Chithra chakra (5 résultats)
- Couverture souple
Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 59,50
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 5 disponible(s)
Taschenbuch. Etat : Neu. 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 (u. a.) | Taschenbuch | 256 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 97…83659917776 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
- Couverture souple
- impression à la demande
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AllemagneBuchWeltWeit Ludwig Meier e.K.
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 69,90
EUR 23,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 2 disponible(s)
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 256 pp. Englisch.
- Couverture souple
- impression à la demande
Vendeur : moluna, Greven, Allemagnemoluna
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 56,63
EUR 48,99 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : Plus de 20 disponibles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chakra N C ChithraDr. 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 D…habi. Her research focus on.
- Couverture souple
- impression à la demande
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagnebuchversandmimpf2000
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 69,90
EUR 60,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 con…structing 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 256 pp. Englisch.
- Couverture souple
- impression à la demande
Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 69,90
EUR 62,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 1 disponible(s)
Taschenbuch. Etat : Neu. 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 cons…tructing 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.
