Smittestopp − A Case Study on Digital Contact Tracing

This open access book describes Smittestopp, the first Norwegian system for digital contact tracing of Covid-19 infections, which was developed in March and early April 2020. The system was deployed after five weeks of development and was active for a little more than two months, when a drop in infection levels in Norway and privacy concerns led to shutting it down. The intention of this book is twofold. First, it reports on the design choices made in the development phase. Second, as one of the only systems in the world that collected population data into a central database and which was used for an entire population, we can share experience on how the design choices impacted the system's operation. By sharing lessons learned and the challenges faced during the development and deployment of the technology, we hope that this book can be a valuable guide for experts from different domains, such as big data collection and analysis, application development, and deployment in a national population, as well as digital tracing

Unlocking the correlation in fluvial outcrops by using a DOM-derived virtual datum: Method description and field tests in the Huesca fluvial fan, Ebro Basin (Spain)

Adequate characterization of depositional architecture is of great importance when studying fluvial outcrops as reservoir analogs. The complex three-dimensional (3D) distribution and lateral and vertical relationships of sandstone bodies require a high degree of stratigraphic control in order to make a proper assessment of the distribution and connectivity of the reservoir facies. This assessment demands the use of reliable correlation datums. Unfortunately, clear marker beds (e.g., ash layers, coal beds, and paleosols) are not always available in fluvial outcrops, and when present, they are often covered by vegetation or debris that prevents their tracking over long distances. A new method to achieve highly accurate and semiautomatic correlations within fluvial digital outcrop models (DOMs) is presented in response to the need for further correlation procedures, especially in the absence of suitable datums. The method is based on the hypothesis that the average depositional paleosurface of a sedimentary system can be represented by a plane at outcrop scale. If this assumption is valid, this plane can be used as a virtual datum to identify along the DOM the sediments that were deposited simultaneously. The method was tested and applied successfully within two kilometer-scale outcrops of the Huesca fluvial fan (Early Miocene, northern Spain), where the virtual datum provided accurate correlations regardless of stratigraphic or topographical complexities. Moreover, all the sedimentary successions of the outcrops could be automatically subdivided into the desired stratigraphic intervals by only moving the virtual datum vertically. These intervals can be subsequently isolated to facilitate the detection of subtle variations and trends of their fluvial properties. Consequently, a virtual datum is the equivalent of having a marker bed crossing the stratigraphic succession of an outcrop at any desired position. The advantages provided by a virtual datum prove to be especially useful in large and topographically complex outcrops that previously could not have been studied with such a high degree of 3D stratigraphic control

Human-centred artificial intelligence for mobile health sensing:challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions

Adequate model complexity and data resolution for effective constraint of simulation models by 4D seismic data

4D seismic data bears valuable spatial information about production-related changes in the reservoir. It is a challenging task though to make simulation models honour it. Strict spatial tie of seismic data requires adequate model complexity in order to assimilate details of seismic signature. On the other hand, not all the details in the seismic signal are critical or even relevant to the flow characteristics of the simulation model so that fitting them may compromise the predictive capability of models. So, how complex should be a model to take advantage of information from seismic data and what details should be matched? This work aims to show how choices of parameterisation affect the efficiency of assimilating spatial information from the seismic data. Also, the level of details at which the seismic signal carries useful information for the simulation model is demonstrated in light of the limited detectability of events on the seismic map and modelling errors. The problem of the optimal model complexity is investigated in the context of choosing model parameterisation which allows effective assimilation of spatial information in the seismic map. In this study, a model parameterisation scheme based on deterministic objects derived from seismic interpretation creates bias for model predictions which results in poor fit of historic data. The key to rectifying the bias was found to be increasing the flexibility of parameterisation by either increasing the number of parameters or using a scheme that does not impose prior information incompatible with data such as pilot points in this case. Using the history matching experiments with a combined dataset of production and seismic data, a level of match of the seismic maps is identified which results in an optimal constraint of the simulation models. Better constrained models were identified by quality of their forecasts and closeness of the pressure and saturation state to the truth case. The results indicate that a significant amount of details in the seismic maps is not contributing to the constructive constraint by the seismic data which is caused by two factors. First is that smaller details are a specific response of the system-source of observed data, and as such are not relevant to flow characteristics of the model, and second is that the resolution of the seismic map itself is limited by the seismic bandwidth and noise. The results suggest that the notion of a good match for 4D seismic maps commonly equated to the visually close match is not universally applicable

