Modelo de predicción y cuantificación de la producción de arena en yacimientos de crudo pesado

En este trabajo se presenta inicialmente la revisión bibliográfica de los modelos asociados a la producción de arena en yacimientos de crudo pesado, a partir de esto se construye un modelo de producción de arena compuesto de un módulo de flujo de fluidos, un módulo de geomecánica y un módulo de producción de arena. Posteriormente se realiza el estudio y modelamiento de una prueba experimental de producción de arena a escala laboratorio. Durante el modelamiento se hace un ajuste de parámetros con el fin de reproducir el comportamiento mecánico y la producción de arena obtenidos de forma experimental. El ajuste encontrado con el modelo de simulación presenta un error cercano al 4% comparado con el caso real. Finalmente, se muestra que para el modelamiento del comportamiento de las formaciones poco consolidadas es indispensable el uso de modelos constitutivos de esfuerzo deformación de tipo elastoplástico. Además se encuentra que la producción de arena depende especialmente de la deformación plástica del material, la velocidad de flujo y del área afectada.Abstract: In this paper a literature review of the models associated with sand production in heavy oil reservoirs is initially presented, from this a sand production model is built. This model consists of a fluid flow module, a geomechanics module and a sand production module. Subsequently the study and modeling of a laboratory scale sand production test is performed. During the modeling, an adjustment of parameters in order to reproduce the mechanical behavior and the sand production is made. The results found with the simulation model presented an error close to 5% compared to the actual case. Finally it is showed that for modeling the behavior of poorly consolidated formations the use of elastoplastic stress-strain constitutive models is indispensable. It is also found that sand production is especially dependent on the plastic deformation of the material, the flow rate and the affected area.Maestrí

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq