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You searched for subject:(Automated drilling). Showing records 1 – 2 of 2 total matches.

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University of Texas – Austin

1. -4766-7868. Self-learning control of automated drilling operations.

Degree: PhD, Mechanical Engineering, 2018, University of Texas – Austin

In recent years, drilling automation has sparked significant interest in both the upstream oil and gas industry and the drilling research community. Automation of various drilling tasks can potentially allow for higher operational efficiency, increased consistency, and reduced risk of trouble events. However, wide adoption of drilling automation has been slow. This can be primarily attributed to the complex nature of drilling, and the high variability in well types and rig specifications that prevent the deployment of off-the-shelf automation solutions. Such complexities justify the need for an automation system that can self-learn by interacting with the drilling environment to reduce uncertainty. The aim of this dissertation is to determine how a drilling automation system can learn from the environment and utilize this learning to control drilling tasks optimally. To provide an answer, the importance of learning, as well as its limitations in dealing with challenges such as insufficient training data, are explored. A self-learning control system is presented that addresses the aforementioned research question in the context of optimization, control, and event detection. By adopting an action-driven learning approach, the control system can learn the parameters that describe system dynamics. An action-driven approach is shown to also enable the learning of the relationship between control actions and user-defined performance metrics. The resulting knowledge of this learning process enables the system to make and execute optimal decisions without relying on simplifying assumptions that are often made in the drilling literature. Detection of trouble drilling events is explored, and methods for reduction of false/missed alarms are presented to minimize false interruptions of the drilling control system. The subcomponents of the self-learning control system are validated using simulated and actual field data from drilling operations to ascertain the effectiveness of the proposed methods. Advisors/Committee Members: Oort, Eric van (advisor), Fernandez, Benito R. (advisor), Chen, Dongmei (committee member), Barr, Ronald E. (committee member), Niekum, Scott (committee member).

Subjects/Keywords: Automated drilling; Drilling optimization; Self-learning control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-4766-7868. (2018). Self-learning control of automated drilling operations. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65829

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 07, 2020. http://hdl.handle.net/2152/65829.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Web. 07 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4766-7868. Self-learning control of automated drilling operations. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/2152/65829.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4766-7868. Self-learning control of automated drilling operations. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65829

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Dayton

2. Srnoyachki, Matthew R. Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation.

Degree: MS(M.S.), Mechanical Engineering, 2018, University of Dayton

An automated drilling system was designed to provide an improved capability to a robotic vehicle prototype used to perform expedient airfields evaluations. The Robotic Assault Zone Terminal Evaluation Kit (RAZTEK) is an autonomous capable Lightweight Tactical All-Terrain Vehicle (LTATV) equipped with an automated dynamic cone penetrometer (DCP) termed “Mosquito.” The combination of a recreational terrain vehicle equipped with an automated DCP system provided the Air Force with its first remote airfield assessment capability. This capability allowed remote measuring of unknown landing zone soil structures around the world without putting an operator in high risk environments to perform the task. An automated drilling system (ADS) was strategically designed in this research to allow the RAZTEK vehicle to perform expedient airfield pavement evaluations on hard paved or concrete surfaces. The assemblies of the “Mosquito Nest,” which include the structures of the vehicle associated with airfield evaluations, were all assessed for their structural integrity and design. A finite element analysis using Solidworks ® 2017 provided stress analysis and displacement calculations proving that each structure designed using a safety factor of 3 or more was structurally safe under the conditions provided by the system dynamics. The electrical design for integrating the ADS into the existing structural geometry, was successful in allowing automation between the Mosquito and ADS points of penetration to be concentric. The objective of the ADS was to provide a self-operating drilling application which could sense when it breaks through the hard upper surface, provide drill depth or surface thickness measurement, and allow the mosquito to measure the substrate below. A test procedure was created to characterize the ADS by drilling through 30, 8 inch depth Portland concrete cement (PCC) blocks without tool change or system failure. The resulting test data showed an average time to drill through each block was 272.43 ± 13.05 seconds. The average depth the drive motor stopped at was 8.9 ± 0.25 inches. The ADS provided an operating drill force of 159.7 lbf to the drill bit while traveling at an average drill depth rate of 0.033 inches per second. The ADS system had an 11.28 % ± 3.08 % error. Advisors/Committee Members: Reissman, Timothy (Committee Chair).

Subjects/Keywords: Robotics; Remote Sensing; Mechanical Engineering; RAZTEK; robotic assault zone terminal evaluation kit; airfield pavement evaluation; expedient airfield evaluation; remote sensing; assault zone survey; DCP; automated drilling system; ADS; runway surface drilling; airfield surface characterization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Srnoyachki, M. R. (2018). Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544537004159348

Chicago Manual of Style (16th Edition):

Srnoyachki, Matthew R. “Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation.” 2018. Masters Thesis, University of Dayton. Accessed August 07, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544537004159348.

MLA Handbook (7th Edition):

Srnoyachki, Matthew R. “Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation.” 2018. Web. 07 Aug 2020.

Vancouver:

Srnoyachki MR. Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation. [Internet] [Masters thesis]. University of Dayton; 2018. [cited 2020 Aug 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544537004159348.

Council of Science Editors:

Srnoyachki MR. Automated Drilling Application for Autonomous Airfield Runway Surveying Vehicles: System Design and Validation. [Masters Thesis]. University of Dayton; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544537004159348

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