Petroleum Engineering Program 1
1. A novel green nanocomposite for EOR: Experimental Investigation of IFT Reduction, wettability Shift, and nanofluid stability
2. A systematic and critical review of application of molecular dynamics simulation in low salinity water injection
3. Atomistic insights into role of low salinity water on montmorillonite-brine interface: Implications for EOR from clay-bearing sandstone reservoirs
4. Influence of brine compositions on wetting preference of montmorillonite in rock/brine/oil system: An in silico study
5. Prediction of asphaltene adsorption capacity of clay minerals using machine learning
6. AI-based models for predicting rock/mineral-hydrogen-brine contact angles using experimental data
7. Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches
8. Novel robust Elman neural network-based predictive models for bubble point oil formation volume factor and solution gas–oil ratio using experimental data
9. New connectionist tools for prediction of CO2 diffusion coefficient in brine at high pressure and temperature ─ implications for CO2 sequestration in deep saline aquifers
10. New intelligent models for predicting wax appearance temperature using experimental data – Flow assurance implications
11. A novel hybrid nano-smart water with soluble chlorides and sulphates and silica-montmorilant-xanthan nanocomposite for EOR: Insights from IFT and contact angle measurements and core flooding tests
12. Impact of a novel biosynthesized nanocomposite (SiO2@Montmorilant@Xanthan) on wettability shift and interfacial tension: Applications for enhanced oil recovery
13. Insights from molecular dynamics on CO2 diffusion coefficient in saline water over a wide range of temperatures, pressures, and salinity: CO2 geological storage implications
14. Novel intelligent models for prediction of hydrogen diffusion coefficient in brine using experimental and molecular dynamics simulation data: Implications for underground hydrogen storage in geological formations
15. Characterization of crude oils and asphaltenes using the PC-SAFT EoS: A systematic review
16. Artificial neural network, support vector machine, decision tree, random forest, and committee machine intelligent system help to improve performance prediction of low salinity water injection in carbonate oil reservoirs
17. Connectionist Models for Asphaltene Precipitation Prediction by n-Alkane Titration─Pressure and Crude Oil Properties Considered
18. Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs
19. A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection
20. A systematic and critical review of asphaltene adsorption from macroscopic to microscopic scale: Theoretical, experimental, statistical, intelligent, and molecular dynamics simulation approaches
21. Effects of asphaltene structure and polythiophene-coated magnetite nanoparticles on surface topography and wettability alteration of silica surface
22. Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models
1