CN113393400A - Processing method and device for eliminating noise characteristics of electric imaging image - Google Patents
Processing method and device for eliminating noise characteristics of electric imaging image Download PDFInfo
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Abstract
The embodiment of the invention discloses a processing method and a device for eliminating noise characteristics of an electric imaging image, wherein the method comprises the following steps: selecting a sample image which meets the sample condition from the electric imaging image; extracting the electric deduction measurement data of the sample image for statistical processing to determine a boundary value of background data and specified characteristic data in the sample image; dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric deduction measurement data of the electric imaging image, and performing binarization processing on the electric deduction measurement data of the electric imaging image according to the boundary value to obtain electric deduction binarization data corresponding to the background data and the specified characteristic data; performing connectivity detection on the electric buckle binary data of the electric imaging image one by one; selecting the electric buckle measurement data corresponding to the background data area, and carrying out filtering processing according to the noise characteristics in the background data area to remove the noise characteristics; and according to the updated electric deduction measurement data, preprocessing and normalizing to generate an electric imaging image.
Description
Technical Field
The embodiment of the invention relates to the technical field of geophysical exploration of oil and gas resources, in particular to a processing method and a processing device for eliminating noise characteristics of an electrical imaging image.
Background
The electric imaging well logging can clearly image well wall stratums in an open hole well, visually display geological phenomena of cracks, holes, bedding, faults, unconformities and the like of the well wall stratums, and can carry out various fine geological researches such as crack identification evaluation, sedimentary structure analysis, sedimentary microfacies division and the like on the basis of stratum characteristics on images. In recent years, with the development of the petroleum industry, deep oil and gas resource exploration is increasingly carried out in domestic oil fields, and the deep lithology mainly comprises high-resistance igneous rocks.
When the electric imaging instrument works underground, the electric imaging instrument emits focusing current into a stratum through the small dense measuring electrodes, the current is diffused after entering the stratum by a certain depth and then returns to the return electrode at the other end of the instrument to form a complete loop, and the current of each electrode can reflect the conductivity of a well wall stratum in a specific area contacted by the electrode. The measurement principle of the electrical imaging instrument is shown in fig. 1a and 1 b. When the electric imaging instrument works underground, a large amount of conductivity information of the well wall stratum is collected in a rotating and upward measuring mode, and a conductivity image of the well wall stratum can be obtained through software processing, so that well wall stratum characteristics can be visually displayed.
The measured data of the electric buckle of the instrument is recorded by the value of an ammeter of the electric buckle, the measured data is influenced by the conductivity of the stratum and noise signals generated by a circuit system of the instrument and a measuring environment, namely the measured data of the electric buckle is a superposition value of the current signals and the noise signals passing through the stratum.
I electric buckle measurement data is I stratum current data + I noise
The transmitting voltage of the electric buckle of the instrument is relatively fixed and has small change, and the current signal of the stratum is much larger than the noise signal for medium and low resistance stratums such as sand shale and the like according to the ohm's law. The influence of the formation resistivity on the measured value is dominant, the image measured by the instrument is shown in figure 2a, the formation characteristics can be clearly shown in a static image and a dynamic image, and the noise characteristics are basically covered. However, for high-resistivity strata such as igneous rocks, the current signal of the strata is greatly reduced, the stratum characteristics of the image are weakened, the noise characteristics are relatively strengthened, and the phenomenon that the stratum characteristics and the noise characteristics coexist on the image is shown as the image, so that the image quality is influenced. If the noise is a regular signal, the noise characteristics on the image will be represented by regular characteristics such as a wave shape, and as shown in fig. 2c, the image acquired by a single polar plate in the electrical imaging instrument in fig. 2c is a dark color part in the image is a crack characteristic, and a light color part in the image is a noise characteristic, wherein the noise characteristics are represented by the wave-shaped regular characteristics.
When the noise features exist on the electrical imaging image, the noise features interfere with effective features on the image, and the stratum feature discrimination is influenced, such as obstacles on fine geological research such as crack hole identification evaluation, stratum interface division and the like. The phenomenon that noise characteristics exist in the electric imaging image is over in the Bohai sea buried hill igneous rock stratum and the Xinjiang deep well carbonate rock stratum in China, and the application effect of electric imaging logging data is seriously influenced.
The home and abroad electrical imaging data processing software generally has the functions of acceleration correction, electrical buckle depth alignment, polar plate non-coplanar correction, bad electrical buckle rejection, electrical buckle equalization processing and static and dynamic image generation, and images for geological workers to explain and evaluate can be obtained through the processing. The above processes have no problem in the process of eliminating the noise characteristics of the electrical imaging image of the low-resistance stratum such as sand shale. However, for the electrical imaging image of the high-resistivity igneous rock formation, the noise characteristics on the image cannot be eliminated.
Disclosure of Invention
In view of the above problems, embodiments of the present invention are proposed to provide a processing method and apparatus for eliminating noise characteristics of an electrical imaging image, which overcome or at least partially solve the above problems.
