Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

Structure and dynamics of thin colloidal films

This thesis begins by studying the thickness of evaporative spin coated colloidal crystals and demonstrates the variation of the thickness as a function of suspension concentration and spin rate. Particularly, the films are thicker with higher suspension concentration and lower spin rate. This study also provides evidence for the reproducibility of spin coating in terms of the thickness of the resulting colloidal films. These colloidal films, as well as the ones obtained from various other methods such as convective assembly and dip coating, usually possess a crystalline structure. Due to the lack of a comprehensive method for characterization of order in colloidal structures, a procedure is developed for such a characterization in terms of local and longer range translational and orientational order. Translational measures turn out to be adequate for characterizing small deviations from perfect order, while orientational measures are more informative for polycrystalline and highly disordered crystals. Finally, to obtain an understanding of the relationship between dynamics and structure, the dynamics of colloids in a quasi-2D suspension as a function of packing fraction is studied. The tools that are used are mean square displacement (MSD) and the self part of the van Hove function. The slow down of dynamics is observed as the packing fraction increases, accompanied with the emergence of 6-fold symmetry within the system. The dynamics turns out to be non-Gaussian at early times and Gaussian at later times for packing fractions below 0.6. Above this packing fraction, the dynamics is non-Gaussian at all times. Also the diffusion coefficient is calculated from MSD and the van Hove function. It goes down as the packing fraction is increased.