172 results on '"Davoodi, Shadfar"'
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2. Machine-Learning Predictive Model for Semiautomated Monitoring of Solid Content in Water-Based Drilling Fluids
3. Interporosity Flow Between Matrix and Fractures in Carbonates: A Study of its Impact on Oil Production
4. Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data
5. Robust machine-learning model for prediction of carbon dioxide adsorption on metal-organic frameworks
6. Deformation of the void space of pores and fractures of carbonates: Comprehensive analysis of core and field data
7. Underground hydrogen storage: A review of technological developments, challenges, and opportunities
8. An integrated intelligent approach to the determination of drilling fluids’ solid content
9. Committee machine learning: A breakthrough in the precise prediction of CO2 storage mass and oil production volumes in unconventional reservoirs
10. A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms
11. Machine learning insights to CO2-EOR and storage simulations through a five-spot pattern – a theoretical study
12. Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids
13. Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm
14. Carbon dioxide sequestration through enhanced oil recovery: A review of storage mechanisms and technological applications
15. Multiscale and diverse spatial heterogeneity analysis of void structures in reef carbonate reservoirs
16. Hole-cleaning performance in non-vertical wellbores: A review of influences, models, drilling fluid types, and real-time applications
17. Synthetic polymers: A review of applications in drilling fluids
18. Recent advances in polymers as additives for wellbore cementing applications: A review
19. Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models
20. Modified-starch applications as fluid-loss reducers in water-based drilling fluids: A review of recent advances
21. Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms
22. Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
23. Review of technological progress in carbon dioxide capture, storage, and utilization
24. Evaluation of facies heterogeneity in reef carbonate reservoirs: A case study from the oil field, Perm Krai, Central-Eastern Russia
25. Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations
26. Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids
27. Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms
28. Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites
29. Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
30. Study of void space structure and its influence on carbonate reservoir properties: X-ray microtomography, electron microscopy, and well testing
31. A comprehensive review of beneficial applications of viscoelastic surfactants in wellbore hydraulic fracturing fluids
32. A critical review of self-diverting acid treatments applied to carbonate oil and gas reservoirs
33. Thermally stable and salt-resistant synthetic polymers as drilling fluid additives for deployment in harsh sub-surface conditions: A review
34. Data driven models to predict pore pressure using drilling and petrophysical data
35. A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques
36. Optimized machine learning models for natural fractures prediction using conventional well logs
37. Experimental and field applications of nanotechnology for enhanced oil recovery purposes: A review
38. Robust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir
39. Nanoparticle applications as beneficial oil and gas drilling fluid additives: A review
40. Mesoscopic theoretical modeling and experimental study of rheological behavior of water-based drilling fluid containing associative synthetic polymer, bentonite, and limestone
41. Robust hybrid machine learning algorithms for gas flow rates prediction through wellhead chokes in gas condensate fields
42. Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
43. Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data
44. Predicting oil flow rate through orifice plate with robust machine learning algorithms
45. Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms
46. Combined Deep Learning and Optimization for Hydrogen-Solubility Prediction in Aqueous Systems Appropriate for Underground Hydrogen Storage Reservoirs.
47. Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms
48. Prediction of oil flow rate through orifice flow meters: Optimized machine-learning techniques
49. Impacts of interactions with low-mineralized water on permeability and pore behavior of carbonate reservoirs.
50. Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids.
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