Image analysis – Applications – Seismic or geological sample measuring
Reexamination Certificate
2001-04-25
2004-08-03
Johns, Andrew W. (Department: 2621)
Image analysis
Applications
Seismic or geological sample measuring
C382S168000
Reexamination Certificate
active
06771800
ABSTRACT:
The present invention relates to a method of chrono-stratigraphic interpretation of a seismic cross section or block, that is to say a geological record of a seismic cross section or block.
The present invention lies in the context of oil exploration and allows a switch from the geophysical domain to the geological domain.
STATE OF THE ART
The method according to the invention applies to seismic cross sections or seismic blocks. A seismic cross section is formed by the juxtaposition in a plane of sampled one-dimensional signals referred to as seismic traces. Likewise, a seismic block is formed by the juxtaposition of seismic traces in a volume. The expression “seismic section” refers either to a seismic cross section or to a slice of seismic block. A seismic section offers a view of the juxtaposition of the seismic traces contained in the plane of section. These views are seismic images, which will be referred to as seismic image sections in the account of the implementation of the method. In a seismic image, the luminous intensity of a pixel is proportional to the seismic magnitude represented by the one-dimensional signals.
The chrono-stratigraphic interpretation of seismic cross sections or seismic blocks involves the synthesis of seismic horizons in the cross section or the block. Several methods have been devised for carrying out syntheses of horizons. Their results are better or worse depending in fact on the geological environment whose image is offered by the seismic section. Thus, in regions where the geological strata are of the monoclinal dominant type, the synthesis of horizons by measurement of similarity between neighbouring traces gives good results. On the other hand, in zones where the geology is more disturbed, it is preferable firstly to calculate the gradient vectors of the luminous intensity between neighbouring pixels and then to implement a horizon synthesis by integrating the orientation field of the calculated gradient vectors.
The thesis by Marc Donias, submitted on Jan. 28, 1999 to the University of Bordeaux I and entitled “Caractérisation de champs d'orientation par analyse en composantes principales et estimation de la courbure. Application aux images sismiques”, [Characterization of orientation fields by principal components analysis and estimation of curvature. Application to seismic images], describes in detail the abovementioned schemes for carrying out horizon synthesis.
Horizon synthesis applied to each pixel of the seismic image section creates as many horizons as there are pixels in the image. The seismic horizons, in the guise of markers of the local geology, cannot intersect. On the other hand, they can converge, merge locally and blend into one, or even diverge. The merging of horizons leads to the concept of accumulation of syntheses.
To carry out an accumulation of the horizon syntheses, one defines a matrix which is identical in size to the seismic image. Each element of the matrix is associated with a pixel of the image and is initially assigned a zero value. For each pixel of the image a continuity curve is calculated which corresponds to the synthesis of the horizon passing through the said pixel. All the continuity curves are transverse to the vertical dimension of the image. When calculating the continuity curves, an element of the matrix is incremented by one unit each time the pixel with which it is associated in the image is crossed by a continuity curve.
The calculation of a continuity curve transverse to the vertical dimension of the image section at a given pixel consists in calculating the gradients of luminous intensity for all the pixels included in a neighbourhood of the chosen pixel, then in calculating a local gradient from the gradient measurements obtained over the neighbourhood and in assigning the local gradient to the chosen pixel. The continuity curve is then developed by marching transversely from pixel to pixel starting from the chosen pixel up to the vertical lateral boundaries of the image, in the two directions indicated by the local gradient and its additive inverse, by iteratively repeating the previous two steps.
When all the pixels of the seismic image section have been scanned, the matrix carrying out the accumulations of syntheses is represented in the form of a new image in which each pixel possesses a luminous intensity proportional to the number aggregated in the corresponding element of the matrix, which number is at least equal to one. The boundaries observed on this image give a good idea of the organization of the geological strata in the subsoil.
CONTRIBUTION OF THE INVENTION
The method according to the invention exploits the image obtained by accumulation of syntheses to determine the geological depositions such as they were deposited and not such as they are observed today in the form of strata. To do this, the method defines a transformation of the vertical scale of the seismic section measured in seismic times into a geological vertical scale measured in geological times. The method thus makes it possible to define the rates of sedimentation which governed the depositions of the geological strata. In particular, it highlights the geological hiatuses, that is to say erosions and gaps.
DEFINITION OF THE INVENTION
The subject of the present invention is a method of chrono-stratigraphic interpretation of a seismic image section S, comprising a horizontal dimension or width and a vertical dimension or height in the direction of the subsoil and consisting of columns of pixels, which consists in:
defining a matrix M identical in size to the image section S and consisting of elements each of which is associated with a pixel of the image section S and is initially assigned a zero value,
for each pixel i of the image section S, calculating a continuity curve Ci passing through the said pixel and transverse to the vertical dimension of the image section S,
incrementing an element of the matrix M by one unit each time the pixel with which it is associated in the image section S is crossed by a curve Ci,
the said method being characterized in that it furthermore consists in:
constructing, for each column c of the matrix M, a histogram Hc consisting of a number of classes which is equal to the number of elements of the said column c, each class corresponding to one of the elements of the column c and containing a number of samples which is equal to the value aggregated in the relevant element of the matrix M, which value is equal to the number of curves passing through the pixel associated with the said element, the total number of samples distributed in the histogram constructed for each column being equal to the total number of pixels in the image section S,
equalizing each histogram Hc so as to produce an equalized histogram Hc′,
defining an empty image section S′, whose width in pixels is identical to the width in pixels of the image section S and whose height in pixels is equal to the number of classes of the histograms Hc′,
assigning the distribution defined by the equalized histogram Hc′ to each column c′ of the image section S′ by allocating each pixel of the column c′ the cardinality of the content of the associated class of Hc′,
delimiting in the image section S′ the groups of contiguous pixels containing samples and labelling each of the said groups,
allocating each pixel of the image section S the label given to the pixel group to which it was assigned in the image section S′ and displaying the labelled image section S.
According to another characteristic, the calculation of the continuity curve Ci transverse to the vertical dimension of the image section S at a given pixel i consists in:
calculating the gradients of luminous intensity for all the pixels included in a neighbourhood Vi of pixel i,
calculating a local gradient Gi from the gradient measurements obtained over the neighbourhood Vi and assigning the gradient Gi to pixel i,
marching transversely from pixel to pixel starting from pixel i up to the vertical l
Baylou Pierre
Donias Marc
Guillon Sébastien
Keskes Naamen
Pauget Fabien
ELF Exploration Production
Johns Andrew W.
Ostrolenk Faber Gerb & Soffen, LLP
LandOfFree
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