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Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems./
Author:	Lashari, Shan e Zehra.
Description:	1 online resource (86 pages)
Notes:	Source: Masters Abstracts International, Volume: 79-11.
Contained By:	Masters Abstracts International79-11.
Subject:	Petroleum engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10793883click for full text (PQDT)
ISBN:	9780355938272

Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems.
Lashari, Shan e Zehra.

Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems. - 1 online resource (86 pages)

Source: Masters Abstracts International, Volume: 79-11.

Thesis (M.S.)--West Virginia University, 2018.

Includes bibliographical references

For the last few decades multiple business sectors have been influenced by the advancement in Artificial Intelligence (AI). Though the oil and gas sector began to utilize the potential of AI comparatively latter than many other sectors, the appreciable amount of work has been done by researchers to equip the industry with AI tools. This work aims to explore various horizons of petroleum engineering by using different AI tools. For providing better decision making in reservoir fluid characterization problem, fuzzy logic has been applied, which is an AI method to drive decisions when data is incomplete or unreliable. The second part of the work is the combination of supervised and unsupervised machine learning has provided an automated version of well log analysis, where the generated algorithm is able to distinguish between different lithological zones on the basis of well log parameters. The majority of the problems such as drilling process optimization, production forecasting, comes under the umbrella of statistical regression. The supervised learning regression algorithm was generated to predict the drilling performance in terms of rate of penetration. The similar model was used for producing regression analysis of reservoir that has been treated by steam assisted gas drainage. The accuracy of both cases were investigated by comparing the prediction with available real time data. The work has been concluded by providing conclusion gathered from comparing different methods and limitations of methodologies derived from Artificial Intelligent (AI) tools.

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

Mode of access: World Wide Web

ISBN: 9780355938272Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

AIIndex Terms--Genre/Form:

542853
Electronic books.

Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems.
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245 1 0 $a Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems.
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300 $a 1 online resource (86 pages)
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500 $a Source: Masters Abstracts International, Volume: 79-11.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Takbiri-Borujeni, Ali.
502 $a Thesis (M.S.)--West Virginia University, 2018.
504 $a Includes bibliographical references
520 $a For the last few decades multiple business sectors have been influenced by the advancement in Artificial Intelligence (AI). Though the oil and gas sector began to utilize the potential of AI comparatively latter than many other sectors, the appreciable amount of work has been done by researchers to equip the industry with AI tools. This work aims to explore various horizons of petroleum engineering by using different AI tools. For providing better decision making in reservoir fluid characterization problem, fuzzy logic has been applied, which is an AI method to drive decisions when data is incomplete or unreliable. The second part of the work is the combination of supervised and unsupervised machine learning has provided an automated version of well log analysis, where the generated algorithm is able to distinguish between different lithological zones on the basis of well log parameters. The majority of the problems such as drilling process optimization, production forecasting, comes under the umbrella of statistical regression. The supervised learning regression algorithm was generated to predict the drilling performance in terms of rate of penetration. The similar model was used for producing regression analysis of reservoir that has been treated by steam assisted gas drainage. The accuracy of both cases were investigated by comparing the prediction with available real time data. The work has been concluded by providing conclusion gathered from comparing different methods and limitations of methodologies derived from Artificial Intelligent (AI) tools.
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
653 $a AI
653 $a Machine learning
653 $a Reservoir fluid characterization
653 $a Rop
653 $a Smart proxy
653 $a Well logs
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0765
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a West Virginia University. $b Statler College of Engineering & Mineral Resources. $3 3170720
773 0 $t Masters Abstracts International $g 79-11.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10793883 $z click for full text (PQDT)