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Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks

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能源与人工智能(英文) 2021年第1期3卷 167-179页

作者： Lucas Abreu Blanes de Oliveira Luiz Felipe Niedermaier Custodio thais bortotti fagundes Carina Ulsen Cleyton de Carvalho Carneiro Petrobras–Petroleo Brasileiro S.A. Avenida Henrique Valadares 28CentroRio de Janeiro20231-030 Rio de JaneiroBrazil Universidade de Sao Paulo USPEscola PolitecnicaPraça Narciso de AndradeVila MathiasSantosSão Paulo 11013-560Brazil

Understanding rock mineralogy is essential for formation evaluation,improving the calculation of porosity and hydrocarbon *** primary method to obtain the mineralogy from a well is by applying a model to the geochemical tool’s chemical ***,creating a mineralogical model presents challenges such as the minerals’chemical composition and the decision to include a mineral in the *** traditional application of machine learning can make mineral models less realistic since conventional training is developed based on a set of minerals with different occurrences,lowering some minerals’*** present research proposes the stepped machine learning(SML),a stepped way to use machine learning to create a mineralogical model from chemical and mineralogical data.A database was assembled with the elemental concentration obtained with XRF analyses and the mineral concentrations obtained with XRD *** chemical elements were Al,Ca,Fe,K,Mg,Mn,Na,Si,and *** minerals were calcite,dolomite,quartz,clays,K-feldspar,plagioclase,and *** algorithms were tested:MLP,GAN,Random Forest,and XGBoost,with XGBoost showing the best *** was applied,where a mineral model results are used to train a subsequent *** allowed for a significant improvement in some models,notably to clays with an increase in R 2 from 0.597 to 0.853,quartz an increase from 0.673 to 0.869,and calcite,from 0.758 to 0.862.A decrease in the mean squared error of these minerals’models was also *** model was applied to the geochemical logs from three wells drilled in the Brazilian pre-salt,and the results were compared with XRD *** SML model was able to honor the mineral concentrations for different *** is demonstrated that the integration between machine learning tools and geological knowledge in SML was crucial for creating a representative mineralogical model.

关键词： minerals quartz reservoir

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The Influence of Surge Arrester Location on Over-Voltages Caused by Direct Lightning Strikes to MV Lines

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Journal of Energy and Power Engineering 2014年第3期8卷 508-514页

作者： Alexandre Piantini thais O. de Carvalho Paulo F. Obase Jorge M. Janiszewski Gilnei J. G. Santos Donorvan R. fagundes Institute of Energy and Environment University of Sao Paulo Sao Paulo 05508-010 Brazil Polytechnic School University ofS6o Paulo S6o Paulo 05508-010 Brazil AES SUL Coordination of Planning and Engineering S6o Leopoldo 93010-060 Brazil

In the period 2003-2011, lightning over-voltages accounted for about 47% of the total number of distribution transformer failures observed in the service area ofAES Sul, a power company that operates in the state of Rio Grande doSul, in South Brazil. This paper presents the results of an investigation on the influence of the distance between transformer and MV arresters on the surges at the transformer windings caused by direct strikes to the MV network. The analysis, performed through simulations using the Alternative Transients Program, shows that in general higher voltages are produced by subsequent strokes. Although in relation to the primary side the surges transferred to the secondary are much less affected by the distance between transformers and primary arresters, such distance should always be kept as short as possible in order to reduce the probability of occurrence of transformer failures due to over-voltages at the MV bushings.

关键词： Distribution transformer lightning surges overvoltages.

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