Acoustic time difference extraction method, device, computing equipment, storage medium and product

Abstract

The present invention discloses a method, device, computing equipment, storage medium and product for extracting acoustic wave time difference, and relates to the field of oil and gas exploration. The method comprises: obtaining acoustic wave logging data corresponding to different depths in a target layer; calculating the Akaike information criterion value of a multi-channel waveform in the acoustic wave logging data corresponding to each depth, and determining the initial arrival of the acoustic wave corresponding to the depth; combining subarray data of different subarray spans by using a multi-source waveform similarity superposition method; performing dynamic windowing according to the initial arrival of the acoustic wave for any subarray span; calculating the two-dimensional correlation function of each subarray according to the slowness-time correlation method, and converting the two-dimensional correlation function into a one-dimensional coherence function; superimposing the one-dimensional coherence functions of all subarrays to estimate the slowness value of the wave on the subarray span, and determining the longitudinal wave time difference after performing depth migration. The present invention effectively improves the accuracy and longitudinal resolution of acoustic wave time difference extraction.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration, and in particular to a method, device, computing equipment, storage medium and product for extracting acoustic wave time difference. Background Art

[0002] With the deepening of unconventional oil and gas exploration and development such as shale, the requirements for the vertical resolution of well logging are getting higher and higher. As one of the important geophysical logging technologies, acoustic wave time difference can be applied to lithology classification, rock mechanical parameter calculation, synthetic seismic record, pore pressure calculation and other aspects. The commonly used acoustic wave time difference extraction methods include slowness-time correlation method (STC), which usually provides the average slowness curve within the coverage range of the receiving transducer array. By setting the longitudinal wave time difference range, the two-dimensional network (i.e., time and slowness) is searched within the time range to find the maximum value of the correlation coefficient to determine the longitudinal wave slowness of the formation. In addition, some people have proposed a real-time extraction method of acoustic wave time difference based on particle swarm optimization. However, the longitudinal resolution of the acoustic wave time difference obtained by the above two methods is low and cannot meet the needs of thin layer exploration. Therefore, improving the longitudinal resolution of the time difference has important engineering application value. How to improve the longitudinal resolution of the acoustic wave time difference has become an urgent problem to be solved. Summary of the invention

[0003] In view of the above problems, the present invention is proposed to provide a method, apparatus, computing device, storage medium and product for extracting acoustic wave time difference that overcome the above problems or at least partially solve the above problems.

[0004] According to one aspect of an embodiment of the present application, a method for extracting acoustic wave time difference is provided, the method comprising:

[0005] Acquire acoustic logging data corresponding to different depths in the target layer;

[0006] For each depth, based on the Akaike information criterion, the Akaike information criterion value of the multi-channel waveform in the acoustic logging data corresponding to the depth is calculated, and the initial arrival of the acoustic wave corresponding to the depth is determined according to the Akaike information criterion value of the multi-channel waveform;

[0007] The sub-array data of different sub-array spans are combined by using the multi-source waveform similarity superposition method;

[0008] For any subarray span, dynamic windowing is performed according to the initial arrival of acoustic waves corresponding to different depths in the target layer segment; the two-dimensional correlation function of each subarray is calculated according to the slowness-time correlation method, and the two-dimensional correlation function is converted into a one-dimensional coherence function;

[0009] The one-dimensional coherence functions of all sub-arrays are stacked, and the slowness of the wave over the sub-array span is estimated based on the stacking results. The longitudinal wave time difference is determined after depth migration.

[0010] Furthermore, obtaining the acoustic logging data corresponding to different depths in the target layer section further includes:

[0011] Multipole acoustic logging is performed at different depths in the target layer to acquire acoustic logging data corresponding to different depths.

