Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

Análise não-linear do campo de temperaturas desenvolvidas durante a soldagem do tipo TIG contínuo em placas delgadas

This study offers an analytical approach in order to provide a determination of the temperature field developed during the DC TIG welding of a thin plate of aluminum. The non-linear characteristics of the phenomenon, such as the dependence of the thermophysical and mechanical properties with temperature were considered in this study. In addition to the conductive heat exchange process, were taken into account the exchange by natural convection and radiation. A transient analysis is performed in order to obtain the temperature field as a function of time. It is also discussed a three-dimensional modeling of the heat source. The results obtained from the analytical model were be compared with the experimental ones and those available in the literature. The analytical results show a good correlation with the experimental ones available in the literature, thus proving the feasibility and efficiency of the analytical method for the simulation of the heat cycle for this welding process.

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities