Characterization method for fine-grained sedimentary rock laminar texture

• US 10,643,321 B1
• Filed: 12/27/2019
• Issued: 05/05/2020
• Est. Priority Date: 07/23/2018
• Status: Active Grant

• Current Assignee: China University of Petroleum
• Original Assignee: China University of Petroleum
• Inventors: Wang, Guanmin, Xiong, Zhouhai, Li, Mingpeng, He, Lingxiao
• Primary Examiner(s): Osinski, Michael S
• Application Number:
  US16/729,260
• Publication Number:
  US 20200134805A1
• Time in Patent Office: 130 Days
• CPC Class Codes: G01N 21/952 Inspecting the exterior surface of cylindrical bodies or wires G01N21/956 takes precedence
• CPC Class Codes: G01N 21/954 Inspecting the inner surface of hollow bodies, e.g. bores
• CPC Class Codes: G01N 33/24 Earth materials G01N33/42 takes precedence
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/30242 Counting objects in image
• CPC Class Codes: G06T 5/30 Erosion or dilatation, e.g. thinning
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G09B 23/40 for geology

Abstract

The present application relates to a characterization method for fine-grained sedimentary rock laminar texture, including S1: image preprocessing; S2: loading the image, and normalizing the image to a specified size; S3: performing mean filtering, dilation operation and binarization processing on the image; S4: determining whether laminars are developed; S5: determining the number of bright laminars and dark laminars; S6: determining the continuity of bright laminars and dark laminars; S7: according to the statistical result and the calculation result, recording the characterized parameters into Excel. The present application can accurately characterize the texture features of fine-grained sedimentary rock laminar. Compared with the prior art, the present application have higher efficiency and satisfies the requirement of symmetrically depicting the growth features of fine-grained sedimentary rock laminar, and a technical support is provided for the exploration and development of shale oil and gas.

Claims

1. A characterization method for a fine-grained sedimentary rock laminar texture, wherein, including the following specific steps:

S1;

image preprocessing comprising the operations of;

acquiring a microscopic image of a fine-grained sedimentary rock thin section, and daubing non-laminar features in the microscopic image of the fine-grained sedimentary rock thin section;

S2;

loading the microscopic image, and normalizing the microscopic image to a specified size;

S3;

performing mean filtering, dilation operation and binarization processing on the microscopic image successively;

S4;

determining whether laminars are developed through the operations of;

counting bright pixel points in each row of the image;

setting a first threshold according to peaks, corresponding to high values of the bright pixel points; and

determining, according to the first threshold, whether laminars are developed in the image;

S5;

determining a number of bright laminars and dark laminars through the operations of;

setting a second threshold, finding the positions of troughs according to adjacent two peaks;

wherein if an elevation difference between a peak and an adjacent trough is greater than the second threshold, determining that there is a trough; and

acquiring accurate positions of peaks and troughs, corresponding to effective peaks and effective troughs, wherein the number of effective peaks is the number of bright laminars and the number of effective troughs is the number of dark laminars;

S6;

determining a continuity of bright laminars and dark laminars through the operations of;

in the bright laminars or the dark laminars, counting a number of corresponding break points by using the bright pixel points in each row of the microscopic image as a unit;

obtaining a reciprocal of an average number of break points; and

using a size of the reciprocal to characterize a strength of the continuity of bright laminars or the continuity of dark laminars; and

,

S7;

according to the statistical result and the calculation result, writing the number of bright laminars, the number of dark laminars, the average width of bright laminars, the average width of dark laminars, the average width of bright and dark laminars, the continuity of bright laminars, the continuity of dark laminar, the continuity of bright and dark laminars, the width variance of bright laminars, the width variance of dark laminars, and the average width variance of bright and dark laminars into an Excel file.

2. The characterization method according to claim 1, wherein, in S3, by the mean filtering, a mean value of neighboring pixel points is used to represent a target pixel point, thus target pixel points are removed, and effective pixels are obtained;

by the dilation operation, the effective pixels are expanded; and

, by the binarization processing, gray values of pixel points on the image are set as 0 or 255, so that an obvious black-white image is presented.

3. The characterization method according to claim 2, wherein, in S4, peaks of the image are acquired by a function “

[v1,l1]”

=attain_peak(“

y1”

,threshold1), where [v1,l1] represents a peak value, y1 is an accumulated value of transverse pixel points, and threshold1 is a peak threshold, corresponding to a first threshold;

wherein if the “

v1”

value exceeds the peak threshold, it is considered that there is a peak, and it is determined that there is a laminar; and

, if the returned “

v1”

value is null, there is no peak, and it is determined that there is no laminar.

4. The characterization method according to claim 3, wherein, in S5, the positions of troughs are determined by a function [bright_stripe,dull_stripe,stripe_map]=cal_index(y1,[v1,l1],bw,threshold2), where “

bright_stripe”

represents a bright laminar, “

dull_stripe”

represents a dark laminar, “

stripe_map”

represents a laminar simulated map, “

bw”

represents a binary image, the size of the laminar simulated map is obtained by the “

bw”

, and “

threshold2”

is the second threshold;

the positions of the troughs are obtained according to adjacent two peaks;

wherein if an elevation difference between a peak and an adjacent trough is greater than the “

threshold2”

, it is determined that there is a trough, so that the positions of peaks and troughs, i.e., effective peaks and effective troughs, are obtained, wherein the number of effective peaks is the number of bright laminars and the number of effective troughs is the number of dark laminars, such that the number of bright and dark laminars is determined.

5. The characterization method according to claim 4, wherein, in S6, in accordance with the result of binarization processing, if the adjacent pixel points in each row of the image are the same in color, it is recorded as a break point.

6. The characterization method according to claim 5, wherein, in S6, the process of determining the continuity of bright laminars includes the operations of:

the number of dark break points among the pixel points in each row of each bright laminar is counted;

an average number of break points is further obtained according to the total number of rows of each bright laminar; and

an average number of dark break points of the image is calculated according to the number of bright laminars, where the reciprocal of the average number of dark break points is the continuity of bright laminars in the image.

7. The characterization method according to claim 6, wherein, in S6, the process of determining the continuity of dark laminars includes the operations of:

the number of bright break points among the pixel points in each row of each dark laminar is counted;

an average number of break points is further obtained according to the total number of rows of each dark laminar; and

an average number of bright break points of the image is calculated according to the number of dark laminars, where the reciprocal of the average number of bright break points is the continuity of dark laminars in the image.

8. The characterization method according to claim 7, wherein, in S6, the continuity of bright laminars and the continuity of dark laminars are averaged to obtain the continuity of bright and dark laminars in the image.