Dipole acoustic logging dispersion correction method based on density clustering and related device

Abstract

The present invention discloses a method for correcting the frequency dispersion of dipole acoustic logging based on density clustering and a related device, wherein the method for correcting the frequency dispersion of dipole acoustic logging based on density clustering comprises: performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; repeating the process until the dispersion curve group of the target layer segment is obtained; processing the dispersion curve group by density clustering to obtain the effective part of the dispersion curve group; fitting the effective part of the dispersion curve group to obtain the correction result. The present invention uses density clustering to perform cluster screening on the dispersion curve, without the need for pre-training of a large amount of data, thereby improving the efficiency of extracting effective data; the correction result is obtained by fitting the effective part of the dispersion curve, thereby improving the accuracy of obtaining the true shear wave velocity of the formation from actual data while ensuring the dispersion correction efficiency.

Description

Technical Field

[0001] The invention relates to the field of geophysical acoustic logging, and in particular to a density clustering-based dipole acoustic logging dispersion correction method and device, a computing device and a computer storage medium. Background Art

[0002] Dipole acoustic logging uses dipole excitation to excite flexural waves in the wellbore that propagate along the fluid in the well. The dipole waveform has an obvious dispersion effect. The time difference result obtained by the time domain processing method is the average result within a frequency range and cannot reflect the true shear wave velocity of the formation. Therefore, in actual processing, it is necessary to perform dispersion correction on the dipole acoustic logging data to obtain the true shear wave velocity of the formation. An effective dispersion correction method is a key technology for the effective application of dipole acoustic logging.

[0003] At present, dispersion correction mainly consists of a dispersion analysis method for obtaining the dispersion characteristics of mode waves and a method for extracting time differences from the dispersion characteristics. Dispersion analysis methods mainly include the Prony method, the weighted spectral coherence method (WSS), the amplitude phase estimation method (APES), etc. After obtaining a high-resolution dispersion curve through dispersion analysis, the dispersion curve can be corrected by using the histogram statistics method and the curve fitting function method. The histogram statistics method is to obtain the histogram of the time difference of the dispersion curve extracted from the frequency time difference correlation diagram, but the data will be affected by noise, so that the edge will cause errors in the measurement results. The curve fitting function method fits the dispersion curve, and after fitting, the time difference is obtained by the cutoff frequency of the fitting curve, but it is necessary to select a suitable fitting function, and the curve quality requirements are high. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a density clustering-based dipole acoustic logging dispersion correction method and apparatus, computing equipment and computer storage medium that overcome the above-mentioned problems of low dispersion correction accuracy and efficiency.

[0005] According to one aspect of the present invention, a method for correcting the dispersion of dipole acoustic logging based on density clustering is provided, comprising:

[0006] Step S1, performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point;

[0007] Step S2, repeatedly executing step S1 until a dispersion curve group of the target layer segment is obtained;

[0008] Step S3, processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group;

[0009] Step S4, fitting the effective part of the dispersion curve group to obtain a correction result.

[0010] In an optional manner, the number of the dispersion curve groups is determined by the layer depth of the target layer.

[0011] In an optional manner, performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point further includes:

[0012] The weighted spectral coherence method is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point.

[0013] In an optional manner, before the process of processing the dispersion curve group by density clustering to obtain a valid part of the dispersion curve group, the method further includes:

[0014] According to the maximum velocity value, the minimum velocity value, the maximum frequency value and the minimum frequency value of each dispersion curve in the dispersion curve group, each dispersion curve is normalized.

[0015] In an optional manner, the specific formula for normalizing each dispersion curve according to the maximum velocity value, the minimum velocity value, the maximum frequency value, and the minimum frequency value of each dispersion curve in the dispersion curve group is:

[0016]

[0017] Among them, x min is the minimum value; x max is the maximum value; x is the original data; x′ is the normalized data.