[0012] Furthermore, the calculation formula of the Akaike information criterion value of each waveform is as follows:

[0013]

[0014] in, represents the Akaike information criterion value of the waveform; M represents the order of the autoregressive model; N represents the length of the waveform data; C is a constant; Indicates the variance of the preceding data of the kth point of the waveform; Indicates the variance of the data after the kth point of the waveform.

[0015] Further, determining the initial wave of the sound wave corresponding to the depth according to the Akaike information criterion value of the multi-channel waveform further includes:

[0016] The minimum value of the Akaike information criterion of each waveform is calculated, and fitting processing is performed based on the minimum value to obtain the initial arrival of the sound wave corresponding to the depth.

[0017] Furthermore, the dynamic windowing according to the initial arrivals of the acoustic waves corresponding to different depths in the target layer section further includes:

[0018] Using the initial arrival of the acoustic wave corresponding to different depths in the target layer as a constraint, the length of the window is set, and the starting position and the ending position of the window are dynamically determined before and after the initial arrival of the acoustic wave;

[0019] Among them, the starting position of the window is t0-t1, the ending position of the window is t0+t2, and t1+t2=Δt is satisfied; t0 represents the initial arrival of the sound wave, Δt represents the length of the window, and t1 and t2 are positive numbers.

[0020] Further, converting the two-dimensional correlation function into a one-dimensional coherence function further comprises:

[0021] For each slowness, find the arrival time when the two-dimensional correlation function is the maximum value, and convert the two-dimensional correlation function into a one-dimensional coherence function at the arrival time.

[0022] According to another aspect of an embodiment of the present application, there is provided a device for extracting a time difference of an acoustic wave, the device comprising:

[0023] An acquisition module, adapted to acquire acoustic logging data corresponding to different depths in a target layer interval;

[0024] The acoustic wave initial arrival calculation module is adapted to calculate the Akaike information criterion value of the multi-channel waveform in the acoustic logging data corresponding to each depth based on the Akaike information criterion, and determine the acoustic wave initial arrival corresponding to the depth according to the Akaike information criterion value of the multi-channel waveform;

[0025] A combination module, adapted to combine sub-array data of different sub-array spans by using a multi-source waveform similarity superposition method;

[0026] The processing module is suitable for dynamically opening windows for any subarray span according to the initial arrivals of acoustic waves corresponding to different depths in the target layer; calculating the two-dimensional correlation function of each subarray according to the slowness-time correlation method, and converting the two-dimensional correlation function into a one-dimensional coherence function; superimposing the one-dimensional coherence functions of all subarrays, estimating the slowness value of the wave on the subarray span according to the superposition result, and determining the longitudinal wave time difference after depth migration.

[0027] According to another aspect of the embodiment of the present application, there is provided a computing device, including: 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;

[0028] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the above-mentioned sound wave time difference extraction method.

[0029] According to another aspect of the embodiments of the present application, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned acoustic wave time difference extraction method.

[0030] According to another aspect of the embodiments of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned acoustic wave time difference extraction method.

[0031] According to the technical solution provided by the present invention, the initial arrival of the acoustic wave is preliminarily calculated as the initial value through the Akaike information criterion, the waveform data combination of different sub-array spans is realized according to the multi-source waveform similarity superposition method, and the initial arrival of the acoustic wave is used as a constraint for dynamic windowing; the multi-source waveform similarity superposition method can be used to realize any combination of different receiver sub-arrays, provide higher-resolution time difference processing results, effectively improve the resolution of the time difference curve, and provide a reliable technical means for the fine evaluation of oil and gas reservoirs; through dynamic windowing, the windowing position can be adaptively adjusted according to the changes in the formation properties, so that the extracted acoustic wave time difference is more accurate, further improving the accuracy of time difference extraction, and the applicability is strong; this solution realizes the extraction of acoustic wave time difference data with different resolutions, and can intelligently and real-time extract high-resolution time difference, which greatly improves the longitudinal resolution of acoustic wave time difference on the basis of ensuring the accuracy of time difference extraction.

