Systems thinking and simulation to help IT/Software professionals to visualize knowledge assets evolution according to digital solutions implementation

Mención Internacional en el título de doctorThis doctoral thesis presents the SIPAC-framework, a methodological proposal created to systemically guide and help software engineers and information technology professionals in the process of proposing a customized technological solution, specifically oriented to propose software or IT solutions that provides business value supported on the status of intangible knowledge assets of organizations, and from this, drive the achievement of the strategic goals that define the organizational operation. To achieve this, the SIPAC-framework comprises three layers clearly differentiated but intimately interrelated and co-dependent on each other: a methodological layer, a mechanisms layer and a technological layer inclusive of the technological artifacts to be used. 1. The methodological layer comprises the SIPAC methodology itself, inspired by Peter Checkland's soft systems approach, but adapted to, from an engineering point of view, addressing the situation given by the underlying knowledge of an organization, which it is usually unstructured and disordered, and whose understanding fits to be addressed as a complex problem. The SIPAC-framework guides the professional in the process of identifying such knowledge, structuring it in knowledge assets, organizing such assets according to the identity of the organization, characterizing them according to their quality and the impact they have in achieving the strategic objectives, exploiting them to propose an appropriate technological solution and envisaging possible future scenarios based on what can happen to them as a consequence of the decision making about the technological solution to be implemented. 2. The mechanisms layer comprises the constructs necessary to be able to carry out the subjacent activities of the methodological layer, mainly a model of identification and valuation of intangible knowledge assets, a model of characterization of the assets according to their quality and impact, a Markovian model of prediction of the re-characterization of intangible knowledge assets, and an instance-based learning model implementation of decisions on the implementation of technological solutions. 3. The technological layer constitutes the artifacts to be used during the deployment of the methodology to support its methodological processes. In detail, this layer presents an instrument for collecting information on the knowledge of a company and its structuring into knowledge assets, a web application for the management of such information through a database, an agent-based model that implements both the automatic characterization of the knowledge assets from the information stored in the database, as well as the simulation and prediction of the behavior of said assets as a product of the decisions made regarding technological implementations. The SIPAC framework has been used in a total of 11 small and medium enterprises, by means of teams of 2-4 software engineers each, who have been in charge of doing the deployment in two different time stages: an initial audit carried out in the pre-project phase and prior to the decision of technological implementation; and an audit carried out after the implementation of the technological solution. The interaction of said professionals with the interested parties by the companies (stakeholders) has been discontinuous, limited to specific audits, interviews and validations on the information and models built. This work has derived in the methodological proposal that constitutes the SIPAC-framework, with its mechanisms and technological artefacts, and whose impact can be evidenced in several aspects: • The effective elicitation and characterization of organizational knowledge of the participating companies. • The success of the goals-aligned digital solution implementation proposals, which is evidenced by the improvement in organizational knowledge assets’ state. • The effective predictive power of the SIPAC-framework’s simulation module. • The satisfaction of software engineers and IT professionals by both the process of d • The improvement of the profession of software engineers and professionals of information and communication technologies, by providing them with an innovative approach that leads them to demonstrate to their clients the knowledge they have, in what state they are, how they can improve and what can happen if they decide to improve it. • The emergence of organizational information that is traditionally hidden and incomprehensible, usually reserved for its management by expensive consultants and the experience of a few; all at a minimum cost, maximizing the visualization of the information and minimizing the complexity of its interpretation. • This thesis is a starting point for the development of the body of knowledge on the valuation of knowledge assets in technological environments as a tool to achieve the strategic goal of an organization. In addition, this work leaves open the way for the future development of decision-making models based on value, as well as the evolution of the presented model, ideally in a single patentable technological device.eploying the methodology and the results obtained.Esta tesis doctoral presenta SIPAC-framework, una propuesta metodológica creada para sistémicamente guiar y ayudar a los ingenieros de software y profesionales de las tecnologías de la información en el proceso de proponer una solución tecnológica customizada, orientada a proporcionar valor a las organizaciones y soportada en los activos intangibles de conocimiento de las organizaciones, de manera que se pueda, a partir de esto, impulsar la consecución de los objetivos estratégicos que dirigen su funcionamiento. Para conseguir esto, el SIPAC-framework comprende tres capas claramente diferenciadas, pero íntimamente interrelacionadas y codependientes entre sí: una capa metodológica, una capa de mecanismos y una capa tecnológica o de artefactos tecnológicos de soporte a ser usados. 