[0018] In an optional manner, the processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group further comprises:

[0019] The radius and the minimum number parameters are set for each dispersion curve in the dispersion curve group respectively, and the DBSCAN clustering method is used to perform cluster analysis on each dispersion curve to obtain the effective part of the dispersion curve group.

[0020] In an optional manner, fitting the effective part of the dispersion curve group to obtain the correction result further includes:

[0021] The effective dispersion points of each dispersion curve in the dispersion curve group are respectively fitted to obtain a multiple linear regression model, and the effective dispersion points of each dispersion curve are corrected according to the multiple linear regression model to obtain a correction result.

[0022] According to another aspect of the present invention, a dipole acoustic logging dispersion correction device based on density clustering is provided, comprising:

[0023] A dispersion analysis module is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point; the operation is repeated until a dispersion curve group of the target layer segment is obtained;

[0024] A dispersion clustering module, used for processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group;

[0025] The dispersion correction module is used to fit the effective part of the dispersion curve group to obtain a correction result.

[0026] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0027] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned density clustering-based dipole acoustic logging dispersion correction method.

[0028] According to another aspect of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned density clustering-based dipole acoustic logging dispersion correction method.

[0029] The solution provided by the above embodiment of the present invention is to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; repeat the execution until the dispersion curve group of the target layer segment is obtained; process the dispersion curve group using density clustering to obtain the effective part of the dispersion curve group; fit the effective part of the dispersion curve group to obtain the correction result. The present invention uses density clustering to cluster and screen the dispersion curves, without the need for pre-training of a large amount of data, thereby improving the efficiency of extracting effective data; the correction result is obtained by fitting the effective part of the dispersion curve, which improves the accuracy of obtaining the true shear wave velocity of the formation from actual data while ensuring the efficiency of dispersion correction.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0032] Figure 1 A schematic flow chart of a method for correcting the dispersion of dipole acoustic logging based on density clustering according to an embodiment of the present invention is shown;

[0033] Figure 2 A schematic flow chart of a method for correcting the frequency dispersion of dipole acoustic logging based on density clustering according to another embodiment of the present invention is shown;

[0034] Figure 3 A schematic diagram of density clustering according to an embodiment of the present invention is shown;

[0035] Figure 4 A schematic diagram of a dispersion curve at a certain depth point according to an embodiment of the present invention is shown;

[0036] Figure 5 A schematic structural diagram of a dipole acoustic logging dispersion correction device based on density clustering according to an embodiment of the present invention is shown;

[0037] Figure 6 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0039] Figure 1 The flowchart of the method for correcting the dispersion of dipole acoustic logging based on density clustering according to an embodiment of the present invention is shown. The method uses density clustering to process the dispersion curve group to obtain the effective part of the dispersion curve group, and fits the effective part of the dispersion curve group to obtain the correction result. Specifically, Figure 1 As shown, the following steps are included:

[0040] Step S101, performing dispersion analysis on dipole acoustic logging data of a target depth point to obtain a dispersion curve corresponding to the target depth point.

[0041] Dipole acoustic logging refers to the use of low-frequency excitation dipole sound sources to generate bending mode waves in the wellbore to achieve the purpose of extracting shear waves. However, bending waves are not shear waves, they are dispersive and change with frequency. Only at extremely low frequencies, the speed of bending waves is very close to that of shear waves. Therefore, it is necessary to consider correcting the bending waves in order to obtain the true shear wave velocity of the formation from actual data.

[0042] First, it is necessary to conduct dispersion analysis on the dipole acoustic logging data at the target depth point, and analyze the characteristics that affect the dispersion curve by consulting the data, including the change rules of the dispersion curve caused by the well diameter, the density of the liquid in the well, the formation density, and the velocity of the fluid in the well. For example, as the well diameter increases, the cutoff frequency of the dispersion curve shifts to a low frequency, and the dispersion phenomenon becomes more serious. In other words, in the case of a large well diameter, if the dispersion correction method is used to obtain the true shear wave velocity of the formation, the required dispersion correction amount is also large.

