CN117665923A

CN117665923A - Real-time determination method and device for slowness corresponding to array waveform data

Info

Publication number: CN117665923A
Application number: CN202211036556.7A
Authority: CN
Inventors: 孙学凯; 周军; 张炳军; 余卫东; 马修刚; 刘先平; 陶钧; 刘建建; 申珍珍; 王文泽; 牛林林
Original assignee: China National Petroleum Corp; China Petroleum Logging Co Ltd
Current assignee: China National Petroleum Corp; China Petroleum Logging Co Ltd
Priority date: 2022-08-25
Filing date: 2022-08-25
Publication date: 2024-03-08

Abstract

The invention discloses a real-time determination method and device for slowness corresponding to array waveform data. The invention comprises the following steps: acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; and determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

Description

Real-time determination method and device for slowness corresponding to array waveform data

Technical Field

The invention relates to the field of acoustic logging, in particular to a real-time determination method and device for slowness corresponding to array waveform data.

Background

In the related technology, in the field of acoustic logging, the real-time extraction of the longitudinal and transverse wave slowness can obviously shorten the processing and interpretation period of acoustic data, and has important practical significance for rapidly evaluating the reservoir quality and making an appropriate exploration and development decision, which is also a research hotspot in the field of acoustic logging. At present, the threshold method is a method for detecting slowness in real time commonly used in industry, wherein the amplitude of a waveform received by each receiver is amplified through a gain mechanism, the slowness is calculated according to the arrival time interval between two ends of a receiver array when the waveform reaches the arrival time corresponding to a preset amplitude threshold value for the first time, although the threshold method is convenient to implement, the method cannot distinguish effective waveform components from noise, the gain mechanism amplifies the influence of the noise to the same extent, the slowness calculation result is easy to be interfered by the noise, and is influenced by factors such as stratum absorption attenuation and anisotropy, and the waveform captured through the threshold value does not correspond to the same phase of a waveform signal at the arrival time, so that the accuracy degree of the slowness calculation result is influenced.

Valero et al establish a set of first arrival detection flow aiming at array acoustic logging, the flow mainly comprises basic steps of waveform preprocessing, self-adaptive localization time window setting, BIC (Bayesian Information Criteria) -based first arrival detection, outlier point rejection and reconstruction, and the like, although the method adopts a plurality of self-adaptive processing and automatic discrimination technologies, the stability in practical application is still low, the calculation cost is high, and the real-time processing requirement is not met; for realizing automatic stable tracking of single-pole acoustic longitudinal and transverse wave first arrivals, sun and Ayadiuno of Saudi Aramco company establish a novel objective function, which comprehensively quantifies four indexes of relativity, phase similarity, energy ratio and continuity, but some indexes used by the method need to be subjected to cross-depth processing and are not suitable for real-time processing of underground digital signals, in addition, a first arrival detection method developed from the fields of seismic exploration and the like is expected to provide new elicitations for automatic slowness of acoustic logging, such as a grid subdivision slowness model, a multi-channel energy ratio method, a credibility threshold constraint and a long-short time average value ratio method, but whether the methods really adapt to automatic extraction of acoustic logging slowness and have timeliness and stability of real-time processing still lack convincing examples at present.

The similar correlation method (STC for short) proposed by Kimball and Marzetta et al is an alternative scheme for realizing automatic extraction of Slowness, and the method comprehensively considers the consistency of waveform phase and amplitude in the calculation process of a correlation spectrum, calculates the correlation coefficient according to a Time window scanning with a certain length in two dimensions of Time and Slowness, has better anti-noise capability, and therefore, gradually develops into a main technical means of the sonic logging Slowness analysis work. A number of targeted STC analysis methods, such as N times Fang Genfa, frequency dispersion STC analysis, etc., are derived, however, the above methods do not relate to how to overcome the defects of waveform data commonly found in practice, and new STC calculation formulas and analysis flows are not proposed, so that the anti-noise capability is relatively insufficient in terms of implementation effect, and the post-processing is relied on to a certain extent.

