O emprego na transição para uma economia verde: cenários para o Brasil com base no consumo de energia pela indústria

Pós-graduação em Economia - FCLAR

Avaliação da toxicidade e caracterização de hidrocarbonetos presentes em águas de rios que recebem efluente de indústria petroquímica

Pós-graduação em Ciências Biológicas (Biologia Celular e Molecular) - IBRC

Avaliação da genotoxicidade e mutagenicidade das águas dos rios Jaguari, Atibaia e Piracicaba, na região de influência da refinaria de Paulínia - SP

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise energética em refino de petróleo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Glycerol: Production, consumption, prices, characterization and new trends in combustion

The demand for petroleum has been rising rapidly due to increasing industrialization and modernization. This economic development has led to a huge demand for energy, most of which is derived from fossil fuel. However, the limited reserve of fossil fuel has led many researchers to look for alternative fuels which can be produced from renewable feedstock. Increasing fossil fuel prices have prompted the global oil industry to look at biodiesel, which is from renewable energy sources. Biodiesel is produced from animal fats and vegetable oils and has become more attractive because it is more environmentally friendly and is obtained from renewable sources. Glycerol is the main by-product of biodiesel production; about 10% of the weight of biodiesel is generated in glycerol. The large amount of glycerol generated may become an environmental problem, since it cannot be disposed of in the environment. In this paper, an attempt has been made to review the different approaches and techniques used to produce glycerol (hydrolysis, transesterification, refining crude glycerol). The world biodiesel/glycerol production and consumption market, the current world glycerin and glycerol prices as well as the news trends for the use of glycerol mainly in Brazil market are analyzed. The technological production and physicochemical properties of glycerol are described, as is the characterization of crude glycerol obtained from different seed oil feedstock. Finally, a simple way to use glycerol in large amounts is combustion, which is an advantageous method as it does not require any purification. However, the combustion process of crude glycerol is not easy and there are technological difficulties. The news and mainly research about the combustion of glycerol was also addressed in this review. © 2013 Elsevier Ltd.

Evidence for euphotic zone anoxia during the deposition of Aptian source rocks based on aryl isoprenoids in petroleum, Sergipe-Alagoas Basin, northeastern Brazil

Four crude oil samples from the Sergipe-Alagoas Basin, northeastern Brazil, were analyzed using full scan gas chromatography-quadrupole mass spectrometry (GC-qMS) for biomarkers, in order to correlate them using aromatic carotenoids thereby enhancing knowledge about the depositional environment of their source rocks. The geochemical parameters derived from saturated fractions of the oils show evidence of little or no biodegradation and similar thermal maturation (Ts/(Ts+Tm) for terpanes, C29 αββ/(αββ+ααα), C27, and C29 20S/(20S+20R) for steranes). Low pristane/phytane ratios and the abundance of gammacerane and β-carotane are indicative of an anoxic and saline depositional environment for the source rocks. Moreover, we identified a large range of diagenetic and catagenetic products of the aromatic carotenoid isorenieratene, including C40, C33, and C32 diaryl isoprenoids and aryl isoprenoid derivatives with short side chains and/or additional rings. These results indicate anoxia in the photic zone during the deposition of the source rocks. © 2013 The Authors.