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Description of fractured reservoir rock under uncertainties in a 3D model and integration with reservoir simulation is still a challenging topic. In particular, mapping the potential zones with a reservoir quality can be very useful for making decisions and support development planning. This mapping can be done through the concept of flow units. In this paper, an integrated approach including a Hierarchical Cluster Analysis (HCA), geostatistical modeling and uncertainty analysis is developed and applied to a fractured carbonate in order to integrate on numerical simulation. The workflow begins with different HCA methods, performed to well-logs in three wells, to identify flow units and rock types. Geostatistical techniques are then applied to extend the flow units, petrophysical properties and fractures into the inter-well area. Finally, uncertainty analysis is applied to combine different types of uncertainties for generating ensemble reservoir simulation models. The obtained clusters from different HCA methods are evaluated by the cophenetic coefficient, correlation coefficient, and variation coefficient, and the most appropriate clustering method is used to identify flow units for geostatistical modeling. We subsequently define uncertainties for static and dynamic properties such as permeability, porosity, net-to-gross, fracture, water-relative permeability, fluid properties, and rock compressibility. Discretized Latin Hypercube with Geostatistical (DLHG) method is applied to combine the defined uncertainties and create an ensemble of 200 simulation models which can span the uncertainty space. Eventually, a base production strategy is defined under operational conditions to check the consistency and reliability of the models created with UNISIM-II-R (reference model) as a real reservoir with known results. Results represent the compatibility of the methodology to characterize fractured reservoirs since those models are consistent with the reference model (used to generate the simulation models). The proposed workflow provides an efficient and useful means of supporting development planning under uncertainty.

Super-giant carbonate fields, such as Ghawar, in Saudi Arabia, and Lula, at the Brazilian pre-salt, show highly heterogeneous behavior that is linked to high permeability intervals in thin layers. This article applies Local Grid Refinements (LGR) integrated with upscaling procedures to improve the representation of highly laminated reservoirs in flow simulation by preserving the static properties and dynamic trends from geological model. This work was developed in five main steps: (1) define a conventional coarse grid, (2) define LGR in the conventional coarse grid according to super-k and well locations, (3) apply an upscaling procedure for all scenarios, (4) define LGR directly in the simulation model, without integrate geological trends in LGR and (5) compare the dynamic response for all cases. To check results and compare upscaling matches, was used the benchmark model UNISIM-II-R, a refined model based on a combination of Brazilian Pre-salt and Ghawar field information. The main results show that the upscaling of geological models for coarse grid with LGR in highly permeable thin layers provides a close dynamic representation of geological characterization compared to conventional coarse grid and LGR only near-wells. Pseudo-relative permeability curves should be considered for (a) conventional coarse grid or (b) LGR scenarios under dual-medium flow simulations as the upscaling of discrete fracture networks and dual-medium flow models presents several limitations. The conventional approach of LGR directly in simulation model, presents worse results than LGR integrated with upscaling procedures as the extrapolation of dynamic properties to the coarse block mismatch the dynamic behavior from geological characterization. This work suggests further improvements for results for upscaling procedures that mask the flow behavior in highly laminated reservoirs.

The link between the geostatistical modeling of multiscale carbonate heterogeneities and representative simulation flow models remains a challenge because conventional upscaling procedures often disregard the complex dynamic behavior that links the geostatistical static properties, dynamic rock/fluid data and reservoir operational conditions. This work proposes a methodology to build robust simulation models for naturally fractured carbonate reservoirs with multiscale geological characterizations. The development of this work follows three main steps: (1) hierarchical upscaling procedure by flow units, (2) integration of flow units into a reservoir-scale simulation model, (3) validation of simulation model. The hierarchical upscaling procedure was applied to three inter-well regions and extrapolated to three flow units. The proposed workflow is applied to a reservoir based on a combination of real and synthetic data from a Brazilian offshore carbonate reservoir. The methodology shows several advantages: it represents static and dynamic behavior from multiscale heterogeneities in reservoir simulation; it minimizes risk in the selection of well position and completion through well characterization of representative static and dynamic data for each flow unit; and, improved selection process for the simulation flow model. The methodology shows that the relative permeability defined in reservoir simulation can be different from the laboratory measured curve because it needs to match the dynamic behavior from the reference solution. This result shows that a proper characterization and upscaling approach are crucial to sufficiently represent geological heterogeneous scenarios in reservoir simulation. The methodology we present here is useful for multidisciplinary areas of expertise as it ensures the appropriate link between the fine scale geomodelling and the coarser scale reservoir simulation, considering the development of complex carbonate reservoirs.

