| EPSRC Reference: |
GR/L08632/01 |
| Title: |
ANALYSING OILWELL DRILLING DATA WITH NEURAL NETWORKS |
| Principal Investigator: |
Nabney, Professor IT |
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| Department: |
Information Engineering |
| Organisation: |
Aston University |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
15 January 1997 |
Ends: |
14 January 2000 |
Value (£): |
71,602
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Summary on Grant Application Form |
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A significant and expensive aspect of exploration drilling for oil and gas is that of ensuring the drilling debris is effectively removed from the bore. The current best practice for monitoring the status of hole cleaning is largely subjective, labour intensive and depends on the experience of site personnel. A new particle measurement device, the PD50, can be used to give a continuous on-line measurement of the rate of cuttings. A dedicated drilling engineer, funded by the industrial collaborators, will spend 12 months at two extended reach wells to gather data. The purpose of this part of the project is to develop suitable data analysis techniques to analyse this rich cource of information. The scientific issues to be addressed arise due to three features of the data; the complex time relationships between parameters, the highly non stationary environment, and the imbalanced priors.We therefore intend to use neural networks and hidden Markov models to model the probabilities of discrete events. On-line training algorithms will address the issue of non-stationarity.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.aston.ac.uk |