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dc.contributor.authorRezaee, M. Reza
dc.contributor.authorSlatt, R.
dc.contributor.authorSigal, R.
dc.date.accessioned2017-01-30T11:23:47Z
dc.date.available2017-01-30T11:23:47Z
dc.date.created2008-11-19T18:02:02Z
dc.date.issued2007
dc.identifier.citationRezaee, M. Reza and Slatt, Roger and Sigal, Richard. 2007. Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir. ASEG Extended Abstracts 2007 1: 1-4.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/11257
dc.description.abstract

Estimation of reservoir parameters has always been a challenge for shale gas reservoirs. This study has concentrated on neural network technique and multiple regression analysis to predict reservoir properties including porosity, permeability, fluid saturation and total organic carbon content from conventional wireline log data for a large North American shale gas reservoir. More than 262 core analysis data from 3 wells were used as "target" and "response" for neural network and multiple regression analysis. Common log data available in three wells including GR, SP, RHOB, NPHI, DT and deep resistivity were used as "input" and "predictor".This study shows that reservoir parameters could be better estimated using the neural network technique than through multiple regression. The neural network method had a correlation coefficient greater than 80% for most of the parameters. Although providing a set of algorithms, multiple regression analysis was less successful for predicting reservoir parameters.

dc.publisherCSIRO Publishing
dc.relation.urihttp://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm.
dc.subjectNorth American
dc.subjectShale Gas reservoir
dc.subjectMulti Regression Analysis
dc.subjectArtificial Neural Network
dc.titleShale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir
dc.typeJournal Article
dcterms.source.volume1
dcterms.source.startPage1
dcterms.source.endPage4
dcterms.source.issn08123985
dcterms.source.titleASEG Extended Abstracts
curtin.note

The definitive version of this article can be found at http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm.

curtin.note

This article was orginally published by CSIRO Publishing

curtin.accessStatusFulltext not available
curtin.facultyDepartment of Petroleum Engineering
curtin.facultyFaculty of Science & Engineering


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