Ultrasonic imaging logging method and system for oil well

Abstract

The application relates to the technical field of ultrasonic imaging logging, in particular to an ultrasonic imaging logging method and system for oil logging, which comprises the following steps: collecting various logging data of each depth, and collecting sound wave velocity data, amplitude data and echo signals of each direction under each depth; obtaining comprehensive abnormal change characteristic values under each depth; dividing the total logging depth into characteristic depth sections; obtaining difference significant values of each depth; obtaining sound wave measurement interference values of each direction under each depth; when the echo signals are processed by using the amplitude limiting filtering method, obtaining the maximum deviation of adjacent sampling allowed when the echo signals of each direction under each depth are filtered, obtaining an initial ultrasonic imaging image of the logging and dividing the initial ultrasonic imaging image, adjusting the regularization parameter when each part of the image is filtered by using a total variation algorithm, obtaining an ultrasonic imaging image of the logging and obtaining a logging result. The application aims to improve the quality of the ultrasonic imaging image of the logging and improve the accuracy of the logging result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ultrasonic imaging logging, in particular to an ultrasonic imaging logging method and system for oil logging. BACKGROUND

[0002] The ultrasonic imaging logging technology can intuitively present the geological structure around the well in the form of a two-dimensional image by emitting high-frequency sound waves and receiving formation reflected signals, and has important significance in identifying fractures, faults, lithologic interfaces and evaluating reservoir properties. The ultrasonic imaging logging technology can realize high-resolution formation visualization, and provides key basis for geologists and engineers to accurately judge the formation structure characteristics and carry out oil and gas exploration and development work.

[0003] However, the current ultrasonic imaging logging technology faces many challenges in practical application, and the imaging quality is limited. In the data acquisition link, the complex and changeable wellbore environment is one of the main interference sources: wellbore collapse, mud invasion, etc. can change the sound wave propagation path, causing signal scattering and refraction; the differences in mud density, viscosity, sand content, etc. will affect the sound wave attenuation characteristics. In addition, the performance limitations of the instrument itself cannot be ignored-the frequency response range, azimuth resolution limit of the ultrasonic probe, and noise interference of the sound wave emission and reception system will all reduce the imaging accuracy; the anisotropy of sound wave propagation caused by the heterogeneity of the formation makes the signal characteristics at different azimuths and depths significantly different, further exacerbating the complexity of the imaging process. In summary, the existence of complex interference factors in the oil logging process seriously affects the accuracy of the logging results, resulting in generally low application accuracy of the ultrasonic imaging technology in oil logging. SUMMARY

[0004] In view of the above, it is necessary to provide an ultrasonic imaging logging method and system for oil logging, which, compared with the traditional ultrasonic imaging logging method and system for oil logging, improves the accuracy of the logging results by improving the quality of the logging ultrasonic imaging image:

[0005] In a first aspect, an embodiment of the present application provides an ultrasonic imaging logging method for oil logging, which comprises the following steps:

[0006] Collecting various logging data at each predetermined depth, and sound wave velocity data, amplitude data and echo signals at each azimuth at each depth;

[0007] all depths are divided into clustering clusters by similarity of logging data between different depths; for each orientation, abnormality degrees of the acoustic velocity data and the amplitude data at each depth in the acoustic velocity data and the amplitude data at all depths are respectively evaluated, and a comprehensive abnormal change feature value at each depth is obtained through the abnormality degrees of all orientations at each depth; the total logging depth is divided into feature depth sections through distribution of the comprehensive abnormal change feature values at all depths;

[0008] each depth in the same clustering cluster and the same feature depth section as each depth is recorded as a comparison depth, a difference significant value of each depth is obtained through acoustic velocity difference and amplitude difference between each depth and its comparison depth, amplitude data of each orientation at each depth is arranged according to depth to form an amplitude vector, and an acoustic measurement interference value of each orientation at each depth is obtained through similarity of amplitude vectors between each orientation and other orientations at each depth and the difference significant value;

[0009] when a limited amplitude filtering method is used to process echo signals, a maximum deviation allowed for adjacent sampling of each orientation at each depth during filtering processing of the echo signals is obtained through the acoustic measurement interference value, an initial ultrasonic imaging image of the logging is obtained after filtering processing, the initial ultrasonic imaging image is divided according to the feature depth section, a regularization parameter for filtering processing of each partial image by using a total variation algorithm is adjusted through the acoustic measurement interference value of all depths in each partial image obtained by the division, an ultrasonic imaging image of the logging is obtained after filtering processing, and a logging result is obtained.

