Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging
In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. During training stages the SVMs use neutron, photoelectric factor and gamma...
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Universidad Nacional de Colombia
2018-04-01
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| Series: | Earth Sciences Research Journal |
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| Online Access: | https://revistas.unal.edu.co/index.php/esrj/article/view/68320 |
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| author | Jorge Alberto Leal Luis Hernan Ochoa Carmen Cecilia Contreras |
| author_facet | Jorge Alberto Leal Luis Hernan Ochoa Carmen Cecilia Contreras |
| author_sort | Jorge Alberto Leal |
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| description | In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. During training stages the SVMs use neutron, photoelectric factor and gamma ray logs as input; also mean and variance of resistivity acquired for image tool and fractal dimension of resistive images. The first SVM employs in the training stage intervals manually interpreted of fossiliferous limestone, performed by a specialized geologist integrating information of core-logs correlation of a pilot well; afterwards, in classification stages, this SVM automatically recognizes intervals with fossiliferous limestone only using logs data of any well of the field. The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing automatically these rock associations during classification stage without interpretations of a geologist as input data. Additionally, a logic function was applied to intervals with photoelectric factor ≥ 4 and all sections not classified by the SVMs were grouped as laminated calcareous rocks. The SVMs and logic function show accuracy of 98.76 %, 94.02 % and 94.60 % respectively in six evaluated wells and might be applied to other wells in the field that have the same dataset. This methodology is highly dependent of the data quality and all intervals affected by bad borehole condition have to be removed prior its application in order to avoid wrong interpretations. Finally, the whole model has to be recalibrated to be applied in other fields of the basin. |
| format | Article |
| id | doaj-art-547804bfbb4b4d09a003f63dfda6a702 |
| institution | OA Journals |
| issn | 1794-6190 2339-3459 |
| language | English |
| publishDate | 2018-04-01 |
| publisher | Universidad Nacional de Colombia |
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| series | Earth Sciences Research Journal |
| spelling | doaj-art-547804bfbb4b4d09a003f63dfda6a7022025-08-20T02:19:15ZengUniversidad Nacional de ColombiaEarth Sciences Research Journal1794-61902339-34592018-04-01222758210.15446/esrj.v22n2.6832048638Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical ImagingJorge Alberto Leal0Luis Hernan Ochoa1Carmen Cecilia Contreras2Universidad Nacional de ColombiaPh.D. Professor, Geosciences Department, Science Faculty, Universidad Nacional de Colombia, Bogota.Principal Geologist, PTS-DFW. Schlumberger TechnologyIn this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. During training stages the SVMs use neutron, photoelectric factor and gamma ray logs as input; also mean and variance of resistivity acquired for image tool and fractal dimension of resistive images. The first SVM employs in the training stage intervals manually interpreted of fossiliferous limestone, performed by a specialized geologist integrating information of core-logs correlation of a pilot well; afterwards, in classification stages, this SVM automatically recognizes intervals with fossiliferous limestone only using logs data of any well of the field. The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing automatically these rock associations during classification stage without interpretations of a geologist as input data. Additionally, a logic function was applied to intervals with photoelectric factor ≥ 4 and all sections not classified by the SVMs were grouped as laminated calcareous rocks. The SVMs and logic function show accuracy of 98.76 %, 94.02 % and 94.60 % respectively in six evaluated wells and might be applied to other wells in the field that have the same dataset. This methodology is highly dependent of the data quality and all intervals affected by bad borehole condition have to be removed prior its application in order to avoid wrong interpretations. Finally, the whole model has to be recalibrated to be applied in other fields of the basin.https://revistas.unal.edu.co/index.php/esrj/article/view/68320machine learningsupport vector machinesborehole logsimage logsfractal dimensioncalcareous lithologiesCatatumbo Basin. |
| spellingShingle | Jorge Alberto Leal Luis Hernan Ochoa Carmen Cecilia Contreras Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging Earth Sciences Research Journal machine learning support vector machines borehole logs image logs fractal dimension calcareous lithologies Catatumbo Basin. |
| title | Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging |
| title_full | Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging |
| title_fullStr | Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging |
| title_full_unstemmed | Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging |
| title_short | Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging |
| title_sort | automatic identification of calcareous lithologies using support vector machines borehole logs and fractal dimension of borehole electrical imaging |
| topic | machine learning support vector machines borehole logs image logs fractal dimension calcareous lithologies Catatumbo Basin. |
| url | https://revistas.unal.edu.co/index.php/esrj/article/view/68320 |
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