The general form of the watercut YW regression is:
Y_W^{-1} = Y_{W0}^{-1} + \mbox{Regression}(\{q_k\}), \quad k=[1..N] |
where
| sandface flowrates |
One can build various types of regression including the Artificial Neural Network or the closed-form regressions.
The simplest form of the linear closed-form regression is:
Y_W^{-1} = Y_{W0}^{-1} + \sum_{k=1}^N w_k \cdot q_k, \quad k=[1..N] |
The simplest form of the non-linear closed-form regression is polynomial:
Y_W^{-1} = Y_{W0}^{-1} + \sum_{k=1}^N w_k \cdot q_k ^ {n_k} |
The more general of the non-linear closed-form regression is rational fraction:
Y_W^{-1} = Y_{W0}^{-1} + \frac{\sum_{k=1}^N w_k \cdot q_k ^ {n_k} }{1 + \sum_{k=1}^N z_k \cdot q_k ^ {m_k} }, \quad |z| = \sqrt{\sum_k z_k} >0 |
Petroleum Industry / Upstream / Subsurface E&P Disciplines / Well Testing (WT) / Flowrate Testing / Flowrate / Production Water cut (Yw)
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