[0010] In one embodiment, the clustering cluster obtaining process is as follows:

[0011] logging data at each depth is arranged to form a logging feature sequence of each depth, and a clustering division result of all logging feature sequences is obtained by using a clustering algorithm to divide all depths into clustering clusters.

[0012] In one embodiment, the comprehensive abnormal change feature value obtaining process is as follows:

[0013] abnormal scores of the acoustic velocity data and the amplitude data at each depth of each orientation are output by using an anomaly detection algorithm with the acoustic velocity data and the amplitude data of all orientations at all depths as input;

[0014] a mean value of the abnormal scores of the acoustic velocity data and the amplitude data of all orientations at each depth is taken as a comprehensive abnormal change feature value at each depth.

[0015] In one embodiment, the feature depth section division process is as follows:

[0016] The depth at which the comprehensive abnormal change characteristic value is greater than the preset comprehensive abnormal change threshold is taken as a feature depth, each feature depth is taken as a segmentation value, and the total logging depth is segmented to obtain a feature depth segment.

[0017] In one embodiment, the difference significant value is obtained by:

[0018] The acoustic velocity data and the amplitude data of all orientations at each depth are arranged in the order of orientation to form an acoustic velocity sequence and an amplitude sequence at each depth.

[0019] The distance between the acoustic velocity sequence and the amplitude sequence at each depth and each contrast depth is calculated and recorded as a first distance and a second distance.

[0020] The difference significant value can be further obtained by all the first distances and all the second distances corresponding to each depth.

[0021] In one embodiment, the difference significant value is calculated by multiplying the first distance and the second distance between each depth and any contrast depth, and the difference significant value is the average of the product between each depth and all contrast depths.

[0022] In one embodiment, the expression of the acoustic measurement interference value is:

[0023] In the formula, represents the acoustic measurement interference value of the yth orientation at the xth depth. represents the difference significant value of the xth depth; m represents the number of orientations at the xth depth; exp() represents the exponential function with the natural constant as the base number. represents the similarity of the amplitude vector between the yth orientation and the zth orientation at the xth depth.

[0024] In one embodiment, the expression of the maximum deviation allowed for adjacent samples during the echo signal filtering process at each orientation at each depth is:

[0025] In the formula, represents the maximum deviation allowed for adjacent samples during the echo signal filtering process at the yth orientation at the xth depth. represents the range of amplitude in the echo signal at the yth orientation at the xth depth. represents the normalized result of the acoustic measurement interference value at the yth orientation at the xth depth.

[0026] In one embodiment, the expression of the regularization parameter when the adjustment adopts the total variation algorithm to filter each partial image is:

[0027] in the formula, denotes the regularization parameter of the fth divided partial image in the filtering process; denotes the minimum value of the preset regularization parameter; denotes the range of the preset regularization parameter; denotes the mean value of the normalization results of the sound wave measurement interference values of all depths and all directions in the fth divided partial image.

[0028] In a second aspect, the embodiments of the present application also provide an ultrasonic imaging logging system for oil logging, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the ultrasonic imaging logging method for oil logging described in any one of the above aspects when executing the computer program.

