Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network
Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW predic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University of Baghdad/College of Engineering
2022-12-01
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| Series: | Iraqi Journal of Chemical and Petroleum Engineering |
| Online Access: | https://ijcpe.uobaghdad.edu.iq/index.php/ijcpe/article/view/916 |
| _version_ | 1797974737011343360 |
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| author | Jassim Mohammed Al Said Naji Ghassan H. Abdul-Majeed Ali K. Alhuraishawy |
| author_facet | Jassim Mohammed Al Said Naji Ghassan H. Abdul-Majeed Ali K. Alhuraishawy |
| author_sort | Jassim Mohammed Al Said Naji |
| collection | DOAJ |
| description |
Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data that was adopted by the ANN study was used here where it is comprised of 1922 measured points of SSW and the other nine parameters of Gamma Ray, Compressional Sonic, Caliper, Neutron Log, Density Log, Deep Resistivity, Azimuth Angle, Inclination Angle, and True Vertical Depth from one Iraqi directional well. Three existing empirical correlations are based only on Compressional Sonic Wave Time (CSW) for predicting SSW. In the same way of developing previous correlations, a fourth empirical correlation was developed by using all measured data points of SSW and CSW. A comparison demonstrated that utilizing ANN was better for SSW predicting with a higher R2 equal to 0.966 and lower other statistical coefficients than utilizing four empirical correlations, where correlations of Carroll, Freund, Brocher, and developed fourth had R2 equal to 0.7826, 0.7636, 0.6764, and 0.8016, respectively, with other statistical parameters that show the new developed correlation best than the other three existing. The use of ANN or new developed correlation in future SSW calculations is relevant to decision makers due to a number of limitations and target SSW accuracy.
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| first_indexed | 2024-04-11T04:24:26Z |
| format | Article |
| id | doaj.art-f3b7b18e5851477db2b0cf3336b1e3a4 |
| institution | Directory Open Access Journal |
| issn | 1997-4884 2618-0707 |
| language | English |
| last_indexed | 2024-04-11T04:24:26Z |
| publishDate | 2022-12-01 |
| publisher | University of Baghdad/College of Engineering |
| record_format | Article |
| series | Iraqi Journal of Chemical and Petroleum Engineering |
| spelling | doaj.art-f3b7b18e5851477db2b0cf3336b1e3a42022-12-30T07:24:17ZengUniversity of Baghdad/College of EngineeringIraqi Journal of Chemical and Petroleum Engineering1997-48842618-07072022-12-0123410.31699/IJCPE.2022.4.7Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural NetworkJassim Mohammed Al Said Naji0Ghassan H. Abdul-Majeed1Ali K. Alhuraishawy2Petroleum Engineering Department, College of Engineering, University of Baghdad, Baghdad, Iraq.Department of Scientific Affairs, University of Baghdad, Baghdad, Iraq.Reservoir and Field Development Directorate, Ministry of Oil, Baghdad, Iraq. Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data that was adopted by the ANN study was used here where it is comprised of 1922 measured points of SSW and the other nine parameters of Gamma Ray, Compressional Sonic, Caliper, Neutron Log, Density Log, Deep Resistivity, Azimuth Angle, Inclination Angle, and True Vertical Depth from one Iraqi directional well. Three existing empirical correlations are based only on Compressional Sonic Wave Time (CSW) for predicting SSW. In the same way of developing previous correlations, a fourth empirical correlation was developed by using all measured data points of SSW and CSW. A comparison demonstrated that utilizing ANN was better for SSW predicting with a higher R2 equal to 0.966 and lower other statistical coefficients than utilizing four empirical correlations, where correlations of Carroll, Freund, Brocher, and developed fourth had R2 equal to 0.7826, 0.7636, 0.6764, and 0.8016, respectively, with other statistical parameters that show the new developed correlation best than the other three existing. The use of ANN or new developed correlation in future SSW calculations is relevant to decision makers due to a number of limitations and target SSW accuracy. https://ijcpe.uobaghdad.edu.iq/index.php/ijcpe/article/view/916 |
| spellingShingle | Jassim Mohammed Al Said Naji Ghassan H. Abdul-Majeed Ali K. Alhuraishawy Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network Iraqi Journal of Chemical and Petroleum Engineering |
| title | Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network |
| title_full | Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network |
| title_fullStr | Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network |
| title_full_unstemmed | Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network |
| title_short | Comparison of Estimation Sonic Shear Wave Time Using Empirical Correlations and Artificial Neural Network |
| title_sort | comparison of estimation sonic shear wave time using empirical correlations and artificial neural network |
| url | https://ijcpe.uobaghdad.edu.iq/index.php/ijcpe/article/view/916 |
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