Design Principles Behind the Construction of an Autonomous Laboratory-Scale Drilling Rig

Loeken, Erik A.; Trulsen, Alexander; Holsaeter, Andrew M.; Wiktorski, Ekaterina; Sui, Dan; Ewald, Robert H.

doi:10.1016/j.ifacol.2018.06.356

Cited by 18 publications

(8 citation statements)

References 3 publications

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“…7) In some cases, the DT is used for the "Design" of a machine [68,72], of a laboratory environment [71], of a product or production environment [69,73] or for the redesign of the working environment [70].…”

Section: Review Of Digital Twin Applications In Literaturementioning

confidence: 99%

“…In some cases the production system is equipped with additional sensors to perform different analyses [37,39,45,67], or for the design of a single process [71]. In other cases, the system is considered with the addition of cameras: in particular, in one case the system is a production robot that will be in the future controlled from data coming from the cameras through its DT [61], in another case is used for remote training of a robot [64] and in the last case is used to evaluate the design of a process line [69].…”

Section: System Featuresmentioning

confidence: 99%

“…In two cases is used a prediction model [25,63], in other cases the representation of the production system is done with a Discrete Event System (DES) model [28,37,41,44]. Eight articles did not use any model, but they extract information from the system experimentally, analysing the acquired data [26,43,45,49,50,52,56,71]. The most used model is the 3D kinematical model, used by the remaining articles in Table 1.…”

Section: Dt Implementation Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Review of digital twin applications in manufacturing

Cimino

Negri

Fumagalli

2019

Computers in Industry

556

169

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Section: Review Of Digital Twin Applications In Literaturementioning

confidence: 99%

Section: System Featuresmentioning

confidence: 99%

Section: Dt Implementation Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Review of digital twin applications in manufacturing

Cimino

Negri

Fumagalli

2019

Computers in Industry

556

169

View full text Add to dashboard Cite

“…Figures 1 and 2 show the laboratory drilling rig and its sketch, respectively. The detailed information about the rig structure, its software and control system was given in Løken and Løkkevik (2019), Løken et al (2018Løken et al ( , 2019, The top drive is controlled by a driver to set the rotary speed (RPM) and maximum torque. The construction is equipped with a complete hoisting system consisting of actuators, stepper motors and brakes.…”

Section: Our Novelty and Contributionsmentioning

confidence: 99%

Data-driven approaches tests on a laboratory drilling system

Løken

Løkkevik

Sui

2020

J Petrol Explor Prod Technol

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In recent years, considerable resources have been invested to exploit vast amounts of data that get collected during exploration, drilling and production of oil and gas. Data-related digital technologies potentially become a game changer for the industry in terms of reduced costs through increasing operational efficiency and avoiding accidents, improved health, safety and environment through strengthening situational awareness and so on. Machine learning, an application of artificial intelligence to offer systems/processes self-learning and self-driving ability, has been around for recent decades. In the last five to ten years, the increased computational powers along with heavily digitized control and monitoring systems have made machine learning algorithms more available, powerful and accurate. Considering the state-of-art technologies that exist today and the significant resources that are being invested into the technologies of tomorrow, the idea of intelligent and automated drilling systems to select best decisions or provide good recommendations based on the information available becomes closer to a reality. This study shows the results of our research activity carried out on the topic of drilling automation and digitalization. The main objective is to test the developed machine learning algorithms of formation classification and drilling operations identification on a laboratory drilling system. In this paper, an algorithm to develop data-driven models based on the laboratory data collocated in many scenarios (for instance, drilling different formation samples with varying drilling operational parameters and running different operations) is presented. Moreover, a testing algorithm based on datadriven models for new formation detection and confirmation is proposed. In the case study, results on multiple experiments conducted to test and validate the developed machine learning methods have been illustrated and discussed.

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“…It mainly consists of five parts-that is, chassis, rotary unit, feeding unit, drilling angle adjustment unit, and electronic hydraulic control unit. 7,8 Working mechanism of drilling rig When the drilling rig works, it moves and arrives at the drilling location required first, and keeps steady attitude under the support role of the support mechanism. Then, the drilling angle is adjusted by the operator (labeled as ''4'' in Figure 2) with the hydraulic cylinder according to the drilling requirement.…”

Section: Structure Of Drilling Rigmentioning

confidence: 99%

Investigation on sticking for drill rod based on finite element analysis and fuzzy proportion–integration–differentiation

Zhang

et al. 2019

Advances in Mechanical Engineering

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In order to investigate the essence of sticking for drill rod served in underground coal mine, a comprehensive solution combing finite element analysis with fuzzy proportion–integration–differentiation on the drilling rig is proposed. First, the structure and working mechanism of the drilling rig are studied; then, the static and modal analyses on the drill rod with finite element analysis are performed according to the actual working conditions of the drilling rig. Furthermore, a load-sensing hydraulic power system of the drilling rig is built, and the co-simulation with AMESim and Simulink is performed on the response, overshoot, and vibration, when the hydraulic power is controlled with the conventional control method, proportion–integration–differentiation control method, and fuzzy proportion–integration–differentiation method, respectively. The simulation results show that the strength, stiffness, and vibration frequency of the drilling rig model meet the working requirements, and no resonance occurs; furthermore, the load-sensing hydraulic power system with fuzzy proportion–integration–differentiation method is preferable than the other two control methods.

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Design Principles Behind the Construction of an Autonomous Laboratory-Scale Drilling Rig

Cited by 18 publications

References 3 publications

Review of digital twin applications in manufacturing

Review of digital twin applications in manufacturing

Data-driven approaches tests on a laboratory drilling system

Investigation on sticking for drill rod based on finite element analysis and fuzzy proportion–integration–differentiation

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