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Use of neural networks for predictio...

Alamoudi, Waleed Ahmad.

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Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia./
Author:	Alamoudi, Waleed Ahmad.
Description:	181 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1756.
Contained By:	Dissertation Abstracts International63-04B.
Subject:	Geophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3051579
ISBN:	0493662022

Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia.
Alamoudi, Waleed Ahmad.

Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia. - 181 p.

Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1756.

Thesis (Ph.D.)--Texas A&M University, 2002.

Reservoir porosity controls the strategies for reservoir management. Porosity is the primary key to a reliable reservoir model. The most economic method of evaluating reservoir porosity on a foot-by-foot basis is from core and well log data analysis. Lateral reservoir porosity is estimated using geostatistical method from well log data or from the integration of well log data and seismic data. However, the petroleum industry needs more accurate, reliable methods to estimate porosity from seismic data. Neural network analysis is one of the latest technologies available to the petroleum industry.

ISBN: 0493662022Subjects--Topical Terms:

535228
Geophysics.

Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia.
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020 $a 0493662022
035 $a (UnM)AAI3051579
035 $a AAI3051579
040 $a UnM $c UnM
100 1 $a Alamoudi, Waleed Ahmad. $3 1953409
245 1 0 $a Use of neural networks for prediction of lateral reservoir porosity from seismic acoustic impedance: A case study from Saudi Arabia.
300 $a 181 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1756.
500 $a Chair: Joel S. Watkins.
502 $a Thesis (Ph.D.)--Texas A&M University, 2002.
520 $a Reservoir porosity controls the strategies for reservoir management. Porosity is the primary key to a reliable reservoir model. The most economic method of evaluating reservoir porosity on a foot-by-foot basis is from core and well log data analysis. Lateral reservoir porosity is estimated using geostatistical method from well log data or from the integration of well log data and seismic data. However, the petroleum industry needs more accurate, reliable methods to estimate porosity from seismic data. Neural network analysis is one of the latest technologies available to the petroleum industry.
520 $a In this study, I report results of an investigation of the use of neural network to predict lateral reservoir porosity. The approach is based on using average seismic acoustic impedances extracted from a 3D seismic volume to predict lateral average porosity for 13 reservoir geological layers. A neural network was trained using different subsets of well log data from 9 hydrocarbon wells and validated using the reminder of the wells. Data from the Unayzah reservoir in CNR field located in central basin of Saudi Arabia was used in this study.
520 $a Model-based post-stack seismic inversion was used to produce a seismic acoustic impedance volume. Average impedance maps were then created for 13 layers from the Unayzah reservoir interval in the CNR field.
520 $a Back-propagation neural network technique successfully estimated lateral reservoir porosity from seismic acoustic impedance and density attributes. The neural network performance using data from 6 wells (C, D, F, G, I, J), more or less distributed along the field axis, provided a better correlation and less scatter than other well training geometries in the testing phase. The A, B, and H wells were used for validation. Goodness of fit was 0.9985. The good neural network prediction in the testing phase reflects the neural network capability to estimate average reservoir porosities. Predicted lateral porosity maps incorporate heterogeneities introduced by the seismic data, and correlates with the seismic and geological interpretations.
520 $a Neural network results show that neural network method can be used to predict lateral reservoir porosities, provided neural network can be trained on the available wellbore data of that reservoir before application to seismic data.
590 $a School code: 0803.
650 4 $a Geophysics. $3 535228
650 4 $a Engineering, Petroleum. $3 1018448
650 4 $a Physics, Acoustics. $3 1019086
690 $a 0373
690 $a 0765
690 $a 0986
710 2 0 $a Texas A&M University. $3 718977
773 0 $t Dissertation Abstracts International $g 63-04B.
790 1 0 $a Watkins, Joel S., $e advisor
790 $a 0803
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3051579