CN111007064A - Intelligent logging lithology identification method based on image identification - Google Patents
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Abstract
The invention relates to an intelligent well logging lithology identification method based on image identification, which comprises the following steps: the method comprises the following steps: constructing a mineral category intelligent identification method based on a neural network; step two: identifying the types of minerals such as quartz, feldspar and rock debris by using the intelligent identification method; step three: intelligently identifying reinforcement learning and updating of the neural network, and updating the neural network method according to the diagnosis result; step four: identifying rock grain edges; step five: identifying the mineral type in each particle by using a neural network method; step six: each particle is named according to the rock name naming method. The method avoids the influence of subjective experience and the loss of information by learning the lithologic particle image sample set, continuously enlarges the sample set space by continuously correcting, realizes lithologic intelligent identification, and can be used for researching strata, reservoir stratum and the like in the process of petroleum exploration and development.
Description
Technical Field
The invention relates to the technical field of image recognition and processing, in particular to an intelligent well logging lithology recognition method based on image recognition.
Background
The lithology identification is a process of accurately naming lithology based on characteristic conditions such as rock color, structure, mineral components and content thereof by using techniques such as rock sample visual observation, microscopic observation, chemical component analysis and the like and taking petrology, mineralogy and geochemistry as theoretical guidance. Lithology recognition is a precondition for the research of strata, reservoirs and the like in the process of petroleum exploration and development, is one of core works for reservoir evaluation, and is also a basis for solving reservoir parameters. At present, in the field of petroleum geology, researchers observe optical characteristics and surface texture characteristics of rocks through a polarization microscope so as to identify and analyze components of different rocks, but in practical application, due to the fact that rock sample targets are numerous in particles, manual identification workload is large, timeliness is poor, and quantitative analysis of samples is difficult to perform.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to overcome the defects in the prior art, the invention provides the intelligent logging lithology identification method based on image identification, which avoids the influence of subjective experience and the loss of information by learning the lithology particle image sample set, avoids the waste of a large amount of manpower and time, and continuously enlarges the sample set space by continuous correction to realize intelligent lithology identification.
The technical scheme adopted by the invention for solving the technical problems is as follows: an intelligent well logging lithology identification method based on image identification comprises the following steps:
the method comprises the following steps: constructing a mineral category intelligent identification method based on a neural network;
step two: identifying the mineral types such as quartz, feldspar and rock debris by using the intelligent identification method;
step three: intelligently identifying reinforcement learning and updating of the neural network, and updating the neural network method according to the diagnosis result;
step four: identifying rock grain edges;
step five: identifying the mineral type in each particle by using a neural network method;
step six: each particle is named according to the rock name naming method.
Specifically, the first step comprises: 1) obtaining a lithologic particle sample set; 2) processing a lithologic particle image; 3) sharpening the Laplace operator; 4) and constructing a BP neural network, and learning a sample set.
Further, the specific method of step 2) is as follows: the gaussian filter uses a two-dimensional convolution operator of the gaussian kernel for image denoising, the two-dimensional gaussian function being as follows:
the specific method of the step 3) is as follows: the image is sharpened by utilizing the Laplacian operator, the definition is improved, the details are highlighted,
the specific application method in the step two is as follows: and (4) judging minerals such as quartz, feldspar and rock debris by using the method constructed in the first step for each pixel point in the test set.
Specifically, the specific method of the third step is as follows: 1) the staff demonstrates the correctness of the recognition conclusion according to the actual mineral distribution condition and the comparison of the neural network recognition result; 2) correcting the error identification result; 3) constructing a new sample set by the image points corresponding to the corrected identification result; 4) retraining the neural network method again with the updated sample set; 5) and after the reinforcement learning process is carried out, updating the intelligent neural network diagnosis method.
