Feature-based estimation for applications in geosciences

Abstract

A reservoir simulator mimics the movement of fluids in the presence of each other through a porous medium under some specified conditions. It is a numerical model of a real-life physical process, therefore, subject to uncertainty. Some uncertainties can be lowered by improving model-parameter estimates. This is where data assimilation plays an important role. Automated data assimilation, using sophisticated techniques, is a widely researched topic in today's applied science. We investigated two research topics in data assimilation that are closely connected to the area of image processing. Images are an integral part of reservoir engineering application in the form of property or variable fields. Reservoir engineering, image processing and data assimilation are the leading themes here. First, we applied an ensemble multiscale filter as a permeability estimator and concluded that the filter can be an efficient localizing tool especially for spatially large observations. Second, we developed a grid deformation technique inspired by grid generation and image warping methods. We presented two- and three-dimensional versions of the method in reservoir and groundwater flow models, and concluded that the grid distortion proved cost efficient and effective.