NLU Meghalaya Library

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Machine learning applications in subsurface energy resource management : (Record no. 5052)

MARC details
000 -LEADER
fixed length control field 03231cam a22004937i 4500
001 - CONTROL NUMBER
control field 9781003207009
003 - CONTROL NUMBER IDENTIFIER
control field FlBoTFG
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240213122826.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu---unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221206s2022 flu o 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency OCoLC-P
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency OCoLC-P
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781003207009
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1003207006
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000823899
Qualifying information (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 100082389X
Qualifying information (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000823875
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000823873
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781032074528
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 1032074523
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1201/9781003207009
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1353293072
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC-P)1353293072
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TJ163.2
072 #7 - SUBJECT CATEGORY CODE
Subject category code TEC
Subject category code subdivision 047000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 062000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code KNAT
Source bicssc
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.0420285631
Edition number 23
245 00 - TITLE STATEMENT
Title Machine learning applications in subsurface energy resource management :
Remainder of title state of the art and future prognosis /
Statement of responsibility, etc. edited by Srikanta Mishra.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Boca Raton :
Name of producer, publisher, distributor, manufacturer CRC Press,
Date of production, publication, distribution, manufacture, or copyright notice 2022.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (1 volume)
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note OCLC-licensed vendor bibliographic record.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Power resources
General subdivision Management
-- Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element TECHNOLOGY / Petroleum
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Data Modeling & Design
Source of heading or term bisacsh
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Mishra, Srikanta,
Dates associated with a name 1958-
Relator term editor.
Real World Object URI https://isni.org/isni/0000000048139782
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Taylor & Francis
Uniform Resource Identifier <a href="https://www.taylorfrancis.com/books/9781003207009">https://www.taylorfrancis.com/books/9781003207009</a>
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified OCLC metadata license agreement
Uniform Resource Identifier <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a>

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