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Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted)./
作者:	Kuo, Tsai-Bao.
面頁冊數:	1 online resource (150 pages)
附註:	Source: Dissertations Abstracts International, Volume: 47-07, Section: B.
Contained By:	Dissertations Abstracts International47-07B.
標題:	Petroleum engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=8625409click for full text (PQDT)
ISBN:	9798206562378

Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted).
Kuo, Tsai-Bao.

Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted). - 1 online resource (150 pages)

Source: Dissertations Abstracts International, Volume: 47-07, Section: B.

Thesis (Ph.D.)--Texas A&M University, 1986.

Includes bibliographical references

Computer-assisted approaches to well log correlation are of interest to petroleum engineers and geologists for two reasons. In large field studies a computer can be used to simply reduce the time required to correlate zones of interest. It is also possible that computer-assisted correlations may suggest zonal matches of interest and originality that might not have been considered. The objective of this study is to develop a new approach to computer-assisted log correlations. The new approach is based on a zonal correlation methodology and an artificial intelligence technique-- rule-based systems. A prototype rule-based computer software system is developed to implement this method. The system corre- lates logs in a fashion parallel to human experts. With this system, correlations are directly derived from the shapes of log traces. Results obtained from applying field data show that this new approach can correlate distinct geological horizons with a high degree of success. Citations adhere to the style of the Journal of Petroleum Technology.

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

Mode of access: World Wide Web

ISBN: 9798206562378Subjects--Topical Terms:

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

542853
Electronic books.

Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted).
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100 1 $a Kuo, Tsai-Bao. $3 3701617
245 1 0 $a Well Log Correlation Using Artificial Intelligence (Rule-Based, Expert System, Computer-Assisted).
264 0 $c 1986
300 $a 1 online resource (150 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertations Abstracts International, Volume: 47-07, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
502 $a Thesis (Ph.D.)--Texas A&M University, 1986.
504 $a Includes bibliographical references
520 $a Computer-assisted approaches to well log correlation are of interest to petroleum engineers and geologists for two reasons. In large field studies a computer can be used to simply reduce the time required to correlate zones of interest. It is also possible that computer-assisted correlations may suggest zonal matches of interest and originality that might not have been considered. The objective of this study is to develop a new approach to computer-assisted log correlations. The new approach is based on a zonal correlation methodology and an artificial intelligence technique-- rule-based systems. A prototype rule-based computer software system is developed to implement this method. The system corre- lates logs in a fashion parallel to human experts. With this system, correlations are directly derived from the shapes of log traces. Results obtained from applying field data show that this new approach can correlate distinct geological horizons with a high degree of success. Citations adhere to the style of the Journal of Petroleum Technology.
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
690 $a 0765
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Texas A&M University. $3 718977
773 0 $t Dissertations Abstracts International $g 47-07B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=8625409 $z click for full text (PQDT)