Identification of natural fractures using resistive image logs, fractal dimension and support vector machines
The purpose of this research is to apply a new approach to identify natural fractures in wells in a hydrocarbon reservoir using resistive image logs, fractal dimension and support vector machines (SVMs). The stratigraphic sequence investigated by each well is composed of Cretaceous calcareous rocks...
- Autores:
-
Leal, Jorge Alberto
Ochoa, Luis Hernán
García, Jerson Andres
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/67607
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/67607
http://bdigital.unal.edu.co/68636/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Fractal dimension
resistive image logs
box counting method
natural fractures
hydrocarbon reservoir
Catatumbo basin
support vector machines (svms)
Dimensión fractal
registros de imágenes resistivas
método del conteo de cajas
fracturas naturales
yacimiento de hidrocarburos
cuenca del Catatumbo
máquinas de soporte vectorial
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | The purpose of this research is to apply a new approach to identify natural fractures in wells in a hydrocarbon reservoir using resistive image logs, fractal dimension and support vector machines (SVMs). The stratigraphic sequence investigated by each well is composed of Cretaceous calcareous rocks from the Catatumbo Basin, Colombia. The box counting method was applied to image logs in order to generate a curve representing variations of fractal dimension in these images throughout each well. The arithmetic mean of fractal dimension showed values ranging from 1,70 to 1,72 at the mineralized fracture intervals, and from 1,72 to 1,76 at the open fracture intervals. Morphological classification between open and mineralized natural fractures is performed using corelogs integration in a pilot well. Fractal dimension of images along with gamma rays and resistivity logs were employed as the input dataset of a SVM model identifying intervals with natural open fractures automatically, shortly after logs acquisition and previous to its interpretation by specialists. Although final results were affected by borehole conditions and logs quality, the SVM model showedaccuracy between 72,3% and 82,2% in 5 wells evaluated in the studied field. |
|---|