A relatively new technique has been applied for the purpose of providing more accurate Permeability information and identify rocks types with good fluid flow characteristics from wireline logs data. The study for which this technique was applied included wells drilled over the last 30 years up to present. When you have a wide range of logging tools and differences in logs scales or presentations it becomes really difficult to identify the existing facies on a depositional cycle just by following the shape of the log curves. Even when you count with a consistent or homogeneous log set of curves, the task of identifying the different facies is often difficult. The technique used applies the clustering K-Means 2 algorithm based on wireline logs characteristics to identify the different Electrofacies. The Electrofacies found were very useful to determine rock types with identifiable fluid flow characteristics. Porosity/Permeability relationships were then assigned to the various Electrofacies and used to calculate permeabilties during the log analysis. The method was applied for 79 wells from La Peña Field and for the 9 wells from the Tundy field and proved to be very successful in dealing with a wide heterogeneous range of wireline log types when working on a multiwell field petrophysical analysis. The application of this technique is very simple if we look at the existing pattern recognition software designed to identify facies that include the complicated stochastic algorithms which usually requires the operation of skilled users in order to obtain impressive results. This paper presents a description of the clustering K-Means 2 algorithm used for the Electrofacies calculations and also shows the permeabilities results obtained in the petrophysical analysis for each of the Facies. Finally some examples of the petrophysical evaluation and the calculated Electrofacies will also be visualized graphically and discussed here.