£22.14

CRC Press Data-Driven Analytics for Geological CO2 Storage

Name: CRC Press Data-Driven Analytics for Geological CO2 Storage
Brand: CRC Press
Price: 22.14 GBP
Availability: InStock

Bioengineering

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Description

As data-driven analytics grows in popularity among oil and gas professionals, this book by CRC Press provides essential insights into the geological storage of CO2. It addresses complex reservoir engineering problems that typically require the development of numerical reservoir simulation models. This text is the first to examine how artificial intelligence and machine learning contribute to data-driven analytics for fluid flow in porous environments. It covers a wide range of applications, including saline aquifers as well as depleted gas and oil reservoirs. By utilizing actual case studies, the book explains the practical application of these technologies in real-world scenarios. Readers will learn how to develop smart proxy models to handle complex numerical reservoir simulations. This resource is designed for professionals looking to integrate modern computational methods into traditional reservoir engineering and carbon sequestration workflows.

Key Features

Explores the application of artificial intelligence and machine learning within the field of data-driven analytics for fluid flow.

Provides specialized knowledge on managing fluid flow in porous environments, including saline aquifers and depleted reservoirs.

Uses actual case studies to demonstrate the practical implementation of data-driven methods in geological storage.

Teaches the development of smart proxy models to simplify and enhance complex numerical reservoir simulations.

Addresses specific reservoir engineering challenges associated with the geological storage of CO2.

Product Specifications

Brand: CRC Press
Format: hardcover
ASIN: 1138197149
Category: Books > Subjects > Science, Nature & Maths > Engineering & Technology > Bioengineering
Domain: Amazon UK
Release Date: 05 June 2018
Listed Since: 02 August 2016

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CRC Press Data-Driven Analytics for Geological CO2 Storage

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