According to an aspect of the embodiments of the present invention, there is provided a processing method for eliminating noise characteristics of an electrical imaging image, the method including:
acquiring an electric imaging image of the whole well section, and selecting a sample image which meets the sample condition from the electric imaging image;
extracting the electric deduction measurement data of the sample image for statistical processing to determine a boundary value of background data and specified characteristic data in the sample image;
dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric deduction measurement data of the electric imaging image, and performing binarization processing on the electric deduction measurement data of the electric imaging image according to the boundary value to obtain electric deduction binarization data corresponding to the background data and the specified characteristic data;
performing connectivity detection on the electric buckle binary data of the electric imaging image one by one, and correcting the detection result into abnormal electric buckle binary data;
selecting the electric deduction measurement data corresponding to the background data area, carrying out filtering processing according to the noise characteristics in the background data area, and updating the electric deduction measurement data in the background data area to remove the noise characteristics;
and according to the updated electric deduction measurement data, preprocessing and normalizing to generate an electric imaging image.
According to another aspect of the embodiments of the present invention, there is provided a processing apparatus for removing noise characteristics of an electrical imaging image, including:
the selection module is suitable for acquiring the electric imaging images of the whole well section and selecting the sample images meeting the sample conditions from the electric imaging images;
the interface value determining module is suitable for extracting the electric deduction measurement data of the sample image to perform statistical processing so as to determine the interface value of the background data and the specified characteristic data in the sample image;
the binarization module is suitable for dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric deduction measurement data of the electric imaging image, and performing binarization processing on the electric deduction measurement data of the electric imaging image according to the boundary value to obtain electric deduction binarization data corresponding to the background data and the specified characteristic data;
the connectivity detection module is suitable for performing connectivity detection on the electric buckle binary data of the electric imaging image one by one and correcting a detection result into abnormal electric buckle binary data;
the filtering module is suitable for selecting the electric buckle measurement data corresponding to the background data area, carrying out filtering processing according to the noise characteristics in the background data area, and updating the electric buckle measurement data in the background data area to remove the noise characteristics;
and the conversion module is suitable for generating an electrical imaging image after preprocessing and normalization processing according to the updated electrical deduction measurement data.
According to still another aspect of an embodiment of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the processing method for eliminating the noise characteristics of the electric imaging image.
According to a further aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the processing method for removing noise characteristics of an electrical imaging image as described above.
According to the processing method and device for eliminating the noise characteristics of the electric imaging image, provided by the embodiment of the invention, the background data area and the designated characteristic data area in the electric imaging image are divided by determining the boundary value of the background data and the designated characteristic data, and the noise characteristics in the background data are filtered, so that the noise characteristics in the electric imaging image can be effectively eliminated, and the image quality is improved.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIGS. 1 a-1 b show schematic views of the measurement principle of an electrical imaging instrument;
FIG. 2a shows an electrical imaging image of a medium and low resistivity formation;
FIG. 2b shows a high resistivity earth formation electrical imaging image;
fig. 2c shows a unipolar plate image with significant noise characteristics;
FIG. 3 shows a flow diagram of a processing method for removing noise characteristics of an electrical imaging image according to one embodiment of the invention;
FIG. 4a shows a schematic representation of a sample image taken from an electrical imaging image of a full wellbore section;
FIG. 4b is a schematic diagram showing the individual deduction measurement data from which the sample image is extracted;
FIG. 4c shows a schematic of a statistical histogram;
FIG. 5 illustrates a flow diagram of connectivity detection for one embodiment of the present invention;
6 a-6 b show schematic diagrams of a filtering process;
FIG. 7a shows a schematic representation of an original image of an electrographic image;
FIG. 7b shows a schematic image after electrographic image processing;
FIG. 8 is a schematic diagram of a processing apparatus for removing noise characteristics from an electrical imaging image according to an embodiment of the present invention;
FIG. 9 shows a schematic structural diagram of a computing device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 3 shows a flow chart of a processing method for removing noise characteristics of an electrical imaging image according to an embodiment of the invention, as shown in FIG. 3, the method comprising the steps of:
step S301, acquiring an electric imaging image of the whole well section, and selecting a sample image meeting the sample condition from the electric imaging image.
The inventor finds that, in the process of implementing the invention, although some electric imaging processing software develops relevant filtering algorithm modules such as mean filtering, median filtering and the like, the following problems still exist in the processing: useful features such as image background noise features and stratum cracks and holes cannot be distinguished, and stratum features concerned by users such as micro cracks and micro dissolving holes on the image can be weakened while the image noise features are weakened through filtering processing.
In this embodiment, after the electrical imaging image of the whole well section is acquired, the electrical imaging image is subjected to image analysis, and a sample image that meets the sample condition is selected. The sample conditions include, for example, high formation resistivity, significant noise characteristics, specified characteristics, etc. Because the prior art cannot eliminate the noise characteristics aiming at the electric imaging images of the high-flame-retardant diagenetic stratum, the sample images are used for pertinently selecting the images with high stratum resistivity so as to eliminate the noise characteristics aiming at the electric imaging images of the high-flame-retardant diagenetic stratum. The sample image with obvious noise characteristics can be used for conveniently and accurately dividing a background data area and a specified characteristic data area. Here, the specified features include formation features of interest to the user, such as valid fracture-hole features. In the present embodiment, the slot-hole feature is taken as an example for explanation, but the specific implementation is not limited to the slot-hole feature.
