The analysis of 3-D seismic reflection data (and their corresponding seismic attributes) is an essential tool for hydrocarbon exploration projects. In this talk we review some work done when applying Machine learning to 3-D seismic reflection data to estimate some petrophysical properties given by the well-logs. The results correspond to an oil field in Colombia called Tenerife located in the Middle Magdalena Valley and we go from 2-D seismic sections to 3-D geo-cubes of petrophysical properties. Using some of these properties (such as the volume of clay) we obtain facies. We are able to identify paleochannels that are seen in the seismic data, its seismic attributes and in the resulting 3-D petrophysical estimations.