A technology commonly used to reduce drilling times is the "casing drilling". It consists of using the casing as drilling string as the well deepens, instead of the common drill pipe. In this way, the casing drilling system integrates the drilling and casing processes, making the construction of the well more efficient. Once the final depth is reached, the well is cemented, leaving it ready for completion. The greatest benefit of this technique is the optimization of costs associated with drilling. In return, this method prevents to execute Open Hole logging. Without these logs, it is not possible to know the depth of the reservoirs or perform a petrophysical analysis for the area of interest.In order to define the well completion, neural nets training techniques have been implemented, to predict resistivity, density and neutron porosity for each well drilled using casing drilling. To achieve this, several training wells were strategically selected in the different areas where casing drilling was going to be performed. Each training well needs to have a triple combo open hole log, and cased hole pulsed neutron log.Pulsed neutron log is key for this application since with it we obtain curves that respond to water formation salinity, hydrogen index and the density of the rock, which are used in the architecture of the neural networks to obtain the predicted triple combo (resistivity-density-neutron).This paper explains the use and reach of neural networks. The project has more than 58 wells drilled and the results were compared with a test well to validate the methodology.