In order to facilitate subsequent use of the sample image, the sample image with a specific length can be selected according to the user requirement when the sample image is selected, and data analysis is performed based on the sample image with the specific length. The sample image may be determined based on the image analysis result, for example, by analyzing the electrical imaging image of the whole wellbore section by using image analysis and other techniques to determine the sample image, or by screening out partial images suitable for obtaining the sample image according to the depth information and the orientation information in the electrical imaging image, and then determining the sample image from the partial images, which is not limited herein.
Step S302, the electric deduction measurement data of the sample image is extracted for statistical processing so as to determine a boundary value of the background data and the designated characteristic data in the sample image.
The whole-well electrical imaging image is shown in fig. 4a, from which a sample image is selected as the partial image outlined in fig. 4 a. According to the depth information and the azimuth information contained in the sample image, the electric deduction measurement data of the sample image can be extracted. The depth information of the sample image is about 7405 m as shown in fig. 4a, and the azimuth information can be determined according to the azimuth information distribution of 0-359 in the image. In the electrical imaging image, 0-359 represents 0 azimuth to 359 azimuth corresponding to different directions from north-east-south-west-north in the well, and azimuth information is determined according to the value in 0-359 corresponding to the sample image. According to the depth information and the azimuth information, electric buckle measurement data of the electric imaging instrument can be extracted when the electric imaging instrument obtains an electric imaging image through measurement. The data of the electrical deduction measurement of the sample image is shown in fig. 4 b. Here, the extracted electric buckle measurement data of the sample image is the original electric buckle measurement data measured by the electric imaging instrument.
And performing statistical processing on the frequency of the electric deduction measurement data appearing in the sample image to generate a statistical histogram. As shown in fig. 4c, the abscissa of the statistical histogram corresponds to the electric buckle measurement data, and the ordinate corresponds to the frequency of occurrence of each electric buckle measurement data in the sample image. The range of noisy data values generated by the circuitry and measurement environment is generally stable and relatively small (e.g., raw meter values are generally less than 30), while the electrical buckle measurement data corresponding to the fracture-hole characteristics of the formation is generally large (e.g., tens to tens of thousands, etc.) and has no fixed range of values. In the generated statistical histogram, a region with small electric deduction measurement data can generate a plurality of peak value intervals with high frequency, the data in the region corresponds to a background data region on a sample image, and the noise characteristic is most obvious on the background data region; in a region with relatively large electric buckle measurement data, a plurality of peak value intervals with relatively small frequency are generated, the peak value intervals of the part mainly correspond to specified characteristics such as slot hole characteristics on a sample image, and if the size of the slot hole data is relatively constant, a relatively obvious single peak can appear; if the numerical value of the data of the slot hole changes greatly, a plurality of peaks which are relatively gentle and have relatively low frequency can be shown. In fig. 4c, in the low value region of the measurement data of the electrical buckle, a peak with a higher frequency occurs. The electrical deduction measurement data with the highest frequency and the electrical deduction measurement data located in the low value area in the statistical histogram is selected, for example, as shown in fig. 4c, the electrical deduction measurement data X with the frequency higher than 0.35 and located within the electrical deduction measurement data 200 is selected. And determining a boundary value of the background data and the designated characteristic data in the sample image according to the selected electric deduction measurement data and the designated coefficient. If the selected abscissa electric deduction measurement data X is used, the designated coefficient is determined according to the specific implementation condition, for example, 2-3, and the X is multiplied by the designated coefficient to obtain a boundary value Y.
In the embodiment, the selected sample image with obvious noise characteristics and obvious designated characteristics is used for obtaining the boundary value of the background data and the designated characteristic data, so that the subsequent utilization of the boundary value is facilitated, the electric imaging image of the whole well section is processed, and the noise characteristics in the electric imaging image of the whole well section are eliminated.
And step S303, dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric deduction measurement data of the electric imaging image, and performing binarization processing on the electric deduction measurement data of the electric imaging image according to the boundary value to obtain electric deduction binarization data corresponding to the background data and the specified characteristic data.
And dividing the electric imaging image according to the obtained boundary value Y and each electric deduction measurement data extracted from the electric imaging image according to the depth information and the azimuth information, and dividing a background data area and a specified characteristic data area in the electric imaging image. And comparing the electric deduction measurement data of the electric imaging image with the boundary value, and determining a background data area and a specified characteristic data area in the electric imaging image according to the comparison result. If the electric deduction measurement data smaller than the boundary value is background data, and the electric deduction measurement data larger than or equal to the boundary value is designated characteristic data. The electric buckle measurement data of the sample image shown in fig. 4b is roughly divided into three regions, the lower left corner and the upper right corner in the figure are background data regions, and the middle stripe part is a designated feature (crack) data region. And dividing the background data area and the designated characteristic data area in the electric imaging image according to the comparison result of the electric deduction measurement data of the electric imaging image and the boundary value.