[0029] The present application has at least the following beneficial effects:

[0030] The present application proposes an ultrasonic imaging logging method and system for oil logging in view of the problem that the quality of ultrasonic imaging results is limited and the judgment of formation structure is difficult due to the complex geological structure and measurement environment interference in oil logging. In the present application, the lithology change characteristics and geological structure differences of different depths and directions are fully considered, the interference influence characteristics of sound wave measurement in the horizontal depth and the vertical direction are accurately analyzed, and the echo signal processing and imaging stage processing in the ultrasonic imaging logging process are comprehensively optimized and adjusted. The beneficial effect is that the fine analysis of ultrasonic measurement interference is realized by closely combining the geological stratum characteristics of oil logging, the accuracy of ultrasonic imaging logging data is significantly improved, and the geological structure judgment and analysis accuracy based on the ultrasonic logging results is improved, thereby providing more reliable geological basis for oil and gas exploration and development. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0032] Figure 1 The step flow chart of the ultrasonic imaging logging method for oil logging provided by an embodiment of the present application is shown in the figure;

[0033] Figure 2 The flow chart of the acquisition of the sound wave measurement interference value is shown in the figure. DETAILED DESCRIPTION

[0034] In the description of embodiments of the present application, the words "example", "or", "for example", and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design presented as an "example" or "for example" in the embodiments of the present application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of "example", "or", "for example", and the like is merely intended to present certain concepts in a particular manner.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. It is to be understood that the use of "or" in the present application is meant to encompass both a and / or b.

[0036] In addition, it should be pointed out that the terms "first", "second" in the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0037] The specific schemes of the ultrasonic imaging logging method and system for oil logging provided by the present application are described in detail below in combination with the accompanying drawings.

[0038] Please refer to Figure 1 which shows the step flowchart of the ultrasonic imaging logging method for oil logging provided by an embodiment of the present application, and the method comprises the following steps:

[0039] Step 1, collecting various logging data at preset depths, and sound wave speed data, amplitude data and echo signals at various orientations at each depth.

[0040] In order to effectively analyze the characteristics of interference factors, multiple types of data are collected in the present application. On the one hand, basic logging data is collected, including well depth, wellbore diameter, mud density, viscosity, sand content parameters, which reflect the wellbore environmental conditions. Changes in mud density and viscosity will directly affect sound wave attenuation, and irregular wellbore diameter may cause abnormal sound wave propagation path. On the other hand, ultrasonic imaging raw data is obtained, including sound wave amplitude, propagation time and frequency components at different depths and orientations. Among them, the sound wave amplitude reflects the formation reflection intensity, the propagation time is related to the formation speed, and the frequency component contains the formation structure detail information.

[0041] Specifically, the ultrasonic imaging logging instrument integrating multiple sensors is used in the present application. The wellbore diameter change is measured in real time by a caliper to obtain the well wall shape information; the mud density, viscosity and sand content parameters are measured at different depths on the ground and in the well by a mud gravity gauge, a mud viscosity gauge and a mud sand content gauge to ensure the accuracy and representativeness of the data. The ultrasonic probe emits acoustic waves to the well wall at a set frequency of 200 kHz-1 MHz, receives the reflected signals at a fixed azimuth angle interval of 2° and a depth sampling interval of 0.01 m, converts the analog signals into digital signals and stores them in the instrument memory to obtain the acoustic velocity data, amplitude data and echo signals at each depth and azimuth.

[0042] Step 2: dividing all depths into clusters according to the similarity of logging data between different depths; obtaining the comprehensive abnormal change characteristic value at each depth; dividing the total logging depth into characteristic depth sections; obtaining the contrast depth of each depth and the difference significant value of each depth; arranging the amplitude values at each depth and azimuth according to the depth to form an amplitude vector, and obtaining the acoustic measurement interference value at each depth and azimuth according to the similarity of the amplitude vector between each depth and azimuth and the rest of the azimuths and the difference significant value.

[0043] When acoustic measurements are made at different depths and azimuths, the influence of interference factors shows significant spatial differences. In the depth direction, the shallow formation is significantly affected by mud invasion and well wall collapse, resulting in severe acoustic amplitude attenuation and time difference increase, causing imaging blur or distortion; although the stability of the deep formation is relatively improved, the heterogeneity of the formation is enhanced, and the acoustic reflection characteristics of different lithology interfaces differ significantly, which easily causes lithology boundary recognition errors. In the azimuth direction, irregular areas of the wellbore, such as the expansion and contraction sections, will cause the acoustic propagation path to deviate, resulting in a decrease in the received signal amplitude. Therefore, in actual measurement, due to complex noise interference, the logging data is significantly different in interference degree, which further affects the accuracy of the logging results.

