Operations in oil and gas companies are naturally dangerous and susceptible to the occurrence of accidents. The financial losses due to accident damages can be elevated. To avoid the risk of high expenses, it is usual for firms to acquire insurance. However, setting the right parameters for an insurance contract requires estimating the firm s risk exposure, which is still a hard task. To handle this issue, some authors suggest uncertainty characterization models based on safety barriers and precursor information. This approach facilitates the definition of consequences and also acts in a more predictive way when compared to usual models based only on historical data. Then, an optimization model is suggested, using the results obtained with the uncertainty characterization method mentioned as one of its inputs. As loss functions are not fully known, in order to solve the stochastic problem, a Sample Average Approximation (SAA) approach is used. The results obtained were compared to the situation where the company does not acquire insurance and to other two insurance contract options. The optimization model proposed was the one that granted greater predictability to the loss values, presenting the smallest standard deviation. The second best option presented a standard deviation 102 percent greater than the one obtained with the optimized insurance. Also, the model provided greater protection against extreme events, characteristic shown by smaller VaR and CVaR values, with the second best option presenting a CVaR 41 percent greater than the optimized model s CVaR.