Data fusion algorithms for assessing sensors’ accuracy in an oil production well : a Bayesian approach
Master thesis
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http://hdl.handle.net/11250/181685Utgivelsesdato
2009Metadata
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- Studentoppgaver (TN-IDE) [823]
Sammendrag
Oil industry faces an underutilization problem of the captured data during the extracting
process. This issue is a consequence of the lack of information regarding sensors’
accuracy. One effect can be a serious obstacle in the development of computer assisted
decision systems.
In a production well, it can be experienced the inexistence of sensor redundancy and
enough information to assess credible probabilities. In this situation, we have to strongly
depend of the experts’ ability to provide alternatives based on their understanding. These
skills can be a critical limitation and turns particularly difficult the establishment of a
prediction model.
With this work we propose a Bayesian Network approach as a promissory data fusion
technique for surveillance of sensors accuracy. We proved the usefulness of this method
when it seems there isn’t enough feasible data to construct a model. In presence of certain
data constrains we suggest an inversion of the causal relationship. This approach can be a
possible solution to help the expert in accessing conditional probabilities.
Beskrivelse
Master's thesis in Information technology