Optimizing Drill Bit Selection using Artificial Neural Networks
| 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.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.description.authorstatus | Student | en_US |
| dc.description.peerreview | yes | en_US |
| dc.identifier.uri | https://hdl.handle.net/10294/3236 | |
| 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 |