[0044] Based on the above analysis, the present application precisely analyzes the acoustic interference influence characteristics at different positions by comparing and analyzing the characteristics of the logging data at different depths and azimuths, and the specific analysis process is as follows:

[0045] (1) dividing all depths into clusters according to the similarity of logging data between different depths; for each azimuth, respectively evaluating the abnormality of the acoustic velocity data and amplitude data at each depth in the acoustic velocity data and amplitude data at all depths, obtaining the comprehensive abnormal change characteristic value at each depth through the abnormality of all azimuths at each depth, and dividing the total logging depth into characteristic depth sections through the distribution of the comprehensive abnormal change characteristic value at all depths.

[0046] Due to the fact that the geological structure and mud properties at different depths and different orientations may have great differences, the sound wave signal measurement at different positions is interfered differently, which leads to great influence on the analysis and judgment of the boundary with the same geological structure and the existence of cracks or faults; therefore, the interference degree of the collected data at different depths and different orientations is analyzed based on the environmental characteristics and the geological characteristics of the adjacent positions at different depths and different orientations.

[0047] Specifically, the sound wave speed and amplitude at different depths in the same lithology section have similarity or gradual trend, the stratigraphic structure at different depths presents a periodic repetition mode, and the cracks or faults have continuity characteristics in the vertical direction. All the logging data at different depths are combined to form a logging feature sequence at each depth, all the logging feature sequences are taken as inputs, the clustering division result of all the logging feature sequences is obtained by using the condensed hierarchical clustering algorithm, and all the depths are divided into clusters, wherein the DTW (Dynamic Time Warping) distance between different logging feature sequences is taken as the distance measurement result, and the purpose is to divide the depths with similar logging data characteristics by clustering division, so as to avoid the error in the interference and influence analysis of the ultrasonic logging data caused by the difference in logging features in the same lithology section. The condensed hierarchical clustering algorithm is a known technology, and will not be described herein.

[0048] Further, since the sound wave speed and amplitude at different depths in the same lithology section have similarity or gradual trend, the sound wave speed data and amplitude data of all depths at each orientation are taken as inputs, and the abnormal score of the sound wave speed data and amplitude data of each depth at each orientation is output by using the anomaly detection algorithm; in order to accurately analyze the influence of the change of the geological structure on the sound wave measurement, the abnormal features of the sound wave speed data and amplitude data of different depths at each orientation are comprehensively judged. Specifically, the mean value of the abnormal scores of the sound wave speed data and amplitude data of all orientations at each depth is taken as the comprehensive abnormal change feature value at each depth. In order to further avoid the influence of the difference in the geological structure at different depths on the judgment of the interference degree of the sound wave measurement, a comprehensive abnormal change threshold is set, and each depth with a comprehensive abnormal change feature value greater than the comprehensive abnormal change threshold is taken as a feature depth.

[0049] In this embodiment, the anomaly detection algorithm is the Local Outlier Factor (LOF) algorithm, and the LOF algorithm is a known technology, which will not be described herein. As other embodiments, on the basis of measuring the abnormal degree of the sound wave speed data and amplitude data of each depth at each orientation, the implementer can use other existing feasible methods, and the present application does not have special limitations.

[0050] In this embodiment, the value of the comprehensive abnormal change threshold is 0.7, and the value of the comprehensive abnormal change threshold is preset by a person, and the implementer can set it by himself, and the present application does not make special limitation.

[0051] Further, in order to accurately analyze the contrast difference of the acoustic wave measurement interference at different depths, each feature depth is taken as each segmentation value, the total logging depth is segmented and divided, and each depth range obtained after the division is taken as a feature depth section for contrast analysis of the acoustic wave measurement interference, so that the specific contrast features of the acoustic wave measurement interference in the depth section with similar lithological characteristics and logging characteristics can be more accurately analyzed.

[0052] (2) Each depth with the same clustering cluster and the same feature depth section is recorded as each contrast depth, the difference significant value of each depth is obtained through the acoustic velocity difference and the amplitude difference between each depth and its contrast depth, and the amplitude value data of each direction at each depth is arranged according to the depth to form an amplitude vector, and the acoustic wave measurement interference value of each direction at each depth is obtained through the similarity of the amplitude vector between each direction at each depth and the remaining directions and the difference significant value.

