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- W2002201734 abstract "Abstract Recent developments in drilling technology, such as increased sensory information, enhanced data processing and transmitting capacity and capability, and developments in computer controlled machinery, together with adaptation of already available process technology and know-how, are opening up new possibilities for drilling operations. Application of these combined technologies, together with advanced computer modeling, enables enhanced monitoring and increased optimization and control of drilling operations. This paper presents such an integrated system for monitoring and control of the drilling process, currently in the test phase. A key element in the methodology used here is that the models for fluid flow and drilling mechanics are continuously updated in real-time according to the measured data using Kalman filtering techniques. By comparing the calibrated models to real-time data, unwanted occurrences can be detected quickly, and mitigating actions may be taken, either through system control or through manual intervention. Using the calibrated models, safe limits for the drilling operation are computed and enforced, and procedures are optimized. The modules developed cover tripping and reaming, pump start up, friction tests, stick-slip prevention, bit load optimization and monitoring. The methodology may be applied to drilling operations where the drilling equipment is computer controlled. Surface and preferably downhole data must be available in real time. Rigorous testing with drilling data from offshore drilling operations has been performed, and several full-scale tests have been run on a test rig. The ability to maintain the drilling operation within critical limits has been demonstrated. The methodology may contribute to increased safety and reduced down time during drilling operations." @default.
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- W2002201734 date "2006-02-21" @default.
- W2002201734 modified "2023-10-16" @default.
- W2002201734 title "Monitoring and Control of Drilling Utilizing Continuously Updated Process Models" @default.
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- W2002201734 doi "https://doi.org/10.2118/99207-ms" @default.
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