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Application of neural network to the determination of well-test interpretation model for horizontal wells.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of neural network to the determination of well-test interpretation model for horizontal wells./
作者:	Sultan, Mir Asif.
面頁冊數:	1 online resource (300 pages)
附註:	Source: Masters Abstracts International, Volume: 63-10.
Contained By:	Masters Abstracts International63-10.
標題:	Petroleum engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1407217click for full text (PQDT)
ISBN:	9780493466040

Application of neural network to the determination of well-test interpretation model for horizontal wells.
Sultan, Mir Asif.

Application of neural network to the determination of well-test interpretation model for horizontal wells. - 1 online resource (300 pages)

Source: Masters Abstracts International, Volume: 63-10.

Thesis (M.S.)--King Fahd University of Petroleum and Minerals (Saudi Arabia), 2001.

Includes bibliographical references

Well-test model identification and, subsequently, model parameters determination is more complex in horizontal wells as compared to vertical wells. This is due to the increase in number of flow regimes occurring during a flow period and due to the fact that strong correlation exists between model parameters. This study presents a new approach for automatic model identification and computer-aided well-test interpretation in horizontal wells. The new approach is based on using neural network to (1) identify the well-test interpretation model; (2) identify flow regimes, and (3) mark the position of identified flow regions on the derivative plot of well test data. This work consists of first generating common model signatures using Ozkan and Ragavan analytical solutions for horizontal well in various reservoir and inner boundary conditions assuming laterally boundless reservoir. Next, these signatures are used to train neural networks for three identification stages, namely, model identification, flow regime identification, and position of flow regime identification. Separate networks were trained, then tested and validated using synthetic as well as field data. Once the three identification stages are completed, specialized plots for data points falling into each flow regime are used to determine initial model parameters. Finally, non-linear regression software was used to determine final model parameters. A comparative study was carried out using different network architectures and data preparation schemes. Modular approach with direct data utilization is found to be most suitable for field implementation of our approach.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9780493466040Subjects--Topical Terms:

566616
Petroleum engineering.
Index Terms--Genre/Form:

542853
Electronic books.

Application of neural network to the determination of well-test interpretation model for horizontal wells.
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500 $a Source: Masters Abstracts International, Volume: 63-10.
500 $a Publisher info.: Dissertation/Thesis.
502 $a Thesis (M.S.)--King Fahd University of Petroleum and Minerals (Saudi Arabia), 2001.
504 $a Includes bibliographical references
520 $a Well-test model identification and, subsequently, model parameters determination is more complex in horizontal wells as compared to vertical wells. This is due to the increase in number of flow regimes occurring during a flow period and due to the fact that strong correlation exists between model parameters. This study presents a new approach for automatic model identification and computer-aided well-test interpretation in horizontal wells. The new approach is based on using neural network to (1) identify the well-test interpretation model; (2) identify flow regimes, and (3) mark the position of identified flow regions on the derivative plot of well test data. This work consists of first generating common model signatures using Ozkan and Ragavan analytical solutions for horizontal well in various reservoir and inner boundary conditions assuming laterally boundless reservoir. Next, these signatures are used to train neural networks for three identification stages, namely, model identification, flow regime identification, and position of flow regime identification. Separate networks were trained, then tested and validated using synthetic as well as field data. Once the three identification stages are completed, specialized plots for data points falling into each flow regime are used to determine initial model parameters. Finally, non-linear regression software was used to determine final model parameters. A comparative study was carried out using different network architectures and data preparation schemes. Modular approach with direct data utilization is found to be most suitable for field implementation of our approach.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Petroleum engineering. $3 566616
655 7 $a Electronic books. $2 lcsh $3 542853
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710 2 $a King Fahd University of Petroleum and Minerals (Saudi Arabia). $3 1030607
773 0 $t Masters Abstracts International $g 63-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1407217 $z click for full text (PQDT)