The Stratal Architecture of the False River Point Bar (Lower Mississippi River, LA)

The False River point bar system is located roughly thirty miles north of Baton Rouge, LA. It is an oxbow lake cut off from the Mississippi River, and represents a large-scale modern analogue to the ancient systems of northeastern Alberta, Canada, specifically those of the Athabasca Oils Sands. While previous work has been done regarding the general architecture of a point bar, there is still omplexity associated with larger scale systems, as studies have been mostly based on small and medium-scale examples. Examination of the point bar was done through a combination of coring and geophysical logging, followed by software analysis of the logs in order to identify facies pack ages and trends within three different areas of the bar (early upstream apex, late stage near apex, and downstream late simple bar stage to late compound bar stage). Three facies packages were interpreted: Top bar, interbedded heterolithic strata, and high-net facies (sand-rich). Although early to late stage simple bar growth demonstrated predictable facies stacking trends, facies related late stage compound bar growth and active abandonment from the main river channel were unpredictable based on small-scale simple models. Permeability differences within sand-rich packages also decreased in the downstream end of the bar, demonstrating a difference in sand quality within seemingly identical facies packages which has previously been overlooked

Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

On a wildlife tracking and telemetry system : a wireless network approach

Includes abstract.Includes bibliographical references (p. 239-261).Motivated by the diversity of animals, a hybrid wildlife tracking system, EcoLocate, is proposed, with lightweight VHF-like tags and high performance GPS enabled tags, bound by a common wireless network design. Tags transfer information amongst one another in a multi-hop store-and-forward fashion, and can also monitor the presence of one another, enabling social behaviour studies to be conducted. Information can be gathered from any sensor variable of interest (such as temperature, water level, activity and so on) and forwarded through the network, thus leading to more effective game reserve monitoring. Six classes of tracking tags are presented, varying in weight and functionality, but derived from a common set of code, which facilitates modular tag design and deployment. The link between the tags means that tags can dynamically choose their class based on their remaining energy, prolonging lifetime in the network at the cost of a reduction in function. Lightweight, low functionality tags (that can be placed on small animals) use the capabilities of heavier, high functionality devices (placed on larger animals) to transfer their information. EcoLocate is a modular approach to animal tracking and sensing and it is shown how the same common technology can be used for diverse studies, from simple VHF-like activity research to full social and behavioural research using wireless networks to relay data to the end user. The network is not restricted to only tracking animals – environmental variables, people and vehicles can all be monitored, allowing for rich wildlife tracking studies

Permeability estimation from time-lapse seismic data for updating the flow-simulation model

The key to increasing reservoir recovery is to provide accurate estimates of the permeable pathways (permeability, transmissibility) and the transmissibility of the barriers that control reservoir heterogeneity. The reservoir-engineering techniques (such as well testing, well logging and production data) supply the estimate of these properties in the reservoir region which is limited to well locations. Providing estimates of the permeability in the reservoir rocks located between the wells is the holy grail of reservoir engineering for history matching. Compared with all other engineering techniques, 4D seismic could play a unique role in providing the property of the reservoir at a good spatial coverage. In this thesis, the estimation of permeability, transmissibility, and the transmissibility multiplier, using 4D seismic, is addressed. First, current methodologies for permeability estimation were applied in synthetic and field examples. Based on the investigations performed, the permeability-estimation method was modified and adjusted to produce an improved result. Consequently, the estimates of permeability provided an introduction to the fast-track history-matching method. The proposed history-matching technique implies a simple and practical approach for quickly updating the simulation to improve the history-matching in the model. In following, the assessment of the uncertainties associated with the permeability estimation that involves using a variety of different attributes, using different time-lapse surveys, tuning effects and method assumptions, were performed. The uncertainties were tackled by addressing these issues; thus, the permeability result was further enhanced, and the uncertainty associated with the estimates was quantified. Next, the relationships between the quantitative estimates of connectivity and the 4D seismic signal were established. Two types of connectivity assessments using 4D seismic (hydraulic sand connectivity and barrier connectivity) were proposed, depending on the fact that 4D-seismic information is either pressure- or saturation-dominant. Accordingly, two types of attributes were introduced, the seismic connectivity attribute (SCA) and the Laplacian attribute. When applied to the Schiehallion field data, an interpretation approach is used to interpret pressure- and saturation-anomalies in frequent time-lapse seismic, using all available sources of data. Following this, a pressure-anomaly map is utilized for locating faults and compartments iii (using the Laplacian attribute), and a saturation-anomaly map is used to calculate the SCA. New approaches were chosen for estimating transmissibility and transmissibility multipliers, based on proposed attributes extracted from 4D seismic