[0032] 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

[0033] 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:

[0034] Figure 1 A schematic diagram showing a flow chart of a method for extracting acoustic wave time difference according to an embodiment of the present application is shown;

[0035] Figure 2 A schematic diagram of subarray data combination for a 0.5 ft subarray span is shown;

[0036] Figure 3 A schematic diagram of subarray data combination for a 1.0 ft subarray span is shown;

[0037] Figure 4 A schematic diagram of subarray data combination for a 1.5 ft subarray span is shown;

[0038] Figure 5 A schematic diagram of subarray data combination for a 2.0 ft subarray span is shown;

[0039] Figure 6 A schematic diagram of subarray data combination for a 2.5 ft subarray span is shown;

[0040] Figure 7A schematic diagram of subarray data combination for a 3.0 ft subarray span is shown;

[0041] Figure 8 A schematic diagram of subarray data combination for a 3.5 ft subarray span is shown;

[0042] Fig. 9 A schematic diagram of dynamic windowing is shown;

[0043] Fig.10 A schematic diagram of processing results with different time difference resolutions is shown;

[0044] Fig.11 A structural block diagram of a device for extracting acoustic wave time difference according to an embodiment of the present application is shown;

[0045] Fig.12 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0047] Figure 1 FIG. 1 shows a flow chart of a method for extracting acoustic wave time difference according to an embodiment of the present application, as shown in FIG. Figure 1 As shown, the method comprises the following steps:

[0048] Step S101, obtaining acoustic logging data corresponding to different depths in a target layer interval.

[0049] The corresponding acoustic logging data is obtained by performing acoustic logging at different depths in the target layer to be logged. Specifically, multipole acoustic logging can be performed at different depths in the target layer to collect the acoustic logging data corresponding to different depths. Among them, multipole acoustic logging (Multipole Acoustic Logging) is an advanced logging technology used to measure the propagation characteristics of sound waves in the wellbore. By emitting and receiving sound waves of different polarization modes, it can obtain detailed information about the formation, including velocity, anisotropy, fractures and fluid properties, etc., and can more comprehensively obtain acoustic information of the wellbore and formation, such as longitudinal waves, shear waves and Stoneley waves.

[0050] Step S102, for each depth, based on the Akaike Information Criterion, calculate the Akaike Information Criterion value of the multi-channel waveform in the acoustic logging data corresponding to the depth, and determine the initial arrival of the acoustic wave corresponding to the depth according to the Akaike Information Criterion value of the multi-channel waveform.

[0051] Among them, Akaike Information Criterion (AIC) is a standard for measuring the goodness of statistical model fitting. It is based on the concept of entropy and can weigh the complexity of the estimated model and the goodness of the model fitting the data to avoid overfitting. The lower the Akaike Information Criterion value (AIC value), the better the model. The embodiment of the present application preliminarily calculates the initial arrival of the sound wave through the Akaike Information Criterion. In step S102, based on the Akaike Information Criterion, the Akaike Information Criterion values ​​of the multi-channel waveforms in the acoustic logging data corresponding to each depth are calculated, and the minimum value of the Akaike Information Criterion value is the initial arrival of the waveform. Among them, a multi-channel waveform refers to a waveform received by multiple receivers, and the calculation formula of the Akaike Information Criterion value of each waveform is as follows:

[0052]

[0053] in, represents the Akaike information criterion value of the waveform; M represents the order of the autoregressive model; N represents the length of the waveform data; C is a constant; Indicates the variance of the preceding data of the kth point of the waveform; Indicates the variance of the data after the kth point of the waveform.

[0054] After the Akaike information criterion values ​​of the multi-channel waveforms are calculated, the initial arrival of the sound wave corresponding to the depth can be determined based on the Akaike information criterion values ​​of the multi-channel waveforms. For example, the minimum value of the Akaike information criterion value of each waveform corresponding to the depth is calculated, and fitting processing is performed based on the minimum value to obtain the initial arrival t0 of the sound wave corresponding to the depth. In other words, the minimum value of the AIC values ​​of different receivers at the same depth is calculated, and the initial arrival t0 of the sound wave corresponding to the depth is obtained by fitting.

