The objective of this work is to develop a Bayesian Network (BN) model to produce environmental risk maps for oil and gas site developments and to demonstrate the model’s scalability from a point to a collection of points. To reach this objective, a benchmark BN model was formulated as a “proof of concept” using Aquifers, Ecoregions and Land Use / Land Cover maps as local and independent input variables. This model was then used to evaluate the probabilistic geographical distribution of the Environmental Sensibility of Oil and Gas (O&G) developments for a given study area. A Risk index associated with the development of O&G operation activities based on the spatial environmental sensibility was also mapped. To facilitate the Risk assessment, these input variables (maps) were discretized into three hazard levels: high, moderate and low. A Geographical Information System (GIS) platform was used (ESRI ArcMap 10), to gather, modify and display the data for the analysis. Once the variables were defined and the hazard data was included on feature classes (layer shapefile format), Python 2.6 software was used as the computational platform to calculate the probabilistic state of all the Bayesian Network’s variables. This allowed to define Risk scenarios both on prognostic and diagnostic analysis and to measure the impact of changes or interventions in terms of uncertainty. The resulting Python – ESRI ArcMap computational script was called “BN+GIS, which populated maps describing the spatial variability of the states of the Environmental Sensibility and of the corresponding Risk index. The latter in particular, represents a tool for decision makers to choose the most suitable location for placing a drilling rig, since it integrates three fundamental environmental variables. Also, results show that is possible to back propagate the information from the Environmental Sensibility to define the inherent triggering scenarios (hazard variables). A case of study is presented to illustrate the applicability of the proposed methodology on a specific geographical setting. The Barnett Shale was chosen as a benchmark study area because sufficient information on this region was available, and the importance that it holds on the latest developments of unconventional plays in the country. The main contribution of this work relies in combining Bayesian Networks and GIS to define environmental Risk scenarios that can facilitate decision-making for O&G stakeholders such as land owners, industry operators, regulators and Non-Governmental Organizations (NGOs), before and during the development of a given site.

Submitted by Secretaria Pós de Produção (tpp@vm.uff.br) on 2017-07-27T19:54:42Z No. of bitstreams: 1 D2016 - Joel Weisz.pdf: 1307010 bytes, checksum: 3fa5137c9ae34e324c34308d8c921c00 (MD5) / Made available in DSpace on 2017-07-27T19:54:42Z (GMT). No. of bitstreams: 1 D2016 - Joel Weisz.pdf: 1307010 bytes, checksum: 3fa5137c9ae34e324c34308d8c921c00 (MD5) / As sucessivas políticas industriais brasileiras, no tocante a inovação tecnológica, desde os últimos anos do século XX, têm se fundamentado na constatação de que, enquanto a produção científica da academia é relativamente alta, o desempenho em inovação tecnológica nos setores produtivos é baixo. Como decorrência, as políticas de inovação formuladas desde então buscaram transformar o conhecimento disponível nas universidades e centros de pesquisa (ICTs) em inovação tecnológica. Em todas políticas industriais, formuladas desde então, tem havido uma ênfase na transferência de tecnologia. Essa foi também a política do Estado, promovendo investimentos vultosos nas ICTs para pesquisa e desenvolvimento relacionada ao setor de exploração e produção de petróleo e gás natural e sua cadeia produtiva (O&G), sobretudo na superação dos desafios tecnológicos representados pela exploração e produção em águas profundas, ultraprofundas e no pré-sal. Este trabalho, ao estudar o recurso a redes cooperativas de inovação tecnológica como instrumento de transferência de tecnologia, lança um olhar para a variável capital social na gestão das redes em contraposição a seu tratamento como uma estrutura insumo/produto. Como segunda contribuição, estudamse as políticas que fundamentaram esse modelo. O estudo conclui que, apesar da adoção de uma visão sistêmica na formulação de políticas, após 1999, uma visão ainda linear do processo de inovação tecnológica, que permeia a prática da interação empresa-ICT, responde por um desempenho insatisfatório desse mecanismo e traduz essa conclusão para proposições relativas a políticas públicas que tenham impacto sobre a estratégia das empresas na gestão da inovação. / Consecutive Brazilian industrial policies, since the late nineteen hundreds, have been based on the assumption, regarding technological innovation, that whereas science output from academia is relatively high, industry technological innovation performance remains rather low. Consequently, innovation policies designed since then have strived to convert knowledge available in universities and research centers into business. Every industrial policy since then has stressed technology transfer. This has also been the state policy in promoting high investment in universities and research centers for R&D on oil and gas exploration and exploitation and its industrial chain (O&G), especially in overcoming the technological challenges faced in deep-water, ultradeep and pre-salt operations. This thesis, upon studying the use of cooperative networks as a tool for technology transfer, investigates the role of the social capital variable in network management, as opposed to viewing networks as a mere input/output concern. A second contribution is na analysis of the policies on which that model was based. This study concludes that despite a systemic approach framework in policy design after 1999, a persistent mindset that still regards industry-university interaction in the framework of the linear model responds for an unsatisfactory performance of this mechanism and translates this conclusion into public policy propositions that interfere strategic management of innovation in industry.