The general form of the Water-Oil Ratio (WOR) regression is:

WOR = WOR_0 + Q_O \cdot \mbox{Regression}(\{q_k\}, \{Q_k\}), \quad k=[1..N]


Watercut Power Regression

WOR = WOR_0 + Q_{O} \cdot \sum_{k=1..N} \big[ a_{O,k} \, Q_{O,k}^{gQ_{O,k}} +  a_{W,k} \, Q_{W,k}^{gQ_{W,k}} + b_{O,k} \, q_{O,k}^{gq_{O,k}} +  b_{W,k} \, q_{W,k}^{gq_{W,k}}  \big]


Watercut Rational Regression

WOR = WOR_0 + \frac {Q_{O} \cdot \sum_{k=1..N} \big[ a_{O,k} \, Q_{O,k}^{gQ_{O,k}} +  a_{W,k} \, Q_{W,k}^{gQ_{W,k}} + b_{O,k} \, q_{O,k}^{gq_{O,k}} +  b_{W,k} \, q_{W,k}^{gq_{W,k}}  \big]  }{1 + \sum_{k=1..N} \big[ c_{O,k} \, Q_{O,k}^{hQ_{O,k}} +  c_{W,k} \, Q_{W,k}^{hQ_{W,k}} + d_{O,k} \, q_{O,k}^{hq_{O,k}} +  d_{W,k} \, q_{W,k}^{hq_{W,k}}  \big]  }


Watercut  Neural Network Regression

WOR = WOR_0 + Q_O \cdot \mbox{ANN}(\{Q_{O,k}\}, \{Q_{W,k}\},\{q_{O,k}\},\{q_{W,k}\}), \quad k=[1..N]

See Also

Petroleum Industry / Upstream / Subsurface E&P Disciplines / Well Testing (WT) / Flowrate Testing / Flowrate  / Production Water cut (Yw)

[ WOR ] [ Watercut Diagnostics ][ Watercut Fractional Flow @model ]