University of Houston
Downhole Automation Using Spline, Catenary and Clothoid Curves Based on Minimum Wellpath Energy
Abstract
dc:description.abstractIn order to design a correction wellpath with minimum energy to bring a deviated path to designed well trajectory, an experimental downhole drilling automation is set up and applied. The criterion used to select the best curve between deviated point and target point is the minimization of well path energy. This criterion considers curvature and torsion together, which supplies an advanced qualification of wellbore quality. In the consideration of reducing wellbore fiction, unconventional curves are coupled in wellpath design. Catenary, spline and clothed curves supply curvature bridging in transition zones as they make wellpath smooth. Several survey points will be set to check coordinates of bit moving. Balanced average method, minimum curvature, and natural curve model are used to build well trajectory. Finally, this thesis compares results of these nine optimization results by using different unconventional curves and calculation trajectory methods.
Degree
thesis:*- Name thesis:degree_name
- Master of Science in Petroleum Engineering
- Level thesis:degree_level
- Masters
- Discipline thesis:degree_discipline
- Petroleum Engineering
- Grantor
- University of Houston
- Year dc:date.issued
- 2014
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Yi, Ping
- Advisor dc:contributor.advisor
-
- Qin, Guan
- Committee members dc:contributor.committeemember
-
- Samuel, Robello
- Song, Gangbing
Subjects
dc:subject × 5Rights
dc:rights- Statement dc:rights
-
- The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/10657/2799
- OAI identifier oai:identifier
- oai:uh-ir.tdl.org:10657/2799