The general form of the Water-Oil Ratio (WOR) regression is:
| (1) |
WOR = WOR_0 + Q_O \cdot \mbox{Regression}(\{q_k\}, \{Q_k\}), \quad k=[1..N] |
Watercut Power Regression
| (2) |
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
| (3) |
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
| (4) |
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 ]
