Soft Computing on Reservoir Characterization & Production Forecasting: Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching - Couverture souple

Chakra N C, Chithra; Song, Ki-Young; M. Gupta, Madan

 
9783659917776: Soft Computing on Reservoir Characterization & Production Forecasting: Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching
Couverture souple
ISBN 10 : 365991777X ISBN 13 : 9783659917776
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

Production forecasting and reservoir modeling play vital roles in optimal field development plan and management of petroleum reservoirs. This motivates engineers to develop computationally efficient and fast numerical methods capable of constructing history matched reservoir models producing reliable production forecasts. Relatively two new soft computing techniques successfully applied for automatic history matching and production forecasting. The first approach is artificial neural networks (ANN) based modeling, and the 2nd is genetic algorithm (GA) based optimization. A higher-order neural network (HONN) with higher-order synaptic operation (HOSO) architecture that embeds linear (conventional), quadratic (QSO) and cubic synaptic operations (CSO) used for forecasting real field oil production. For automatic history matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) was employed. Adaptive genetic operators of AGA dynamically adjusts control parameters during evolution. The performance of both soft computing methods in achieving fast convergence rate and reduced computational efforts are presented in this book.

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Présentation de l'éditeur

Production forecasting and reservoir modeling play vital roles in optimal field development plan and management of petroleum reservoirs. This motivates engineers to develop computationally efficient and fast numerical methods capable of constructing history matched reservoir models producing reliable production forecasts. Relatively two new soft computing techniques successfully applied for automatic history matching and production forecasting. The first approach is artificial neural networks (ANN) based modeling, and the 2nd is genetic algorithm (GA) based optimization. A higher-order neural network (HONN) with higher-order synaptic operation (HOSO) architecture that embeds linear (conventional), quadratic (QSO) and cubic synaptic operations (CSO) used for forecasting real field oil production. For automatic history matching problem through reservoir characterization, a global optimization method called adaptive genetic algorithm (AGA) was employed. Adaptive genetic operators of AGA dynamically adjusts control parameters during evolution. The performance of both soft computing methods in achieving fast convergence rate and reduced computational efforts are presented in this book.

Biographie de l'auteur

Dr. Chithra Chakra holds Ph.D. in Computer Science & Engineering from University of Petroleum & Energy Studies, India, working as Research Engineer in ADRIC- The Petroleum Institute, Abu Dhabi. Her research focus on reservoir modeling and simulation, evolutionary algorithms, gradient and stochastic production optimization methods.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
365991777X
ISBN 13 
9783659917776
Reliure
Broché
Nombre de pages
256
Coordonnées du fabricant
non disponible
Personne responsable
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