Browsing by Author "Wu, Mingqi"
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Item Modeling of carbon nanotube array separation in electrolytes and by chemical functionalization(2007) Wu, Mingqi; Yakobson, Boris I.Due to the accumulated van der Waals attraction, the separation of single-walled carbon nanotubes (SWNTs) has been a challenge. This thesis focus on theoretical modeling and study of separating SWNT array in electrolytes and by chemical functionalization. First, the swelling behavior of SWNT fibers in superacid as well as the sudden collapse of such dispersion with introduction of water are studied by Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. A simple kinetic model for the charging mechanism of SWNT is proposed. Optimization of the controlled parameters of the model has been carried out. Second, we designed a tensegrity structure with three (5,5) SWNTs connected by nine polyethylene chains. One of relative strong connection ways between polyethylene and tube cap is constructed. Structure optimization and molecular dynamics simulation are performed to analyze the stability changes in such and several similar structures by adding some or removal one chain.Item Stochastic clustering and pattern matching for real-time geosteering(Society of Exploration Geophysicists, 2019) Wu, Mingqi; Miao, Yinsen; Panchal, Neilkunal; Kowal, Daniel R.; Vannucci, Marina; Vila, Jeremy; Liang, FamingWe have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is measured by a correlation coefficient; then stochastic searching is performed on the space of all possible earth models to maximize the similarity under constraints of the prior understanding of the drilling process and target formation; finally, an inference is made based on the samples simulated from the posterior distribution using stochastic approximation Monte Carlo in which we extract the most likely earth model and the associated credible intervals as a quantified confidence indicator. We extensively test our method using synthetic and real geosteering data sets. Our method consistently achieves good performance on synthetic data sets with high correlations between the interpreted and the reference logs and provides similar interpretations as the geosteering geologists on four real wells. We also conduct a reliability performance test of the method on a benchmark set of 200 horizontal wells randomly sampled from the Permian Basin. Our Bayesian framework informs geologists with key drilling decisions in real time and helps them navigate the drilling bit into the target formation with confidence.