CN110716239A - Fine evaluation method for lithology of well logging gravel rock mass based on electrical imaging - Google Patents
Fine evaluation method for lithology of well logging gravel rock mass based on electrical imaging Download PDFInfo
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Abstract
The invention belongs to the technical field of well logging in oil exploration and development industries, and particularly relates to a well logging gravel rock lithology fine evaluation method based on electrical imaging. The evaluation method is more reliable in evaluation mode and more accurate in calculation result. The evaluation method comprises the following steps: carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanned image; carrying out edge detection on the conglomerate particles in the image after filling pretreatment, and segmenting the conglomerate particles from the background; according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles; based on the sliding window and the particle size, a particle size spectrum and lithology curve are calculated.
Description
Technical Field
The invention belongs to the technical field of well logging in oil exploration and development industries, and particularly relates to a well logging gravel rock lithology fine evaluation method based on electrical imaging.
Background
The exploration of gravel rock reservoirs has become one of the major tasks for energy exploration. The gravel rock body generally has strong heterogeneity in longitudinal and transverse directions due to the particularity of the source and the deposition process, the lithofacies on a vertical section changes rapidly, the lithology of a reservoir is complex, and the lithology identification and division of the reservoir are difficult by conventional well logging.
The electric imaging well logging obtains a two-dimensional image of the well periphery by an array electronic scanning technology, the resolution of well wall stratum description reaches 0.2in, and the structure and the characteristics of the well wall can be reflected more intuitively and clearly. The imaging logging can accurately describe the deposition environment of a reservoir, clearly reflect pores, argillaceous substances, cracks, bedding, holes, biological disturbance and the like, and can effectively perform sedimentary facies research and lithology recognition research.
The existing lithology automatic identification method based on electrical imaging mainly comprises an image segmentation method and an identification method based on image template matching. However, the image segmentation method does not consider the borehole coverage rate of the electrical imaging, and blank strips of the electrical imaging are not processed, so that the calculation result is inaccurate; the identification method based on image template matching excessively depends on a standard template, so that the algorithm has poor generalization capability and low identification accuracy.
Disclosure of Invention
The invention provides a well logging gravel rock lithology fine evaluation method based on electrical imaging, the evaluation method is more reliable in evaluation mode, and the calculated result is more accurate.
In order to solve the technical problems, the invention adopts the following technical scheme:
a fine evaluation method for lithology of a logging gravel rock body based on electrical imaging comprises the following steps:
(1) carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanning image;
(2) carrying out edge detection on the conglomerate particles in the image after the filling pretreatment, and segmenting the conglomerate particles from the background;
(3) according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles;
(4) and calculating a particle size spectrum and a lithology curve based on the sliding window and the particle size.
Further, the step (3) can be specifically described as follows:
setting the roundness of the conglomerate particles according to the geological deposition constraint conditions of the block;
if the roundness is sub-round, round or very round, the conglomerate particle segmentation result is represented by merging and looking up, and the mass center of each segmentation area is obtained and contains a pixel and a boundary pixel set;
according to the gray average value of the pixels contained in each divided area; judging whether the conglomerate exists or not;
according to the boundary pixel set, the curvature of the conglomerate area is calculated in a self-adaptive mode; according to the curvature threshold value, selecting concave points on the boundary, and positioning adjacent convex points on two sides; if the centroid of the adjacent area is within the coordinate range set by the pit and the adjacent convex point, merging the adjacent area and the conglomerate area;
and calculating the chord length between each concave point pair in the conglomerate region, calculating the corresponding arc length for the concave point pair smaller than the threshold value of the chord length, and splitting the conglomerate region according to the chord if the arc length is larger than the threshold value of the arc-chord ratio.
Further, the step (4) can be specifically described as follows:
setting the longitudinal size of the sliding window;
calculating the maximum conglomerate particle size in the evaluation depth range, and obtaining a statistical range of the conglomerate particle size according to a BIN value of a particle size spectrum; according to the size of the conglomerate particles, calculating the number of the conglomerate particles in the sliding window in each particle size range, and normalizing to obtain a particle size spectrum;
dividing the fine conglomerate, the middle conglomerate and the coarse conglomerate in the sliding window according to the threshold values of the sizes of the fine conglomerate and the middle conglomerate, and calculating the area ratio of the fine conglomerate, the middle conglomerate and the coarse conglomerate to obtain lithology curves of the fine conglomerate, the middle conglomerate and the coarse conglomerate; the area ratio of the non-conglomerate region is output as a sandstone lithology curve.
The invention provides a fine evaluation method for lithology of a logging gravel rock mass based on electrical imaging, which comprises the following steps: carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanned image; carrying out edge detection on the conglomerate particles in the image after filling pretreatment, and segmenting the conglomerate particles from the background; according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles; based on the sliding window and the particle size, a particle size spectrum and lithology curve are calculated. The fine evaluation method for the lithological character of the logging gravel rock body based on the electrical imaging uses the electrical imaging blank strip filling technology, so that the evaluation result can better accord with the manual evaluation result, and the calculation result is more accurate.
