Soft Computing for Reservoir Characterization and Modeling (Hardcover)

Patrick Wong

ISBN 10: 3790814210 / ISBN 13: 9783790814217

Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2001

Beschreibung

Hardcover. The volume is the first comprehensive book in the area of intelligent reservoir characterization written by leading experts in academia and industry. It contains state-of-the-art techniques to be applied in reservoir geophysics, well logging, reservoir geology, and reservoir engineering. It introduces the basic concepts of soft computing techniques including neural networks, fuzzy logic and evolutionary computing applied to reservoir characterization. Some advanced statistical and hybrid models are also presented. The specific applications include different reservoir characterization topics such as prediction of petrophysical properties from well logs and seismic attributes. For example, if you want to increase the strength of a device, without adding too much extra weight to it, the contradiction matrix tells you that you can use "Principle 1: Segmentation," or "Principle 8: Counterweight," or "Principle 15: Dynamicity," or "Principle 40: Composite Materials. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Bestandsnummer des Verkäufers 9783790814217

Bibliografische Details

• Titel: Soft Computing for Reservoir Characterization and Modeling (Hardcover)
• Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg
• Erscheinungsjahr: 2001
• Sprache: Englisch
• ISBN-10: 3790814210
• ISBN-13: 9783790814217
• Einband: Hardcover
• Zustand: new

Über diesen Titel

Inhaltsangabe

The volume is the first comprehensive book in the area of intelligent reservoir characterization written by leading experts in academia and industry. It contains state-of-the-art techniques to be applied in reservoir geophysics, well logging, reservoir geology, and reservoir engineering. It introduces the basic concepts of soft computing techniques including neural networks, fuzzy logic and evolutionary computing applied to reservoir characterization. Some advanced statistical and hybrid models are also presented. The specific applications include different reservoir characterization topics such as prediction of petrophysical properties from well logs and seismic attributes.