Published

2016-09-01

Identification of natural fractures using resistive image logs, fractal dimension and support vector machines

Identificación de fracturas naturales utilizando registro de imágenes resistivas, dimensión fractal y máquinas de soporte vectorial

DOI:

https://doi.org/10.15446/ing.investig.v36n3.56198

Keywords:

Fractal dimension, resistive image logs, box counting method, natural fractures, hydrocarbon reservoir, Catatumbo basin, support vector machines (svms) (en)
Dimensión fractal, registros de imágenes resistivas, método del conteo de cajas, fracturas naturales, yacimiento de hidrocarburos, cuenca del Catatumbo, máquinas de soporte vectorial (es)

Downloads

Authors

  • Jorge Alberto Leal Universidad Nacional de Colombia
  • Luis Hernán Ochoa Associated Professor at Sciences Faculty, Geosciences Department, Universidad Nacional de Colombia (Colombia).
  • Jerson Andres García Universidad de los Andes. Ecopetrol S.A.

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 showed
accuracy between 72,3% and 82,2% in 5 wells evaluated in the studied field.

El propósito de esta investigación es aplicar un nuevo enfoque para identificar fracturas naturales en pozos de un yacimiento de hidrocarburo utilizando registros de imágenes resistivas, dimensión fractal y máquinas de soporte vectorial (MSV). La secuencia estratigráfica alcanzada por cada pozo está compuesta por rocas calcáreas cretácicas de la Cuenca del Catatumbo, Colombia. El método del conteo de cajas se aplicó a registros de imágenes, generando una curva que representa variaciones de dimensión fractal en las imágenes a lo largo de cada pozo. La media aritmética de dimensión fractal mostró valores desde 1,70 a 1,72 en intervalos con fracturas mineralizadas y desde 1,72 a 1,76 en intervalos con fracturas abiertas. La clasificación morfológica entre fracturas naturales abiertas y mineralizadas es realizada utilizando integración núcleo-registro de un pozo piloto. La dimensión fractal de las imágenes junto con registros de rayos gamma y resistividad son empleados como datos de entrada a un modelo de MSV identificando intervalos con fracturas naturales abiertas automáticamente, poco después de adquirir los registros y previo a su interpretación por especialistas. Aunque los resultados finales están afectados por condiciones del hoyo y calidad de registros, el modelo de MSV mostró exactitud entre 72,3% y 82,2% en 5 pozos evaluados del campo estudiado.

References

Asquith, G., & Krygowski , D. (2004). Basic Well Log Analysis.(II ed., Vol. 16). Tulsa, Oklahoma, United States of America: The American Association of Petroleum Geologists.

Barrero, D., Pardo, A., Vargas, C., & Martínez, J. (2007). Colombian Sedimentary Basins. Bogota, D.C., Colombia:Agencia Nacional de Hidrocarburos - ANH.

Leal, J., Ochoa, L., & Garcia, J. (2014). Fractal Dimension and Image Logs, a New Approach to Enhance the Characterization of Naturally Fractured Reservoirs and Its Application in Catatumbo Basin-Colombia . AAPG’s Annual Convention & Exhibition (p. 90189). Houston: The American Association of Petroleum Geologists.

Mandelbrot, B. (1983). The Fractal Geometry of Nature. New York, New York, United States of America: W. H. Freeman and Company.

Moreno, G., & García, O. (2006). Quantitative Characterization of Fracture Patterns with Circular Windows and Fractal Analysis. Geología Colombiana (31), 73-74.

Nelson, R. (2001). Geologic Analysis of Naturally Fractured Reservoirs (II ed.). Houston, Texas, United States of America: Gulf Professional Publishing.

Roy, A., Perfect, E., Dunne, W., & Mackay, L. (2007). Fractal Characterization of Fracture Networks: An Improved Box-Counting Technique. Journal of Geophysical Research, 112, 1-2. DOI: 10.1029/2006JB004582

Taylor, J., & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge, United Kingdom: Cambridge University Press. DOI: 10.1017/CBO9780511809682

Turcotte, D. (1997). Fractal and Chaos in Geology and Geophysics. Cambridge, United Kingdom: Cambridge University Press. DOI: 10.1017/CBO9781139174695

Vivas, M. (1992). Techniques for Inter Well Description by Applying Geostatistic and Fractal Geometry Methods to Well Logs and Core Data. Ph.D. Thesis, xvi-xvii. Norman,

Oklahoma, United States of America: The University of Oklahoma.

Watanabe, K., & Takahashi, H. (1993). Fractal Characterization of Subsurface Fracture Network for Geothermal Energy Extraction System. In S. University (Ed.), Eighteenth Workshop on Geothermal reservoir Engineering (pp. 119-120.). Stanford: Stanford University.

Witten, I., & Franck, E. (2005). Data Mining Practical Machine Learning Tool and techniques (II ed.). San Francisco, California, United States of America: Morgan Kaufmann Publisher.

Dimensions

PlumX

Article abstract page views

1793

Downloads

Download data is not yet available.

How to Cite

Identification of natural fractures using resistive image logs, fractal dimension and support vector machines. (2016). Ingeniería E Investigación, 36(3), 125-132. https://doi.org/10.15446/ing.investig.v36n3.56198