Empirical analysis of localized casing wear with variations in contact pressure and drilling conditions

Im Wettkampf die Grenzen der Kohlenwasserstoff- und Geothermie Förderung sowie Speicherung stetig zu übertreffen und optimieren, wurden horizontale Bohrungen, tiefe Ablenkungsbohrungen und Bohrungen mit großem Neigungswinkel (ERD Bohrungen) zum heutigen Standard. Bei der Planung solcher Projekte sowie Ablenkungen, wirken sich Vorgänge wie Räumen (reaming), Bohren (drilling), Rotation off-bottom und das Ein- und Ausfahren der Bohrgestänge enorm auf die Futterrohr Beschaffenheit aus. Durch das Aufeinandertreffen und dem herrschenden Kontakt zwischen dem Bohrstrang und der Innenwand des Futterrohrs, tritt eine stärkere Abnutzung des Materials auf seitens des Futterrohrs, besonders an den Verbindungsstellen des Bohrstrangs, auf. Dies führt entweder zu einem ungleichmäßigen kreisförmigen Schwund der Futterrohr-Stärke, oder bei längerem kontinuierlichem Kontakt gegen die Innenwand des Futterrohrs, zu einer punktuellen tiefen Verschleißrille. Ein dynamischer Futterrohr-Verschleiß ist aufgrund der Vielzahl von Variablen, ein komplexes, zu simulierendes Phänomen, da die Variablen das nicht-lineare Verschleißverhalten beeinflussen. Die Verschleißintensität wird durch individuelle Kombinationen von Betriebsbelastungen (verändernde Kraft-Flächen-Verteilungen) beeinflusst. Metallurgische Eigenschaften (Werkstoffhärte und Streckgrenze) und vorherrschende tribologische Mechanismen (Oberflächenrauheit, Reibungsfaktoren und Verschleißart) kommen zum Tragen. Damit Verschleißsimulationsgleichungen möglichst genau sind, müssen die erforderlichen Reibungs- und Verschleißfaktoren experimentell durch umfangreiche Versuche bestimmt werden. Um diese Anforderung zu erfüllen, besteht der erste Schritt dieser Forschung darin, vorhandene mathematische Modelle und konventionelle Bohrlochplanungssoftwares zu analysieren. Daraus folgt die Bestimmung von Parametern, die für ein umfassendes Verschleißtestverfahren in Bezug auf variable Seitenkraft, Bohrstrangdrehzahl, axiale Bewegung und Fluidtyp, notwendig sind. Der zweite Schritt und somit der Fokus dieser Studie ist die Planung, Konstruktion und Anwendung eines vollwertigen Verschleißmodels zur Reproduktion von Reibungs- und Verschleißfaktoren unter Feldbedingungen. Die entwickelte Verschleißanlage ermöglicht Abrieb verschiedener Futterrohr-Materialien unter diversen Betriebslasten und verschieden Arten von Schmiermitteln zu simulieren. Unter Anwendung nahezu gleicher Betriebslasten, wurden die Verschleißprozesse für Stahl-, Glasfaser- und Kohlefaser-Futterrohrs untereinander verglichen. Der Umfang der Testreihe beinhaltete bislang 14 Tests unter Anwendung eines Schlammbasierenden Schmiermittels oder Wasser. Vergleiche zwischen den erbrachten Ergebnissen, in Bezug auf Reibungs- und Verschleißfaktoren, zeigen eine starke Ähnlichkeit und stehen somit im Einklang mit früheren experimentellen Studien. Nachdem der höchste Verschleißfaktor binnen kurzer Testdauer erreicht wurde, ist zu beobachten, dass dieser nach einer Verringerung des Druckkontaktes drastisch und stetig sinkt. Dieses Phänomen ist bei allen getesteten Materialien unter bestimmten Betriebslasten und Bohrszenarien zu verzeichnen und kann als einen genaueren Richtwert für Feld Ereignisse genutzt werden. Des Weiteren können mithilfe der gemessenen Werte des Reibungsfaktors, die Spanne der Belastungsdruckgrenze (Contact Pressure Threshold) an den Futterrohren genauer bestimmt werden. Eine Verschleißvolumengleichung, basierend auf den experimentellen Ergebnissen, wird als Teil der Testergebnisse präsentiert. Anhand der ermittelten Werte sowie Trends der Versuchsergebnisse, können Vorhersagen, bezüglich des Verschleißes, getroffen werden. Indem weiterer Stahlsorten, Durchmesser und neue Futterrohr-Materialien in den experimentellen Umfang einbezogen werden, kann die Anlage unter praxisnahen Bedingungen