And besides dividing the background data area and the specified characteristic data area, carrying out binarization processing on the electric deduction measurement data of the electric imaging image. Specifically, the electrical deduction binary data of the background data is set, the electrical deduction binary data corresponding to the electrical deduction measurement data smaller than the boundary value is set to be 0, the electrical deduction binary data of the characteristic data is designated, and the electrical deduction binary data corresponding to the electrical deduction measurement data larger than or equal to the boundary value is set to be 1.
And step S304, performing connectivity detection on the electric buckle binary data of the electric imaging image one by one, and correcting the detection result into abnormal electric buckle binary data.
After obtaining the electrical buckle binary data of the electrical imaging, performing connectivity detection on the electrical buckle binary data of the electrical imaging image one by one, and detecting whether each electrical buckle binary data is communicated or not, whether the detection result reaches the connectivity of the specified characteristics or not, whether the connectivity of the specified characteristics is met or not, and the like.
Specifically, the connectivity detection comprises the following steps:
step S3041, sequentially obtaining any unmarked electric buckle binary data i in the electric imaging image, and marking the electric buckle binary data i as detection data.
For connectivity detection, it is necessary to detect all the electrocuted binary data in the electrographic image. During detection, any unmarked electric deduction binary data i in the electric imaging image can be sequentially obtained according to the sequence from top to bottom (from small to large according to depth information) and from left to right (from 0 azimuth to 359 azimuth according to azimuth information) in the electric imaging image. And marking the acquired electric deduction binary data as detection data, and executing subsequent detection processing to avoid repeated acquisition.
Step S3042, determining whether the electric deduction binary data i is 1; if yes, go to step S3043; if not, go to step S3041.
The obtained electrical deduction binary data i is judged whether to be 1, if so, the electrical deduction binary data i is specified characteristic data, and the following description is taken as an example of the slot characteristic data. The characteristic data of the seam holes has connectivity, only one data is 1, and when the electric buckle binary data i is detected to be 1, the step S3043 is executed to continuously detect whether the adjacent data around the first detection data are connected. If the electric buckle binary data i is not detected to be 1, which indicates that the electric buckle binary data is not the hole characteristic data, returning to the step S3041, and recovering the next unmarked electric buckle binary data for detection.
In step S3043, the electric deduction binary data i is marked as first detection data.
And when detecting that the electric deduction binary data i is 1, marking the electric deduction binary data i as first detection data, and continuing connectivity detection based on the first detection data.
Step S3044 of determining whether each other electrical deduction binary data j adjacent to the first detection data in the up, down, left, and right directions is 1; if yes, go to step S3045; if not, go to step S3048.
And if the other electric buckle binary data j with the value of 1 exists in the other electric buckle binary data j, executing the step S3045 and continuing the connectivity detection. If no electric buckle binary data with the value of 1 exists in the other electric buckle binary data j, it indicates that only the first detection data is 1, which is not communicated with other electric buckle binary data adjacent to the upper, lower, left and right directions, the first detection data does not conform to the seam hole characteristic data, the detection result is abnormal, and the other adjacent electric buckle binary data is no longer detected, and step S3048 is executed.
In step S3045, the electric deduction binary data j is marked as second detection data.
And marking the electric deduction binary data j communicated with the first detection data as second detection data, and continuing to detect. And if a plurality of electric buckle binary data j communicated with the first detection data exist, marking the plurality of electric buckle binary data j as second detection data, and sequentially detecting the connectivity of each second detection data.
Step S3046 of determining whether other electroconvulsive binary data k of the second detection data, which is adjacent in the four directions of up, down, left, and right, and is not the first detection data, is 1; if yes, go to step S3047; if not, go to step S3048.
When detecting that 1 exists in other electric buckle binary data adjacent to the first detection data in the up, down, left and right directions, the first detection data is communicated with the other electric buckle binary data, the electric buckle binary data marked with the electric buckle binary data j of 1 is the second detection data, and connectivity detection is continuously carried out on the second detection data. For the second detection data, it is determined whether other electrical deduction binary data k of the non-first detection data adjacent to the second detection data in the up, down, left, and right directions is 1, that is, it is determined whether there is data of 1 in the other electrical deduction binary data k of the non-first detection data adjacent to the electrical deduction binary data i in the up, down, left, and right directions of the electrical deduction binary data j, if it is determined that the other electrical deduction binary data k of the non-first detection data adjacent to the second detection data in the up, down, left, and right directions is 0, the second detection data is not communicated with the other electrical deduction binary data k, and step S3048 is executed. If the other electric deduction binary data k is 1, it is determined that the second detection data has the other connected electric deduction binary data k, executing step S3047, marking the original second detection data as the first detection data, and marking the electric deduction binary data k as the second detection data, so as to continue to circularly perform connectivity detection on the electric deduction binary data.
In step S3047, the second detection data is marked as the first detection data, the electric buckle binarization data k is marked as the second detection data, and step S3046 is executed.