Drawings
FIG. 1 is a schematic flow chart of a method for finely evaluating the lithological character of a well logging gravel rock mass based on electrical imaging, provided by the invention;
FIG. 2 is an effect diagram for performing fine evaluation on lithology of a Y920 well in a two-stage interpretation depth range by using the well logging gravel rock lithology fine evaluation method based on electrical imaging provided by the invention.
Detailed Description
The invention provides a well logging gravel rock lithology fine evaluation method based on electrical imaging, the evaluation method is more reliable in evaluation mode, and the calculated result is more accurate.
As shown in figure 1, the method for finely evaluating the lithological character of the logging gravel rock mass based on the electrical imaging comprises the following steps:
step 1: carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanned image;
for example, the description will be made by taking the example of performing the blank stripe filling preprocessing by using the Morphological Component Analysis (MCA) algorithm based on the signal sparse representation. The specific implementation steps of the blank stripe filling preprocessing based on the MCA algorithm refer to the following steps:
(1) and initializing:
selecting iteration times N, a total variation regularization parameter gamma, an ending standard value Stop, a noise standard deviation sigma, a Curvelet transformation coarsest decomposition scale, an LDCT transformation ringing and a window width; and carrying out Curvelet and LDCT transformation on the original input image, calculating an initial threshold value delta by using a transformation coefficient, and calculating a descending step length lambda. Initializing the structural component Xn0, initial texture component Xt＝0。
(2) Iteration is carried out for N times:
①, calculating residual R ═ M (X-X)n-Xt) X is image data to be restored, and M is a mask template;
②, PartA structural component update:
a. to Xn+ R carries out Curvelet transformation to obtain transformation coefficient alphan；
b. For alphanUsing delta to perform soft threshold shrinkage, then performing Curvelet inverse transformation, and reconstructing Xn。
PartB: and (3) texture component updating:
a. to Xt+ R to make LDCT conversion to obtain conversion coefficient alphat；
b. For alphatUsing delta to perform soft threshold shrinkage, then performing LDCT inverse transformation, and reconstructing Xt。
PartC using a parameter gamma for a structural component XnTV correction is performed.
③, updating the threshold value delta, if the value is exponentially decreased, the value delta is delta multiplied by lambda, and if the value is linearly decreased, the value delta is delta-lambda.
Step 2: carrying out edge detection on the conglomerate particles in the image after filling pretreatment, and segmenting the conglomerate particles from the background;
and (3) further carrying out edge detection on the conglomerate particles in the pretreated graph obtained in the step 1 on the basis of finishing the step 1.
Specifically, a multi-scale edge detection method based on an edge stream is described as an example, and the multi-scale edge detection method includes the following specific implementation steps:
(1) setting an initial scale, and finishing the scale and the scale jump interval;
(2) and calculating the edge flow energy E (s, theta) in an initial scale according to the following calculation formula:
in the formula, Gσ(x, y) is a gaussian function of the scale σ, and n ═ is (cos θ, sin θ) a unit vector in the gradient direction;
projecting the edge flow energy of each pixel point to X and Y directions, and accumulating to obtain an initial edge flow vector field;
(3) obtaining a current scale according to the scale jump interval, and calculating a current edge flow vector field; if the amplitude of the initial edge flow vector field is smaller than the amplitude threshold value, the initial edge flow vector field is assigned as the current vector field; if the angle between the initial vector field and the current vector field is smaller than the angle threshold, overlapping the current vector field to the initial vector field;
(4) repeating the steps (2) and (3) until the scale is finished, wherein the initial scale vector field is the final multi-scale edge flow vector field S;
(5) calculating the divergence of the vector field S and solving the following Possion equation
An edge flow function C is obtained.
Then, the detected edge part is segmented from the background, and specifically, the edge part is segmented by taking an image segmentation algorithm based on curve evolution as an example. The specific implementation steps of the image segmentation algorithm based on curve evolution are as follows:
(1) setting an error threshold lambda, a smoothing weight coefficient w and an iteration number N;
(2) according to the following level set equation:
wherein, the iterative computation curve evolvement result image is
；
(3) If, if
If the difference value with the original image is smaller than the error threshold value or the iteration times is larger than N, stopping the iteration;
(4) and convolving the curve evolution result image with the first derivatives of the Gaussian functions in the X and Y directions respectively, and calculating edge points on the image according to the amplitude values.
And step 3: according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles;
and (3) further carrying out region merging and splitting on the segmented conglomerate particles on the basis of finishing the step 2. As a preferred embodiment of the present invention, the region merging and splitting method based on geological deposition constraints can be specifically described as follows:
(1) setting the roundness of the conglomerate particles according to the geological deposition constraint conditions of the block;
(2) if the roundness is sub-round, round or very round, the conglomerate particle segmentation result is represented by a union set, and the mass center of each segmentation area and a set containing pixels and boundary pixels are obtained;
(3) the gray level average value of the pixels is contained according to each divided area; judging whether the conglomerate exists or not;
(4) and calculating the curvature of the conglomerate region in a self-adaptive manner according to the boundary pixel set. Based on the curvature threshold, a pit on the boundary is selected and adjacent bumps on both sides are located. If the centroid of the adjacent area is within the coordinate range set by the pit and the adjacent convex point, merging the adjacent area and the conglomerate area;
(5) and calculating the chord length between each concave point pair in the conglomerate region, calculating the corresponding arc length for the concave point pair smaller than the threshold value of the chord length, and splitting the conglomerate region according to the chord if the arc length is larger than the threshold value of the arc-chord ratio.
