The qOW plotis based on the following correlation between oil production rate and water production rate:
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q_W = a \, \cdot q_O + b |
where
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a = J^{-1}_O \cdot ( J_{1W} + J_{2W}) |
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b = J_{2W} \cdot (p^*_2 - p^*_1) |
where
| | water production rate | | oil production rate |
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| body | --uriencoded--p%5e*_1 |
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| formation pressure in oil pay reservoir | | LaTeX Math Inline |
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| body | --uriencoded--J_%7B1W%7D |
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| water productivity index of oil pay reservoir | | LaTeX Math Inline |
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| body | --uriencoded--J_%7B1O%7D |
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| oil productivity index of oil pay reservoir |
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| body | --uriencoded--p%5e*_2 |
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| formation pressure water reservoir | | LaTeX Math Inline |
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| body | --uriencoded--J_%7B2W%7D |
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| water productivity index of oil pay reservoir | |
In practical applications, the equation
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is often considered through the averaged value:
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<q_W> = a \, \cdot <q_O> + \, b |
where
| are weighted average of and |
There are different ways to calculate weighted average of the dynamic variable, for example:
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| < A >_t \ = \frac{1}{t} \int_o^t A(t) \, dt |
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| <A>_q \ = \frac{1}{Q(t)} \int_o^t A(t) \, q(t) \, dt |
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See Also
Petroleum Industry / Upstream / Production / Subsurface Production / Field Study & Modelling / Production Analysis / Watercut Diagnostics
