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Books on the topic 'Reservoir computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2025

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1

Nakajima, Kohei, and Ingo Fischer, eds. Reservoir Computing. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-13-1687-6.

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2

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande, eds. Photonic Reservoir Computing. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496.

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3

Wong, Patrick, Fred Aminzadeh, and Masoud Nikravesh, eds. Soft Computing for Reservoir Characterization and Modeling. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1807-9.

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4

Bruk, Stevan. Methods of computing sedimentation in lakes and reservoirs: A contribution to the International Hydrological Programme, IHP - II Project A. 2.6.1 panel. Paris: UNESCO, 1985.

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5

1959-, Nikravesh Masoud, Aminzadeh Fred, and Zadeh Lotfi Asker, eds. Soft computing and intelligent data analysis in oil exploration. Amsterdam: Elsevier, 2003.

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6

Stevan, Bruk, and International Hydrological Programme, eds. Methods of computing sedimentation in lakes and reservoirs: A contribution to the International Hydrological Programme, IHP - II Project A. 2.6.1 panel. Paris: UNESCO, 1985.

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7

Reservoir Computing. Springer Nature, 2021.

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8

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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9

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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10

Aminzadeh, Fred, Masoud Nikravesh, and Patrick Wong. Soft Computing for Reservoir Characterization and Modeling. Physica-Verlag, 2013.

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11

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande. Photonic Reservoir Computing: Optical Recurrent Neural Networks. de Gruyter GmbH, Walter, 2019.

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12

Fischer, Ingo, and Kohei Nakajima. Reservoir Computing: Theory, Physical Implementations, and Applications. Springer, 2020.

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13

Aminzadeh, Fred, Masoud Nikravesh, and Patrick Wong. Soft Computing for Reservoir Characterization and Modeling. Physica, 2010.

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14

(Editor), Patrick Wong, Fred Aminzadeh (Editor), and Masoud Nikravesh (Editor), eds. Soft Computing for Reservoir Characterization and Modeling (Studies in Fuzziness and Soft Computing). Physica-Verlag Heidelberg, 2002.

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15

Nikravesh, Masoud. Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization And Modeling. Springer, 2010.

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16

Nikravesh, Masoud, Lofti A. Zadeh, and Victor Korotkikh. Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling. Springer, 2014.

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17

Nikravesh, Masoud, Lofti A. Zadeh, and Victor Korotkikh. Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling. Springer, 2013.

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18

Kuchuk, Fikri J., T. S. Ramakrishnan, and Mustafa Onur. Wireline Formation Testing: Hardware, Pressure Transient Testing, Interpretation and Sampling. Society of Petroleum EngineersRichardson, Texas, USA, 2021. http://dx.doi.org/10.2118/9781613998434.

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Delivering a comprehensive exploration of modeling through hardware, advanced formation testing, and new interpretation techniques, Wireline Formation Testing will appeal to engineers looking for a deeper understanding of the potential insights and limitations of these important computations and analytics. Since improving reservoir characterization is paramount to computing reliable predictors and uncertainties in reservoir performance, the advanced techniques presented here will help with meeting that goal. Wireline Formation Testing is the must-have resource for engineers who wish to evaluate reservoirs simultaneously with the scope of the testing results, as well as for engineering students seeking understanding of this primary source of wireline formation pressure data collection.
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19

(Editor), Masoud Nikravesh, Lotfi A. Zadeh (Editor), and Victor Korotkikh (Editor), eds. Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling (Studies in Fuzziness and Soft Computing). Springer, 2004.

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20

Kamal, Medhat M. Transient Well Testing. Society of Petroleum EngineersRichardson, Texas, USA, 2009. http://dx.doi.org/10.2118/9781555631413.

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Transient well testing provides indirect determination of reservoir and well parameters. It is one of the most important in a spectrum of diagnostic tools used by petroleum engineers to characterize hydrocarbon assets and predict their future performance. This new monograph is the go-to handbook for designing, running, and analyzing different types of transient tests in oil and gas reservoirs. Since the publication of SPE’s last monograph on well testing, considerable advances in testing tools, computing technology, and theoretical development have continued to enhance the information that can be obtained from transient testing and change the methods of interpretation. A gap in the petroleum engineer’s library for a comprehensive book covering the state of the art in well-test analysis has existed for some time now. Practicing petroleum engineers need a single up-to-date reference for transient testing of oil, gas, and water wells; Transient Well Testing has been written to fill that gap. Errata (http://go.spe.org/TWTerrata)
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21

Aminzadeh, Fred, M. Nikravesh, and L. A. Zadeh. Soft Computing and Intelligent Data Analysis in Oil Exploration. Elsevier Science & Technology Books, 2003.

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