And (3) updating and marking the original second detection data as first detection data, marking the electric buckle binary data k as second detection data, executing the step S3046 in a circulating manner, and continuing to detect the connectivity of the second detection data from the step S3046 until other electric buckle binary data k, adjacent to the second detection data in the upper, lower, left and right directions, of the non-first detection data are 0 and are not communicated with other electric buckle binary data k, and at the moment, finishing the connectivity detection of the second detection data, and executing the subsequent step S3048.
Step S3048, counting the number of the first detection data and the second detection data, and determining whether the number is greater than a preset value; if not, go to step S3049.
In step S3049, the electroporation binary data of the first detection data and the second detection data is corrected to 0.
And counting the number of the first detection data and the second detection data marked in the detection process, wherein if the second detection data does not exist, the number of the second detection data is 0. And judging whether the number is larger than a preset value or not according to the number, wherein if the preset value is set to be 20 according to the characteristics of the slot, when the number is smaller than or equal to 20, the detected result of the detected first detection data and the connected detection result of other electrical buckle binary data are abnormal detection results, and the abnormal detection results do not accord with the characteristic data of the slot, and the abnormal detection results need to be corrected. In the correction, the two-valued data of the electric deduction marked as the first detection data and the second detection data are both corrected to 0. If the number is greater than 20, the detected first detection data and the detection results of the communicated other electric deduction binary data are normal detection results and accord with the characteristic data of the seam hole, and the electric deduction binary data of the first detection data and the second detection data do not need to be processed. The preset value is set according to the connectivity of the specified feature, and is not limited herein.
And S3050, judging whether the two-valued data of the electric buckle in the electric imaging image are all marked, if not, executing the step S3041.
Judging whether each electric buckle binary data in the electric imaging image is marked, if so, judging whether the electric buckle binary data are marked as detection data, first detection data or second detection data, and if so, judging that all the electric buckle binary data in the electric imaging image are subjected to connectivity detection, and finishing the detection; if the unlabeled electric buckle binary data exists, continuing to execute the step S3041, and detecting the unlabeled electric buckle binary data until all the electric buckle binary data in the electric imaging image are labeled, so as to complete connectivity detection.
Through the connectivity detection, the electric buckle binary data with the abnormal connectivity detection in the electric imaging image is corrected, the data with the abnormal connectivity is removed, and the accuracy of the specified characteristic data is guaranteed.
Step S305, selecting the electrical deduction measurement data corresponding to the background data area, performing filtering processing according to the noise characteristics in the background data area, and updating the electrical deduction measurement data in the background data area to remove the noise characteristics.
The background data area and the designated feature data area both have the influence of noise features, the designated feature data in the designated feature data area can cover the influence of the noise features, the background data area has no other features, and is more obviously influenced by the noise features, as shown in fig. 2c, the noise features in the background data area are wavy. According to the noise characteristics, the electric buckle measurement data of the background data area is processed, and the noise characteristics can be effectively removed.
Specifically, the distance between two adjacent noise features is determined according to the electric imaging image, and the filtering radius of the filtering processing is determined according to the distance. According to the corresponding relation between the noise features and the depth, for example, a certain depth A meter contains B noise features, according to the electric imaging image, the distance between two adjacent noise features contained in the electric imaging image can be determined, for example, K, and according to the distance K, the filtering radius of the filtering process is determined, for example, K x a, wherein a is a distance coefficient determined according to the implementation situation, and can be selected to be 2.0-3.0. After the filtering radius is determined, sequentially selecting the electric buckle binary data with the numerical value of 0 in the background data area, drawing a circle according to the filtering radius by taking the electric buckle binary data as a circle center, and determining the obtained circle range as a filtering area. As shown in fig. 6a, the electrical deduction binary data of 0, which is thickened, in the background data area is selected as the center of a circle, and a filtering area is selected by drawing a circle according to the filtering radius. If the electrical deduction binary data at the center of the circle is located at the edge of the electrical imaging image, as shown in fig. 6b, the electrical deduction binary data selected in the filtering region is not a complete circle, and the data with the electrical deduction binary data of 0 located in the filtering region is processed during processing. The method comprises the steps of obtaining each electric deduction measurement data of the data with the electric deduction binary data of 0 in a filtering area, calculating the average value of the electric deduction measurement data with the electric deduction binary data of 0 in the filtering area, and updating the electric deduction measurement data corresponding to the electric deduction binary data of the circle center by using the average value to enable the noise characteristic to be smooth. And repeating the step, and re-acquiring the electric deduction binary data with the value of 0 in the background data area until the electric deduction measurement data corresponding to the electric deduction binary data with the value of 0 in the background data area are updated, wherein the electric deduction measurement data in the background data area tend to be an average value, and the influence of noise characteristics is eliminated.
And S306, preprocessing and normalizing the electric deduction measurement data according to the updated electric deduction measurement data to generate an electric imaging image.