1. La capa metodológica comprende la metodología SIPAC en sí misma, inspirada en el enfoque de sistemas blandos de Peter Checkland, pero adaptada a, desde un punto de vista ingenieril, abordar la situación dada por el conocimiento subyacente en una organización, el cual usualmente está desestructurado y desordenado, y cuya comprensión debe ser abordada como un problema complejo. SIPAC-framework guía al profesional en el proceso de identificar tal conocimiento, estructurarlo en activos de conocimiento, organizarlos en función de la identidad de la organización, caracterizarlos en función de su calidad y el impacto que estos tienen en la consecución de los objetivos estratégicos, explotarlos para proponer una adecuada solución tecnológica y visualizar posibles escenarios futuros en función de lo que puede pasar con ellos como consecuencia de la toma de decisiones sobre la solución tecnológica a implementar. 2. La capa de mecanismos comprende los constructos conceptuales necesarios para poder llevar a cabo las actividades de la capa metodológica, principalmente un modelo de identificación y valoración de activos intangibles de conocimiento, un modelo de caracterización de los activos en función de su calidad e impacto, un modelo markoviano de predicción de la re-caracterización de activos intangibles de conocimiento, y una implementación del modelo basado en instancias (IBL-model) sobre las decisiones estratégicas con respecto a la implementación de soluciones tecnológicas. 3. La capa tecnológica se constituye por los artefactos utilizados durante el despliegue de la metodología para soportar sus procesos. En detalle, esta capa presenta un instrumento de recolección de información sobre el conocimiento de una empresa y su estructuración en activos de conocimiento, un aplicativo web para la gestión de dicha información por medio de una base de datos, un modelo basado en agentes que implementa tanto la caracterización automática de los activos de conocimiento a partir de la información almacenada en la base de datos, como la simulación y predicción del comportamiento de dichos activos como producto de las decisiones de implementación tecnológica tomadas. El SIPAC-framework se ha usado en un total de 11 pequeñas y medianas empresas, por medio de equipos de entre 2 y 4 profesionales de la ingeniería del software cada uno, que han estado a cargo de hacer el despliegue metodológico en dos estadios de tiempo diferentes: una auditoría inicial llevada a cabo en la fase de pre-proyecto y con anterioridad a la decisión de implementación tecnológica; y una auditoría llevada a cabo con posterioridad a la implementación de la solución tecnológica. La interacción de dichos profesionales con los interesados por parte de las empresas ha sido discontinua, limitándose a auditorías concretas, entrevistas y validaciones sobre la información y modelos construidos. Este trabajo ha derivado en la propuesta metodológica que constituye el SIPAC-framework, con sus mecanismos y artefactos tecnológicos, y cuyo impacto se puede ver en varios aspectos: • La elicitación y caracterización efectiva del conocimiento organizativo de las empresas participantes. • El éxito que han tenido las propuestas de implementación de solución tecnológica alineadas con los objetivos, lo que se evidencia por la mejora en el estado de los activos organizativos de conocimiento. • El efectivo poder predictivo del módulo de simulación del SIPAC-framework. • La satisfacción de los ingenieros de software y los profesionales de TI, tanto por el proceso de implementación de la metodología como por los resultados obtenidos. • La mejora de la profesión de los ingenieros de software y profesionales de las tecnologías de la información y la comunicación, al dotarles de un enfoque innovador que les conduce a evidenciar ante sus clientes el conocimiento que tienen, en qué estado se encuentra, cómo lo pueden mejorar y lo que puede ocurrir si deciden mejorarlo. • La emergencia de información organizativa que tradicionalmente está oculta e incomprensible, usualmente reservada a costosas consultoras y a la experiencia de unos pocos; todo a un coste mínimo, maximizando la visualización de la información y minimizando la complejidad de su interpretación. Esta tesis es un punto de partida para el desarrollo de la base de conocimiento sobre la valoración de activos de conocimiento en entornos tecnológicos como herramienta para conseguir los objetivos estratégicos de una organización. Además, este trabajo deja abierto el camino para el futuro desarrollo de modelos de toma de decisiones basados en el valor, así como la evolución del modelo presentado, idealmente en un solo artefacto tecnológico patentable.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Raúl Álvaro Espejo Ballivian.- Secretario: José María Álvarez Rodríguez.- Vocal: Stefano Armeni