And step 4: based on the sliding window and the particle size, a particle size spectrum and lithology curve are calculated.
And (4) further calculating a particle size spectrum and a lithology curve on the basis of finishing the step 3. As a preferred embodiment of the present invention, the specific implementation steps of the method for calculating the particle size spectrum and the lithology curve are as follows:
(1) setting the longitudinal size of the sliding window;
(2) calculating and evaluating the maximum conglomerate particle size in the depth range, and according to a BIN value of a particle size spectrum; obtaining the statistical range of the sizes of the conglomerate particles; according to the size of the conglomerate particles, calculating the number of the conglomerate particles in the sliding window in each particle size range, and normalizing to obtain a particle size spectrum;
(3) dividing the fine conglomerate, the middle conglomerate and the coarse conglomerate in the sliding window according to the threshold values of the sizes of the fine conglomerate and the middle conglomerate, and calculating the area ratio of the fine conglomerate, the middle conglomerate and the coarse conglomerate to obtain lithology curves of the fine conglomerate, the middle conglomerate and the coarse conglomerate; the area ratio of the non-conglomerate region is output as a sandstone lithology curve.
It is noted that fig. 2 shows an effect graph of fine evaluation of lithology in a two-stage interpretation depth range of a Y920 well based on the evaluation method provided by the present invention. From fig. 2, it can be seen that after the blank band is filled in the original electrical imaging image, the constructed particle size spectrum and the calculated lithology profile can effectively reflect the size distribution of conglomerate particles in the well section and the detailed lithology division.
The invention provides a fine evaluation method for lithology of a logging gravel rock mass based on electrical imaging, which comprises the following steps: carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanned image; carrying out edge detection on the conglomerate particles in the image after filling pretreatment, and segmenting the conglomerate particles from the background; according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles; based on the sliding window and the particle size, a particle size spectrum and lithology curve are calculated. The fine evaluation method for the lithological character of the logging gravel rock body based on the electrical imaging uses the electrical imaging blank strip filling technology, so that the evaluation result can better accord with the manual evaluation result, and the calculation result is more accurate.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (3)
1. A fine evaluation method for lithology of a logging gravel rock body based on electrical imaging is characterized by comprising the following steps:
(1) carrying out blank strip filling pretreatment on the two-dimensional static microresistivity scanning image;
(2) carrying out edge detection on the conglomerate particles in the image after the filling pretreatment, and segmenting the conglomerate particles from the background;
(3) according to geological deposition constraint conditions, carrying out region merging and splitting on the segmented conglomerate particles;
(4) and calculating a particle size spectrum and a lithology curve based on the sliding window and the particle size.
2. The electrical imaging based fine evaluation method for the lithology of the logging gravel rock mass is characterized in that the step (3) can be specifically described as follows:
setting the roundness of the conglomerate particles according to the geological deposition constraint conditions of the block;
if the roundness is sub-round, round or very round, the conglomerate particle segmentation result is represented by merging and looking up, and the mass center of each segmentation area is obtained and contains a pixel and a boundary pixel set;
according to the gray average value of the pixels contained in each divided area; judging whether the conglomerate exists or not;
according to the boundary pixel set, the curvature of the conglomerate area is calculated in a self-adaptive mode; according to the curvature threshold value, selecting concave points on the boundary, and positioning adjacent convex points on two sides; if the centroid of the adjacent area is within the coordinate range set by the pit and the adjacent convex point, merging the adjacent area and the conglomerate area;
and calculating the chord length between each concave point pair in the conglomerate region, calculating the corresponding arc length for the concave point pair smaller than the threshold value of the chord length, and splitting the conglomerate region according to the chord if the arc length is larger than the threshold value of the arc-chord ratio.
3. The electrical imaging based logging gravel rock lithology fine evaluation method according to claim 1, wherein the step (4) can be specifically described as follows:
setting the longitudinal size of the sliding window;
calculating the maximum conglomerate particle size in the evaluation depth range, and obtaining a statistical range of the conglomerate particle size according to a BIN value of a particle size spectrum; according to the size of the conglomerate particles, calculating the number of the conglomerate particles in the sliding window in each particle size range, and normalizing to obtain a particle size spectrum;
dividing the fine conglomerate, the middle conglomerate and the coarse conglomerate in the sliding window according to the threshold values of the sizes of the fine conglomerate and the middle conglomerate, and calculating the area ratio of the fine conglomerate, the middle conglomerate and the coarse conglomerate to obtain lithology curves of the fine conglomerate, the middle conglomerate and the coarse conglomerate; the area ratio of the non-conglomerate region is output as a sandstone lithology curve.
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