Estimation of GOR at reservoir pressures Below bubble point pressure using GMDH (Group Method of Data Handling)
A GMDH neural networks modelling approach is proposed to estimate gas oil ratio at pressures below bubble point pressure. A new correlation is developed by the use of 385 PVT data collected from available literature on GOR measurement and ranging from 100scf/STB to a little over 1500scf/STB. GMDH ap...
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| Format: | Final Year Project |
| Language: | English |
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Universiti Teknologi PETRONAS
2014
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| Online Access: | http://utpedia.utp.edu.my/14342/1/Final%20Dissertation%20Moctar%20Bebaha%2014209.pdf http://utpedia.utp.edu.my/14342/ |
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| Summary: | A GMDH neural networks modelling approach is proposed to estimate gas oil ratio at pressures below bubble point pressure. A new correlation is developed by the use of 385 PVT data collected from available literature on GOR measurement and ranging from 100scf/STB to a little over 1500scf/STB. GMDH approach is explained and an overview of the available literature on GMDH modelling is laid out. The new correlation is multinomial algebraic in nature, and is tested against the currently widely used correlations in the petroleum industry. Correlations factors for all correlations are produced. |
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