[0053] Since the lithological difference will cause the acoustic velocity and the amplitude to present significant changes, and the difference in porosity and fluid properties will cause the acoustic travel time and the amplitude to present obvious differences, after the feature depth section division is completed based on the lithological characteristics, if the interference influence degree in a certain depth section is larger, it will directly affect the correlation characteristics of the amplitude value of different directions with the depth. Therefore, the present application carries out the contrast analysis of the acoustic wave measurement data for different feature depth sections: first, the acoustic velocity data and the amplitude data of all directions at each depth are arranged in order of direction to form the acoustic velocity sequence and the amplitude sequence of each depth; second, each depth with the same clustering cluster and the same feature depth section is recorded as each contrast depth of each depth, and the degree of influence of the acoustic wave measurement affected by the interference is obtained by analyzing the acoustic velocity difference and the amplitude difference between each depth and its contrast depth.

[0054] Based on the above analysis, the difference significant value of each depth is obtained through the acoustic velocity difference and the amplitude difference between each depth and its contrast depth, and the expression is:

[0055] ; in the formula, the difference significant value of the xth depth is represented; the number of contrast depths of the xth depth is represented; , the distance between the acoustic velocity sequence and the amplitude sequence between the xth depth and the ith contrast depth thereof is represented respectively. , the first distance and the second distance are recorded respectively.

[0056] In this embodiment, the distance between the acoustic velocity sequences and the distance between the amplitude sequences are both DTW (Dynamic Time Warping) distances. As other embodiments, on the basis of measuring the difference between the data in the two acoustic velocity sequences and the difference between the data in the two amplitude sequences, the implementer can use other existing technologies such as the Euclidean distance, and the present application does not make special limitations.

[0057] It should be noted that the greater the difference significance value calculated is, the more significant the relative characteristics of each depth that are different from its comparative depth due to the interference are in the analysis of the lithology difference and the logging feature difference at different depths in the comprehensive oil logging process.

[0058] Further, considering the borehole diameter change and the existence of fractures and faults in the oil logging process, the acoustic test results in different directions will be interfered differently. With the increase of the logging depth, these interferences become more complex due to the difference in the geological distribution characteristics, which may lead to an increase in the error in the judgment and analysis of the echo signals in different directions. Therefore, it is necessary to perform a relative comparative analysis based on the interference degree characteristics at different depths and further combine the amplitude change difference between different directions to analyze the echo interference characteristics in the acoustic measurement process at each position; specifically, the amplitude data of each direction at each depth and its comparative depth are arranged according to the depth to form an amplitude vector of each direction at each depth, and the lower the similarity of the amplitude change characteristics of each direction with the depth compared with the amplitude change characteristics of the rest directions with the depth is, the greater the echo interference of each direction is analyzed based on the correlation characteristics of the amplitude change with the depth in different directions.

[0059] Based on the above analysis, the acoustic measurement interference value of each direction at each depth is obtained through the similarity of the amplitude vectors between each direction and the rest directions at each depth and the difference significance value, and the expression is:

[0060] In the formula, the acoustic measurement interference value of the yth direction at the xth depth is represented by The difference significance value of the xth depth is represented bym represents the number of directions at the xth depth; exp() represents an exponential function with a natural constant as the base number; and the similarity of the amplitude vectors between the yth direction and the zth direction at the xth depth is represented by Since the correlation between the interference characteristics of different depths and the amplitude change with the depth in different directions is inversely proportional, in order to comprehensively consider the interference of the horizontal direction characteristics and the vertical depth characteristics on the acoustic measurement at different positions, the similarity of the amplitude vectors between the yth direction and the zth direction at the xth depth is calculated by The result of the similarity is mapped to (0, 1].

[0061] In this embodiment, the similarity between the amplitude vectors is the cosine similarity. As other embodiments, on the basis of measuring the similarity between the amplitude vectors, the implementer can use other existing technologies, and the present application does not make special limitations.