Subsea fluid sampling to maximise production asset in offshore field development

The acquisition of representative subsea fluid sampling from offshore field development asset is crucial for the correct evaluation of oil reserves and for the design of subsea production facilities. Due to rising operational expenditures, operators and manufacturers have been working hard to provide systems to enable cost effective subsea fluid sampling solutions. To achieve this, any system has to collect sufficient sample volumes to ensure statistically valid characterisation of the sampled fluids. In executing the research project, various subsea sampling methods used in the offshore industry were examined and ranked using multi criteria decision making; a solution using a remote operated vehicle was selected as the preferred method, to compliment the subsea multiphase flowmeter capability, used to provide well diagnostics to measure individual phases – oil, gas, and water. A mechanistic (compositional fluid tracking) model is employed, using the fluid properties that are equivalent to the production flow stream being measured, to predict reliable reservoir fluid characteristics on the subsea production system. This is applicable even under conditions where significant variations in the reservoir fluid composition occur in transient production operations. The model also adds value in the decision to employ subsea processing in managing water breakthrough as the field matures. This can be achieved through efficient processing of the fluid with separation and boosting delivered to the topside facilities or for water re-injection to the reservoir. The combination of multiphase flowmeter, remote operated vehicle deployed fluid sampling and the mechanistic model provides a balanced approach to reservoir performance monitoring. Therefore, regular and systematic field tailored application of subsea fluid sampling should provide detailed understanding on formation fluid, a basis for accurate prediction of reservoir fluid characteristic, to maximize well production in offshore field development

Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments

In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance

UNCERTAINTY QUANTIFICATION OF PETROLEUM RESERVOIRS: A STRUCTURED BAYESIAN APPROACH

This thesis proposes a systematic Bayesian approach for uncertainty quantification with an application for petroleum reservoirs. First, we demonstrated the potential of additional misfit functions based on specific events in reservoir management, to gain knowledge about reservoir behaviour and quality in probabilistic forecasting. Water breakthrough and productivity deviation were selected and provided insights of discontinuities in simulation data when compared to the use of traditional misfit functions (e.g. production rate, BHP) alone. Second, we designed and implemented a systematic methodology for uncertainty reduction combining reservoir simulation and emulation techniques under the Bayesian History Matching for Uncertainty Reduction (BHMUR) approach. Flexibility, repeatability and scalability are the main features of this high-level structure, incorporating innovations such as phases of evaluation and multiple emulation techniques. This workflow potentially turns the practice of BHMUR more standardised across applications. It was applied for a complex case study, with 26 uncertainties, outputs from 25 wells and 11+ years of historical data based on a hypothetical reality, resulting in the construction of 115 valid emulators and a small fraction of the original search space appropriately considered non-implausible by the end of the uncertainty reduction process. Third, we expanded methodologies for critical steps in the BHMUR practice: (1) extension of statistical formulation to two-class emulators; (2) efficient selection of a combination of outputs to emulate; (3) validation of emulators based on multiple criteria; and (4) accounting for systematic and random errors in observed data. Finally, a critical step in the BHMUR approach is the quantification of model discrepancy which accounts for imperfect models aiming to represent a real physical system. We proposed a methodology to quantify the model discrepancy originated from errors in target data that are set as boundary conditions in a numerical simulator. Its application demonstrated that model discrepancy is dependent on both time and location in the input space, which is a central finding to guide the BHMUR practice in case of studies based on real fields

Quantificação de incertezas em reservatórios de petróleo : uma abordagem Bayesiana estruturada

