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Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools./
Author:	Tariq, Zeeshan.
Description:	1 online resource (174 pages)
Notes:	Source: Masters Abstracts International, Volume: 81-08.
Contained By:	Masters Abstracts International81-08.
Subject:	Petroleum engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743451click for full text (PQDT)
ISBN:	9781392412923

Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools.
Tariq, Zeeshan.

Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools. - 1 online resource (174 pages)

Source: Masters Abstracts International, Volume: 81-08.

Thesis (M.S.)--King Fahd University of Petroleum and Minerals (Saudi Arabia), 2016.

Includes bibliographical references

Rock Mechanical parameters are critical in alleviating the risks associated with the drilling and maximizing the reservoir productivity. These parameters are used in the optimization of the well placement, well bore instability, completion design, draw-down limits to avoid sanding, hydraulic fracturing, and many more. The appropriate estimation of these parameters is very essential for the development and production phases of hydrocarbon recovery. Incorrect estimation of rock mechanical parameters may wrongly lead to heavy investment decisions and inappropriate field development plans.To carry out any operation, a continuous profile of rock mechanical parameters is needed. Retrieving reservoir rock samples throughout the depth of the reservoir and performing laboratory tests are extremely expensive and time consuming. Therefore, these parameters are estimated from the sonic and compressional wave velocities obtained from Well-logs. Parameters obtained from laboratory tests are termed as static parameters while those obtained from Well-logs are dynamic parameters. The former case represents closely the condition in the reservoir. Since the Well-logs provide a continuous profile of parameters, they have to be calibrated with respect to the static parameters.Since rock properties change with the depth, a realistic estimation of static values remains a big challenge. In carbonate rocks because of heterogeneity the problem is more critical compared to sandstone. In addition to that shear and compressional wave velocity data is not always available from Well logs that makes the problem more challenging.This research deals with the development of new artificial intelligence models to predict both the acoustic waves and the rock mechanical parameters. The proposed models will use different wire-line logs as an input. Three different AI techniques will be implemented and the one with the optimal performance on the basis of maximum coefficient of determination and minimum average absolute percentage error will be selected.

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

Mode of access: World Wide Web

ISBN: 9781392412923Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

Rock mechanical parametersIndex Terms--Genre/Form:

542853
Electronic books.

Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools.
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100 1 $a Tariq, Zeeshan. $3 3701426
245 1 0 $a Estimation of Acoustic Velocities and Rock Mechanical Parameters Using Artificial Intelligence Tools.
264 0 $c 2016
300 $a 1 online resource (174 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 81-08.
500 $a Advisor: Abdulraheem, Abdulazeez; Elkatatny, Salaheldin A.
502 $a Thesis (M.S.)--King Fahd University of Petroleum and Minerals (Saudi Arabia), 2016.
504 $a Includes bibliographical references
520 $a Rock Mechanical parameters are critical in alleviating the risks associated with the drilling and maximizing the reservoir productivity. These parameters are used in the optimization of the well placement, well bore instability, completion design, draw-down limits to avoid sanding, hydraulic fracturing, and many more. The appropriate estimation of these parameters is very essential for the development and production phases of hydrocarbon recovery. Incorrect estimation of rock mechanical parameters may wrongly lead to heavy investment decisions and inappropriate field development plans.To carry out any operation, a continuous profile of rock mechanical parameters is needed. Retrieving reservoir rock samples throughout the depth of the reservoir and performing laboratory tests are extremely expensive and time consuming. Therefore, these parameters are estimated from the sonic and compressional wave velocities obtained from Well-logs. Parameters obtained from laboratory tests are termed as static parameters while those obtained from Well-logs are dynamic parameters. The former case represents closely the condition in the reservoir. Since the Well-logs provide a continuous profile of parameters, they have to be calibrated with respect to the static parameters.Since rock properties change with the depth, a realistic estimation of static values remains a big challenge. In carbonate rocks because of heterogeneity the problem is more critical compared to sandstone. In addition to that shear and compressional wave velocity data is not always available from Well logs that makes the problem more challenging.This research deals with the development of new artificial intelligence models to predict both the acoustic waves and the rock mechanical parameters. The proposed models will use different wire-line logs as an input. Three different AI techniques will be implemented and the one with the optimal performance on the basis of maximum coefficient of determination and minimum average absolute percentage error will be selected.
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 Rock mechanical parameters
653 $a Reservoir rock samples
653 $a Rock properties
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 King Fahd University of Petroleum and Minerals (Saudi Arabia). $b Petroleum Engineering Department. $3 3701427
773 0 $t Masters Abstracts International $g 81-08.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743451 $z click for full text (PQDT)