[0062] It should be noted that: when the difference significant value of each depth is greater, and the correlation between each orientation at each depth and the remaining orientations is lower due to interference during the sound wave measurement process, the sound wave measurement interference value of each depth calculated is greater, indicating that the environment at each orientation at each depth during the sound wave measurement process has a greater interference effect on the echo signal. The acquisition process of the sound wave measurement interference value is shown in Figure 2 .

[0063] Step 3, when the echo signal is processed by the amplitude limiting filter method, the maximum deviation allowed by the adjacent sampling of the echo signal filtering processing of each orientation at each depth is obtained by the sound wave measurement interference value, the initial ultrasonic imaging image of the well logging is obtained after the filtering processing, the initial ultrasonic imaging image is divided according to the feature depth section, and the regularization parameter of the filtering processing of each part of the image at all depths by the sound wave measurement interference value is adjusted. The full variation algorithm is used to adjust the regularization parameter of the filtering processing of each part of the image.

[0064] Based on the above analysis, the interference characteristics of the sound wave measurement at different depth positions in the ultrasonic imaging process are comprehensively analyzed, further, based on the analysis result, the echo signal stage and the filtering processing of the ultrasonic imaging stage of the sound wave measurement at each depth position are optimized and adjusted, and then the error of the ultrasonic imaging measurement under the complex environment of oil logging is reduced.

[0065] Specifically, in the echo signal stage, the echo signal is processed by the amplitude limiting filter method, wherein the maximum deviation allowed by the adjacent sampling of the echo signal filtering processing of each orientation at each depth is obtained by the sound wave measurement interference value of each orientation at each depth, and the expression is:

[0066] ; In the formula, represents the maximum deviation allowed by the adjacent sampling of the echo signal filtering processing of the y-th orientation at the x-th depth; represents the range of the amplitude in the echo signal of the y-th orientation at the x-th depth; represents the normalization result of the sound wave measurement interference value of the y-th orientation at the x-th depth.

[0067] In this embodiment, the Min-Max normalization method is used to obtain the normalization result of the sound wave measurement interference value.

[0068] It should be noted that the greater the calculated acoustic measurement interference value of the xth depth and the yth orientation, the greater the influence of the environment at each depth and each orientation on the echo signal during acoustic measurement, and the smaller the corresponding should be set , and the processing effect of the interference factors in the echo signal is improved.

[0069] Further, after the above echo stage processing is completed, an initial ultrasonic imaging image is obtained by the ultrasonic imaging logging instrument, and the initial ultrasonic imaging image data needs to be further processed. Specifically, the filtering processing of the ultrasonic imaging image in the logging process adopts a total variation (TV) algorithm, and the ultrasonic imaging image is divided according to the characteristic depth section. When each part of the image after the division is filtered by the TV algorithm, the adjustment formula of the filtering parameter is:

[0070] ; in the formula, denotes the regularization parameter of the fth part of the image after the division during the filtering processing; denotes the minimum value of the preset regularization parameter; denotes the range of the preset regularization parameter; denotes the mean value of the normalized results of the acoustic measurement interference values of all orientations at all depths in the fth part of the image after the division.

[0071] In this embodiment, the minimum value of the preset regularization parameter is 10, and the value range of the preset regularization parameter is [10, 200].

[0072] In this embodiment, the Min-Max normalization method is used to obtain the normalized results of the acoustic measurement interference values.

[0073] It should be noted that the greater the acoustic measurement interference influence of all orientations at all depths in each characteristic depth section, the greater the regularization parameter is set, the noise interference influence is reduced, and the ultrasonic imaging quality is improved.

[0074] Based on the above processing, based on the interference influence characteristic analysis of the acoustic measurement of different depths and different orientations, in actual logging, according to the real-time analysis of the interference influence degree, the filtering parameter is optimized and adjusted in different filtering processing stages, the intelligent comprehensive optimization processing of the filtering processing in the ultrasonic imaging in the oil logging process is realized, and the accurate ultrasonic imaging result is obtained.

[0075] Step 4, the ultrasonic imaging image of the logging is obtained after the filtering processing, and the logging result is obtained based on the ultrasonic imaging image.

