| 000 | 03231cam a22004937i 4500 | ||
|---|---|---|---|
| 001 | 9781003207009 | ||
| 003 | FlBoTFG | ||
| 005 | 20240213122826.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 221206s2022 flu o 000 0 eng d | ||
| 040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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| 020 |
_a9781003207009 _q(electronic bk.) |
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_a1003207006 _q(electronic bk.) |
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_a9781000823899 _q(electronic bk. : EPUB) |
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| 020 |
_a9781000823875 _q(electronic bk. : PDF) |
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| 020 |
_a1000823873 _q(electronic bk. : PDF) |
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| 020 | _z9781032074528 | ||
| 020 | _z1032074523 | ||
| 024 | 7 |
_a10.1201/9781003207009 _2doi |
|
| 035 | _a(OCoLC)1353293072 | ||
| 035 | _a(OCoLC-P)1353293072 | ||
| 050 | 4 | _aTJ163.2 | |
| 072 | 7 |
_aTEC _x047000 _2bisacsh |
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_aKNAT _2bicssc |
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| 082 | 0 | 4 |
_a621.0420285631 _223 |
| 245 | 0 | 0 |
_aMachine learning applications in subsurface energy resource management : _bstate of the art and future prognosis / _cedited by Srikanta Mishra. |
| 264 | 1 |
_aBoca Raton : _bCRC Press, _c2022. |
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| 300 | _a1 online resource (1 volume) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 520 | _aThe 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 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 0 |
_aPower resources _xManagement _xData processing. |
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| 650 | 0 | _aMachine learning. | |
| 650 | 7 |
_aTECHNOLOGY / Petroleum _2bisacsh |
|
| 650 | 7 |
_aCOMPUTERS / Data Modeling & Design _2bisacsh |
|
| 700 | 1 |
_aMishra, Srikanta, _d1958- _eeditor. _1https://isni.org/isni/0000000048139782 |
|
| 856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003207009 |
| 856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
| 999 |
_c5052 _d5052 |
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