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With the recent advance in high speed data communication offered by wired drill pipe (WDP) telemetry, it is now possible to design automated control systems that directly utilize downhole data (e.g. pressure) to optimize drilling procedures. This research couples drilling hydraulics, rate of penetration (ROP), and rotational speed (RPM) control into a single controller for managed pressure drilling systems. This novel multivariate controller improves drilling performance during normal drilling operations and enhances safety during abnormal drilling conditions such as unwanted gas influx situations. New advances in drilling automation have made the closed loop control of downhole weight on bit (WOB) and drill string rotational speed (RPM) possible. This study uses two feedback controllers that control the downhole WOB and RPM using surface data. A multivariate nonlinear model predictive controller (NMPC) uses downhole and surface measurements to simultaneously regulate the bottom hole assembly (BHA) pressure and maximize the ROP. For this purpose, NMPC provides the necessary set points for the WOB and RPM feedback controllers as well as manipulates the choke valve opening and pump flow rates. Controller performance is enhanced via a nonlinear estimator that works continuously online with the NMPC and provides the necessary estimated parameter values (i.e. annulus density, friction factor, and gas influx) for precise and efficient drilling control. The designed NMPC controller has a multi-priority approach which is described in the following three scenarios: (1) during unexpected gas influx, the NMPC gives priority to BHA pressure control and attenuates the influx effectively via a novel kick attenuation method that switches the control objective from BHA pressure to choke valve pressure; (2) during connection procedures when adding a new stand, ROP is stopped and the NMPC focuses on maintaining the BHA pressure constant; (3) during normal drilling operation, which involves changes in the rock formation and differential pressures, NMPC gives priority to ROP maximization while maintaining RPM, WOB, and BHA pressure within specified bounds. Preliminary results suggest that this multivariate controller for ROP and BHA pressure control will decrease drilling costs, reduce operator workload, and minimize risk significantly. Specific improvements in drilling performance include higher ROP, effective kick attenuation, and more uniform cuttings. The use of a multivariate NMPC allows for better ROP optimization and BHA pressure control than would be possible with the use of two independent controllers. These benefits are demonstrated across the three scenarios mentioned above. This technology has potential to deliver significant performance improvements during managed pressure drilling and further the development of auto driller systems.
With the recent advance in high speed data communication offered by wired drill pipe (WDP) telemetry, it is now possible to design automated control systems that directly utilize downhole data (e.g. pressure) to optimize drilling procedures. This research couples drilling hydraulics, rate of penetration (ROP), and rotational speed (RPM) control into a single controller for managed pressure drilling systems. This novel multivariate controller improves drilling performance during normal drilling operations and enhances safety during abnormal drilling conditions such as unwanted gas influx situations. New advances in drilling automation have made the closed loop control of downhole weight on bit (WOB) and drill string rotational speed (RPM) possible. This study uses two feedback controllers that control the downhole WOB and RPM using surface data. A multivariate nonlinear model predictive controller (NMPC) uses downhole and surface measurements to simultaneously regulate the bottom hole assembly (BHA) pressure and maximize the ROP. For this purpose, NMPC provides the necessary set points for the WOB and RPM feedback controllers as well as manipulates the choke valve opening and pump flow rates. Controller performance is enhanced via a nonlinear estimator that works continuously online with the NMPC and provides the necessary estimated parameter values (i.e. annulus density, friction factor, and gas influx) for precise and efficient drilling control. The designed NMPC controller has a multi-priority approach which is described in the following three scenarios: (1) during unexpected gas influx, the NMPC gives priority to BHA pressure control and attenuates the influx effectively via a novel kick attenuation method that switches the control objective from BHA pressure to choke valve pressure; (2) during connection procedures when adding a new stand, ROP is stopped and the NMPC focuses on maintaining the BHA pressure constant; (3) during normal drilling operation, which involves changes in the rock formation and differential pressures, NMPC gives priority to ROP maximization while maintaining RPM, WOB, and BHA pressure within specified bounds. Preliminary results suggest that this multivariate controller for ROP and BHA pressure control will decrease drilling costs, reduce operator workload, and minimize risk significantly. Specific improvements in drilling performance include higher ROP, effective kick attenuation, and more uniform cuttings. The use of a multivariate NMPC allows for better ROP optimization and BHA pressure control than would be possible with the use of two independent controllers. These benefits are demonstrated across the three scenarios mentioned above. This technology has potential to deliver significant performance improvements during managed pressure drilling and further the development of auto driller systems.