And preprocessing and normalizing the updated electric deduction measurement data according to the electric deduction measurement data updated by the electric imaging image. The preprocessing comprises acceleration correction, electric buckle alignment, pole plate non-coplanar correction, bad electric buckle removal, electric buckle equalization processing and the like, and the preprocessing is processed according to the updated electric buckle measurement data and is not described herein. The normalization processing is to convert the updated electric buckle measurement data into a numerical value in a designated numerical value interval, for example, the electric buckle measurement data is normalized to be in a numerical value interval of 0-255, so as to obtain a corresponding color numerical value, and each color numerical value has a corresponding relation with different designated colors. The color values after the normalization process are corresponding to gradation values 0 to 255 of 8 bits as three different ones of R (red), G (green), and B (blue), thereby generating an electrical imaging image. The process of converting the electric buckle measurement data into color values and generating the electric imaging image according to the image color values can also be realized by adopting ways such as converting the color values in a linear change manner, setting different color values corresponding to different colors and the like, and the method is not limited here.
Fig. 7a shows an initial image of an electrical imaging image of a full-wellbore section, and fig. 7b shows a processed electrical imaging image, which effectively eliminates the noise characteristics in fig. 7a, improves the image quality, and facilitates the user to analyze the processed image.
According to the processing method for eliminating the noise characteristics of the electrical imaging image, provided by the embodiment of the invention, the background data area and the specified characteristic data area in the electrical imaging image are divided by determining the boundary value of the background data and the specified characteristic data, and the noise characteristics in the background data are filtered, so that the noise characteristics in the electrical imaging image can be effectively eliminated, and the image quality is improved.
Fig. 8 is a schematic structural diagram of a processing apparatus for removing noise characteristics of an electrical imaging image according to an embodiment of the present invention. As shown in fig. 8, the apparatus includes:
the selecting module 810 is suitable for acquiring the electric imaging images of the whole well section and selecting the sample images meeting the sample conditions from the electric imaging images;
a boundary value determining module 820, adapted to extract the electric deduction measurement data of the sample image for statistical processing to determine a boundary value between the background data and the specified feature data in the sample image;
the binarization module 830 is adapted to divide a background data area and a specified characteristic data area in the electrical imaging image according to the boundary value and the electrical deduction measurement data of the electrical imaging image, and perform binarization processing on the electrical deduction measurement data of the electrical imaging image according to the boundary value to obtain electrical deduction binarization data corresponding to the background data and the specified characteristic data;
the connectivity detection module 840 is suitable for performing connectivity detection on the electric buckle binary data of the electric imaging image one by one and correcting the detection result into abnormal electric buckle binary data;
a filtering module 850, adapted to select the electrical deduction measurement data corresponding to the background data area, perform filtering processing according to the noise characteristics in the background data area, and update the electrical deduction measurement data in the background data area to remove the noise characteristics;
and the conversion module 860 is suitable for performing preprocessing and normalization processing according to the updated electric deduction measurement data to generate an electric imaging image.
Optionally, the boundary value determining module 820 is further adapted to: extracting the electric buckle measurement data of the sample image according to the depth information and the azimuth information contained in the sample image; counting the electric buckle measurement data, and generating a statistical histogram according to the frequency of the electric buckle measurement data in the sample image; counting the frequency of the electricity deduction measurement data corresponding to the abscissa in the histogram, and the frequency of the electricity deduction measurement data corresponding to the ordinate in the sample image; and selecting the electric buckle measurement data with the highest frequency and the electric buckle measurement data positioned in the low value area in the statistical histogram, and determining the boundary value of the background data and the designated characteristic data in the sample image according to the multiplication of the selected electric buckle measurement data by a designated coefficient.
Optionally, the binarization module 830 is further adapted to: comparing the electric deduction measurement data of the electric imaging image with the boundary value, and determining a background data area and an appointed characteristic data area in the electric imaging image according to a comparison result; wherein, the electric buckle measurement data smaller than the boundary value is background data; the electric buckle measurement data which is greater than or equal to the boundary value is designated characteristic data; carrying out binarization processing on the electric buckle measurement data of the electric imaging image according to the dividing value; wherein the electric deduction binary data of the background data is set to be 0, and the electric deduction binary data of the specified characteristic data is set to be 1.
Optionally, the connectivity detection module 840 further comprises:
the data acquisition unit 841 is suitable for sequentially acquiring any unlabeled electric deduction binary data i in the electric imaging image and labeling the electric deduction binary data i into detection data;
a first judging unit 842 adapted to judge whether the electrical deduction binary data i is 1; if yes, marking the electric buckle binary data i as first detection data, and executing a second judging unit 843; if not, the data acquisition unit 841 is executed;
a second judging unit 843 adapted to judge whether each other electrical deduction binary data j adjacent to the first detection data in the four directions of up, down, left, and right is 1; if yes, marking the electric buckle binary data j as second detection data; a third judging unit 844 is executed; if not, execute the fourth determining unit 845;
a third judging unit 844 adapted to judge whether or not other electrically deducted binarized data k of the non-first detected data adjacent to the second detected data in the four directions of up, down, left, and right is 1; if yes, marking the second detection data as first detection data, marking the electric deduction binary data k as second detection data, and executing a third judging unit 844 in a circulating mode until other electric deduction binary data are all 0; if not, execute the fourth determining unit 845;
a fourth determining unit 845, adapted to count the number of the first detection data and the second detection data, and determine whether the number is greater than a preset value; if not, correcting the electric deduction binary data of the first detection data and the second detection data to be 0;
the fifth determining unit 846 is adapted to determine whether the two-valued data of the electrical deduction in the electrical imaging image are all marked, and if not, the data obtaining unit 841 is executed.
