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

Chithra Chakra N C

ISBN 10: 365991777X / ISBN 13: 9783659917776

Editore: LAP LAMBERT Academic Publishing, 2017

Riguardo questo articolo

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9783659917776

Dati bibliografici

• Titolo: Soft Computing on Reservoir Characterization & Production Forecasting : Application of Higher-order Neural Network on Production Forecasting and Adaptive Genetic Algorithm for History Matching
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 365991777X
• ISBN 13: 9783659917776
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 16 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 16 millimetri di profondità
• Peso: 399 grammi

Descrizione articolo

Riassunto

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.