Summary With the recent advance in high-speed data communication offered by wired-drillpipe (WDP) telemetry, it is now possible to design automated control systems that directly use downhole data (e.g., pressure) to optimize drilling procedures. This research couples drilling hydraulics, rate of penetration (ROP), and rotational-speed (rev/min) control into a single controller for managed-pressure-drilling (MPD) systems. This novel multivariate controller improves drilling performance during normal drilling operations and enhances safety during abnormal drilling conditions such as unwanted gas-influx situations. New advances in drilling automation have made the closed-loop control of downhole weight on bit (WOB) and drillstring rotational speed (rev/min) possible. This study uses two feedback controllers that control the downhole WOB and rev/min by use of surface data. A multivariate nonlinear model-predictive controller (NMPC) uses downhole and surface measurements to simultaneously regulate the bottomhole-assembly (BHA) pressure and maximize the ROP. For this purpose, NMPC provides the necessary set points for the WOB and rev/min feedback controllers and manipulates the choke-valve opening and pump-flow rates. Controller performance is enhanced by means of a nonlinear estimator that works continuously online with the NMPC and provides the necessary estimated parameter values (such as annulus density, friction factor, and gas influx) for precise and efficient drilling control. The designed NMPC controller has a multipriority approach that is described in the following three scenarios: during unexpected gas influx, the NMPC gives priority to BHA pressure control and attenuates the influx effectively by means of a novel kick-attenuation method that switches the control objective from BHA pressure to choke-valve pressure; during connection procedures when adding a new stand, ROP is stopped and the NMPC focuses on maintaining the BHA pressure constant; and during normal drilling operation, which involves changes in the rock formation and differential pressures, NMPC gives priority to ROP maximization while maintaining rev/min, WOB, and BHA pressure within specified bounds. Preliminary results suggest that this multivariate controller for ROP and BHA-pressure control decreases drilling costs, reduces operator workload, and minimizes risk significantly. Specific improvements in drilling performance include higher ROP, effective kick attenuation, and more-uniform cuttings. The use of a multivariate NMPC allows for better ROP optimization and BHA-pressure control than is possible with the use of two independent controllers. These benefits are demonstrated across the three scenarios mentioned previously. In simulation, this technology delivers significant performance improvements during MPD and furthers the development of automated-driller systems.
The process of drilling a borehole is very complex, involving surface and downhole drilling systems, which interact with the drilling fluid and the surrounding rocks. Modeling and simulating every aspect of the drilling process and drilling system is still considered too complex to be realized. However, many areas of modeling and simulation are currently undergoing very aggressive development. These areas include rig systems, downhole dynamics, rock-bit interaction, drilling/formation fluid, and the Earth model. High-fidelity models in these well-defined areas have demonstrated some success. Lately, drilling modelling and simulation has become one of the key factors for advancing drilling systems automation/control, intelligent managed pressure drilling and drilling optimization by understanding and/or predicting downhole dynamics. Modeling the magnitude and spatiotemporal distribution of uncertainty in the actual drilling process poses serious challenges in constructing high-fidelity models of the entire drilling system. However, the advancement of technology may dramatically improve the future of such an attempt to accurately model and simulate the whole drilling process. The topics presented in this paper include the current state of drilling process simulation software and simulators, the challenges of modelling drilling systems for automation and control, adaptive simulations for downhole drilling systems and the operator's perspective on drilling modelling. This paper examines the current state of drilling modelling and simulation and identifies its future goals.
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