[0055] Through the above processing method, the initial arrival t0 of the acoustic wave corresponding to different depths in the target layer section can be obtained conveniently.

[0056] Step S103, combining sub-array data of different sub-array spans by using a multi-source waveform similarity superposition method.

[0057] Multi-source Waveform Similarity Stacking (MWSS) is an advanced technology used in seismic data processing. It is mainly used to improve the quality and resolution of seismic records. This method enhances useful signals and suppresses noise by identifying and stacking similar waveforms generated by multiple seismic sources. It is particularly suitable for seismic data processing under complex geological conditions.

[0058] In the embodiment of the present application, the multi-source waveform similarity superposition method is used to realize the combination of different sub-array span data. Taking an instrument with 8 receivers as an example, there are 7 sub-array combination methods, and the specific combination methods are as follows: Figures 2 to 8 As shown, Figures 2 to 8 The following are schematic diagrams of sub-array data combinations with sub-array spans ranging from 0.5 ft to 3.5 ft (0.1524 m to 1.067 m). Figures 2 to 8 As shown, the circle represents the receiver array position, and the square represents the transmission source position, wherein the black circle identifies the data of the repeated sub-array, and the repeatability of the repeated array data can be used to improve the time difference longitudinal resolution. Figure 2 The subarray shown has a span of 0.5 ft, which requires 14 waveform stacks; Figure 3 The subarray shown has a span of 1.0 ft, which requires 18 waveform stacks; Figure 4 The subarray shown has a span of 1.5 ft, which requires 20 waveform stacks; Figure 5 The subarray shown has a span of 2.0 ft, which requires 20 waveform stacks; Figure 6 The subarray shown has a span of 2.5 ft, which requires 16 waveform stacks; Figure 7 The subarray shown has a span of 3.0 ft, which requires 14 waveform stacks; Figure 8 The subarray shown has a span of 3.5 ft, which requires 8 waveform stacks.

[0059] Step S104, for any subarray span, dynamic windowing is performed according to the initial arrivals of acoustic waves corresponding to different depths in the target layer segment; the two-dimensional correlation function of each subarray is calculated according to the slowness-time correlation method, and the two-dimensional correlation function is converted into a one-dimensional coherence function.

[0060] Select any subarray span (or the number of repeated subarrays spanning the same depth interval) from the multiple subarray spans in step S103, use the initial arrival of the sound wave corresponding to different depths in the target layer segment as a constraint, and dynamically open windows before and after the initial arrival range of the sound wave. Specifically, the length of the window can be set based on the initial arrival of the sound wave obtained in step S102, and the starting position and the ending position of the window can be dynamically determined before and after the initial arrival of the sound wave; wherein, the starting position of the window is t0-t1, and the ending position of the window is t0+t2, and t1+t2=Δt is satisfied; t0 represents the initial arrival of the sound wave, Δt represents the length of the window, and t1 and t2 are positive numbers. When t0 changes with depth, the position of the opened window will also change dynamically with depth. By adopting the construction of dynamic time windows, the accuracy of time difference extraction is further improved.

[0061] In step S104, the two-dimensional correlation function of each subarray is calculated according to the slowness-time correlation method, and for each slowness, the arrival time T when the two-dimensional correlation function reaches a maximum value is found. max , retain this extreme value, and convert the two-dimensional correlation function into a one-dimensional coherence function at time t to time t. Specifically, convert the two-dimensional correlation function into a one-dimensional coherence function of the variable s, and the one-dimensional coherence function is as follows:

[0062]

[0063] in, represents the one-dimensional coherence function; s represents the variable, which is the slowness; T max represents the time when the two-dimensional correlation function reaches its maximum value; N represents the number of subarrays.

