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Título del libro: Water-Rock Interaction - Proceedings Of The 13th International Conference On Water-Rock Interaction, Wri-13
Título del capítulo: Determination of flowing pressure gradients in producing geothermal wells by using artificial neural networks

Autores UNAM:
OCTAVIO GARCIA VALLADARES; EDGAR ROLANDO SANTOYO GUTIERREZ;
Autores externos:
Idioma:
Inglés
Año de publicación:
2010
Palabras clave:

Computational architecture; Hidden layers; Hyperbolic tangent; Inclination angles; Input datas; Levenberg-Marquardt optimization; Mass flow rate; Physical data; Predictive models; Simulated data; Training data sets; Wellbore; Algorithms; Neural networks; Oil field equipment; Pressure gradient; Temperature measurement; Geothermal wells

Resumen:

predictive model based on an application of the artificial neural networks (ANN) for obtained the flowing pressure gradients in geothermal wells was applied. The ANNmodel uses geometrical and physical data commonly measured in producing wells. The prediction of pressure gradients was successfully achieved using the following input data: wellbore geometry (i.e., wellbore depth, inclination angle of pipe, and wellbore diameter), mass flow rate and bottomhole temperature measurements collected from a world database of geothermal wellbores. For the ANN, several computational architectures based on the Levenberg-Marquardt optimization algorithm, the hyperbolic tangent sigmoid transfer-function, and the linear transfer-function were effectively used. The best fitting training data set was obtained with an ANNarchitecture of 15 neurons in the hidden layer, which made possible to predict the flowing pressure gradients with a satisfactory efficiency (R 2 = 0.9910). The results provided by the ANNmodel between measured and simulated data were in good agreement. © 2010 Taylor & Francis Group, London.

Entidades citadas de la UNAM:
Fuente:


ISBN:
9780415604260
Editorial:

Tipo de producto:
Conference Paper