000			03249cam a2200445 i 4500
001			on1246283594
003			GHU
005			20260605070437.0
006			m o d
007			cr |n|||||||||
008			210416s2021 mau ob 001 0 eng d
020			_a9780128219300 _q(electronic bk.)
020			_a0128219300 _q(electronic bk.)
020			_a9780128219294 _q(electronic bk.)
020			_a0128219297 _q(electronic bk.)
035			_a(OCoLC)1246283594 _z(OCoLC)1246142343 _z(OCoLC)1249100538
037			_a6546221 _bProquest Ebook Central
037			_a9780128219300 _bO'Reilly Media
040			_aYDX _beng _erda _epn _cGHU _dGHLIB
049			_aMAIN
050		4	_aTN871 BEL
082	0	4	_a622.3380285631 _223
100	1	0	_aBelyadi, Hoss, _eauthor. _1https://id.oclc.org/worldcat/entity/E39PCjrh9dKhH4tMDdWMvwgdwC _0http://id.loc.gov/authorities/names/no2018029339.
245	1	0	_aMachine learning guide for oil and gas using Python : _ba step-by-step breakdown with data, algorithms, codes, and applications / _cHoss Belyadi, Alireza Haghighat.
264		1	_aCambridge, MA : _bGulf Professional Publishing, _c2021.
300			_a1 online resource (xiii, 462 pages)
336			_atext _btxt _2rdacontent.
337			_acomputer _bc _2rdamedia.
338			_aonline resource _bcr _2rdacarrier.
504			_aIncludes bibliographical references and index.
520			_aMachine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
588			_aDescription based on online resource; title from digital title page (ProQuest Ebook Central, viewed on September 12, 2024).
650		0	_aPetroleum engineering _xData processing.
650		0	_aNatural gas _xData processing.
650		0	_aMachine learning. _0http://id.loc.gov/authorities/subjects/sh85079324.
700	1		_aHaghighat, Alireza, _eauthor. _1https://id.oclc.org/worldcat/entity/E39PCjJTDbQVb4YbCyT33Fd9tC _0http://id.loc.gov/authorities/names/no2015009580.
776	0	8	_iPrint version: _aBelyadi, Hoss. _tMachine learning guide for oil and gas using Python _z0128219297 _z9780128219294 _w(OCoLC)1195448296.
856	4	0	_uhttp://ezproxy.library.qmul.ac.uk/login?url=https://app.knovel.com/hotlink/toc/id:kpMLGOGUP4/machine-learning-guide?kpromoter=marc
942			_2lcc _cBOOK _n0
999			_c678 _d678