Orientadores: Denis José Schiozer e Camila Caiado; Ian Vernon; Michael Goldstein, Guilherme Daniel AvansiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Durham UniversityResumo: Essa tese propõe uma abordagem Bayesiana sistemática para quantificação de incertezas de reservatórios de petróleo. No primeiro artigo, demonstramos o potencial de funções-objetivo adicionais que são baseadas em eventos específicos da fase de gerenciamento de reservatórios, a fim de melhorar a representação do comportamento do reservatório e a qualidade da previsão probabilística. Irrupção de água e desvio de produtividade foram selecionados, proporcionando um entendimento de descontinuidades no modelo numérico e nos dados de simulação quando comparado com o uso exclusivo de funções objetivo tradicionais (por exemplo, taxa de produção). No segundo artigo, definimos e implementados uma metodologia sistemática para redução de incertezas que combina simulação de reservatórios e técnicas de emulação em uma abordagem de Ajuste de Histórico Bayesiano para Redução de Incertezas (BHMUR, Bayesian History Matching for Uncertainty Reduction, acrônimo em inglês). Flexibilidade, repetitividade e escalabilidade são as características principais dessa estrutura geral que incorpora inovações tais como fases de avaliação e múltiplas técnicas de emulação. Esse procedimento potencialmente transforma a prática de BHMUR em uma mais padronizada para diversas aplicações. Aplicamos em um estudo de caso com 26 atributos incertos, dados de produção de 25 poços e 11+ anos de dados de histórico de produção baseado em uma realidade hipotética, resultando na construção de 115 emuladores validados e uma pequena fração do espaço de busca apropriadamente considerada não-implausível ao final do processo de redução de incertezas. No terceiro artigo, expandimos metodologias para estágios críticos na prática de BHMUR: (1) extensão da formulação estatística de BHMUR para acomodar emuladores do tipo classificadores; (2) seleção efetiva de uma combinação de dados de produção para emulação; (3) validação de emuladores baseados em múltiplos critérios; e (4) consideração de erros sistemáticos e aleatórios em dados observados. No último artigo, avaliamos um passo crítico para a prática de BHMUR, que é a quantificação de discrepância do modelo para contabilizar a representação de sistemas físicos a partir de modelos imperfeitos. Propusemos uma metodologia para quantificar a discrepância do modelo originada em erros de dados medidos e informados ao simulador numérico como condição de contorno (target). A aplicação da metodologia demonstrou que a discrepância do modelo é simultaneamente dependente de tempo e da posição no espaço de busca: uma descoberta importante para orientar o processo de quantificação de incertezas em estudos de caso baseados em reservatórios de petróleo reaisAbstract: This thesis proposes a systematic Bayesian approach for uncertainty quantification with an application for petroleum reservoirs. First, we demonstrated the potential of additional misfit functions based on specific events in reservoir management, to gain knowledge about reservoir behaviour and quality in probabilistic forecasting. Water breakthrough and productivity deviation were selected and provided insights of discontinuities in simulation data when compared to the use of traditional misfit functions (e.g. production rate, BHP) alone. Second, we designed and implemented a systematic methodology for uncertainty reduction combining reservoir simulation and emulation techniques under the Bayesian History Matching for Uncertainty Reduction (BHMUR) approach. Flexibility, repeatability and scalability are the main features of this high-level structure, incorporating innovations such as phases of evaluation and multiple emulation techniques. This workflow potentially turns the practice of BHMUR more standardised across applications. It was applied for a complex case study, with 26 uncertainties, outputs from 25 wells and 11+ years of historical data based on a hypothetical reality, resulting in the construction of 115 valid emulators and a small fraction of the original searching space appropriately considered non-implausible by the end of the uncertainty reduction process. Third, we expanded methodologies for critical steps in the BHMUR practice: (1) extension of statistical formulation to two-class emulators; (2) efficient selection of a combination of outputs to emulate; (3) validation of emulators based on multiple criteria; and (4) accounting for systematic and random errors in observed data. Finally, a critical step in the BHMUR approach is the quantification of model discrepancy which accounts for imperfect models aiming to represent a real physical system. We proposed a methodology to quantify the model discrepancy originated from errors in target data that are set as boundary conditions in a numerical simulator. Its application demonstrated that model discrepancy is dependent on both time and location in the input space, which is a central finding to guide the BHMUR practice in case of studies based on real fieldsDoutoradoReservatórios e GestãoDoutora em Ciências e Engenharia de Petróleo206985/2017-7CNPQFUNCAM

A holistic methodology for the non-destructive experimental characterization and reliability-based structural assessment of historical steel bridges

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGNowadays, several historical steel structures present damage and an advanced deterioration state induced by human or natural actions, causing fluctuations in geometrical, physical, and mechanical properties that dramatically affect their mechanical behavior. Due to the economic, cultural, and heritage value, these constructions must be comprehensively assessed to verify their current condition state. This work presents a holistic methodology aimed at the non-destructive experimental characterization and reliability-based structural assessment of historical steel bridges. It comprehends from the experimental data acquisition to the finite element model updating and the probabilistic-based structural assessment to obtain the reliability indexes of serviceability and ultimate limit states. Several sources of information are considered in the evaluation process, thus, results are more realistic and accurate and can be used for optimal decision-making related to maintenance and retrofitting actions. The feasibility of the methodology has been tested on O Barqueiro Bridge, an aging riveted bridge located in Galicia, Spain. The study first involved a comprehensive experimental campaign to characterize the bridge effectively at multiple levels: geometry, material, and structural system by the synergetic combination of different tools and methods: in-depth visual inspection, terrestrial laser scanner survey, ultrasonic testing, and ambient vibration test. Subsequently, a detailed FE model was developed and calibrated with an average relative error in frequencies of 2.04% and an average MAC value of 0.94. Finally, the reliability-based structural assessment was performed, yielding reliability indexes of 1.80 and 1.99 for the serviceability and ultimate limit states, respectively. Thus, the bridge could not withstand traffic loads with satisfactory structural performance in its current condition.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-095893-B-C21European Regional Development Fund | Ref. EAPA_826/201