Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field
Expanding exploration activities into new fields has significantly boosted oil production. Well logging is a key method in petroleum exploration, used to evaluate hydrocarbon zones by analyzing parameters such as gamma ray, porosity, density, resistivity, and wave propagation velocity. These paramet...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/08/bioconf_srcm24_07002.pdf |
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| author | Rasif Nahari Utama Widya Ardhya Garini Sherly Fitri Indriani Rista Pratama Novian Putra Dhea |
| author_facet | Rasif Nahari Utama Widya Ardhya Garini Sherly Fitri Indriani Rista Pratama Novian Putra Dhea |
| author_sort | Rasif Nahari |
| collection | DOAJ |
| description | Expanding exploration activities into new fields has significantly boosted oil production. Well logging is a key method in petroleum exploration, used to evaluate hydrocarbon zones by analyzing parameters such as gamma ray, porosity, density, resistivity, and wave propagation velocity. These parameters are displayed as vertical log curves against well depth. However, logging tools sometimes fail to capture formation parameters accurately, creating gaps in well log data. Sonic log data are particularly prone to such gaps, as they are newer and less common in older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. Gradient boosting employs an ensemble of decision trees, iteratively correcting errors to model complex data patterns. This method is especially suitable for handling the intricate nature of well log data. In this study, Python was used to develop predictions for missing data, demonstrating the capability of machine learning to enhance data reliability and improve petroleum exploration processes. By bridging data gaps, machine learning ensures more accurate assessments of hydrocarbon zones, supporting better exploration outcomes. |
| format | Article |
| id | doaj-art-6e1853400b2c40f486e01a2b1afeca12 |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-6e1853400b2c40f486e01a2b1afeca122025-02-07T08:20:29ZengEDP SciencesBIO Web of Conferences2117-44582025-01-011570700210.1051/bioconf/202515707002bioconf_srcm24_07002Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' FieldRasif Nahari0Utama Widya1Ardhya Garini Sherly2Fitri Indriani Rista3Pratama Novian Putra Dhea4Department of Geophysics Engineering, Institut Teknologi Sepuluh NopemberDepartment of Geophysics Engineering, Institut Teknologi Sepuluh NopemberDepartment of Geophysics Engineering, Institut Teknologi Sepuluh NopemberDepartment of Geophysics Engineering, Institut Teknologi Sepuluh NopemberDepartment of Geophysics Engineering, Institut Teknologi Sepuluh NopemberExpanding exploration activities into new fields has significantly boosted oil production. Well logging is a key method in petroleum exploration, used to evaluate hydrocarbon zones by analyzing parameters such as gamma ray, porosity, density, resistivity, and wave propagation velocity. These parameters are displayed as vertical log curves against well depth. However, logging tools sometimes fail to capture formation parameters accurately, creating gaps in well log data. Sonic log data are particularly prone to such gaps, as they are newer and less common in older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. Gradient boosting employs an ensemble of decision trees, iteratively correcting errors to model complex data patterns. This method is especially suitable for handling the intricate nature of well log data. In this study, Python was used to develop predictions for missing data, demonstrating the capability of machine learning to enhance data reliability and improve petroleum exploration processes. By bridging data gaps, machine learning ensures more accurate assessments of hydrocarbon zones, supporting better exploration outcomes.https://www.bio-conferences.org/articles/bioconf/pdf/2025/08/bioconf_srcm24_07002.pdf |
| spellingShingle | Rasif Nahari Utama Widya Ardhya Garini Sherly Fitri Indriani Rista Pratama Novian Putra Dhea Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field BIO Web of Conferences |
| title | Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field |
| title_full | Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field |
| title_fullStr | Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field |
| title_full_unstemmed | Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field |
| title_short | Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field |
| title_sort | prediction of sonic log values using a gradient boosting algorithm in the ab field |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2025/08/bioconf_srcm24_07002.pdf |
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