Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

The application of multiobjective optimisation to protein-ligand docking

Despite the intense efforts that have been devoted to the development of scoring functions for protein-ligand docking, they are still limited in their ability to identify the correct binding pose of a ligand within a protein binding site. A deeper understanding of the intricacies of scoring functions is therefore essential in order to develop these effectively. The aim of the work described in this thesis is to analyse the individual interaction energy types which form the individual components of a force field-based scoring function. To do this, & protein-ligand docking algorithm that is based on multiobjective optimisation has been developed. Multiobjective optimisation allows for the optimisation of several objectives simultaneously and this has been applied to the individual interaction energy types of the GRID scoring function. Traditionally these interaction energy types are summed together and the total energy is used to guide the search. By using individual energy types during optimisation, their roles can be better understood. The interaction energy types that have been used here are the electrostatic and hydrogen bond interactions combined, and van der Waals interactions. The algorithm is first tested on two datasets containing twenty complexes. The results show that the different interaction energy types have varying influences when it comes to successfully docking certain complexes, and that it is important to fmd the right balance of interaction energy types so as to find correct solutions. Ofthe twenty complexes, the algorithm found correct solutions for fifteen. To improve the performance of the algorithm, a few enhancements were introduced. This includes a simplex minimisation process with a Lamarckian element. The algorithm was retested on the twenty complexes, and the newer version was found to outperform the original version, finding correct solutions for seventeen of the twenty complexes. To extensively study the capabilities of the algorithm, it was tested on varied datasets, including the FlexX dataset. The algoritlun's performance was also compared to a single-objective docking tool, Q-fit. The comparison betw~en the multiobjective and single-objective methodologies revealed that single-objective methods can sometimes fail at finding correct docked solutions because they are unable to correctly balance the interaction energy types comprising a scoring function. The study also showed that a multiobjective optimisation method can reveal the reasons why a given docking algorithm may fail at fmding a correct solution. Finally, the algorithm was extended to incorporate desolvation energy as a third objective. Though these results are preliminary, they revealed some interesting relationships between the different objectives.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modelling Early Transitions Toward Autonomous Protocells

This thesis broadly concerns the origins of life problem, pursuing a joint approach that combines general philosophical/conceptual reflection on the problem along with more detailed and formal scientific modelling work oriented in the conceptual perspective developed. The central subject matter addressed is the emergence and maintenance of compartmentalised chemistries as precursors of more complex systems with a proper cellular organization. Whereas an evolutionary conception of life dominates prebiotic chemistry research and overflows into the protocells field, this thesis defends that the 'autonomous systems perspective' of living phenomena is a suitable - arguably the most suitable - conceptual framework to serve as a backdrop for protocell research. The autonomy approach allows a careful and thorough reformulation of the origins of cellular life problem as the problem of how integrated autopoietic chemical organisation, present in all full-fledged cells, originated and developed from more simple far-from-equilibrium chemical aggregate systems.Comment: 205 Pages, 27 Figures, PhD Thesis Defended Feb 201

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc

Biological Nanowires: Integration of the silver(I) base pair into DNA with nanotechnological and synthetic biological applications