[0043] Then, based on the dispersion analysis results, the dispersion curve of the dipole sound wave (such as multiple modes and single mode sound waves) is extracted to obtain the dispersion curve corresponding to the target depth point. For example, based on the type of pre-input mode wave, the dispersion curve of multiple mode waves is extracted by the Prony method, the matrix beam method, etc.; the dispersion curve of a single mode wave is extracted by the phase unwrapping method, the weighted dispersion coherence method, etc.

[0044] Step S102, repeating step S101 until a dispersion curve group of the target layer segment is obtained.

[0045] The dispersion curve of each depth point in the target layer is calculated repeatedly and finally integrated into a dispersion curve group.

[0046] Step S103: Process the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group.

[0047] The dispersion curve group includes at least one dispersion curve, and each dispersion curve usually includes some noise points or outlier data points. It is necessary to remove the noise points or outlier data points in each dispersion curve, also called invalid parts, or remove all noise points and outlier data points from the overall dispersion curve group to obtain the valid part in the dispersion curve group.

[0048] Each dispersion curve in the dispersion curve group is usually non-convexly distributed. In this embodiment, the dispersion curve group is processed by density clustering to obtain the effective part of the dispersion curve group. The density clustering algorithm has high automation and can well identify non-convex noise points and outlier data points. It has good robustness and does not need to determine the number of borehole mode waves and mode wave parameters in advance. Since the density clustering algorithm does not require a large amount of data training in advance, it only needs to perform clustering screening according to the density distribution of the two-dimensional dispersion curve data, which improves the efficiency of extracting effective data.

[0049] In an optional manner, a density clustering algorithm such as DBSCAN, MDCA, OPTICS, or DENCLUE is used to process the dispersion curve group to obtain a valid portion of the dispersion curve group.

[0050] For example, compared with partition-based clustering methods and hierarchical clustering methods, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) density clustering algorithm can divide areas with sufficiently high density into clusters, does not require a given number of clusters, and can find clusters of arbitrary shapes in noisy spatial data sets. The OPTICS (Ordering Points to Identify the Clustering Structure) density clustering algorithm is also a density-based clustering algorithm. Its goal is to cluster data in space according to density distribution. Its idea is very similar to DBSCAN and can be regarded as a generalization of the DBSCAN algorithm. The OPTICS algorithm relaxes the requirement of the scanning radius (eps) from a single value to a range. However, unlike DBSCAN, the OPTICS algorithm can obtain clusters of different densities. After processing by the OPTICS algorithm, clusters of arbitrary density can be obtained in theory.

[0051] Step S104, fitting the effective part of the dispersion curve group to obtain a correction result.

[0052] The effective part of the dispersion curve group is fitted to obtain a multivariate linear regression model (such as the curve equation of velocity changing with frequency). The actual dipole acoustic logging data is input into the multivariate linear regression model to obtain the dispersion prediction value of the target layer segment. According to the dispersion difference between the actual value and the estimated value, the logging data is dispersion corrected to obtain the true shear wave velocity of the formation.

[0053] The solution provided by the above embodiment of the present invention is to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; repeat the execution until the dispersion curve group of the target layer segment is obtained; process the dispersion curve group using density clustering to obtain the effective part of the dispersion curve group; fit the effective part of the dispersion curve group to obtain the correction result. The present invention uses density clustering to cluster and screen the dispersion curves, without the need for pre-training of a large amount of data, thereby improving the efficiency of extracting effective data; the correction result is obtained by fitting the effective part of the dispersion curve, which improves the accuracy of obtaining the true shear wave velocity of the formation from actual data while ensuring the efficiency of dispersion correction.

