Identifying the Optimum Zone for Reducing Drill String Vibrations

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dc.contributor.advisorShor, Roman J.
dc.contributor.authorEtaje, Darlington Christian
dc.contributor.committeememberGates, Ian Donald
dc.contributor.committeememberHassay, Derek N.
dc.date2018-11
dc.date.accessioned2018-07-12T13:50:23Z
dc.date.available2018-07-12T13:50:23Z
dc.date.issued2018-07-10
dc.description.abstractThis thesis was written to address the vibration problems that occur during drilling operations. Due to the rotational motion effected on the drill string while drilling, vibrations occur, and when these vibrations become excessive, the drill string may oscillate in a manner that could damage the pipes and damage other tools attached to the drill string. Machine learning may be used to identify the vibration prone zones and provide recommendations to the driller to change the operating weight on bit (WOB) and rotation speed (RPM) to achieve drilling efficiency while reducing the possibility of damages downhole. Data received from the rig is processed through a dimension reduction process and then categorized using a decision tree classification method. The rules behind the decision tree was created by reversing conventional ways of curbing vibration problems during drilling operations. In the course of the research, it was discovered that there is a need for additional safety gap away from the usual boundary for vibration problems. Quantitative risk analysis was used to identify this gap. This report explains the process of identifying that safety gap. The machine learning model used throughout this research was trained on recorded downhole data and tested with surface data from the electronic drilling recorder. The reports also highlight the findings from market research done to identify the possibility of deploying this research as a startup in Calgary, Canada. Detailed competitor analysis is shown based on customer discovery and customer validation interviews. This has led to the development of business model canvas which is described in this report. A blue ocean strategy was graphed showing that the startup, “Optimum Zone Identifier, OZI” can be differentiated from competitors by being in a market segment that has unique needs with OZI being the only player fitting this category.en_US
dc.identifier.citationEtaje, D. C. (2018). Identifying the Optimum Zone for Reducing Drill String Vibrations (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32358en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32358
dc.identifier.urihttp://hdl.handle.net/1880/107136
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectDrill String Vibrations
dc.subject.classificationEngineering--Petroleumen_US
dc.titleIdentifying the Optimum Zone for Reducing Drill String Vibrations
dc.typemaster thesis
thesis.degree.disciplineChemical and Petroleum Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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