Detection of reservoir fractures in imaging logs using directional filtering
Every rock, which has pores connected together, can be considered as a reservoir rock. Existing fractures in the reservoir rock are the most effective factors, which increase the production and harvesting of the well. Detection of reservoir rock fractures is a very complicated process, due to factors such as poor contrast, fracture variable thickness, noise, discontinuities in the curve of a fracture, and the existence of some phenomenon like fracture bedding. In the well-log images, some sinusoidal curves are fractures. The directions of fractures are shown as some special curves. According to this concept, in this paper, fractures are extracted based on an optimized algorithm using directional filters. The first order derivative of the imaging log is calculated for each pixel. Then, the direction filters are designed based on a different direction of each pixel, and applied to the original imaging log. Finally, using the Hough transform, some parameters of each fracture are obtained, and are displayed on a real imaging log.
An Implementation of the Hough Transformation for the Identification and Labelling of Fixed Period Sinusoidal Curves
Hough transforms have long been used as computationally efficient methods for detecting particular shapes in images. This paper describes a practical implementation of a Hough transform for the characterisation of rock fractures in acoustic borehole images, where planar fractures ideally result in sinusoidal curves with a period equal to the perimeter of the borehole. The three remaining degrees of freedom of the sinusoid, namely its amplitude, baseline position, and phase, are successfully determined in ultrasound return images with considerable noise and clutter.
Multivariate fracture intensity prediction: Application to Oil Mountain anticline, Wyoming
The geometric characteristics of natural fractures significantly impact the hydraulic behavior of fractured reservoirs. Prediction of fracture geometry is therefore important for reservoir development decisions and production forecasting. Although many geometric, kinematic, mechanical, geomechanical, petrophysical, sedimentary, and geophysical attributes correlate to fracture intensity, typically, only the attribute with the highest absolute value correlation is chosen to be carried forward to influence prediction. We employ a geostatistical Bayesian updating approach that quantitatively accounts for multiple important attributes together impacting fracture geometry prediction. The resulting models are more representative of the true geological complexity. This methodology is applied to the Oil Mountain anticline outcrop near Casper, Wyoming.