Leveraging Data Science and Data Analytics To Enable Automation and Improve Drilling and Completion Practices
Tuesday, 2 May
606
Technical / Poster Session
This session highlights exciting applications of leveraging data analytics, data science, digital applications and artificial intelligence to improve drilling and completion operations. Specific topics discussed include real time image processing, time-series analysis, smart systems, neural networks, and AI frameworks. These methods are applied in various applications of tripping, wellbore stability, trajectory control, and kick detection, among others. This session will be a great opportunity to exchange knowledge around practical applications of various data uses to the areas of drilling optimization, well completion and automation.
Chairperson
Sponsoring Society:
- American Society of Mechanical Engineers (ASME)
- Institute of Electrical and Electronics Engineers, Oceanic and Engineering Society (IEEE-OES)
- Society of Petroleum Engineers (SPE)
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0930-0948 32248An Advanced In-Line Sensing AI Framework for Enhancing Drilling Operations
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0950-1008 32428Time Series Data Analysis With Recurrent Neural Network for Early Kick Detection
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1010-1028 32532Hybrid Approach Using Physical Insights and Data Science for Early Stuck Detection
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1030-1048 32456Case Study: Effective and Economical Approach To Prevent Scale Formation Using Scale Inhibitor Squeeze Into Reservoir
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1050-1108 32549Using LWD Borehole Image interpretation for Drilling Optimization: A Brazilian Pre-Salt Case Study
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1110-1128 32592Transforming the Trajectory Control From Conventional Motor Drilling to Autonomous Rotary Steerable Systems
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1130-1148 32642Reduction of Well Construction Time With Innovative Design: Tot-3p (True One Trip - 3 Phases)