There is a gap in ensemble-based techniques for generating prior models suitable for data assimilation, while maintaining geological consistency and properly assessing and mitigating uncertainties. This work proposes a semi-automated approach to build an ensemble of prior models calibrated with the observed production data while maintaining geological consistency. The methodology consists of: (1) generating the first ensemble of models (base ensemble)—not calibrated with observed production data—with enough variability and encompassing the observed production data, (2) re-parameterizing uncertainties by combining the normalized quadratic distance with sign (NQDS) indicator with Gaussian kernel density estimation (KDE) and (3) iterating the previous step until a considerable number of approved models (models within the confidence interval for the NQDS of each objective function) are achieved within the full ensemble of models. The methodology is applied to a giant Brazilian pre-salt field. The base ensemble consists of 200 models combining static and dynamic uncertainties and reproducing models with enough variability to encompass the observed production data. After two iterations, by combining the NQDS indicator with Gaussian KDE, an increasing of 181% of models calibrated with observed production data were achieved, when compared to the base case. Therefore, a much higher number of accurate models were obtained by rebuilding the probabilistic distribution functions (PDFs) for each uncertain variable based on observed production data. The mean permeability showed the greatest improvement as the well-log-derived permeability was based on empirical correlations with pore size. Based on a multidisciplinary effort, this work successfully improved the accuracy of prior models.

The market for interactive platforms is rapidly growing, and touchscreens have been incorporated in an increasing number of devices. Thus, the area of smart objects and devices is strongly increasing by adding interactive touch and multimedia content, leading to new uses and capabilities. In this work, a flexible screen printed sensor matrix is fabricated based on silver ink in a polyethylene terephthalate (PET) substrate. Diamond shaped capacitive electrodes coupled with conventional capacitive reading electronics enables fabrication of a highly functional capacitive touchpad, and also allows for the identification of marked objects. For the latter, the capacitive signatures are identified by intersecting points and distances between them. Thus, this work demonstrates the applicability of a low cost method using royalty-free geometries and technologies for the development of flexible multitouch touchpads for the implementation of interactive and object recognition applications.

The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

In the last decades there has been an increase in sensing systems applied in a variety of situations with a large variety of sensor ranges. This represents a growing area with high potential. One of the areas of sensor development that require a great deal of attention is the area of sensor for biomedical applications and biosensors. These sensors have to overcome a number of challenges and limitations inherent to the environment where they are introduced. These difficulties lead to the necessity of using new materials and new techniques for their construction together with the more traditional materials, e.g. silicon based, which have already proven their potential in this area. Among the various materials, polymers have proven to be a good choice, due to a set of advantages such as simple processing, flexibility and facility of being obtained in different shapes. Therefore it is interesting to fabricate polymer based piezoresistive sensors for functional coatings of implantable hip prosthesis. These sensors will allow coating the prosthesis and provide new functionalities to the implants such as the possibility to measure forces and deformations between the prosthesis and the bone and therefore improving the postoperative diagnostic. In this works, a model of hip prosthesis with coated sensors was developed. For this purpose, flexible piezoresistive sensors have been developed that allow being implanted. Strain sensors were fabricated based on thin films of n+-nc-si.H by the technique of hot-wire chemical vapor deposition at a temperature of 150 ºC on a polymeric substrate, using the lithographic technique to construct the various layers of the sensors. The sensor has a gauge factor of -28 for low frequency deformation cycles. In the last decades there has been an increase in sensing systems applied in a variety of situations with a large variety of sensor ranges. This represents a growing area with high potential. One of the areas of sensor development that require a great deal of attention is the area of sensor for biomedical applications and biosensors. These sensors have to overcome a number of challenges and limitations inherent to the environment where they are introduced. These difficulties lead to the necessity of using new materials and new techniques for their construction together with the more traditional materials, e.g. silicon based, which have already proven their potential in this area. Among the various materials, polymers have proven to be a good choice, due to a set of advantages such as simple processing, flexibility and facility of being obtained in different shapes. Therefore it is interesting to fabricate polymer based piezoresistive sensors for functional coatings of implantable hip prosthesis. These sensors will allow coating the prosthesis and provide new functionalities to the implants such as the possibility to measure forces and deformations between the prosthesis and the bone and therefore improving the postoperative diagnostic. In this works, a model of hip prosthesis with coated sensors was developed. For this purpose, flexible piezoresistive sensors have been developed that allow being implanted. (Abstract shortened by ProQuest.).