[0064] Step S105, superimposing the one-dimensional coherence functions of all sub-arrays, estimating the slowness of the wave on the sub-array span according to the superposition result, and determining the longitudinal wave time difference after depth migration.

[0065] The one-dimensional coherence functions of all sub-arrays can be superimposed using the geometric mean or algebraic mean, as shown in the following formula:

[0066]

[0067] in, represents the mean function; represents a one-dimensional coherence function; N represents the number of subarrays.

[0068] In specific applications, the maximum value of the above formula can be used to estimate the slowness of the wave on the subarray span, and the longitudinal wave time difference can be output after depth migration.

[0069] In view of the problem of insufficient longitudinal resolution of existing acoustic time difference, the embodiments of the present application adopt Akaike information criterion, multi-source waveform similarity superposition method, dynamic windowing and other processing methods to extract longitudinal wave time difference, which can effectively improve the accuracy and longitudinal resolution of acoustic time difference extraction. Compared with the conventional slowness-time correlation method that uses a fixed time window to process the entire well section, the dynamic time window technology used in the present application can provide more accurate longitudinal wave time difference information. When the traditional time window processing method is used for strata with many thin interlayers, the extracted time difference will have a large error. The dynamic time window method can adaptively adjust the window position according to the change of stratum properties, and the extracted acoustic time difference is more accurate and has strong applicability. The conventional slowness-time correlation method provides the average slowness curve within the coverage range of the receiving transducer array. The present application adopts the multi-source waveform similarity superposition method technology. The multi-source waveform similarity superposition method can realize any combination of different receiver subarrays, and can provide a higher resolution time difference processing result, effectively improving the resolution of the time difference curve, and providing a reliable technical means for the fine evaluation of oil and gas reservoirs.

[0070] Fig. 9 A schematic diagram of dynamic windowing is shown, such as Fig. 9 As shown, the first curve (GR) is the natural gamma, and the second is the comparison of the results of the conventional time window (represented by a solid line) and the dynamic time window (represented by a dotted line). <1> It can be seen that since the conventional slowness-time correlation method uses a fixed time window to process the entire well section, errors will occur in the time difference results extracted for sudden formations, while the dynamic time window can adaptively change the window position according to the changes in formation properties, and can more accurately extract the formation P-wave time difference information.

[0071] Fig.10 Schematic diagram of processing results with different time difference resolutions is shown, Fig.10As shown, the first one is a single-pole variable density waveform, where the TT_1 curve represents the arrival of the longitudinal wave, WVOUTST_1 <1> The second and third tracks are the coherence diagrams of 4 and 3 receivers, respectively, where the DT_STC4 curve represents the acoustic time difference extracted by 4 receivers, DT_STC3 represents the acoustic time difference extracted by 3 receivers, CORCMB_STC4 represents the variable density amplitude of the slowness-time correlation diagram of 4 receivers, and CORCMB_STC3 represents the variable density amplitude of the slowness-time correlation diagram of 3 receivers; the fourth track is the acoustic time difference extracted by 4 receivers (corresponding to The first track is a comparison of the acoustic time difference extracted by the three receivers (corresponding to the curve represented by DT_STC4) and the acoustic time difference extracted by the threshold value (corresponding to the curve represented by DT24C); the fifth track is a comparison of the acoustic time difference extracted by the three receivers (corresponding to the curve represented by DT_STC3) and the acoustic time difference extracted by the threshold value (corresponding to the curve represented by DT24C); the sixth track is a conventional curve, in which the M1R1 curve represents the resistivity curve, the GR curve represents the natural gamma, and the CAL curve represents the time difference extracted by the high-resolution acoustic time difference extraction method provided in this application. It can be seen from the conventional curve that there are many thin layers in the layer section from 556 meters to 562 meters, and the time difference extracted by the threshold method cannot distinguish these thin layers, while the time difference extracted by the high-resolution acoustic time difference extraction method provided in this application (corresponding to the curve represented by CAL) has a good correspondence with the conventional curve, thereby further verifying the effectiveness of this application through the example processing results, and can greatly improve the vertical resolution of the acoustic time difference.

