Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
1999-08-09
2001-07-24
McElheny, Jr., Donald E. (Department: 2862)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
Reexamination Certificate
active
06266618
ABSTRACT:
The present invention relates to a method for automatic detection of planar heterogeneities crossing the stratification of an environment or medium, from images of borehole walls or developments of core samples of the said environment.
Tools which are referred to by the references FMI (Fullbore Formation Micro Imager) and FMS (Formation Micro Scanner) and are marketed by the company SCHLUMBERGER, make it possible to acquire electrical images from measurements of the local electrical conductivity of the wall of a borehole.
An electrical image of the wall of a borehole is a developed view which has, on a plane, a horizontal axis x representing the azimuthal distribution of the electrodes of the pads of the tool which is used, and a vertical axis y along which the depth (dimension) of the tool in the borehole is defined.
The electrical image of the wall of a borehole or the image of a core sample development is analysed in terms of planar heterogeneities and point heterogeneities.
In terms of image analysis, the planar heterogeneities present on the image can be categorized by their conductivity relative to the background of the image, their sharpness (grey scale contrast), their organization (isolated or grouped by family), their frequency (high or low frequency according to direction and depth) and their visibility (visible on the entire image or only on a part of the image).
Thus, on a high-resolution image of the wall of a borehole and/or on a developed core sample image, two main types of geological heterogeneities can be observed. The first type is generally a geological event which intersects the borehole and which has an extent greatly in excess of the diameter of the well, such as stratification and fracture planes, whereas the second type has a radial and vertical extension limited to the scale of the borehole and of the acquisition device, such as vesicles, nodules or perturbations of the bioturbation type, etc.
A planar heterogeneity is observed on an image in the form of a sinusoid with a general equation y=d+A (sin x+&PHgr;, in which the amplitude A and the phase &PHgr; correspond respectively to the dip and to the azimuth of the plane intersecting the well when the plane and the axis of the well are not parallel, d being the depth at which the sinusoid is located.
The categorization criteria indicated above often make it possible to recognize the geological significance of the planar heterogeneity: stratification or fracturing. Stratification is generally regarded as the dominant planar heterogeneity on the image; it is the most visible event, indicates dominant orientation of the image and is organized in families (one family per level).
Fracturing is a more infrequent isolated event which intersects the stratification and is often partially visible, and several different families of fractures can be recognized on a given level.
Methods for automatic detection of the stratification planes have been proposed. One of the methods relates to high-frequency bedding planes, and another method relates to bed boundaries. Such methods are, in particular, described in Patent Application FR-A-2 749 405 and in publications such as that by S-J. Ye, J. Shen and N.Keskes (1995), “Automatic Identification of bedding planes from electrical borehole images”, 9
th
Scandinavian Conference on Image Analysis, Jun. 6-9 1995, Uppsala, Sweden, and S-J. Ye, Ph. Rabiller & N. Keskes (1997), “Automatic High resolution sedimentary dip detection on borehole imagery”, SPWLA 38
th
Annual Logging Symposium, paper O. These methods make it possible to detect the dominant planar heterogeneity without being perturbed by the other planar or point heterogeneities.
In most cases, because of the wide variety of facies which are encountered, automatic detection of fractures is perturbed by the interference of various types of planes and other heterogeneities.
Other methods for detecting heterogeneities have been proposed in the literature, such as those disclosed by J. N. Antoine & J. P. Delhomme (1990), “A method to derive dips from bed boundaries in borehole images”, paper SPE 20540 &OHgr;, p. 131-130; by D. Torres, R. Strickland, & M. Gianzero (1990), “A new approach to determining dip and strike using borehole images”, SPWLA 31st Annual Logging Symposium, June 24-27, K, 20 p or by J. Hall, M. Ponzi, M. Gonfalini, & G. Maletti (1996), “Automatic extraction and characterisation of geological features and textures from borehole images and core photographs”, SPWLA 37
th
Annual Logging Symposium, paper CCC.
The method of Antoine et al. consists in detecting the stratification planes from contours, referred to as flow lines, which are located on the pad image, then, while complying with certain criteria, in matching the flow lines from pad to pad using a dynamic programming algorithm. The flow lines are obtained from tracing the local orientations of the stratification throughout the image and from selection of the flow lines which lie at the points of inflection. This method detects the slightest details of the flow lines in the image. When there are complex zones in which the planar and point heterogeneities are mixed, and as the pad images obtained are narrow, a complex technique ensues which has serious implementation difficulties. This is because, in spite of a highly developed contour-matching algorithm, it is difficult to obtain satisfactory results in the various geological situations encountered, when this is on the basis of flow lines which are too detailed, unless numerous parameters are set as a function of the type of facies encountered, which would lead to an algorithm which is difficult to use under operational conditions.
The method advocated by Torres et al. consists in using the Hough transform which makes it possible to determine, from an image, the specific parameters characterizing a geometrical shape such as a straight line, a circle, an ellipse or a sinusoid, then in projecting points of the said shape into the parameter space referred to as the Hough space. The point of intersection of these projections in the Hough space represents the parameters of the desired shape.
One drawback with this method resides in the fact that the depth of the sinusoid is not integrated in the parameter space, which leads to inaccuracy in terms of the depth and therefore limitation of the amplitude of the sinusoid because of the window size used by Torres et al.; another drawback is that it requires a great deal of computation time and memory, increasing very rapidly as a function of the dimension of the Hough space, that is to say the number of parameters which are desired.
The method advocated by Hall et al. also uses the Hough transform, but by characterizing the Hough space in three dimensions, namely dip, azimuth and depth of the plane. The Hough transform is applied after contour detection which is carried out either on the basis of the binarized image or after classification of neighbouring pixels. It should be noted that binarizing an image with multiple grey levels by thresholding involves a significant loss of information, and that it would therefore be difficult to detect and distinguish contours with different contrasts in the moving window which is used.
The last methods described in brief above seek to detect all the types of planes by a single algorithm without hierarchization. However, the planes which are to be detected have very different characteristics, such as contrast, frequency, etc. For this reason, these methods cannot be efficiently employed to detect fracturing heterogeneities reliably and securely.
The object of the present invention is to overcome the drawbacks of the prior art methods and to provide a method which, taking into account the different characteristics of the stratification and fracturing heterogeneities, makes it possible to eliminate the stratification of the image in order to better visualize the planes intersecting the stratification in order to facilitate their detection.
The present invention relates to a method for automatic detection
Keskes Naamen
Rabiller Philippe
Ye Shin-Ju
Elf Exploration Production
McElheny Jr. Donald E.
Ostrolenk Faber Gerb & Soffen, LLP
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