# Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization
## 3 Well Placement and Numerical Simulation Results  
The electrofacies model derived from the committee machine is shown in Figure 8. The electrofacies of interest, Facies 2, is designated as favorable pay facies by the machine learning model in contrast with other facies, deemed less favorable for well placement. Well placement is implemented by selecting three locations with the maximum number of layers of the electrofacies of interest, Facies 2, vertically; for comparison/control, we also place three wells at three locations with the least number of layers of Facies 2 vertically (Figure 8).  
Core-calibrated porosity and permeability measurements were used to generate a petrophysical model. A production constraint of 4000 STB/d of oil and 2000 STB/d of water was imposed on all six wells. As a secondary constraint, a bottom-hole pressure of at least 1500 psi and a water-cut monitoring constraint of 0.95 maximum will result in the cessation of operation of the worst-performing well perforations if exceeded. A fifteen-year forecast of the model was generated, and a comparative analysis of cumulative well performance across all regions was conducted.  
Based on the well cumulative performance shown in Figure 9, oil-producing wells placed in the electrofacies of interest showed a higher cumulative output than those placed in other areas. Well_N2 showed the highest performance for wells placed in the other  
Figure 8: Location of wells penetrating a maximum number of layers with Facies 2 (Well_P1, Well_P2, and Well_P3) and Other Facies (Well_N1, Well_N2, and Well_N3) within the reservoir zone.  
electrodes, which can be attributed to the fact that it is surrounded laterally by Facies 2. Well_P3, which had the poorest performance among the wells penetrating Facies 2, outperforms all wells in the other electrofacies. The cumulative oil performance of wells placed in Facies 2 shows the wells had 73.75% incremental oil produced compared to wells in other facies (Figure 10). An observation of the well placement in Figure 8 shows all wells for both regions were placed on the periphery of the reservoir and hence both have an equal chance of water conning, which depresses the oil production performance. Figure 10 shows that the water production recorded for the green area was significantly lower than the white area, proving that the ML model can predict favorable well locations that maximize oil production. The simulation results presented show the workflow can be applied for well placement decisions. The simulation results are based on a greenfield. This principle applies to brownfield regions with high oil saturation distribution. We also performed sensitivity analysis and uncertainty quantification on cumulative oil production by generating 100 multiple realizations of petrophysical properties (Figure 11) and implementing numerical simulation. The results indicate wells placed in Facies 2 have a significantly higher incremental oil production, by 72.7 \(\pm\) 2.8%.  
Figure 9: Cumulative oil production for all wells. Wells penetrating Facies 2 (Well_P1, Well_P2, and Well_P3) as predicted by the committee machine show significantly higher production.