Input parameters for basin modeling simulations are often highly uncertain, and this strongly affects the output results. A probabilistic approach to basin modeling seems more suitable than the traditional deterministic approach for estimation of the quality of the results and for prospect evaluation purpose.

In this chapter, we illustrate a methodology we have developed to show how to apply a probabilistic approach to secondary migration. This is easily done by performing many deterministic simulations, combining the results, and obtaining probability distributions for the output data.

However, a straight application of the Monte Carlo method, without any calibration with observed data, produces results that could be absolutely misleading. For this reason, a partial sensitivity analysis is performed to investigate the response of the system to the simultaneous variation of several input parameters. Input data for these simulations are obtained by optimal sampling of the uncertainty hypercube defined by the input ranges of a set of potentially critical parameters. Afterward, secondary migration results are calibrated to oil and gas volumes in place in the drilled traps by selecting, among all the simulations performed, the ones that best match the data. The calibration allows us to obtain distributions for input parameters that can be used as input for the conventional Monte Carlo method. The final results of this procedure are the probability distributions for oil and gas volumes in all the traps of the area.