Self-learning control of automated drilling operations

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dc.contributor.advisorOort, Eric van
dc.contributor.advisorFernandez, Benito R.
dc.contributor.committeeMemberChen, Dongmei
dc.contributor.committeeMemberBarr, Ronald E.
dc.contributor.committeeMemberNiekum, Scott
dc.creatorPournazari, Parham
dc.creator.orcid0000-0003-4766-7868
dc.date.accessioned2018-07-26T15:05:31Z
dc.date.available2018-07-26T15:05:31Z
dc.date.created2018-05
dc.date.issued2018-06-22
dc.date.submittedMay 2018
dc.date.updated2018-07-26T15:05:32Z
dc.description.abstractIn 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.
dc.description.departmentMechanical Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2707X617
dc.identifier.urihttp://hdl.handle.net/2152/65829
dc.language.isoen
dc.subjectAutomated drilling
dc.subjectDrilling optimization
dc.subjectSelf-learning control
dc.titleSelf-learning control of automated drilling operations
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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