Risk assessment of drilling and completion operations in petroleum wells using a Monte Carlo and a neural network approach

This paper intends to show how two different methodologies, a Monte Carlo simulation method and a connectionist approach can be used to estimate the total time assessment in drilling and completion operations of oil wells in deep waters. The former approach performs a Monte Carlo simulation based on data from field operations. In the later one, correlations and regularities in parameters selected from a petroleum company database were detected using a competitive neural network, and then, a feedforward neural network was trained to estimate the average, standard deviation and total time wasted in the accomplishment of the well. At the end, the results obtained by both models are compared. The analyst could evaluate the precision of the estimated total-time based on geometric and technological parameters provided by the neural network tool, with those supplied by the traditional Monte Carlo method based on data of the drilling and completion operations.