[0054] Figure 2The flowchart of the density clustering-based dipole acoustic logging dispersion correction method according to another embodiment of the present invention is shown. The method uses the weighted spectral coherence method to perform dispersion analysis on the dipole acoustic logging data of the target depth point, uses the DBSCAN clustering method to perform cluster analysis on each dispersion curve in the dispersion curve group, and fits and corrects the effective dispersion points according to the multivariate linear regression model. Specifically, Figure 2 As shown, the following steps are included:

[0055] Step S201: using a weighted spectral coherence method to perform dispersion analysis on dipole acoustic logging data of a target depth point to obtain a dispersion curve corresponding to the target depth point.

[0056] The weighted spectral coherence method can be used to stably extract the fast and slow bending wave dispersion curves. The weighted spectral coherence method is suitable for processing waveforms with relatively simple waveform components. Its basic principle is to use the frequency points around each frequency and perform weighted processing, search for the maximum value of the weighted function within a certain slowness value range, find the true vibration mode, and discard meaningless zero solutions. This not only increases the amount of dispersion data, but also makes the calculation results stable and reliable.

[0057] Specifically, the weighted spectral coherence method is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point, and the dispersion curve corresponding to the target depth point is extracted, such as Figure 4 The dispersion curve at a certain depth point is shown in the figure. The horizontal axis represents the frequency of the bending wave (the frequency range is 0-7kHz), and the vertical axis represents the speed of the bending wave. If the number of dispersion data points is too small, it will lead to too few samples in the frequency domain, which will affect the calculation accuracy. Optionally, the frequency sampling can be encrypted by padding zeros after the time domain waveform data.

[0058] Step S202 is repeatedly executed until a dispersion curve group of the target layer segment is obtained, and the number of dispersion curve groups is determined by the layer segment depth of the target layer segment.

[0059] In an optional manner, the number of dispersion curve groups is determined by the layer depth of the target layer.

[0060] Further, the number of dispersion curve groups is determined by at least one of the following: wellbore diameter of the target layer, formation density, fluid velocity in the well, liquid density in the well, and acoustic velocity of wellbore fluid.

[0061] Step S203, respectively setting the radius and the minimum number parameters for each dispersion curve in the dispersion curve group, performing cluster analysis on each dispersion curve using the DBSCAN clustering method, and obtaining the valid part of the dispersion curve group.

[0062] The DBSCAN clustering method does not require a given number of clusters and can find clusters of any shape in a noise data set. The DBSCAN clustering method requires setting two parameters, namely the scan radius (eps) and the minimum number of included points (minPts). The main steps are: starting from any unvisited point, find all nearby points within the scan radius (eps) (including points equal to eps) from the point; if the number of nearby points is greater than or equal to the minimum number of included points (minPts), the current point and its nearby points form a cluster, and the starting point is marked as visited; then all points in the cluster that are not marked as visited are recursively processed in the same way to expand the cluster; if the number of nearby points is less than the minimum number of included points (minPts), the point is temporarily marked as a noise point; if the cluster is fully expanded, that is, all points in the cluster are marked as visited, and then the unvisited points are processed with the same algorithm.

[0063] In this embodiment, the DBSCAN clustering method is used to perform cluster analysis on each dispersion curve, and the radius (eps) and the minimum number parameter (minPts) are set for each dispersion curve in the dispersion curve group according to experience to obtain the effective part of the dispersion curve group. For example, the radius is set to 0.6 and the minimum number is set to 2, and the following is obtained: Figure 3 As shown in the density clustering diagram, the valid points in multiple cross circles are clustered into one cluster, and the dark gray scattered points are outliers and are clustered into another cluster.

[0064] For the DBSCAN clustering method, the radius will directly affect the clustering results. If the radius (eps) is large, the number of clusters will decrease; conversely, the number of clusters will increase. It usually takes many attempts to set the appropriate radius (eps).

[0065] Furthermore, the radius (eps) is set by K-distance. Specifically, according to the K-distance set E of all points, the set E is sorted in ascending order to obtain the K-distance set E′, and the K-distance change curve in the K-distance set E′ is fitted, and the K-distance value corresponding to the position with a sharp change is determined as the radius (eps) value. Among them, K-distance refers to the Kth (the size of K can be set based on experience) closest distance between any point p(i) and all points (except point p(i)). The K-distance is calculated for each point in the cluster set, and the K-distance set E={e(1), e(2),…, e(n)} of all points is obtained in turn.