Modern computing and mobile device technologies are now based on semiconductor technology with nanoscale components, i.e., nanoelectronics, and are used in an increasing variety of consumer, scientific, and space-based applications. This rise to global prevalence has been accompanied by a similarly precipitous rise in fabrication cost, toxicity, and technicality; and the vast majority of modern nanotechnology cannot be repaired in whole or in part. In combination with looming scaling limits, it is clear that there is a critical need for fabrication technologies that rely upon clean, inexpensive, and portable means; and the ideal nanoelectronics manufacturing facility would harness micro- and nanoscale fabrication and self-assembly techniques. The field of molecular electronics has promised for the past two decades to fill fundamental gaps in modern, silicon-based, micro- and nanoelectronics; yet molecular electronic devices, in turn, have suffered from problems of size, dispersion and reproducibility. In parallel, advances in DNA nanotechnology over the past several decades have allowed for the design and assembly of nanoscale architectures with single-molecule precision, and indeed have been used as a basis for heteromaterial scaffolds, mechanically-active delivery mechanisms, and network assembly. The field has, however, suffered for lack of meaningful modularity in function: few designs to date interact with their surroundings in more than a mechanical manner. As a material, DNA offers the promise of nanometer resolution, self-assembly, linear shape, and connectivity into branched architectures; while its biological origin offers information storage, enzyme-compatibility and the promise of biologically-inspired fabrication through synthetic biological means. Recent advances in DNA chemistry have isolated and characterized an orthogonal DNA base pair using standard nucleobases: by bridging the gap between mismatched cytosine nucleotides, silver(I) ions can be selectively incorporated into the DNA helix with atomic resolution. The goal of this thesis is to explore how this approach to “metallize” DNA can be combined with structural DNA nanotechnology as a step toward creating electronically-functional DNA networks. This work begins with a survey of applications for such a transformative technology, including nanoelectronic component fabrication for low-resource and space-based applications. We then investigate the assembly of linear Ag+-functionalized DNA species using biochemical and structural analyses to gain an understanding of the kinetics, yield, morphology, and behavior of this orthogonal DNA base pair. After establishing a protocol for high yield assembly in the presence of varying Ag+ functionalization, we investigate these linear DNA species using electrical means. First a method of coupling orthogonal DNA to single-walled carbon nanotubes (SWCNTs) is explored for self-assembly into nanopatterned transistor devices. Then we carry out scanning tunneling microscope (STM) break junction experiments on short polycytosine, polycationic DNA duplexes and find increased molecular conductance of at least an order of magnitude relative to the most conductive DNA analog. With an understanding of linear species from both a biochemical and nanoelectronic perspective, we investigate the assembly of nonlinear Ag+-functionalized DNA species. Using rational design principles gathered from the analysis of linear species, a de novo mathematical framework for understanding generalized DNA networks is developed. This provides the basis for a computational model built in Matlab that is able to design DNA networks and nanostructures using arbitrary base parity. In this way, DNA nanostructures are able to be designed using the dC:Ag+:dC base pair, as well as any similar nucleobase or DNA-inspired system (dT:Hg2+:dT, rA:rU, G4, XNA, LNA, PNA, etc.). With this foundation, three general classes of DNA tiles are designed with embedded nanowire elements: single crossover Holliday junction (HJ) tiles, T-junction (TJ) units, and double crossover (DX) tile pairs and structures. A library of orthogonal chemistry DNA nanotechnology is described, and future applications to nanomaterials and circuit architectures are discussed

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Analysis of microbial communities associated with Tilapia Aquaculture in Malawi

