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

Simulador de dano à formação, aplicado ao Projeto e à execução da perfuração de poços de petróleo

With the high oil price variability, the petroleum and the reservoir engineers are usually face to face on how they can evaluate the well performance and productivity. They can improve high productivity from the well construction to the secondary recoveries, but they have never tried a measurement in the drilling operations about the lower productivity index. As a rule, frequently the drilling operations hear from the reservoir engineering and geology that, if there is a formation damage, probably some drilling operations practices were not done properly or the good practice in petroleum engineering or mud engineering were not observed. The study in this working search is an attempt of how to measure a formation damage just from the project drilling to the drilling operations, with datum from the fields in Brazilian northeast and putting into practice a Simulator developed from the modeling on the theory offered by different experts and sources in formation damage

Avaliação tribológica dos polímeros NBR, PTFE e PTFE gravitado em contato com aço AISI 52100

Low cost seals are made of NBR, Nitrile Butadiene Rubber, a family of unsaturated copolymers that is higher resistant to oils the more content of nitrile have in its composition, although lower its flexibility. In Petroleum Engineering, NBR seal wear can cause fluid leakage and environmental damages, promoting an increasing demand for academic knowledge about polymeric materials candidate to seals submitted to sliding contacts to metal surfaces. This investigation aimed to evaluate tribological responses of a commercial NBR, hardness 73 ± 5 Sh A, polytetrafluoroethylene (PTFE), hardness 60 ± 4 HRE and PTFE with graphite, 68 ± 6 HRE. The testings were performed on a sliding tribometer conceived to explore the tribological performance of stationary polymer plane coupons submitted to rotational cylinder contact surface of steel AISI 52100, 20 ± 1 HRC Hardness, under dry and lubricated (oil SAE 15W40) conditions. After screening testings, the normal load, relative velocity and sliding distance were 3.15 N, 0.8 m/s and 3.2 km, respectively. The temperatures were collected over distances of 3.0±0.5 mm and 750±50 mm far from the contact to evaluate the heating in this referential zone due to contact sliding friction by two thermocouples K type. The polymers were characterized through Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA). The wear mechanisms of the polymer surfaces were analyzed by Scanning Electron Microscopy (SEM) and EDS (Energy-Dispersive X-ray Spectroscopy). NBR referred to the higher values of heating, suggesting higher sliding friction. PTFE and PTFE with graphite showed lower heating, attributed to the delamination mechanism

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

Simulador de dano à formação, aplicado ao Projeto e à execução da perfuração de poços de petróleo

With the high oil price variability, the petroleum and the reservoir engineers are usually face to face on how they can evaluate the well performance and productivity. They can improve high productivity from the well construction to the secondary recoveries, but they have never tried a measurement in the drilling operations about the lower productivity index. As a rule, frequently the drilling operations hear from the reservoir engineering and geology that, if there is a formation damage, probably some drilling operations practices were not done properly or the good practice in petroleum engineering or mud engineering were not observed. The study in this working search is an attempt of how to measure a formation damage just from the project drilling to the drilling operations, with datum from the fields in Brazilian northeast and putting into practice a Simulator developed from the modeling on the theory offered by different experts and sources in formation damage