[0066] Furthermore, the minimum number parameter (minPts) is set according to the value of K in the K-distance. The minimum number parameter (minPts) is usually set to the value of K. For example, if K=5, then minPts=5.

[0067] In an optional manner, before the dispersion curve group is processed by density clustering to obtain a valid part of the dispersion curve group, the method further includes:

[0068] According to the maximum velocity value, the minimum velocity value, the maximum frequency value and the minimum frequency value of each dispersion curve in the dispersion curve group, each dispersion curve is normalized respectively.

[0069] The purpose of normalization is to limit the preprocessed data to a certain range (such as [0,1] or [-1,1]), thereby eliminating the adverse effects caused by singular sample data.

[0070] In an optional manner, each dispersion curve in the dispersion curve group is normalized according to the maximum velocity value, the minimum velocity value, the maximum frequency value, and the minimum frequency value. The specific formula is:

[0071]

[0072] Among them, x min is the minimum value; x max is the maximum value; x is the original data; x′ is the normalized data.

[0073] For example, for each dispersion curve, the maximum frequency value (7000) and the minimum frequency value (0) in each dispersion curve are obtained respectively, and a dispersion point with a frequency value of 2000 in a dispersion curve is processed according to the above normalization formula to obtain the normalized frequency value of the dispersion point:

[0074]

[0075] Similarly, the normalized velocity value of the frequency dispersion point is calculated, and then the normalized coordinate value of the frequency dispersion point is obtained (the horizontal coordinate is the frequency, and the vertical coordinate is the velocity).

[0076] Step S204, respectively fitting the effective dispersion points of each dispersion curve in the dispersion curve group to obtain a multiple linear regression model, and correcting the effective dispersion points of each dispersion curve according to the multiple linear regression model to obtain a correction result.

[0077] For example, the highest power of the multiple linear regression equation is set to cubic, a multiple linear regression model is obtained by curve fitting the effective frequency dispersion points, and the effective frequency dispersion points are input into the multiple linear regression model to obtain the final correction result.

[0078] The solution provided by the above embodiment of the present invention uses the weighted spectral coherence method to perform dispersion analysis on the dipole acoustic logging data of the target depth point, which not only increases the amount of dispersion data, but also makes the calculation results stable and reliable. The DBSCAN clustering method is used to perform cluster analysis on each dispersion curve in the dispersion curve group, which further improves the efficiency of extracting effective data, thereby improving the efficiency of dispersion correction. In addition, the effective dispersion points are fitted and corrected according to the multivariate linear regression model, which further improves the accuracy of obtaining the true shear wave velocity of various formations.

[0079] Figure 5 The schematic diagram of the structure of the dipole acoustic logging dispersion correction device based on density clustering according to the embodiment of the present invention is shown. The dipole acoustic logging dispersion correction device based on density clustering 500 comprises: a dispersion analysis module 610 , a dispersion clustering module 520 and a dispersion correction module 530 .

[0080] The dispersion analysis module 610 is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; the process is repeated until a dispersion curve group of the target layer segment is obtained;

[0081] The dispersion clustering module 520 is used to process the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group;

[0082] The dispersion correction module 530 is used to fit the effective part of the dispersion curve group to obtain a correction result.

[0083] In an optional manner, the number of the dispersion curve groups is determined by the layer depth of the target layer.

[0084] In an optional manner, the dispersion analysis module 610 is further used to:

[0085] The weighted spectral coherence method is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point.

[0086] In an optional manner, before the dispersion clustering module 520 processes the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group, the method further includes:

[0087] According to the maximum velocity value, the minimum velocity value, the maximum frequency value and the minimum frequency value of each dispersion curve in the dispersion curve group, each dispersion curve is normalized.