Aquaculture is of major and increasing importance to global food security, particularly in Low Income, Food Deficit Countries (LIFDCs), where it also serves as a significant contribution to poverty alleviation. Disease is widely acknowledged as the prominent bottleneck to achieving global food security and poverty alleviation targets relating to aquaculture, with annual losses exceeding >$6bn (Food and Agriculture Organization 2014). Outbreaks of disease caused by endemic and emerging pathogens impact directly on farmer income and their nutritional security. Avoidance of yield-limiting disease outbreaks is a fundamental requirement for future growth and resilience of aquaculture in LIFDCs. Advances in molecular techniques coupled with next-generation sequencing have provided a step-change in understanding the role of host-associated bacteria, archaea, protists and viruses (the microbiome) in host homeostasis. Shifts in microbiome communities under stressful conditions can contribute to disease states. However, the role of microbiomes in the emergence of diseases in aquaculture, where stressors include feeding, antibiotic and disinfectant use and over-stocking, is poorly studied. Here our study presents an evaluation of the microbiomes (bacteria and viruses) associated with tilapia and their pond environments in aquaculture, using 16S rRNA community profiling techniques and viral amplicon sequencing. Samples investigated in this project were collected from Malawi tilapia fish farms; their skin community composition and diversity were examined across geographical scales. The high variability observed of the microbial communities in small geographic regions, showed that future sampling to detect shifts due to dysbiosis will require time-resolved sampling of ponds under study. Nanopore sequencing of full length 16S rRNA genes, using MinION, allowed us to examine the microbial communities at higher taxonomic resolution than short read sequencing techniques. Its success lays the foundation for in-situ microbial profiling of aquaculture ponds for disease, and offers independence to farmers to monitor their own ponds. Successful amplification of the T4-like Myoviridae phylogenetic markers from one rearing water sample was achieved, although the required degeneracy of the primers inhibited multiplexing. Therefore, our findings suggest that inclusion of bacteriophages in microbiome studies is better served using shotgun metagenomic methods, rather than amplicon sequencing. Finally, we investigated the use of skin swabbing as an alternative to bucket incubations to minimise animal stress when categorising the fish skin microbiome. Skin swabbing successfully captured similar microbial communities in comparison to bucket incubations, with greater diversity and variance between fish

Influenza and the Respiratory Microbiome

Despite the availability of vaccines, influenza causes approximately 3-5 million cases of severe illness and 400,000 deaths each year. Prevention efforts might potentially be strengthened by harnessing the host microbiome, which plays an important role in maintaining human health by promoting host immunity and colonization resistance. Although vaccines are the best available means of prevention, vaccine effectiveness has been low to moderate in recent years and vaccine coverage remains low, especially in low- to middle-income countries. Exploring the relationship between influenza virus and the respiratory microbiome may contribute to alternative strategies of prevention. This dissertation explores the relationship between influenza virus and the respiratory microbiome. In chapter 2, we describe our current understanding of respiratory virus-bacteria interactions using systematic and targeted literature searches. We explore whether respiratory viruses can place selective pressures on bacteria in the upper respiratory tract. Further, as colonization in the upper respiratory tract is a necessary precursor for many respiratory pathogens, we explore whether virus-associated changes in the upper respiratory tract microbiome can influence the etiology of bacterial pneumonia. We found strong biological support for a link between respiratory viruses, the upper respiratory tract microbiome, and bacterial pneumonia. However, we found a lack of longitudinal studies among human populations that examined all three components. To address this knowledge gap, we used a household transmission study of influenza in Nicaragua to explore potential relationships between influenza and the respiratory microbiome. In chapter 3, we examine whether the respiratory microbiome mediates susceptibility to influenza virus infection and characterize structural changes to the respiratory microbiome during influenza virus infection. We used Dirichlet multinomial mixture models to assign nose/throat samples to bacterial community types and generalized linear mixed effects models which account for clustering by household. We found a single community type associated with decreased susceptibility to influenza. Further, we found high rates of change in the microbiome structure following influenza virus infection as well as among household contacts who were never infected with influenza during follow up. In chapter 4, we use secondary cases from the Nicaraguan household transmission study to investigate whether the respiratory microbiome impacts influenza symptomology and viral shedding. We used generalized linear mixed effects models to examine the presentation of symptoms and viral shedding. Further, we used accelerated failure time models with a generalized estimating equation approach to examine time-to-event outcomes including symptom duration, shedding duration, and time to infection. The duration of symptoms varied by bacterial community type both prior to and during influenza virus infection. Further, a community type with low diversity was associated with shorter duration of viral shedding and delayed time to infection among secondary cases. The results of these various analyses suggest the respiratory microbiome may be a potential target for reducing influenza risk, household transmission, and disease severity. In the final chapter, I review the skills I learned and the challenges I encountered during the dissertation process. Finally, I review future research directions that focus on deciphering the complex dynamics between the host, pathogen, and microbiome.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143948/1/kyuhan_1.pd