Análise estratigráfica do Campo de Namorado (Bacia de Campos) com base na interpretação sísmica tridimensional

O presente trabalho analisa os reservatórios turbidíticos do Campo de Namorado, Bacia de Campos – RJ, com a apresentação de um novo modelo evolutivo para o intervalo entre o Albiano superior e Cenomaniano, na área do referido campo. As ferramentas utilizadas neste estudo consistiram da interpretação sísmica em ambiente tridimensional com o software VoxelGeo®, e da análise faciológica junto à perfilagem de poços do referido campo. A análise desenvolvida permitiu a individualização e a posterior visualização tridimensional de um paleocanal meandrante na base do intervalo estudado, feição esta até então não relatada em interpretações anteriores neste reservatório. Como resultado das análises sísmicas e faciológicas, foi possível elaborar um modelo deposicional, onde foram definidos quatro sistemas turbidíticos distintos, inclusos em duas seqüências de 3ª Ordem. Esses sistemas turbidíticos estariam, portanto, associados às seqüências de 4ª Ordem, que são interpretadas como parasseqüências, inseridas nos dois ciclos de 3ª Ordem. As seqüências de 3ª Ordem, que englobam os reservatórios do Campo de Namorado, representariam intervalos de alta freqüência no registro estratigráfico, dentro do contexto de afogamento (2ª Ordem) da Bacia de Campos. Pelas características da calha deposicional observada para o Campo de Namorado, é possível concluir que o sistema, como um todo, foi depositado em um complexo de canais, junto a sistemas de frentes deltaicas. Esses canais, provavelmente, foram esculpidos por fluxos hiperpicnais, formados a partir de inundações catastróficas. As informações provenientes deste estudo, proporcionaram uma melhor compreensão da gênese dos depósitos turbidíticos, acumuladores de hidrocarbonetos, no intervalo estudado, e cuja ocorrência está relacionada com etapas de rebaixamento relativo do nível do mar.

A BDI-based approach for the assessment of driver's decision-making in commuter scenarios

The rapid growth of urban areas has a significant impact on traffic and transportation systems. New management policies and planning strategies are clearly necessary to cope with the more than ever limited capacity of existing road networks. The concept of Intelligent Transportation System (ITS) arises in this scenario; rather than attempting to increase road capacity by means of physical modifications to the infrastructure, the premise of ITS relies on the use of advanced communication and computer technologies to handle today’s traffic and transportation facilities. Influencing users’ behaviour patterns is a challenge that has stimulated much research in the ITS field, where human factors start gaining great importance to modelling, simulating, and assessing such an innovative approach. This work is aimed at using Multi-agent Systems (MAS) to represent the traffic and transportation systems in the light of the new performance measures brought about by ITS technologies. Agent features have good potentialities to represent those components of a system that are geographically and functionally distributed, such as most components in traffic and transportation. A BDI (beliefs, desires, and intentions) architecture is presented as an alternative to traditional models used to represent the driver behaviour within microscopic simulation allowing for an explicit representation of users’ mental states. Basic concepts of ITS and MAS are presented, as well as some application examples related to the subject. This has motivated the extension of an existing microscopic simulation framework to incorporate MAS features to enhance the representation of drivers. This way demand is generated from a population of agents as the result of their decisions on route and departure time, on a daily basis. The extended simulation model that now supports the interaction of BDI driver agents was effectively implemented, and different experiments were performed to test this approach in commuter scenarios. MAS provides a process-driven approach that fosters the easy construction of modular, robust, and scalable models, characteristics that lack in former result-driven approaches. Its abstraction premises allow for a closer association between the model and its practical implementation. Uncertainty and variability are addressed in a straightforward manner, as an easier representation of humanlike behaviours within the driver structure is provided by cognitive architectures, such as the BDI approach used in this work. This way MAS extends microscopic simulation of traffic to better address the complexity inherent in ITS technologies.