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    Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data During Horizontal Drilling 

    Source: Journal of Energy Resources Technology:;2021:;volume( 144 ):;issue: 008:;page 83002-1
    Author(s): Saadeldin, Ramy; Gamal, Hany; Elkatatny, Salaheldin; Abdulraheem, Abdulazeez
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Drillstring vibration is a major concern during drilling wellbore, and it can be split into three types: axial, torsional, and lateral. Many problems associate with the high drillstring vibrations as tear and wear in ...
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    Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations 

    Source: Journal of Energy Resources Technology:;2022:;volume( 144 ):;issue: 010:;page 103201-1
    Author(s): Suleymanov, Vagif; Gamal, Hany; Elkatatny, Salaheldin; Glatz, Guenther; Abdulraheem, Abdulazeez
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The rock acoustic data that provide important information about the formation petrophysics and geomechanics are highly needed to design the wells drilling programs, in addition to, reservoir stimulation and field development ...
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    Rock Strength Prediction in Real-Time While Drilling Employing Random Forest and Functional Network Techniques 

    Source: Journal of Energy Resources Technology:;2021:;volume( 143 ):;issue: 009:;page 093004-1
    Author(s): Gamal, Hany; Alsaihati, Ahmed; Elkatatny, Salaheldin; Haidary, Saleh; Abdulraheem, Abdulazeez
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical ...
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