[0088] In an optional manner, the specific formula for normalizing each dispersion curve according to the maximum velocity value, the minimum velocity value, the maximum frequency value, and the minimum frequency value of each dispersion curve in the dispersion curve group is:

[0089]

[0090] Among them, x min is the minimum value; x max is the maximum value; x is the original data; x′ is the normalized data.

[0091] In an optional manner, the dispersion clustering module 520 is further used to:

[0092] The radius and the minimum number parameters are set for each dispersion curve in the dispersion curve group respectively, and the DBSCAN clustering method is used to perform cluster analysis on each dispersion curve to obtain the effective part of the dispersion curve group.

[0093] In an optional manner, the dispersion correction module 530 is further used to:

[0094] The effective dispersion points of each dispersion curve in the dispersion curve group are respectively fitted to obtain a multiple linear regression model, and the effective dispersion points of each dispersion curve are corrected according to the multiple linear regression model to obtain a correction result.

[0095] The solution provided by the above embodiment of the present invention is to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; repeat the execution until the dispersion curve group of the target layer segment is obtained; process the dispersion curve group using density clustering to obtain the effective part of the dispersion curve group; fit the effective part of the dispersion curve group to obtain the correction result. The present invention uses density clustering to cluster and screen the dispersion curves, without the need for pre-training of a large amount of data, thereby improving the efficiency of extracting effective data; the correction result is obtained by fitting the effective part of the dispersion curve, which improves the accuracy of obtaining the true shear wave velocity of the formation from actual data while ensuring the efficiency of dispersion correction.

[0096] Figure 6 The schematic diagram of the structure of the computing device embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0097] like Figure 6 As shown, the computing device may include: a processor (processor) 602 , a communication interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0098] The processor 602, the communication interface 604, and the memory 606 communicate with each other via a communication bus 608. The communication interface 604 is used to communicate with other devices such as a client or other server network elements. The processor 602 is used to execute a program 610, which can specifically execute the relevant steps in the above-mentioned embodiment of the density clustering-based dipole acoustic logging dispersion correction method.

[0099] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0100] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0101] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0102] The program 610 may be specifically configured to enable the processor 602 to perform the following operations:

[0103] Step S1, performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point;

[0104] Step S2, repeatedly executing step S1 until a dispersion curve group of the target layer segment is obtained;

[0105] Step S3, processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group;

[0106] Step S4, fitting the effective part of the dispersion curve group to obtain a correction result.

[0107] In an optional manner, the number of the dispersion curve groups is determined by the layer depth of the target layer.

[0108] In an optional manner, the program 610 enables the processor to perform the following operations:

[0109] The weighted spectral coherence method is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point.

[0110] In an optional manner, before the process of processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group, the program 610 enables the processor to perform the following operations:

[0111] According to the maximum velocity value, the minimum velocity value, the maximum frequency value and the minimum frequency value of each dispersion curve in the dispersion curve group, each dispersion curve is normalized.

[0112] In an optional manner, the program 610 enables the processor to perform the following operations:

[0113] The maximum velocity value, the minimum velocity value, the maximum frequency value, and the minimum frequency value of each dispersion curve in the dispersion curve group are normalized according to the following normalization formula:

[0114]

[0115] Among them, x min is the minimum value; x max is the maximum value; x is the original data; x′ is the normalized data.

[0116] In an optional manner, the program 610 enables the processor to perform the following operations:

[0117] The radius and the minimum number parameters are set for each dispersion curve in the dispersion curve group respectively, and the DBSCAN clustering method is used to perform cluster analysis on each dispersion curve to obtain the effective part of the dispersion curve group.

[0118] In an optional manner, the program 610 enables the processor to perform the following operations:

[0119] The effective dispersion points of each dispersion curve in the dispersion curve group are respectively fitted to obtain a multiple linear regression model, and the effective dispersion points of each dispersion curve are corrected according to the multiple linear regression model to obtain a correction result.

