Drilling-Systems Automation: Current State, Initiatives, and Potential Impact

Macpherson, John; Wardt, John P. de; Florence, Fred; Chapman, Clinton; Zamora, Mario; Laing, Moray L.; Iversen, Fionn

doi:10.2118/166263-pa

Cited by 41 publications

(6 citation statements)

References 62 publications

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“…Yet, the automation of the drilling process can be addressed at very different levels. Macpherson et al (2013) [1] have defined ten levels of automation (LOA), starting with being completely manual at level 1 and reaching full automation at level 10. At the highest LOA, all the monitoring, generating, selecting, and implementing functions are performed by a computer system.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Decision-Making While Drilling

et al. 2021

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The drilling process is complex because unexpected situations may occur at any time. Furthermore, the drilling system is extremely long and slender, therefore prone to vibrations and often being dominated by long transient periods. Adding the fact that measurements are not well distributed along the drilling system, with the majority of real-time measurements only available at the top side and having only access to very sparse data from downhole, the drilling process is poorly observed therefore making it difficult to use standard control methods. Therefore, to achieve completely autonomous drilling operations, it is necessary to utilize a method that is capable of estimating the internal state of the drilling system from parsimonious information while being able to make decisions that will keep the operation safe but effective. A solution enabling autonomous decision-making while drilling has been developed. It relies on an optimization of the time to reach the section total depth (TD). The estimated time to reach the section TD is decomposed into the effective time spent in conducting the drilling operation and the likely time lost to solve unexpected drilling events. This optimization problem is solved by using a Markov decision process method. Several example scenarios have been run in a virtual rig environment to test the validity of the concept. It is found that the system is capable to adapt itself to various drilling conditions, as for example being aggressive when the operation runs smoothly and the estimated uncertainty of the internal states is low, but also more cautious when the downhole drilling conditions deteriorate or when observations tend to indicate more erratic behavior, which is often observed prior to a drilling event.

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Section: Introductionmentioning

confidence: 99%

Autonomous Decision-Making While Drilling

et al. 2021

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“…A study examines and defines drilling systems’ automation, its drivers, enablers and barriers, and its current state and goals. 29 In particular, it studies the drilling systems’ automation among all segments of the drilling industry. Vishnumolakala 30 develops a simulation environment, models an automatic managed pressure drilling using constant BHA pressure technique by a PID controller, and optimizes the drilling operations.…”

Section: Introductionmentioning

confidence: 99%

Robust and adaptive control design of a drilling rig during the operating modes

Nam

Arıkan

Ozbek

et al. 2019

Measurement and Control

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Oil well drilling towers have different operating modes during a real operation, like drilling, tripping, and reaming. Each mode involves certain external disturbances and uncertainties. In this study, using the nonlinear model for the modes of the operation, robust and/or adaptive control systems are designed based on the models. These control strategies include five types of controllers: cascaded proportional–integral–derivative, active disturbance rejection controller, loop shaping, feedback error learning, and sliding mode controller. The study presents the design process of these controllers and evaluates the performances of the proposed control systems to track the reference signal and reject the uncertain forces including the parametric uncertainties and the external disturbances. This comparison is based on the mathematical performance measures and energy consumption. In addition, three architectures are presented to control the weight on bit during drilling process, and also to maintain a preset constant weight on bit, two control approaches are designed and presented.

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“…Because the derrick of the land rig has a special structure and cannot be directly compatible with the mature rig system of the ocean, we have a long way to go to develop a simple and reliable rig system (Saeed et al 2012). Many scholars have done a lot of research on how to achieve drilling automation and how to control drilling systems (Godhavn 2009;Breyholtz and Nikolaou 2012;Macpherson et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Automatic drill pipe emission control system based on machine vision

Zhang

Guan

2019

J Petrol Explor Prod Technol

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Drilling is not as modern as other industries and still has great potential for automatic control. Automatic drill pipe emission control system is mostly used for offshore drilling, and the derrick of the land drilling rig cannot be directly compatible with the mature drill pipe emission system of the offshore drilling rig because of its unique structure. Drill pipe emissions from land drilling are usually done by worker-assisted mechanical arm. In this kind of working environment, workers are labor intensive and prone to fatigue and safety cannot be effectively guaranteed. In order to reduce labor intensity and improve drilling efficiency, this paper presents an automatic control system of drill pipe emission based on machine vision. In the control process of the tripping operations, visual servo control is added, and the visual sensor is used to detect the centerline of the drill pipe in real time. After the system finding the deviation between the drill pipe and the mechanical arm, the posture of the arm is adjusted in real time to compensate for the deviation. When the claw mechanism is aligned with the centerline of the drill pipe, the drill pipe manipulator grabs the drill pipe to complete the emission task. In this paper, four methods for extracting the centerline are compared, and the linear regression method is the best. Keywords Machine vision • Mechanical arm • Drill pipe emission • Image processing List of symbols mm A unit of length in the metric system (millimeter) kN Common unit of mechanical calculation (1 kN = 1000 kg × 1 m/s 2) EX The abscissa of the intersection minus the abscissa of the center point of the lens (pixel) FPS Frame per second (Hz)

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Drilling-Systems Automation: Current State, Initiatives, and Potential Impact

Cited by 41 publications

References 62 publications

Autonomous Decision-Making While Drilling

Autonomous Decision-Making While Drilling

Robust and adaptive control design of a drilling rig during the operating modes

Automatic drill pipe emission control system based on machine vision

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