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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems./
作者:	Lashari, Shan e Zehra.
面頁冊數:	1 online resource (86 pages)
附註:	Source: Masters Abstracts International, Volume: 79-11.
Contained By:	Masters Abstracts International79-11.
標題:	Petroleum engineering. -
電子資源:	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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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
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773 0 $t Masters Abstracts International $g 79-11.
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