Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir
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 c...
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| Format: | Journal Article |
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CSIRO Publishing
2007
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| Online Access: | http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm. http://hdl.handle.net/20.500.11937/11257 |
| _version_ | 1848747756994166784 |
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| author | Rezaee, M. Reza Slatt, R. Sigal, R. |
| author_facet | Rezaee, M. Reza Slatt, R. Sigal, R. |
| author_sort | Rezaee, M. Reza |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:54:13Z |
| format | Journal Article |
| id | curtin-20.500.11937-11257 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:54:13Z |
| publishDate | 2007 |
| publisher | CSIRO Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-112572018-12-14T00:48:21Z Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir Rezaee, M. Reza Slatt, R. Sigal, R. North American Shale Gas reservoir Multi Regression Analysis Artificial Neural Network 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. 2007 Journal Article http://hdl.handle.net/20.500.11937/11257 http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm. CSIRO Publishing restricted |
| spellingShingle | North American Shale Gas reservoir Multi Regression Analysis Artificial Neural Network Rezaee, M. Reza Slatt, R. Sigal, R. Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title | Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title_full | Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title_fullStr | Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title_full_unstemmed | Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title_short | Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir |
| title_sort | shale gas rock properties prediction using artificial neural network technique and multi regression analysis, anexample from a north american shale gas reservoir |
| topic | North American Shale Gas reservoir Multi Regression Analysis Artificial Neural Network |
| url | http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm. http://hdl.handle.net/20.500.11937/11257 |