Optionally, the filtering module 850 is further adapted to: determining the distance between two adjacent noise features according to the electric imaging image, and determining the filtering radius of filtering processing according to the distance; sequentially selecting electric buckle binary data with the numerical value of 0 in the background data area, and determining a filtering area according to a circle range obtained by a filtering radius by taking the electric buckle binary data as a circle center; calculating the average value of the electric buckle measurement data with the electric buckle binary data of 0 in the filtering area, and updating the electric buckle measurement data corresponding to the circle center by using the average value; and repeating the steps until the electric buckle measurement data corresponding to the electric buckle binary data with the numerical value of 0 in the background data area are updated.
Optionally, the conversion module 860 is further adapted to: preprocessing according to the updated electric buckle measurement data, wherein the preprocessing comprises the following steps: acceleration correction, electric buckle alignment, polar plate non-coplanar correction, bad electric buckle removal and/or electric buckle equalization processing; normalizing the preprocessed electric buckle measurement data, and generating an electric imaging image according to the color value of the normalization processing; the normalization processing is to convert the electric buckle measurement data into color numerical values in a specified numerical value interval, and each color numerical value in the specified numerical value interval has a corresponding relation with a specified color so as to be converted into a corresponding specified color according to the color numerical values.
Optionally, the selecting module 810 is further adapted to: carrying out image analysis on the electric imaging image, and selecting a sample image which meets the sample condition; the sample conditions included: the formation resistivity is high, the noise signature is significant and/or has a specified signature.
The descriptions of the modules refer to the corresponding descriptions in the method embodiments, and are not repeated herein.
The embodiment of the invention also provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the executable instruction can execute the processing method for eliminating the noise characteristics of the electric imaging image in any method embodiment.
Fig. 9 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and a specific embodiment of the present invention does not limit a specific implementation of the computing device.
As shown in fig. 9, the computing device may include: a processor (processor)902, a communication Interface 904, a memory 906, and a communication bus 908.
The method is characterized in that:
the processor 902, communication interface 904, and memory 906 communicate with one another via a communication bus 908.
A communication interface 904 for communicating with network elements of other devices, such as clients or other servers.
The processor 902 is configured to execute the program 910, and may specifically execute relevant steps in the above-described processing method for removing noise characteristics of an electrical imaging image.
In particular, the program 910 may include program code that includes computer operating instructions.
The processor 902 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
A memory 906 for storing a program 910. The memory 906 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 910 may be specifically configured to cause the processor 902 to execute the processing method for eliminating the noise feature of the electrical imaging image in any of the method embodiments described above. For specific implementation of each step in the procedure 910, reference may be made to corresponding steps and corresponding descriptions in units in the above processing embodiment for removing noise characteristics of an electrophotographic image, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose preferred embodiments of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. Embodiments of the invention may also be implemented as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A processing method for removing noise characteristics of an electrical imaging image, the method comprising:
acquiring an electric imaging image of the whole well section, and selecting a sample image which meets the sample condition from the electric imaging image;
extracting the electric deduction measurement data of the sample image for statistical processing to determine a boundary value of background data and specified characteristic data in the sample image;
dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric buckle measurement data of the electric imaging image, and performing binarization processing on the electric buckle measurement data of the electric imaging image according to the boundary value to obtain electric buckle binarization data corresponding to the background data and the specified characteristic data;
performing connectivity detection on the electric buckle binary data of the electric imaging image one by one, and correcting the detection result into abnormal electric buckle binary data;
selecting the electric deduction measurement data corresponding to the background data area, carrying out filtering processing according to the noise characteristics in the background data area, and updating the electric deduction measurement data in the background data area to remove the noise characteristics;
and according to the updated electric deduction measurement data, preprocessing and normalizing to generate an electric imaging image.
2. The method of claim 1, wherein the extracting the electricity deduction measurement data of the sample image for statistical processing to determine the boundary value of the background data and the designated feature data in the sample image further comprises:
extracting the electric deduction measurement data of the sample image according to the depth information and the azimuth information contained in the sample image;
counting the electric buckle measurement data, and generating a statistical histogram according to the frequency of the electric buckle measurement data in the sample image; the abscissa in the statistical histogram corresponds to the electric buckle measurement data, and the ordinate corresponds to the frequency of the electric buckle measurement data in the sample image;
and selecting the electric buckle measurement data with the highest frequency and the electric buckle measurement data positioned in a low value area in the statistical histogram, and determining the boundary value of the background data and the designated characteristic data in the sample image according to the multiplication of the selected electric buckle measurement data by a designated coefficient.
