عنوان

Data-Driven Analytics for the Geological Storage of CO2 /

(Number (ISBN

1138197149

(Number (ISBN

1315280795

(Number (ISBN

1315280809

(Number (ISBN

1315280817

(Number (ISBN

9781138197145

(Number (ISBN

9781315280790

(Number (ISBN

9781315280806

(Number (ISBN

9781315280813

Erroneous ISBN

9781138197145

Title Proper

Data-Driven Analytics for the Geological Storage of CO2 /

General Material Designation

[Book]

First Statement of Responsibility

Shahab Mohaghegh.

Edition Statement

First edition.

Place of Publication, Distribution, etc.

Boca Raton, FL :

Name of Publisher, Distributor, etc.

CRC Press,

Date of Publication, Distribution, etc.

2018.

Specific Material Designation and Extent of Item

1 online resource :

Other Physical Details

text file, PDF

Text of Note

Includes bibliographical references and index.

Text of Note

Cover; Halftitle Page; Title Page; Copyright Page; Dedication; Contents; Nomenclature; Acknowledgments; Author; Contributors; Introduction; 1. Storage of CO2 in Geological Formations; 2. Petroleum Data Analytics; 3. Smart Proxy Modeling; 4. CO2 Storage in Depleted Gas Reservoirs; 5. CO2 Storage in Saline Aquifers; 6. CO2 Storage in Shale Using Smart Proxy; 7. CO2-EOR as a Storage Mechanism; 8. Leak Detection in CO2 Storage Sites; Bibliography; Index.

0

Text of Note

"Data driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of Artificial Intelligence and Machine Learning in data driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of Artificial Intelligence and Machine Learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects."--Provided by publisher.

Source for Acquisition/Subscription Address

Ingram Content Group

Stock Number

9781315280790

International Standard Book Number

9781315280790

Electric power systems.

Electrical engineering.

Technology.

BUSINESS & ECONOMICS-- Infrastructure.

Electric power systems.

Electrical engineering.

SOCIAL SCIENCE-- General.

Technology.

BUS-- 032000

PHDY

SCI026000

SOC-- 000000

TEC009010

TEC010000

TEC031030

Number

Number

Number

Number

Number

Number

Number

Number

Number

Class number

Mohaghegh, Shahab

Date of Transaction

20200822171628.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y