Verschleißverhalten darstellen. Hersteller für Futterrohre und Verbindungen profitieren von diesen Simulationen, um genauere Werte bei ihrer Herstellungsweise integrieren zu können. Durch die Anlage simulierten Feld Bedingungen, können die Werte der Belastungsdruckgrenzen (Contact Pressure Threshold) und Verschleißfaktoren erlangt werden. Diese Werte werden benötigt, um eine empirische Daten Gliederung zu erstellen, welche daraufhin in eine Software integriert werden. Dies vermag den Futterrohr-Verschleiß im Voraus abzuwägen und zu reduzieren, aber ermöglicht gleichzeitig die Flexibilität bei Betriebslasten beizubehalten.In the race to push the limits of hydrocarbon and geothermal production (and storage), deep inclined, horizontal, and ERD wells have become the drilling norm. In building such well trajectories, processes such as drilling, reaming, rotation off-bottom and tripping always affect casing wall thickness due to its interaction with the drillstring (particularly at the tool joints) under high contact forces. This results in either an uneven circumferential thickness reduction or, in case of long continuous contact of a tense drillstring pressed against the casing inner wall, a localized deep wear-groove. Localized dynamic casing wear is a complex phenomenon to simulate due to the number of control variables influencing the non-linear wear behavior. Wear intensity is influenced by individual combinations of service loads (changing force-area distributions), metallurgical properties (material hardness and yield strength) and prevailing tribological mechanisms (surface roughness, friction factors and wear type). For wear simulation models to be accurate, the required friction- and wear factors must be experimentally determined by full-scale tests. To fulfill this requisite, the first step of this research is to analyze existing mathematical models and conventional well planning software to establish parameters for a full-scale wear test method in terms of variable side force, drillstring RPM, axial reciprocation, and fluid type. The design, construction, and application of a full-scale wear frame to reproduce friction- and wear factors under field conditions is the second step, and the core focus of this study. The wear frame is designed to incorporate different casing materials under a range of operational loads and lubrication conditions, and wear scenarios under similar service loads have been compared for steel, fibered glass and fibered carbon casings. A total of 14 wear tests have been carried out in the study time-frame for the casing materials under water and mud lubrication conditions. A comparison of test results shows good consistency and agreement with previous experimental studies in terms of friction and wear factors. After the initial peak values, the wear factor is observed to decline drastically to a steady-drop range upon contact pressure reduction. For all tested materials, this steady range of values can provide a good estimate of field wear volume over time under particular service loads and drilling scenarios. Also, the measured steady values of friction factors help determine close ranges on contact pressure threshold for the casings. A proposed wear volume equation based on the experimental results is presented as a part of test results. It has been observed from the detected trends in the test results that repeated wear tests can make casing wear predictable. With the inclusion of more steel grades, diameters and new casing materials into the experimental scope, the wear frame can be used to develop a comprehensive record of wear performance under different field scenarios for casing and tool joint manufacturing industry. Attribution of wear factors and contact pressure thresholds to specific field conditions via a wear test database, and its integration into a software solution, can fill gaps to help reduce casing wear while retaining flexibility on operational loads