[0072] According to the acoustic wave time difference extraction method provided in the embodiment of the present application, the initial arrival of the acoustic wave is preliminarily calculated as the initial value through the Akaike information criterion, and the waveform data combination of different sub-array spans is realized according to the multi-source waveform similarity superposition method, and the initial arrival of the acoustic wave is used as a constraint for dynamic windowing; the multi-source waveform similarity superposition method can be used to realize any combination of different receiver subarrays, provide higher resolution time difference processing results, effectively improve the resolution of the time difference curve, and provide a reliable technical means for the fine evaluation of oil and gas reservoirs; through dynamic windowing, the windowing position can be adaptively adjusted according to the changes in the formation properties, so that the extracted acoustic wave time difference is more accurate, further improving the accuracy of the time difference extraction, and the applicability is strong; this scheme realizes the extraction of acoustic wave time difference data with different resolutions, and can intelligently and real-time extract high-resolution time difference, and greatly improves the longitudinal resolution of the acoustic wave time difference on the basis of ensuring the accuracy of time difference extraction.

[0073] Fig.11 FIG. 4 shows a structural block diagram of a device for extracting acoustic wave time difference according to an embodiment of the present application. Fig.11As shown, the device includes: an acquisition module 1101, a sound wave first arrival calculation module 1102, a combination module 1103 and a processing module 1104.

[0074] The acquisition module 1101 is adapted to acquire acoustic logging data corresponding to different depths in the target layer interval.

[0075] The acoustic wave initial arrival calculation module 1102 is suitable for: for each depth, based on the Akaike information criterion, calculating the Akaike information criterion value of the multi-channel waveform in the acoustic logging data corresponding to the depth, and determining the acoustic wave initial arrival corresponding to the depth based on the Akaike information criterion value of the multi-channel waveform.

[0076] The combining module 1103 is adapted to combine sub-array data of different sub-array spans by using a multi-source waveform similarity superposition method.

[0077] The processing module 1104 is suitable for: for any subarray span, dynamically opening windows according to the initial arrivals of acoustic waves corresponding to different depths in the target layer segment; calculating the two-dimensional correlation function of each subarray according to the slowness-time correlation method, and converting the two-dimensional correlation function into a one-dimensional coherence function; superimposing the one-dimensional coherence functions of all subarrays, estimating the slowness value of the wave on the subarray span according to the superposition result, and determining the longitudinal wave time difference after depth migration.

[0078] Optionally, the acquisition module 1101 is further adapted to: perform multipole acoustic logging at different depths in the target layer interval, and acquire acoustic logging data corresponding to different depths.

[0079] Optionally, the calculation formula of the Akaike information criterion value of each waveform is as follows:

[0080]

[0081] in, represents the Akaike information criterion value of the waveform; M represents the order of the autoregressive model; N represents the length of the waveform data; C is a constant; Indicates the variance of the preceding data of the kth point of the waveform; Indicates the variance of the data after the kth point of the waveform.

[0082] Optionally, the sound wave initial arrival calculation module 1102 is further adapted to: calculate the minimum value of the Akaike information criterion value of each waveform, and perform fitting processing based on the minimum value to obtain the sound wave initial arrival corresponding to the depth.

[0083] Optionally, the processing module 1104 is further adapted to: using the initial arrival of the sound wave corresponding to different depths in the target layer segment as a constraint, setting the length of the window, and dynamically determining the starting position and the ending position of the window before and after the initial arrival of the sound wave; wherein the starting position of the window is t0-t1, the ending position of the window is t0+t2, and satisfies t1+t2=Δt; t0 represents the initial arrival of the sound wave, Δt represents the length of the window, and t1 and t2 are positive numbers.

