skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

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

Leal, Jorge Alberto ; Ochoa, Luis Hernan ; Contreras, Carmen Cecilia

Earth sciences research journal, 2018-06, Vol.22 (2), p.75-82 [Periódico revisado por pares]

Bogata: Universidad Nacional de Colombia

Texto completo disponível

Citações Citado por
  • Título:
    Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging
  • Autor: Leal, Jorge Alberto ; Ochoa, Luis Hernan ; Contreras, Carmen Cecilia
  • Assuntos: borehole logs ; Boreholes ; calcareous lithologies ; Carbonate rocks ; Carbonates ; Catatumbo Basin ; Classification ; Data ; Data processing ; Electrical resistivity ; fractal dimension ; Fractals ; Gamma rays ; Geologists ; GEOSCIENCES, MULTIDISCIPLINARY ; Image acquisition ; image logs ; Imaging techniques ; Intervals ; Limestone ; Logic ; machine learning ; Photoelectricity ; Rock ; Rocks ; Shales ; Support vector machines ; Training ; Wells
  • É parte de: Earth sciences research journal, 2018-06, Vol.22 (2), p.75-82
  • Descrição: 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.
  • Editor: Bogata: Universidad Nacional de Colombia
  • Idioma: Inglês;Português
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.