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Descrizione libro Condizione: New. Codice articolo ABLIING23Mar3113020189208
Descrizione libro Condizione: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Codice articolo ria9783639244267_lsuk
Descrizione libro PF. Condizione: New. Codice articolo 6666-IUK-9783639244267
Descrizione libro Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the major objectives of the petroleum industry is to obtain an accurate estimate of initial hydrocarbon in place before investing in development and production.Porosity, permeability and fluid saturation are the key variables for characterizing a reservoir in order to estimate the volume of hydrocarbons and their flow patterns to optimize the production of a field. Many empirical equations are available to transform well log data to predict these properties. Researchers have utilized Artificial neural networks, particularly feed forward back propagation neural networks (FFNN), to develop more accurate predictions. Unfortunately, the developed FFNN correlations have some drawbacks, and as a result several improvements have been proposed. Our efforts is directed towards investigating the suitability of some of the recently proposed advances in neural networks technique including, functional networks (FN),cascaded correlation neural networks, polynomial networks, and general regression neural networks for predicting porosity and water saturation from well logs. We compared the performance of these techniques with standard FFNN as well as the empirical correlation models. 156 pp. Englisch. Codice articolo 9783639244267
Descrizione libro PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9783639244267
Descrizione libro Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - One of the major objectives of the petroleum industry is to obtain an accurate estimate of initial hydrocarbon in place before investing in development and production.Porosity, permeability and fluid saturation are the key variables for characterizing a reservoir in order to estimate the volume of hydrocarbons and their flow patterns to optimize the production of a field. Many empirical equations are available to transform well log data to predict these properties. Researchers have utilized Artificial neural networks, particularly feed forward back propagation neural networks (FFNN), to develop more accurate predictions. Unfortunately, the developed FFNN correlations have some drawbacks, and as a result several improvements have been proposed. Our efforts is directed towards investigating the suitability of some of the recently proposed advances in neural networks technique including, functional networks (FN),cascaded correlation neural networks, polynomial networks, and general regression neural networks for predicting porosity and water saturation from well logs. We compared the performance of these techniques with standard FFNN as well as the empirical correlation models. Codice articolo 9783639244267
Descrizione libro PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9783639244267
Descrizione libro Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Adeniran AhmedAhmed had B.Sc. in Electrical/Electronics Engineering, (2001) and M.Sc.in Computer Science (2007) from University of Ibadan, Nigeria. He later moved to KFUPM, Saudi Arabia to study M.S. in Systems Engineering, (2009). H. Codice articolo 4970413