[0084] Optionally, the processing module 1104 is further adapted to: for each slowness, find the arrival time when the two-dimensional correlation function is a maximum value, and convert the two-dimensional correlation function into a one-dimensional coherence function at the arrival time.

[0085] According to the acoustic wave time difference extraction device provided in the embodiment of the present application, the initial arrival of the acoustic wave is preliminarily calculated as the initial value through the Akaike information criterion, and the waveform data combination of different sub-array spans is realized according to the multi-source waveform similarity superposition method, and the initial arrival of the acoustic wave is used as a constraint for dynamic windowing; the multi-source waveform similarity superposition method can be used to realize any combination of different receiver sub-arrays, provide higher resolution time difference processing results, effectively improve the resolution of the time difference curve, and provide a reliable technical means for the fine evaluation of oil and gas reservoirs; through dynamic windowing, the windowing position can be adaptively adjusted according to the changes in the formation properties, so that the extracted acoustic wave time difference is more accurate, further improving the accuracy of the time difference extraction, and the applicability is strong; this scheme realizes the extraction of acoustic wave time difference data with different resolutions, and can intelligently and real-time extract high-resolution time difference, which greatly improves the longitudinal resolution of the acoustic wave time difference on the basis of ensuring the accuracy of time difference extraction.

[0086] The present invention also provides a non-volatile computer storage medium, which stores at least one executable instruction, and the executable instruction can execute the acoustic wave time difference extraction method in any of the above method embodiments.

[0087] An embodiment of the present invention provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the acoustic wave time difference extraction method in any of the above method embodiments.

[0088] Fig.12 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0089] like Fig.12 As shown, the computing device may include: a processor 1202 , a communication interface 1204 , a memory 1206 , and a communication bus 1208 .

[0090] The processor 1202, the communication interface 1204, and the memory 1206 communicate with each other via a communication bus 1208. The communication interface 1204 is used to communicate with other devices such as a client or other server network elements. The processor 1202 is used to execute a program 1210, which can specifically execute the relevant steps in the above-mentioned embodiment of the method for extracting the time difference of sound waves for a computing device.

[0091] Specifically, the program 1210 may include program codes, which include computer operation instructions.

[0092] The processor 1202 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 application. 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.

[0093] The memory 1206 is used to store the program 1210. The memory 1206 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.

[0094] The program 1210 can be specifically used to enable the processor 1202 to execute the acoustic wave time difference extraction method in any of the above-mentioned method embodiments. The specific implementation of each step in the program 1210 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned acoustic wave time difference extraction embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0095] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0096] 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.

[0097] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features 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 intention that the claimed invention requires more features than those expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all of the features of the individual embodiments previously disclosed. Therefore, the claims that follow the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present invention.

[0098] 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 further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0099] In addition, those skilled in the art will appreciate that, although some embodiments described 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, any one of the claimed embodiments may be used in any combination.

[0100] The various component embodiments of the present invention may 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 a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0101] 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.

Claims

1. A method for extracting acoustic wave time difference, characterized in that: The method comprises: Acquire acoustic logging data corresponding to different depths in the target layer; For each depth, based on the Akaike information criterion, the Akaike information criterion value of the multi-channel waveform in the acoustic logging data corresponding to the depth is calculated, and the initial arrival of the acoustic wave corresponding to the depth is determined according to the Akaike information criterion value of the multi-channel waveform; The sub-array data of different sub-array spans are combined by using the multi-source waveform similarity superposition method; For any subarray span, dynamic windowing is performed according to the initial arrivals of acoustic waves corresponding to different depths in the target layer segment; the two-dimensional correlation function of each subarray is calculated according to the slowness-time correlation method, and the two-dimensional correlation function is converted into a one-dimensional coherence function; The one-dimensional coherence functions of all sub-arrays are stacked, and the slowness of the wave in the sub-array span is estimated based on the stacking results. The longitudinal wave time difference is determined after depth migration. The calculation formula of the Akaike information criterion value of each waveform is as follows: in, represents the Akaike information criterion value of the waveform; M represents the order of the autoregressive model; N represents the length of the waveform data; C is a constant; Indicates the variance of the data before the kth point of the waveform; Indicates the variance of the data at the kth point of the waveform; The two-dimensional correlation function is converted into a one-dimensional coherence function of the variable s, which is as follows: in, represents the one-dimensional coherence function; s represents the variable, which is the slowness; T max represents the time when the two-dimensional correlation function reaches its maximum value; N represents the number of subarrays.

