In this thesis is presented, analyzed and evaluated a new method to forecast well logs (for a pseudo-well), based on information of the neighboring vertical wells and the 3D seismic data. This method consists of four main parts. In the first part, the gamma ray logs from wellbores are processed to produce a stratified set of signals. In the second part, the stratified signals are used to perform a method of lateral correlation of wells with Genetic Algorithms; whose results are similar intervals strata that would indicate the existence of continuous rock structures in the subsurface. In the third part, a method, based on Genetic Algorithms, are used to estimate displacements in depth between some strata of the pseudo-well and real wells. That method correlates events between 3D seismic traces at positions near the real wells and the pseudo-well. In the fourth part, results from the other three parts are used to interpolate several types of logs of the pseudo-well, first of the similar strata and then of the not similar strata, adjusted in depth. The results of this part are the forecasted measurements. The weights for interpolation, by the method of Shepard, are calculated using a function that depends on the distance between the pseudo-well and each real well. A study of seven cases is presented to evaluate the effectiveness of the forecasting method. These cases use arrangements of two and three wells of the Namorado Field in the Campos Basin (RJ) and additionally a real well near each array serves as a standard for evaluating the forecasted measurements by the correlation coefficient and the mean absolute percentage error. Overall the results showed a strong linear correlation and the mean absolute percentage errors are between 5 per cent and 30 per cent in relation of standard data.