[0120] The solution provided by the above embodiment of the present invention is to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point; repeat the execution until the dispersion curve group of the target layer segment is obtained; process the dispersion curve group using density clustering to obtain the effective part of the dispersion curve group; fit the effective part of the dispersion curve group to obtain the correction result. The present invention uses density clustering to cluster and screen the dispersion curves, without the need for pre-training of a large amount of data, thereby improving the efficiency of extracting effective data; the correction result is obtained by fitting the effective part of the dispersion curve, which improves the accuracy of obtaining the true shear wave velocity of the formation from actual data while ensuring the efficiency of dispersion correction.

[0121] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the density clustering-based dipole acoustic logging dispersion correction method in any of the above method embodiments.

[0122] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other equipment. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description made to specific languages ​​above is for disclosing the best mode of the present invention.

[0123] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0124] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0125] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0126] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0127] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0128] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for frequency dispersion correction of dipole acoustic logging based on density clustering, characterized in that: include: Step S1, performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point; Step S2, repeatedly executing step S1 until a dispersion curve group of the target layer segment is obtained; Step S3, processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group; Step S4, fitting the effective part of the dispersion curve group to obtain a correction result.

2. The method for correcting the dipole acoustic logging dispersion based on density clustering according to claim 1, characterized in that: The number of the dispersion curve groups is determined by the layer depth of the target layer.

3. The method for dipole acoustic logging dispersion correction based on density clustering according to claim 1, characterized in that: The performing dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point further comprises: The weighted spectral coherence method is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain the dispersion curve corresponding to the target depth point.

4. The method for correction of dipole acoustic logging dispersion based on density clustering according to claim 1, characterized in that: Before processing the dispersion curve group by density clustering to obtain a valid part of the dispersion curve group, the method further includes: According to the maximum velocity value, the minimum velocity value, the maximum frequency value and the minimum frequency value of each dispersion curve in the dispersion curve group, each dispersion curve is normalized.

5. The method for correction of dipole acoustic logging dispersion based on density clustering according to claim 4, characterized in that: The specific formula for normalizing each dispersion curve according to the maximum velocity value, the minimum velocity value, the maximum frequency value, and the minimum frequency value of each dispersion curve in the dispersion curve group is: Among them, x min is the minimum value; x max is the maximum value; x is the original data; x′ is the normalized data.

6. The method for correcting the dispersion of dipole acoustic logging based on density clustering according to any one of claims 1 to 5, characterized in that: The processing of the dispersion curve group by density clustering to obtain a valid part of the dispersion curve group further comprises: The radius and the minimum number parameters are set for each dispersion curve in the dispersion curve group respectively, and the DBSCAN clustering method is used to perform cluster analysis on each dispersion curve to obtain the effective part of the dispersion curve group.

7. The method for correcting the frequency dispersion of dipole acoustic logging based on density clustering according to claim 1, characterized in that: The fitting of the effective part of the dispersion curve group to obtain the correction result further comprises: The effective dispersion points of each dispersion curve in the dispersion curve group are respectively fitted to obtain a multiple linear regression model, and the effective dispersion points of each dispersion curve are corrected according to the multiple linear regression model to obtain a correction result.

8. A dipole acoustic logging dispersion correction device based on density clustering, characterized in that: include: A dispersion analysis module is used to perform dispersion analysis on the dipole acoustic logging data of the target depth point to obtain a dispersion curve corresponding to the target depth point; the operation is repeated until a dispersion curve group of the target layer segment is obtained; A dispersion clustering module, used for processing the dispersion curve group by using density clustering to obtain a valid part of the dispersion curve group; The dispersion correction module is used to fit the effective part of the dispersion curve group to obtain a correction result.

9. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the density clustering-based dipole acoustic logging dispersion correction method according to any one of claims 1 to 7.

10. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform operations corresponding to the density clustering-based dipole acoustic logging dispersion correction method as described in any one of claims 1 to 7.

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