellia nilotica (Babul) biomass-based producer gas and optimization using RSM14
Research progress of green scale inhibitors: a mini review14
Estimation of shale pore-size-distribution from N2 adsorption characteristics employing modified BJH algorithm13
Characterization of multi-component and multi-phase fluids in the Upper Cretaceous oil shale from the Songliao basin (NE China) using T1–T2 NMR correlation maps13
Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR13
Prediction of shale gas horizontal wells productivity after volume fracturing using machine learning – an LSTM approach13
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