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

Caracterização geométrica e parametrização de depósitos transacionais recentes e sua aplicação na modelagem de reservatórios petrolíferos

In this beginning of the XXI century, the Geology moves for new ways that demand a capacity to work with different information and new tools. It is within this context that the analog characterization has important in the prediction and understanding the lateral changes in the geometry and facies distribution. In the present work was developed a methodology for integration the geological and geophysical data in transitional recent deposits, the modeling of petroliferous reservoirs, the volume calculation and the uncertainties associate with this volume. For this purpose it was carried planialtimetric and geophysics (Ground Penetrating Radar) surveys in three areas of the Parnaíba River. With this information, it was possible to visualize the overlap of different estuary channels and make the delimitation of the channel geometry (width and thickness). For three-dimensional visualization and modeling were used two of the main reservoirs modeling software. These studies were performed with the collected parameters and the data of two reservoirs. The first was created with the Potiguar Basin wells data existents in the literature and corresponding to Açu IV unit. In the second case was used a real database of the Northern Sea. In the procedures of reservoirs modeling different workflows were created and generated five study cases with their volume calculation. Afterwards an analysis was realized to quantify the uncertainties in the geological modeling and their influence in the volume. This analysis was oriented to test the generating see and the analogous data use in the model construction

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