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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Autores:: Leal, Jorge Alberto
Ochoa, Luis Hernan
Contreras, Carmen Cecilia

Tipo de recurso:: Article of journal

Fecha de publicación:: 2018

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

Description
Summary:	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.

Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging

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