Search Results - "Rasool, Muhammad Hammad"

Correction to: Rheological characterization of potassium carbonate deep eutectic solvent (DES) based drilling mud.

Authors: Rasool, Muhammad Hammad Zamir, Asif Elraies, Khaled A. et al.

Source: Journal of Petroleum Exploration & Production Technology. Oct2022, Vol. 12 Issue 10, p2883-2885. 3p.

Subject Terms: *Drilling muds, *Potassium carbonate, *Drilling fluids, *Solvents, *Petroleum prospecting, *Grain size

Experimental analysis and predictive modelling of surge and swab pressure mitigation using graphene-based drilling fluid in shallow wells.

Authors: Sabo, Umar Nabil Ridha, Syahrir Dzulkarnain, Iskandar B. et al.

Source: Journal of Petroleum Exploration & Production Technology; Aug2026, Vol. 16 Issue 8, p1-30, 30p

A Mineralogical Perspective on Rare Earth Elements (REEs) Extraction from Drill Cuttings: A Review.

Authors: Rasool, Muhammad Hammad Ridha, Syahrir Ahmad, Maqsood et al.

Source: Minerals (2075-163X). May2025, Vol. 15 Issue 5, p533. 42p.

Novel Deep Learning Framework for Efficient Pressure Zone Detection Via Analysis of Pore Pressure Profiling.

Authors: Rasool, Muhammad Hammad Jaffari, Rabeea Ahmad, Maqsood et al.

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Jan2025, Vol. 50 Issue 1, p237-248. 12p.

Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir.

Authors: Efras, Muhammad Ridho Dzulkarnain, Iskandar Ridha, Syahrir et al.

Source: Journal of Petroleum Exploration & Production Technology; Nov2024, Vol. 14 Issue 11, p3055-3077, 23p

Reactivity of Basaltic Minerals for CO 2 Sequestration via In Situ Mineralization: A Review.

Authors: Rasool, Muhammad Hammad Ahmad, Maqsood

Source: Minerals (2075-163X). Sep2023, Vol. 13 Issue 9, p1154. 34p.

Epsom Salt-Based Natural Deep Eutectic Solvent as a Drilling Fluid Additive: A Game-Changer for Shale Swelling Inhibition.

Authors: Rasool, Muhammad Hammad Ahmad, Maqsood

Source: Molecules. Aug2023, Vol. 28 Issue 15, p5784. 14p.

Rheological characterization of potassium carbonate deep eutectic solvent (DES) based drilling mud.

Authors: Rasool, Muhammad Hammad Zamir, Asif Elraies, Khaled A. et al.

Source: Journal of Petroleum Exploration & Production Technology. Jul2022, Vol. 12 Issue 7, p1785-1795. 11p.

Subject Terms: *Drilling muds, *Potassium carbonate, *Slurry, *Drilling fluids, *Sustainable chemistry, *Rheology

Eco-Friendly Drilling Fluid: Calcium Chloride-Based Natural Deep Eutectic Solvent (NADES) as an All-Rounder Additive.

Authors: Rasool, Muhammad Hammad Ahmad, Maqsood Siddiqui, Numair Ahmed et al.

Source: Energies (19961073). Jul2023, Vol. 16 Issue 14, p5533. 17p.

Process monitoring and dynamic fusion of complex industrial systems: A reconstruction-based Bayesian framework.

Authors: Ali, Husnain Safdar, Rizwan Liu, Jinfeng et al.

Source: Computers & Chemical Engineering; Dec2025, Vol. 203, pN.PAG-N.PAG, 1p

Influence of alkyl chain length in ionic liquid based drilling mud for rheology modification: a review.

Authors: Zamir, Asif Elraies, Khaled A. Rasool, Muhammad Hammad et al.

Source: Journal of Petroleum Exploration & Production Technology; Feb2022, Vol. 12 Issue 2, p485-492, 8p

A Combined Dimensionless Number in Low Salinity Waterflood Alternating Gas Immiscible CO2 Injection to Predict Oil Recovery Factor in Sandstone Reservoir.

Authors: Efras, Muhammad Ridho Dzulkarnain, Iskandar B. Ridha, Syahrir et al.

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Jul2025, Vol. 50 Issue 13, p10441-10463. 23p.

Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir.

Authors: Efras, Muhammad Ridho Dzulkarnain, Iskandar Ridha, Syahrir et al.

Source: Journal of Petroleum Exploration & Production Technology. Nov2024, Vol. 14 Issue 11, p3055-3077. 23p.

Subject Terms: *Machine learning, *Enhanced oil recovery, *Field research, *Sensitivity analysis, *Machine performance