Electrofacies are log-related signatures that reflect specific physical and compositional characteristics of rock units. The concept was developed to encapsulate a collection of recorded well-log responses, enabling the characterization and differentiation of one rock unit from another. The analysis of the lateral and vertical distribution of electrofacies is crucial for understanding reservoir properties; however, well-log analysis can be labor-intensive, time-consuming, and prone to inaccuracies due to the subjective nature of the process. In addition, there is no unique way of reliably classifying logs or deriving electrofacies due to the varying accuracy of different methods. In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. The results from the different methods are used to constitute a committee machine, which is then used to implement electrofacies-guided well placement. 3D distributed petrophysical properties are also computed from core-calibrated porosity and permeability data for reservoir simulation. The results indicate that wells producing from a specific electrofacies, as predicted by the committee machine, have significantly better production than wells producing from other electrofacies. This proposed detailed machine learning workflow allows for strategic decision-making in development and the practical application of these findings for improved oil recovery.