The general form of the watercut YW regression is:
| (1) | Y_W^{-1} = Y_{W0}^{-1} + \mbox{Regression}(\{q_k\}), \quad k=[1..N] |
where
(\{q_k\} = \{ q_1, \, q_2, \, ... q_N \} | 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:
| (2) | Y_W = \frac{1}{Y_{W0}^{-1} + \sum_k w_k \cdot q_k } |
The simplest form of the non-linear closed-form regression is:
| (3) | Y_W = \frac{1}{Y_{W0}^{-1} + \sum_k w_k \cdot q_k ^ {n_k}} |