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- Author: Fatai Anifowose
- Publisher: VDM Verlag
- ISBN-10: 3639143124
- ISBN-13: 9783639143126
- Format: 15.2 x 22.9 x 0.9 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Hybrid AI Models for the Characterization of Oil and Gas Reservoirs (e-book) (used book) | bookbook.eu
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Oil and Gas remain the most exploited source of energy in the world today and have been predicted that they will continue to be available for exploitation in many decades to come. Hence, there is the need to develop accurate and robust predictive models for their effective and efficient exploration, exploitation and management to ensure consistent availability. Various Artificial Intelligence techniques have been used but with dire needs for improvement. Recently, hybrid schemes have been reported to offer better performance and reliability. The capabilities of these schemes have not been well utilized in Oil and Gas. This book explains how these schemes have been utilized in the prediction of porosity and permeability, two important indicators of oil and gas reserves, based on the hybridization of Type-2 Fuzzy Logic, Support Vector Machines and Functional Networks, using real-world well logs. The results are very promising. This book will be of great benefit to researchers and practitioners in the application of AI techniques in oil and gas as well as in Data Mining and Machine Learning.
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- Author: Fatai Anifowose
- Publisher: VDM Verlag
- ISBN-10: 3639143124
- ISBN-13: 9783639143126
- Format: 15.2 x 22.9 x 0.9 cm, softcover
- Language: English English
Oil and Gas remain the most exploited source of energy in the world today and have been predicted that they will continue to be available for exploitation in many decades to come. Hence, there is the need to develop accurate and robust predictive models for their effective and efficient exploration, exploitation and management to ensure consistent availability. Various Artificial Intelligence techniques have been used but with dire needs for improvement. Recently, hybrid schemes have been reported to offer better performance and reliability. The capabilities of these schemes have not been well utilized in Oil and Gas. This book explains how these schemes have been utilized in the prediction of porosity and permeability, two important indicators of oil and gas reserves, based on the hybridization of Type-2 Fuzzy Logic, Support Vector Machines and Functional Networks, using real-world well logs. The results are very promising. This book will be of great benefit to researchers and practitioners in the application of AI techniques in oil and gas as well as in Data Mining and Machine Learning.
Reviews