Parameter Estimation and Uncertainty Quantification for Core Flooding

In light of our depleting fossil fuel reserves and the relatively `cheap' extraction of oil and in spite of the highly nonlinear nature of reservoirs, waterflooding has become big business. In recent times, the use of numerical reservoir simulation has not only become possible but has increasingly been used in the petroleum industry in the...

Ensemble-based Data Assimilation For High-uncertainty systems: a case of study with Particulate Matter in the Aburra Valley

In order to avoid the adverse effects of air pollution, efforts have been made to monitor when air pollution reaches dangerous levels. A Chemical Transport Model (CTM) can simulate trace gases and particles concentration in specific areas. These models are not entirely reliable, owing to incomplete knowledge about emissions and meteorological...

Permeability Estimation Using a Non-Parametric Bayesian Network

This thesis addresses the problem of permeability estimation of an oil reservoir using oil production data, a process known in the oil industry as computer-assisted history matching or data assimilation. Zilko (2012) compared the well-known Ensemble Kalman Filter method for data assimilation with the Non-Parametric Bayesian Beliefs Network (NPBN...

Improving the Algal Bloom Prediction on the North Sea by Dual Ensemble Kalman Filter in GEM Model

Increasingly, the phytoplankton blooms (algal blooms) become a public health concern and a worldwide ecological problem. The Generic ecological model (GEM) plays a very important role to assess ecological quality of the Dutch coastal waters and the southern North Sea. Algal blooms can also be predicted by the GEM model mainly through chlorophyll...