[0076] Based on the optimized ultrasonic imaging data, fine interpretation and analysis of formation characteristics are performed. First, a high gradient area in the image is extracted by using an edge detection algorithm, the input is the obtained ultrasonic imaging image, and the output is a binary edge image containing boundary information of lithologic interface, fracture edge and the like. By performing feature matching on the binary edge image and a geological knowledge base, the geological knowledge base stores acoustic velocity and amplitude feature templates of different lithologies; a pattern recognition algorithm is used to realize automatic division of a lithologic section, the input is an acoustic parameter of an edge position in the binary edge image, the acoustic parameter including a velocity value and an amplitude value, and the output is a lithology category label of each depth section, such as sandstone, mudstone and limestone.

[0077] In the embodiment, a Canny operator is used to obtain the binary edge image, the Canny operator is a known technology, and details are not described herein again. As other embodiments, on the basis of being able to obtain the binary edge image, implementers can use other existing technologies, such as a Sobel operator, and the present application does not have special limitations.

[0078] In the embodiment, the pattern recognition algorithm is specifically a template matching algorithm, the template matching algorithm is a known technology, and details are not described herein again. Implementers can use other existing algorithms.

[0079] For fracture feature analysis, a threshold segmentation algorithm, such as an Otsu threshold segmentation algorithm, is used to extract a low-amplitude abnormal area from the denoised ultrasonic imaging image, the input is the ultrasonic imaging image, and the output is a mask image of a fracture candidate area. A morphological processing algorithm of dilation and erosion is used to optimize the connected domain of the candidate area, remove isolated noise points and retain continuous strip-shaped structures. Further, a Hough transform algorithm is used to realize automatic tracking of the fractures, the input is the optimized fracture mask image and azimuth-depth coordinate information, and the output is geometric parameters and spatial distribution characteristics of the fractures, wherein the geometric parameters include length, width, dip angle and azimuth angle. Statistical methods, including fracture density and porosity calculation, are combined to evaluate the development degree of the fractures and reservoir permeability, and to obtain logging results of the ultrasonic imaging.

[0080] Based on the same inventive concept as the above method, the present application embodiment further provides an ultrasonic imaging logging system for oil logging, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the methods in the above ultrasonic imaging logging method for oil logging when the computer program is executed.

[0081] In summary, the present application aims at the problem that the quality of ultrasonic imaging result is limited and the formation structure is difficult to judge due to complex geological structure and complex measurement environment interference in petroleum logging, and proposes an ultrasonic imaging logging method and system for petroleum logging. In the present application, the lithology change characteristics and geological structure differences of different depths and directions are fully considered, the interference influence characteristics of the transverse depth and the longitudinal direction of the sound wave measurement at different positions are accurately analyzed, and the echo signal processing and imaging stage processing in the ultrasonic imaging logging process are comprehensively optimized and adjusted. Its beneficial effect lies in closely combining the geological stratum characteristics of petroleum logging, realizing fine analysis of ultrasonic measurement interference, significantly improving the accuracy of ultrasonic imaging logging data, and then improving the geological structure judgment and analysis accuracy based on the ultrasonic logging result, providing more reliable geological basis for oil and gas exploration and development.

[0082] The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logic function. In some alternative implementations, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the drawings, the operations or steps corresponding to different blocks can also occur in different order from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0083] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the essential characteristics of the present application. Therefore, the above-described embodiments of the present application should be regarded as exemplary and non-limiting from any point of view.

