911 resultados para Petroleum contracts
Resumo:
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.
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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.
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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.
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Includes bibliography
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Includes bibliography
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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.
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Incluye Bibliografía
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Incluye Bibliografía
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Includes bibliography
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Includes bibliography