In summary, the acoustic logging field still lacks a slowness automatic analysis method with good real-time performance and high reliability, and the main challenges faced by the method are summarized as follows: 1) The slowness detection mechanism based on the threshold method depends on amplitude gain setting and a preset threshold value, does not distinguish noise from signals, is not applicable to low signal-to-noise ratio data, and is limited in accuracy due to the fact that the detection is performed on the same phase which is not strictly defined in time; 2) Most of the first arrival detection methods have high calculation cost and long flow, and do not have timeliness and stability of real-time processing; 3) Most of the existing STC analysis means focus on improving efficiency and precision, lack of exploration on how to overcome defects (such as direct current component, low signal-to-noise ratio, high waveform attenuation and the like) of actual waveform data, and some of the disclosed technical means have obvious stability deficiency in actual application.

In view of the above problems in the related art, no effective solution has been proposed at present.

Disclosure of Invention

The invention mainly aims to provide a real-time determination method and device for slowness corresponding to array waveform data, which are used for solving the problems of insufficient precision and low stability of a slowness real-time extraction technology of sound waves in the related technology.

In order to achieve the above object, according to one aspect of the present invention, there is provided a real-time determination method of slowness corresponding to array waveform data. The invention comprises the following steps: acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; and determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

Further, the preprocessing operation is performed on the original array waveform data to obtain target array waveform data, including: acquiring the number of time sampling points corresponding to the original array waveform dataThe method comprises the steps of carrying out a first treatment on the surface of the Performing differential operation on the original array waveform data according to a formula I, and obtaining array waveform data after the removal operation, wherein the differential operation is used for representing the operation of removing the direct current component contained in the original array waveform data, and the formula I is as follows:x is the original array waveform data, < >>In order to remove the array waveform data after operation, T is the number of time sampling points; performing amplitude equalization operation on the array waveform data after the removal operation according to a formula II, and determining the data after the amplitude equalization operation as target array waveform data, wherein the amplitude equalization operation is used for representing the operation of correcting the array waveform data after the removal operation, and the formula II is as follows:x is the original array waveform data, < >>For the array waveform data after differential operation, +.>For target array waveform data, T represents the number of time sampling points.

Further, determining an initial STC spectrum from the target array waveform data includes: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of second slowness according to the plurality of acquisition times and a preset STC algorithm, wherein the plurality of second slowness corresponds to the plurality of acquisition times one by one.

Further, determining a plurality of second slowness according to the plurality of acquisition times and the preset STC algorithm includes: acquiring a time window, a receiver index and a receiver interval corresponding to original array waveform data, wherein the time window is a waveform period corresponding to an initial STC spectrum, the receiver index is a constant, and the receiver interval is a numberAccording to the interval between every two original array waveform data received by the receiver; calculating a plurality of second slowness by presetting a third formula in the STC algorithm, wherein the third formula is thats ₁ Is the slowness index of the initial STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

Further, determining a weighted STC spectrum from the target array waveform data, comprising: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of third slowness according to the plurality of acquisition times and the weighted STC algorithm, wherein the plurality of third slowness corresponds to the plurality of acquisition times one by one.

Further, determining a plurality of third slowness according to the plurality of acquisition times and the weighted STC algorithm includes: acquiring a time window, a receiver index and a receiver interval corresponding to original array waveform data, wherein the time window is a waveform period corresponding to an initial STC spectrum, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of third slowness by a fourth formula in the weighted STC algorithm, wherein the fourth formula is that CE is->TE iss ₂ For the slowness index of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

Further, determining an EMA STC spectrum from the target array waveform data, comprising: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of fourth slowness according to the plurality of acquisition times and the EMA STC algorithm, wherein the plurality of fourth slowness corresponds to the plurality of acquisition times one by one.

Further, determining a plurality of fourth slowness according to the plurality of acquisition times and the EMA STC algorithm includes: acquiring the current depth of the acquired original array waveform data and smoothing control parameters corresponding to the acquired original array waveform data; calculating a plurality of fourth slowness by a fifth formula in the EMA STC algorithm, wherein the fifth formula is thats ₃ And (3) indexing the slowness of the EMA STC spectrum, j is the current depth, and beta is a smoothing control parameter.

