An innovative computational tool developed at Oak Ridge National Laboratory could reduce uncertainties and the time required to decide where to drill for gas and oil. Compared to previous approaches, this multilevel Monte Carlo method, which involves computational algorithms that rely on repeated random sampling, provides a better understanding of the subsurface’s influence on oil and gas production. “The heterogeneous nature of the Earth’s surface causes a large number of model parameters, making this approach especially effective,” said Clayton Webster, who led the team from the lab’s Computational and Applied Mathematics group. Ultimately, this new approach could reduce computational costs and help industry make better decisions.