Soft Computing on Reservoir Characterization & Production Forecasting

Chithra Chakra N C

ISBN 10: 365991777X / ISBN 13: 9783659917776

Published by LAP LAMBERT Academic Publishing Feb 2017, 2017

About this Item

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.

Seller Inventory # 9783659917776

Bibliographic Details

• Title: Soft Computing on Reservoir Characterization & Production Forecasting
• Publisher: LAP LAMBERT Academic Publishing Feb 2017
• Publication Date: 2017
• Language: English
• ISBN 10: 365991777X
• ISBN 13: 9783659917776
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 16D millimeters 220 millimeters width by 150 millimeters height by 16 millimeters depth
• Weight: 399 grams

About this title

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.

About the Author

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.