This paper shows the implementation of a neurosimulation technique for the well placement applied to the development of a heterogeneous hydrocarbon field with an irregular geometry. During the development of a hydrocarbon field the well placement is a major task, because a small change in location can make gains or losses of money during the remaining productive life of the field; this paper presents a neurosimulation technique as an alternative to conventional methods of well placement which are expensive and consume large amounts of time. This technique is a bridge between hard-computing and soft-computing; effectively mixes artificial neural networks (ANN) and numerical reservoir simulation, in this way using the numerical reservoir simulation in a combination of training wells, production data are obtained along with other data which are used to train and adjust the network, then a large number of scenarios are generated which are evaluated by the trained ANN, the best results are verified whit the numerical reservoir simulation, and then it is possible to predict the rate at which the wells will produce and the cumulative hydrocarbon production. For the development of this work open source tools and free software was used to encourage their use and development in research, in academia and in hydrocarbon industry. This work shows an alternative method of selecting wells that produce fast and accurate results, with which it is easy to take the decision about where is the best place to drill new wells during the development of a hydrocarbon field.