Síntese e caracterização de peneiras moleculares mesoporosas do tipo MCM-41 e AlMCM-41 a partir de fontes alternativas de sílica e de alumínio

The mesoporous molecular sieves of MCM-41 and AlMCM-41 type are considered as promising support for metal in the refining processes of petroleum-based materials as catalysts and adsorbents for environmental protection. In this work the molecular sieves MCM-41 and AlMCM-41 were synthesized by replacing the source of silica conventionally used, for quartz, an alternative and abundant, and the use of waste from the production of diatomaceous earth, an aluminum-silicate, as a source aluminum, due to abundant reserves of diatomaceous earth in the state of Rio Grande do Norte in the city of Ceará-Mirim, with the objective of producing high-value materials that have similar characteristics to traditional commercial catalysts in the market. These materials were synthesized by the method of hydrothermal synthesis at 100 º C for 7 days and subjected to calcination at 500 º C for 2 hours under flow of nitrogen and air. The molecular sieves were characterized by X-ray diffraction (XRD), differential thermal analysis (DTA) and thermogravimetric analysis (TG), adsorption of N2 (BET and BJH methods), spectroscopy in the infra red (FTIR), microscopy scanning electron (SEM) and transmission electron microscopy (TEM). The analysis indicated that the synthesized materials showed characteristic hexagonal structure of mesopores materials with high specific surface area and sort and narrow distribution of size of pores

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

Desenvolvimento de sistemas a base de tensoativos para recuperação avançada de petróleo

Petroleum is a complex combination of various classes of hydrocarbons, with paraffinic, naphtenic and aromatic compounds being those more commonly found in its composition. The recent changes in the world scenario, the large reserves of heavy oils and also the lack of new discoveries of large petroleum fields are indications that, in the near future, the oil recovery by conventional methods will be limited. In order to increase the efficiency of the extraction process, enhanced recovery methods are cited in applications where conventional techniques have proven to be little effective. The injection of surfactant solutions as an enhanced recovery method is advantageous in that surfactants are able to reduce the interfacial tensions between water and oil, thus augmenting the displacement efficiency and, as a consequence, increasing the recovery factor. This work aims to investigate the effects of some parameters that influence the surfactant behavior in solution, namely the type of surfactant, the critical micelle concentration (CMC) and the surface and interface tensions between fluids. Seawater solutions containing the surfactants PAN, PHN and PJN have been prepared for presenting lower interfacial tensions with petroleum and higher stability under increasing temperature and salinity. They were examined in an experimental apparatus designed to assess the recovery factor. Botucatu (Brazil) sandstone plug samples were submitted to assay steps comprising saturation with seawater and petroleum, conventional recovery with seawater and enhanced recovery with surfactant solutions. The plugs had porosity between 29.6 and 32.0%, with average effective permeability to water of 83 mD. The PJN surfactant, at a concentration 1000% above CMC in water, had a higher recovery factor, causing the original oil in place to be recovered by an extra 20.97%, after conventional recovery with seawater

Síntese e caracterização de peneiras moleculares mesoporosas do tipo MCM-41 e AlMCM-41 a partir de fontes alternativas de sílica e de alumínio

The mesoporous molecular sieves of MCM-41 and AlMCM-41 type are considered as promising support for metal in the refining processes of petroleum-based materials as catalysts and adsorbents for environmental protection. In this work the molecular sieves MCM-41 and AlMCM-41 were synthesized by replacing the source of silica conventionally used, for quartz, an alternative and abundant, and the use of waste from the production of diatomaceous earth, an aluminum-silicate, as a source aluminum, due to abundant reserves of diatomaceous earth in the state of Rio Grande do Norte in the city of Ceará-Mirim, with the objective of producing high-value materials that have similar characteristics to traditional commercial catalysts in the market. These materials were synthesized by the method of hydrothermal synthesis at 100 º C for 7 days and subjected to calcination at 500 º C for 2 hours under flow of nitrogen and air. The molecular sieves were characterized by X-ray diffraction (XRD), differential thermal analysis (DTA) and thermogravimetric analysis (TG), adsorption of N2 (BET and BJH methods), spectroscopy in the infra red (FTIR), microscopy scanning electron (SEM) and transmission electron microscopy (TEM). The analysis indicated that the synthesized materials showed characteristic hexagonal structure of mesopores materials with high specific surface area and sort and narrow distribution of size of pores

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