Borehole imaging

1. A method of detecting significant events intersecting a borehole from an image of the borehole wall, which image comprises a two-dimensional array of numbers, each number being the magnitude of a relevant borehole parameter at a point defined by the circumferential direction and the depth, which method comprises the steps of:

(a) converting the image into a three-dimensional orientation space consisting of a stack of two-dimensional images, wherein each two-dimensional image is obtained by applying an edge-detecting or line-detecting filter to the image with a particular orientation of the filter;

(b) selecting a parameter relation that represents the intersection of an event with the borehole wall, wherein the intersection is characterized by n parameters;

(c) creating an n-dimensional parameter space consisting of numbers as a function of the n parameters, wherein each number is a measure of the support in the orientation space for an intersection characterized by the parameters that pertain to that number;

(d) selecting in the parameter space a set of the largest numbers, wherein the parameters that pertain to each of these largest numbers represent well supported intersections with the borehole wall; and

(e) presenting the intersections pertaining to the set of the largest numbers as a list of data representing significant events.

2. The method according to claim 1 , wherein step (d) further includes the steps of:

d1) selecting k intersections having a high support to form a set of k candidate intersections and sorting the set by support;

d2)storing the parameters that represent the candidate intersection having the largest support in an array of intersections;

d3)removing the data pertaining to the candidate intersection having the largest support from the orientation space;

d4)recalculating the support in the orientation space for the remaining candidate intersections to obtain a reduced set of candidate intersections, sorting the reduced set by support, and adding the parameters that represent the candidate intersection having the largest support to the array of intersections;

d5)repeating steps d3) and d4) for all candidate intersections to obtain an ordered set of intersections; and

d6)selecting from the ordered set of intersections the k−i intersections having the largest support, and presenting the k−i intersections as a list of data representing significant events, wherein i can be any number less than k.

3. The method according to claim 1 , wherein step (d) further comprises determining for every point in the orientation space that belongs to a well-supported candidate intersection a support set comprising those orientations for which the support is larger than a threshold value times the support of actual orientation of the well-supported candidate intersection; and removing the candidate intersection from the list of candidate intersections if the number of elements in the support set is relatively large compared to the number of filter orientations.

4. The method according to claim 2 , wherein step (d) further comprises determining for every point in the orientation space that belongs to a well-supported candidate intersection a support set comprising those orientations for which the support is larger than a threshold value times the support of actual orientation of the well-supported candidate intersection; and removing the candidate intersection from the list of candidate intersections if the number of elements in the support set is relatively large compared to the number of filter orientations.

5. The method according to claim 1 , wherein, in step (a), the stack of two-dimensional images comprises images obtained by applying the edge-detecting or line-detecting filter to the image at different orientations of the filter.

6. The method according to claim 1 , wherein in step (a) the filter is applied at each point of the image with the particular orientation of the filter.

7. The method according to claim 1 , wherein one dimension in the three dimensional orientation space in step (a) is formed by the filter orientation.

8. The method according to claim 1 , wherein a Fourier transform of the filter is a product of a radial part of the filter, an angular part of the filter, and a quadrature factor.

9. The method according to claim 1 , wherein the filter is a quadrature filter.

10. The method according to claim 1 , wherein the transfer function of the filter is a complex function.