Probabilistic Time Estimation for Drilling Operations on NCS Using Monte Carlo.

The purpose of this thesis is to present a probabilistic methodology for developing time estimates for drilling operations on the NCS, which can further be expanded to supplementary continental shelfs in the world, or for more specific drilling areas within a continental shelf. The Monte Carlo-model take advantage of the real historical data, where the P90-, P50- and P10 percentiles are derived and used in the probabilistic model. Utilizing risk analysis in the model makes the probabilistic methodology strong and not biased. By taking advantage of historical data together with analytics and providing the dataset into the Monte Carlo model, it is simulated probabilistic ranges expected for one specific well. Historical data are often stored and forgotten in every company and not exploited to further increase the decision making and result. By leveraging historical data and make data-driven decision making it will ultimately generate more value in the long term. By comparing the presented Monte Carlo model with a traditional deterministic and a 3rd party probabilistic model, the results show promising outcome based on two compared drilled wells. Together with additional and richer data, the proposed Monte Carlo model leveraging historical data will increase its robustness and deliver better decision making foundation for the future opposed to the biased and heuristic methods used today