Specifically, the specific method of step four is:
1) the sobel method detects the edges of the particles: the Sobel operator detects the edge according to the gray weighting difference of upper, lower, left and right adjacent points of the pixel point, and the phenomenon that the edge reaches an extreme value; the method has a smoothing effect on noise and provides more accurate edge direction information; if the gradient value G is larger than a certain threshold value, the point (x, y) is considered as an edge point,
2) determining the edge of each particle by a convex function algorithm; setting the search radius to each edge point[xi]Searching all edge points in the radius range for the search center, and searching for the edge points which can satisfy [ x ] by taking the edge points as the centeri-1]+[xi+1]＜2[xi]A point of (a);
3) and (3) taking the point meeting the condition of 2) as a new search center, repeating the step 2) until the edge search of the particle is completed, and searching for the edge point of the next particle.
The concrete application method of the step five is as follows: and C, judging minerals such as quartz, feldspar and rock debris by utilizing the pixel points in the edges of the particles detected in the step four for each pixel point in the test set.
The concrete application method of the step six is as follows: counting the number of pixel points of each mineral type according to the identification result of the mineral type in the edge of each particle, further calculating the volume content, naming each particle according to a rock naming method, pushing the result to workers, comparing the result with a drilling curve, and guiding the drilling work.
The invention has the beneficial effects that: in view of the existing lithology recognition method, based on the current mainstream image intelligent recognition field, firstly, processing a lithology particle image through Gaussian (guass) filtering, sharpening the image by utilizing a Laplacian operator, then distinguishing the boundary of the lithology particle through a sobel method, obtaining the boundary of the lithology particle through a convex function, recognizing the category attribution of each pixel in the boundary through a neural network method, counting the contents of quartz, feldspar and detritus in the particle, and finally determining the logging lithology type, so that the influence of a large amount of effective information lost in the characteristic extraction process and the subjective judgment of people on a recognition result is avoided.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of the operation of the present invention.
FIG. 2 is an image of a pristine lithologic particle.
Fig. 3 is an image after gaussian filter processing.
FIG. 4 is an image of a lithologic particle after edge enhancement.
Fig. 5 is an image after edge detection by the sobel algorithm.
FIG. 6 is an image of the interior of a particular grain boundary as determined by a convex function.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in fig. 1 to 6, an intelligent method for identifying lithology of a well log based on image identification includes the following steps:
1) acquiring an original lithologic particle image from a laboratory;
2) classifying the collected original lithologic particle images according to known lithologic conditions;
3) 2/3 of each class of the classified data set is taken as a training data set, 1/3 is taken as a testing data set;
4) processing images of lithologic grains using gaussian (guass) filtering
5) Sharpening the image by using a Laplacian operator;
6) training a neural network method by adopting a sample set;
7) designing a test program, inputting a test data set into a neural network, testing the training accuracy, printing the model identification accuracy, and stopping training when the accuracy reaches a certain value;
8) pushing the identification result to a worker;
9) the staff verifies the correctness of the recognition conclusion and corrects the wrong recognition result according to the actual lithology condition and the neural network recognition result; constructing a new lithologic particle image sample set by the corrected recognition result; retraining the neural network method for the updated lithologic particle image sample set; and after the reinforcement learning process is carried out, updating the intelligent neural network diagnosis method.
10) Method for distinguishing particle edge by using sobel
11) Obtaining single lithologic grain boundary by convex function method
12) Identifying a category to which each pixel within the lithologic grain boundary belongs;
13) counting the contents of quartz, feldspar and rock debris in each lithologic particle;
14) the name of each lithologic particle is determined.
In view of the existing lithology identification method, based on the current mainstream image intelligent identification field, firstly, processing lithology particle images through Gaussian (guass) filtering, sharpening the images by utilizing Laplacian, then distinguishing the boundaries of the lithology particles through a sobel method, obtaining the lithology particle boundaries through a convex function, identifying the category attribution of each pixel in the boundaries through a neural network method, counting the contents of quartz, feldspar and detritus in the particles, and finally determining the well logging lithology type, so that the influence of a large amount of effective information lost in the characteristic extraction process and the subjective judgment of people on the identification result is avoided, and the well logging lithology intelligent identification method based on image identification is constructed.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (9)
1. An intelligent well logging lithology identification method based on image identification is characterized in that: the method comprises the following steps:
the method comprises the following steps: constructing a mineral category intelligent identification method based on a neural network;
step two: identifying the mineral types such as quartz, feldspar and rock debris by using the intelligent identification method;
step three: intelligently identifying reinforcement learning and updating of the neural network, and updating the neural network method according to the diagnosis result;
step four: identifying rock grain edges;
step five: identifying the mineral type in each particle by using a neural network method;
step six: each particle is named according to the rock name naming method.