Claims

1. An ultrasonic imaging logging method for oil well logging, characterized in that: The method comprises the following steps: Collect various logging data at preset depths, as well as acoustic wave velocity data, amplitude data and echo signals in all directions at each depth; All depths are divided into clusters based on the similarity of logging data between different depths; for each direction, the abnormality of the acoustic velocity data and amplitude data at each depth is evaluated respectively. The comprehensive abnormal change characteristic value at each depth is obtained based on the abnormality of all directions at each depth; the total logging depth is divided into characteristic depth segments based on the distribution of the comprehensive abnormal change characteristic values ​​at all depths; Each depth of the same characteristic depth segment of the same cluster at each depth is recorded as each comparison depth, and the difference in acoustic wave velocity and amplitude between each depth and its comparison depth is used to obtain the difference significance value of each depth; the amplitude data of each direction at each depth and its comparison depth are arranged according to depth to form an amplitude vector, and the acoustic wave measurement interference value of each direction at each depth is obtained by the similarity of the amplitude vector between each direction at each depth and the remaining directions, as well as the difference significance value; When the limiting filtering method is used to process the echo signal, the maximum deviation allowed by adjacent sampling during the filtering processing of the echo signal in all directions at all depths is obtained through the acoustic wave measurement interference value. After the filtering processing, the initial ultrasonic imaging image of the well logging is obtained, and the initial ultrasonic imaging image is divided according to the characteristic depth segment. The regularization parameter when the total variation algorithm is used to filter the partial images is adjusted based on the acoustic wave measurement interference value at all depths in each partial image obtained by the division. After the filtering processing, the ultrasonic imaging image of the well logging is obtained and the logging result is obtained.

2. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The process of obtaining the cluster is as follows: The logging data at each depth are combined into a logging feature sequence of each depth. A clustering algorithm is used to obtain the clustering results of all logging feature sequences, and all depths are divided into clusters.

3. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The process of obtaining the comprehensive abnormal change characteristic value is as follows: The acoustic wave velocity data and amplitude data at all depths in all directions are used as input, and an anomaly detection algorithm is used to output anomaly scores of the acoustic wave velocity data and amplitude data at all depths in all directions; The average of the anomaly scores of the acoustic wave velocity data and amplitude data in all directions at each depth is taken as the comprehensive anomaly change characteristic value at each depth.

4. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The process of dividing the characteristic depth segments is as follows: Each depth whose comprehensive abnormal change characteristic value is greater than the preset comprehensive abnormal change threshold is taken as each characteristic depth, each characteristic depth is taken as each segmentation value, and the total logging depth is segmented to obtain each characteristic depth segment.

5. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The process of obtaining the significant difference value is as follows: Arrange the acoustic wave velocity data and amplitude data of all directions at each depth in the order of directions to form the acoustic wave velocity sequence and amplitude sequence at each depth; Calculate the distances of the acoustic wave velocity sequence and amplitude sequence between each depth and its respective comparison depth, and record them as the first distance and the second distance; The difference significance value may be further obtained by obtaining all the first distances and all the second distances corresponding to each depth.

6. The ultrasonic imaging logging method for oil well logging according to claim 5, wherein: The method for calculating the difference significance value is: calculating the product of the first distance and the second distance between each depth and any of its comparison depths, and the difference significance value is the average of the products between each depth and all of its comparison depths.

7. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The expression of the acoustic wave measurement interference value is: Where, Indicates the acoustic wave measurement interference value at the y-th direction at the x-th depth; represents the difference significance value at the x-th depth; m represents the number of positions at the x-th depth; exp() represents the exponential function with a natural constant as the base; Indicates the similarity of the magnitude vector between the y-th orientation and the z-th orientation at the x-th depth.

8. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The expression for the maximum deviation allowed by adjacent samples during the filtering processing of the echo signals in all directions at each depth is: Where, Indicates the maximum deviation allowed between adjacent samples when filtering the echo signal at the y-th direction at the x-th depth; Indicates the range of the amplitude of the echo signal at the y-th direction at the x-th depth; Represents the normalized result of the acoustic wave measurement interference value at the y-th direction at the x-th depth.

9. The ultrasonic imaging logging method for oil well logging according to claim 1, wherein: The expression for adjusting the regularization parameter when filtering each part of the image using the total variation algorithm is: Where, Represents the regularization parameter of the f-th part of the image after division during filtering; Indicates the minimum value of the preset regularization parameter; Represents the range of values ​​within the preset regularization parameter; It represents the mean of the normalized results of the acoustic wave measurement interference values ​​at all depths and all directions in the divided image of the f-th part.

10. An ultrasonic imaging logging system for oil well logging, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the ultrasonic imaging logging method for petroleum logging as claimed in any one of claims 1 to 9 are implemented.

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