Analysis of Drilling Data and ROP Optimization Using Artificial Intelligence Techniques with Statistical Regression Coupling

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dc.contributor.advisorKostarelos, Konstantinos
dc.contributor.committeeMemberSamuel, Robello
dc.contributor.committeeMemberHolley, Thomas K.
dc.creatorMantha, Bharat
dc.date.accessioned2018-11-30T21:43:45Z
dc.date.available2018-11-30T21:43:45Z
dc.date.createdAugust 2016
dc.date.issued2016-08
dc.date.submittedAugust 2016
dc.date.updated2018-11-30T21:43:46Z
dc.description.abstractPredictive data‐driven analytics has the potential to successfully predict the downhole environment in Drilling Engineering. In general, rate of penetration (ROP) optimization involves adjustment of the weight on bit (WOB) and rotary speed (RPM) for efficient drilling. ROP has a complex relationship with several other parameters, such as formation properties, mud properties, mud hydraulics, borehole deviation, as well as the size/type of bit. In this study, a new workflow based on statistical regression and artificial intelligence (AI) techniques was designed to forward predict ROP using field data gathered from the North Sea horizontal wells. Several machine‐learning models such as such as step‐wise regression, neural networks, support vector regression, classification‐regression trees, random forests, and boosting, were applied for prediction. A web based prediction app was developed that could perform predictive analytics and uncertainty analysis on any data. The app was further tested on other wells and was shown to predict with significant accuracy.
dc.description.departmentPetroleum Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/3580
dc.language.isoeng
dc.rightsThe 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).
dc.subjectRate of penetration (ROP) optimization
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectPredictive analytics
dc.subjectDrilling
dc.subjectRegression
dc.titleAnalysis of Drilling Data and ROP Optimization Using Artificial Intelligence Techniques with Statistical Regression Coupling
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentPetroleum Engineering, Department of
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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