A região de Serra Leste, inserida na Província Mineral de Carajás, Brasil, compreende diversos contextos geológicos relacionados a uma grande variedade de depósitos e ocorrências minerais, dando sustento a diversos estudos de prospecção (geológico, geofísico e sensoriamento remoto), desenvolvendo a pesquisa científica e apoiando empresas actuantes na região. Como auxílio ao campo da prospecção geofísica, os dados magnéticos têm sido genericamente usados para determinar a profundidade de topo das fontes magnéticas, sendo útil para diversas áreas de actuação desde a exploração de hidrocarbonetos, exploração mineral e resolução de problemas ambientais. No âmbito de fornecer uma análise mais eficaz no cálculo e interpretação de profundidades magnéticas, o objetivo deste trabalho é avaliar quatro métodos de estimativa de profundidades: (i) Deconvolução de Euler; (ii) Sinal Analítico; (iii) Gradiente Horizontal; e (iv) Número de Onda, através da aplicação aos dados aeromagnéticos da região de Serra Leste, fornecidos com o préprocessamento. No levantamento de dados, é usado um aeromagnetômetro CS-2 de vapor de césio, de precisão de 0,01 nT e um GPS da Trimble para controle da trajectoria, de precisão entre 5 a 10 m. A direcção das linhas de vôo é NS e as de controle EW, com espaçamento de 250 e 6000 metros, respectivamente. Com o auxílio simultâneo do software OASIS-Montaj 6.3, da GEOSOFT e Potential-Field, versão 2.2, foram processados mapas de soluções da distribuição das fontes magnéticas e suas profundidades, e tabelas referentes à distribuição das soluções em profundidade e erros associados, para cada método de estimativa.O método do gradiente horizontal apresenta alguma coerência na localização das fontes magnéticas, mas pouca consistência na estimativa das profundidades devido às premissas deste método que não são totalmente satisfeitas. As soluções para o número de onda local, a nível regional, mostram-se correlacionáveis com as estruturas geológicas com magnetismo remanescente, porém a nível local, especificamente na localização dos depósitos, não apresentam nenhuma evidência associada a magnetismo. Na estimativa da profundidade, aparenta ser bastante sensível à condição de espessura infinita da fonte magnética que é uma das premissas do método. O método do sinal analítico apresenta os melhores resultados na localização das fontes magnéticas e o método da deconvolução de Euler apresenta os melhores resultados na estimativa das profundidades. De uma maneira geral, é possível concluir que estes dois últimos métodos citados, portanto, produzem soluções mais estáveis e geologicamente plausíveis, sendo os mais recomendados para serem utilizados de forma integrada em estudos desta natureza.

Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster.

Brown macroalgae are an important source of polysaccharides, mainly fucose-containing sulphated polysaccharides (FCSPs), associated with several biological activities. However, the structural diversity and structure–function relationships for their bioactivities are still undisclosed. Thus, the aim of this work was to characterize the chemical structure of water-soluble Saccharina latissima polysaccharides and evaluate their immunostimulatory and hypocholesterolemic activities, helping to pinpoint a structure–activity relationship. Alginate, laminarans (F1, neutral glucose-rich polysaccharides), and two fractions (F2 and F3) of FCSPs (negatively charged) were studied. Whereas F2 is rich in uronic acids (45 mol%) and fucose (29 mol%), F3 is rich in fucose (59 mol%) and galactose (21 mol%). These two fractions of FCSPs showed immunostimulatory activity on B lymphocytes, which could be associated with the presence of sulphate groups. Only F2 exhibited a significant effect in reductions in in vitro cholesterol’s bioaccessibility attributed to the sequestration of bile salts. Therefore, S. latissima FCSPs were shown to have potential as immunostimulatory and hypocholesterolemic functional ingredients, where their content in uronic acids and sulphation seem to be relevant for the bioactive and healthy properties.