2. The intelligent image recognition-based well logging lithology recognition method of claim 1, wherein: the first step comprises the following steps: 1) obtaining a lithologic particle sample set; 2) processing a lithologic particle image; 3) sharpening the Laplace operator; 4) and constructing a BP neural network, and learning a sample set.
3. The intelligent image recognition-based well logging lithology recognition method of claim 2, wherein: the specific method of the step 2) comprises the following steps: a two-dimensional convolution operator using a gaussian kernel for image denoising, the two-dimensional gaussian function being as follows:
4. the intelligent image recognition-based well logging lithology recognition method of claim 2, wherein: the specific method of the step 3) is as follows: the image is sharpened by utilizing the Laplacian operator, the definition is improved, the details are highlighted,
5. the intelligent image recognition-based well logging lithology recognition method of claim 1, wherein: the specific application method in the step two is as follows: and (4) judging minerals such as quartz, feldspar and rock debris by using the method constructed in the first step for each pixel point in the test set.
6. The intelligent image recognition-based well logging lithology recognition method of claim 1, wherein: the concrete method of the third step is as follows: 1) the staff demonstrates the correctness of the recognition conclusion according to the actual mineral distribution condition and the comparison of the neural network recognition result; 2) correcting the error identification result; 3) constructing a new sample set by the image points corresponding to the corrected identification result; 4) retraining the neural network method again with the updated sample set; 5) and after the reinforcement learning process is carried out, updating the intelligent neural network diagnosis method.
7. The intelligent image recognition-based well logging lithology recognition method of claim 1, wherein: the concrete method of the step four is as follows:
1) the sobel method detects the edges of the particles: the Sobel operator detects the edge according to the gray weighting difference of upper, lower, left and right adjacent points of the pixel point, and the phenomenon that the edge reaches an extreme value; the method has a smoothing effect on noise and provides more accurate edge direction information; if the gradient value G is larger than a certain threshold value, the point (x, y) is considered as an edge point,
2) convex function algorithmDetermining the edge of each particle; setting the search radius to each edge point [ x ]i]Searching all edge points in the radius range for the search center, and searching for the edge points which can satisfy [ x ] by taking the edge points as the centeri-1]+[xi+1]＜2[xi]A point of (a);
3) and (3) taking the point meeting the condition of 2) as a new search center, repeating the step 2) until the edge search of the particle is completed, and searching for the edge point of the next particle.
8. The intelligent image recognition-based well logging lithology recognition method of claim 7, wherein: the concrete application method of the step five is as follows: and C, judging minerals such as quartz, feldspar and rock debris by utilizing the pixel points in the edges of the particles detected in the step four for each pixel point in the test set.
9. The intelligent image recognition-based well logging lithology recognition method of claim 1, wherein: the concrete application method of the step six is as follows: counting the number of pixel points of each mineral type according to the identification result of the mineral type in the edge of each particle, further calculating the volume content, naming each particle according to a rock naming method, pushing the result to workers, comparing the result with a drilling curve, and guiding the drilling work.
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| CN112282739A (en) * | 2020-11-18 | 2021-01-29 | 中国石油天然气集团有限公司 | Method for identifying debris scatterers in borehole diameter measurement while drilling |
| CN112354874A (en) * | 2020-09-03 | 2021-02-12 | 江苏旷博智能技术有限公司 | Coal and gangue identification method and gangue automatic separation system |
| CN113128477A (en) * | 2021-05-18 | 2021-07-16 | 西南石油大学 | Clastic rock lithology identification method and system based on deep learning method |
| WO2022166232A1 (en) * | 2021-02-08 | 2022-08-11 | 中国石油化工股份有限公司 | Rock identification method, system and apparatus, terminal, and readable storage medium |
| CN118097641A (en) * | 2024-04-18 | 2024-05-28 | 陕西合兴硅砂有限公司 | Intelligent identification method for high-purity quartz sandstone |
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