These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

151 related articles for article (PubMed ID: 32422960)

  • 1. Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks.
    Gowida A; Elkatatny S; Abdelgawad K; Gajbhiye R
    Sensors (Basel); 2020 May; 20(10):. PubMed ID: 32422960
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks.
    Alsabaa A; Elkatatny S
    ACS Omega; 2021 Jun; 6(24):15816-15826. PubMed ID: 34179625
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System.
    Alsabaa A; Gamal H; Elkatatny S; Abdulraheem A
    Sensors (Basel); 2020 Mar; 20(6):. PubMed ID: 32192144
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network.
    Gouda A; Khaled S; Gomaa S; Attia AM
    ACS Omega; 2021 Dec; 6(48):32948-32959. PubMed ID: 34901646
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids.
    Abdelaal A; Ibrahim AF; Elkatatny S
    ACS Omega; 2023 Apr; 8(16):14371-14386. PubMed ID: 37125126
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud.
    Alsabaa A; Gamal H; Elkatatny S; Abdelraouf Y
    ACS Omega; 2022 May; 7(18):15603-15614. PubMed ID: 35571769
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A New Model for Predicting Rate of Penetration Using an Artificial Neural Network.
    Elkatatny S; Al-AbdulJabbar A; Abdelgawad K
    Sensors (Basel); 2020 Apr; 20(7):. PubMed ID: 32268597
    [TBL] [Abstract][Full Text] [Related]  

  • 8. High energy dissipation-based process to improve the rheological properties of bentonite drilling muds by reducing the particle size.
    Jo HJ; Hong SH; Lee BM; Kim YJ; Hwang WR; Kim SY
    Ultrason Sonochem; 2023 Jan; 92():106246. PubMed ID: 36463782
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Experimental dataset of enhanced rheological properties and lubricity of Nigerian bentonite mud using kelzanĀ® xcd polymer and identifying it optimal combination.
    Oguntade T; Rotimi O; Mojisole A; Solomon A; Angye G
    Data Brief; 2018 Aug; 19():1804-1809. PubMed ID: 30229054
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Real-time prediction of formation pressure gradient while drilling.
    Abdelaal A; Elkatatny S; Abdulraheem A
    Sci Rep; 2022 Jul; 12(1):11318. PubMed ID: 35790798
    [TBL] [Abstract][Full Text] [Related]  

  • 11. H
    Onaizi SA; Gawish MA; Murtaza M; Gomaa I; Tariq Z; Mahmoud M
    ACS Omega; 2020 Dec; 5(47):30729-30739. PubMed ID: 33283121
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data.
    Gamal H; Abdelaal A; Elkatatny S
    ACS Omega; 2021 Oct; 6(41):27430-27442. PubMed ID: 34693164
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Utilization of Artificial Neural Network in Predicting the Total Organic Carbon in Devonian Shale Using the Conventional Well Logs and the Spectral Gamma Ray.
    Siddig O; Abdulhamid Mahmoud A; Elkatatny S; Soupios P
    Comput Intell Neurosci; 2021; 2021():2486046. PubMed ID: 34349796
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The Effect of Biopolymer Chitosan on the Rheology and Stability of Na-Bentonite Drilling Mud.
    Abu-Jdayil B; Ghannam M; Alsayyed Ahmed K; Djama M
    Polymers (Basel); 2021 Sep; 13(19):. PubMed ID: 34641175
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Synergetic Effects of Graphene Nanoplatelets/Tapioca Starch on Water-Based Drilling Muds: Enhancements in Rheological and Filtration Characteristics.
    Ahmad M; Ali I; Bins Safri MS; Bin Mohammad Faiz MAI; Zamir A
    Polymers (Basel); 2021 Aug; 13(16):. PubMed ID: 34451195
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models.
    Elkatatny S
    Sensors (Basel); 2020 Jun; 20(12):. PubMed ID: 32575868
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dataset on the beneficiation of a Nigerian bentonite clay mineral for drilling mud formulation.
    Afolabi RO; Ogunkunle TF; Olabode OA; Yusuf EO
    Data Brief; 2018 Oct; 20():234-241. PubMed ID: 30140719
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Rheological Behavior and Filtration of Water-Based Drilling Fluids Containing Graphene Oxide: Experimental Measurement, Mechanistic Understanding, and Modeling.
    Rafieefar A; Sharif F; Hashemi A; Bazargan AM
    ACS Omega; 2021 Nov; 6(44):29905-29920. PubMed ID: 34778663
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters.
    Abdelaal A; Elkatatny S; Abdulraheem A
    ACS Omega; 2021 Jun; 6(21):13807-13816. PubMed ID: 34095673
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Innovative Applications of Red Mud: Converting an Environmental Challenge to a Drilling Asset.
    AlBoraikan R; Bageri B; Solling TI
    ACS Omega; 2023 Jan; 8(1):614-625. PubMed ID: 36643499
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.