3. The method according to claim 1, wherein the dividing a background data area and a designated feature data area in the electrical imaging image according to the boundary value and the electrical deduction measurement data of the electrical imaging image, and performing binarization processing on the electrical deduction measurement data of the electrical imaging image according to the boundary value to obtain electrical deduction binarization data corresponding to the background data and the designated feature data further comprises:
comparing the electric deduction measurement data of the electric imaging image with the boundary value, and determining a background data area and a specified characteristic data area in the electric imaging image according to a comparison result; wherein the electricity deduction measurement data smaller than the boundary value is background data; the electric buckle measurement data which is greater than or equal to the boundary value is designated characteristic data;
carrying out binarization processing on the electric buckle measurement data of the electric imaging image according to the dividing value; wherein the electric deduction binary data of the background data is set to be 0, and the electric deduction binary data of the specified characteristic data is set to be 1.
4. The method according to claim 3, wherein the step of performing connectivity detection on the electrical deduction binary data of the electrical imaging image one by one, and the step of correcting the electrical deduction binary data with abnormal detection result further comprises the following steps:
step 1, sequentially obtaining any unmarked electric buckle binary data i in an electric imaging image, and marking the electric buckle binary data i as detection data;
step 2, judging whether the electric deduction binary data i is 1 or not; if yes, marking the electric buckle binary data i as first detection data, and executing the step 3; if not, executing the step 1;
step 3, judging whether other adjacent electric buckle binary data j in the upper, lower, left and right directions of the first detection data is 1; if yes, marking the electric buckle binary data j as second detection data; executing the step 4; if not, executing the step 5;
step 4, judging whether other electric deduction binary data k of the second detection data, adjacent to the first detection data in the upper, lower, left and right directions, and not the first detection data is 1; if yes, marking the second detection data as first detection data, marking the electric buckle binary data k as second detection data, and executing the step 4 in a circulating mode until other electric buckle binary data are all 0; if not, executing the step 5;
step 5, counting the number of the first detection data and the second detection data, and judging whether the number is larger than a preset value or not; if not, correcting the electric deduction binary data of the first detection data and the second detection data to be 0;
and 6, judging whether the electric deduction binary data in the electric imaging image are all marked, and if not, executing the step 1.
5. The method of claim 3, wherein the selecting the electrical deduction measurement data corresponding to the background data area, performing filtering processing according to the noise characteristics in the background data area, and updating the electrical deduction measurement data of the background data area to remove the noise characteristics further comprises:
determining the distance between two adjacent noise features according to the electric imaging image, and determining the filtering radius of filtering processing according to the distance;
sequentially selecting electric buckle binary data with the numerical value of 0 in the background data area, and determining a filtering area according to a circle range obtained by the filtering radius by taking the electric buckle binary data as a circle center; calculating the average value of the electric buckle measurement data with the electric buckle binary data of 0 in the filtering area, and updating the electric buckle measurement data corresponding to the circle center by using the average value; and repeating the steps until the electric buckle measurement data corresponding to the electric buckle binary data with the numerical value of 0 in the background data area are updated.
6. The method according to any one of claims 1-5, wherein generating an electrical imaging image after preprocessing and normalizing based on the updated electrical deduction measurement data further comprises:
preprocessing according to the updated electric buckle measurement data, wherein the preprocessing comprises the following steps: acceleration correction, electric buckle alignment, polar plate non-coplanar correction, bad electric buckle removal and/or electric buckle equalization processing;
normalizing the preprocessed electric buckle measurement data, and generating an electric imaging image according to the color value of the normalization processing; the normalization processing is to convert the electric buckle measurement data into color numerical values in a specified numerical value interval, and each color numerical value in the specified numerical value interval has a corresponding relation with a specified color so as to be converted into a corresponding specified color according to the color numerical values.
7. The method of any one of claims 1-5, wherein selecting the sample image from the electrical imaging images that meets the sample condition further comprises:
carrying out image analysis on the electric imaging image, and selecting a sample image which meets the sample condition; the sample conditions include: the formation resistivity is high, the noise signature is significant and/or has a specified signature.
8. A processing apparatus for characterizing noise in an electrical imaging image, the apparatus comprising:
the selection module is suitable for acquiring the electric imaging images of the whole well section and selecting the sample images meeting the sample conditions from the electric imaging images;
the interface value determining module is suitable for extracting the electric deduction measurement data of the sample image to perform statistical processing so as to determine the interface value of the background data and the specified characteristic data in the sample image;
the binarization module is suitable for dividing a background data area and a specified characteristic data area in the electric imaging image according to the boundary value and the electric deduction measurement data of the electric imaging image, and performing binarization processing on the electric deduction measurement data of the electric imaging image according to the boundary value to obtain electric deduction binarization data corresponding to the background data and the specified characteristic data;
the connectivity detection module is suitable for performing connectivity detection on the electric buckle binary data of the electric imaging image one by one and correcting a detection result into abnormal electric buckle binary data;
the filtering module is suitable for selecting the electric buckle measurement data corresponding to the background data area, carrying out filtering processing according to the noise characteristics in the background data area, and updating the electric buckle measurement data in the background data area to remove the noise characteristics;
and the conversion module is suitable for generating an electrical imaging image after preprocessing and normalization processing according to the updated electrical deduction measurement data.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the processing method for eliminating the noise characteristics of the electrical imaging image according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the processing method for denoising an electrogram image according to any one of claims 1-7.
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