2. The method for extracting acoustic wave time difference according to claim 1, characterized in that: The step of obtaining acoustic logging data corresponding to different depths in the target layer interval further includes: Multipole acoustic logging is performed at different depths in the target layer interval to acquire acoustic logging data corresponding to different depths.

3. The method for extracting acoustic wave time difference according to claim 1, characterized in that: Determining the initial wave of the sound wave corresponding to the depth according to the Akaike information criterion value of the multi-channel waveform further includes: The minimum value of the Akaike information criterion value of each waveform is calculated, and fitting processing is performed based on the minimum value to obtain the initial arrival of the sound wave corresponding to the depth.

4. The method for extracting acoustic wave time difference according to claim 1, characterized in that: The dynamic windowing according to the initial arrival of the acoustic wave corresponding to different depths in the target layer section further comprises: Using the initial arrivals of acoustic waves corresponding to different depths in the target layer as constraints, setting the length of the window, and dynamically determining the starting position and the ending position of the window before and after the initial arrival of the acoustic wave; The starting position of the window is t0-t1, the ending position of the window is t0+t2, and t1+t2=Δt is satisfied; t0 represents the initial arrival of the sound wave, Δt represents the length of the window, and t1 and t2 are positive numbers.

5. An acoustic wave time difference extraction device, characterized in that: The device comprises: An acquisition module, adapted to acquire acoustic logging data corresponding to different depths in a target layer interval; The acoustic wave initial arrival calculation module is adapted to calculate the Akaike information criterion value of the multi-channel waveform in the acoustic logging data corresponding to each depth based on the Akaike information criterion, and determine the acoustic wave initial arrival corresponding to the depth according to the Akaike information criterion value of the multi-channel waveform; A combination module, adapted to combine sub-array data of different sub-array spans by using a multi-source waveform similarity superposition method; The processing module is suitable for dynamically opening windows for any subarray span according to the initial arrivals of acoustic waves corresponding to different depths in the target layer; calculating the two-dimensional correlation function of each subarray according to the slowness-time correlation method, and converting the two-dimensional correlation function into a one-dimensional coherence function; performing superposition processing on the one-dimensional coherence functions of all subarrays, estimating the slowness value of the wave on the subarray span according to the superposition result, and determining the longitudinal wave time difference after depth migration; The calculation formula of the Akaike information criterion value of each waveform is as follows: in, represents the Akaike information criterion value of the waveform; M represents the order of the autoregressive model; N represents the length of the waveform data; C is a constant; Indicates the variance of the data before the kth point of the waveform; Indicates the variance of the data at the kth point of the waveform; The two-dimensional correlation function is converted into a one-dimensional coherence function of the variable s, which is as follows: in, represents the one-dimensional coherence function; s represents the variable, which is the slowness; T max represents the time when the two-dimensional correlation function reaches its maximum value; N represents the number of subarrays.

6. A computing device, characterized in that include: 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 acoustic wave time difference extraction method according to any one of claims 1 to 4.

7. A computer storage medium, characterized in that: The computer storage medium stores at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the acoustic wave time difference extraction method according to any one of claims 1 to 4.

8. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the method for extracting the acoustic wave time difference according to any one of claims 1 to 4.

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