Optimizing Drill Bit Selection using Artificial Neural Networks

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dc.contributor.author Lertnimoolchai, Yanee
dc.contributor.author Ogunrinde, Kanyin
dc.date.accessioned 2011-04-05T20:30:01Z
dc.date.available 2011-04-05T20:30:01Z
dc.date.issued 2011-04-02
dc.identifier.uri http://hdl.handle.net/10294/3236
dc.description.abstract The proper bit can increase the rate of penetration (ROP) and reduce overall drilling costs. Conventional analytical optimization methods alone are not sufficient due to the complexity and non linearity of the factors affecting ROP. Artificial Neural Networks (ANNs), capable of handling complex relationships, are used to predict bit performance. A model created using ANNs allows for the selection of the optimal bit for any given pre-specified set of data. en_US
dc.language.iso en en_US
dc.publisher Fa en_US
dc.relation.ispartofseries PSE 2 en_US
dc.title Optimizing Drill Bit Selection using Artificial Neural Networks en_US
dc.type Technical Report en_US
dc.description.authorstatus Student en_US
dc.description.peerreview yes en_US

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