Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

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.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Integration in new product development: Case study in a large Brazilian high-technology company

Proficiency in management activities undertaken in product development processes is regarded as a key competitive advantage for companies, particularly for high-tech industrial firms, which benefit from the important competitiveness factor of launching products with a differentiated technological content. This paper's objective was to identify, through case study, practices for integration between the roles of R & D with others involved in product development in a large Brazilian company of industrial automation. The results suggest some management practices to improve the integration in new products development, such as the use of employees from marketing with knowledge and experience previously gained from R & D activities and uses the heavyweight product manager to solve synchronization problems between product and technology development. © Universidad Alberto Hurtado, Facultad de Economía y Negocios.

Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company

Goal Programming (GP) is an important analytical approach devised to solve many realworld problems. The first GP model is known as Weighted Goal Programming (WGP). However, Multi-Choice Aspirations Level (MCAL) problems cannot be solved by current GP techniques. In this paper, we propose a Multi-Choice Mixed Integer Goal Programming model (MCMI-GP) for the aggregate production planning of a Brazilian sugar and ethanol milling company. The MC-MIGP model was based on traditional selection and process methods for the design of lots, representing the production system of sugar, alcohol, molasses and derivatives. The research covers decisions on the agricultural and cutting stages, sugarcane loading and transportation by suppliers and, especially, energy cogeneration decisions; that is, the choice of production process, including storage stages and distribution. The MCMIGP allows decision-makers to set multiple aspiration levels for their problems in which the more/higher, the better and the less/lower, the better in the aspiration levels are addressed. An application of the proposed model for real problems in a Brazilian sugar and ethanol mill was conducted; producing interesting results that are herein reported and commented upon. Also, it was made a comparison between MCMI GP and WGP models using these real cases. © 2013 Elsevier Inc.

Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company

Due to an increased awareness and significant environmental pressures from various stakeholders, companies have begun to realize the significance of incorporating green practices into their daily activities. This paper proposes a framework using Fuzzy TOPSIS to select green suppliers for a Brazilian electronics company; our framework is built on the criteria of green supply chain management (GSCM) practices. An empirical analysis is made, and the data are collected from a set of 12 available suppliers. We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology. Then a Spearman rank correlation coefficient is used to find the statistical difference between the ranks obtained by the three methods. Finally, a sensitivity analysis has been performed to examine the influence of the preferences given by the decision makers for the chosen GSCM practices on the selection of green suppliers. Results indicate that the four dominant criteria are Commitment of senior management to GSCM; Product designs that reduce, reuse, recycle, or reclaim materials, components, or energy; Compliance with legal environmental requirements and auditing programs; and Product designs that avoid or reduce toxic or hazardous material use. © 2013 Elsevier B.V. All rights reserved.

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.

Avaliação da presença de vapores no solo em antiga área industrial contaminada por hidrocarbonetos: concentrações medidas e simuladas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uso da técnica de análise de óleo lubrificante em motores diesel estacionários, utilizando-se misturas de biodiesel e diferentes níveis de contaminação do lubrificante

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da combustão de carvões e misturas com o coque verde de petróleo injetadas em altos fornos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Biodegradation and Phytotoxicity of Biodiesel, Diesel, and Petroleum in Soil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Sorocabana Railway Company: a relação de uma empresa ferroviária privada com as diretrizes governamentais (1907-1919)

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

Assessing Bacillus subtilis biosurfactant effects on the biodegradation of petroleum products

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Petroleum products biodegradation profile similarity evaluated by F test comparison of weekly CO2 production

Strategies applied to bioremediation contaminated environments are necessary to identify limitations towards biodegradation and to predict remediation performance and thereby rule out technologies that may be inappropriate for the clean-up of the substances of concern. Respirometry applied to bioremediation offers a series of advantages for obtaining CO2 production data when compared to other procedures. It was possible to determinate information regarding the atmosphere's CO2 concentration inside a respirometer containing petroleum products. Afterwards, the CO2 data obtained underwent an in depth statistical analysis by F test. There are some noticeable biodegradation similarity among some substances, such as weathered motor oil and gasoline. Such data provides reliability when revealing important information about how the biodegradation processes happens in those residual oils.