Further, determining a first slowness corresponding to the waveform data of the target array according to the weighted STC spectrum and the EMA STC spectrum includes: acquiring a first slowness peak value, wherein the first slowness peak value is a slowness peak value which appears for the first time in a weighted STC spectrum; acquiring a second slowness peak value, wherein the second slowness peak value is a slowness peak value which appears for the first time in the EMA STC spectrum; calculating a difference value between the first slowness and the second slowness; under the condition that the difference value is larger than a preset value, determining the slowness corresponding to the first slowness peak value as a first slowness; and under the condition that the difference value is smaller than a preset value, determining the slowness corresponding to the second slowness peak value as the first slowness.

Further, determining, according to the initial STC spectrum and the first slowness, a final slowness corresponding to the waveform data of the target array through a calibration mechanism includes: determining the corresponding slowness of the first slowness in the initial STC spectrum, and determining the peak value closest to the position corresponding to the slowness as a third slowness peak value; and determining the slowness corresponding to the third slowness peak as a target slowness, and calibrating the target slowness through a calibration mechanism to obtain the final slowness.

In order to achieve the above object, according to another aspect of the present invention, there is provided a real-time determination apparatus of slowness corresponding to array waveform data. The device comprises: the first acquisition unit is used for acquiring original array waveform data acquired in the target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; the pre-operation unit is used for performing pre-processing operation on the original array waveform data to obtain target array waveform data; the first determining unit is used for determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; the second determining unit is used for determining a first slowness corresponding to the waveform data of the target array according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the waveform data of the original array; and the third determining unit is used for determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

In order to achieve the above object, according to another aspect of the present application, there is provided a computer-readable storage medium including a stored program, wherein the program performs a method of determining a slowness corresponding to one of the above-described array waveform data in real time.

To achieve the above object, according to another aspect of the present application, there is provided a processor for executing a program, wherein the program executes a method for determining slowness corresponding to array waveform data of any one of the above.

According to the invention, the following steps are adopted: acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; according to the initial STC spectrum and the first slowness, the final slowness corresponding to the waveform data of the target array is determined through a calibration mechanism, the problems of insufficient precision and low stability of the slowness real-time extraction technology of the sound waves in the related technology are solved, and the effects of real-time accurate identification and automatic stable extraction of the slowness are achieved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:

FIG. 1 is a flow chart of a method for determining slowness corresponding to array waveform data in real time according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for determining slowness corresponding to array waveform data in real time according to an embodiment of the present invention;

FIG. 3 is a graph showing the effect of a real-time determination method for slowness corresponding to array waveform data in a preset standard well according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a slowness retrieval result corresponding to array waveform data of a digital acoustic log applied in a southwest oilfield according to an embodiment of the present invention;

FIG. 5 is a graph of the effect of a method for determining the slowness of an array waveform data in real time on a formation with a relatively large slowness according to an embodiment of the present invention;

fig. 6 is a schematic diagram of a real-time determining device for determining slowness corresponding to array waveform data according to an embodiment of the present invention.

Detailed Description

It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

According to the embodiment of the invention, a real-time determination method of slowness corresponding to array waveform data is provided.

Fig. 1 is a flowchart of a method for determining slowness corresponding to array waveform data in real time according to an embodiment of the present invention. As shown in fig. 1, the invention comprises the following steps:

step S101, acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information.

The method comprises the steps of collecting original array waveform data of an oil well through a digital acoustic logging method, a variable density acoustic logging method, an array acoustic logging method and the like, wherein the acoustic logging method is an acoustic logging method with a multi-probe acoustic system and measuring multi-wave trains, the original array waveform data comprises longitudinal wave waveform data and transverse wave waveform data, the longitudinal wave waveform data is waveform data with the vibration direction of particles coaxial with the wave propagation direction, and the transverse wave waveform data is waveform data with the vibration direction of the particles perpendicular to the wave propagation direction. Specifically, the acoustic logging method using the multi-probe acoustic system to measure the multi-wave trains can obtain the original array waveform data from a certain depth, wherein the array waveform data at least comprises: longitudinal, transverse and stoneley waves.

Step S102, preprocessing operation is performed on the original array waveform data to obtain target array waveform data.

In the above way, the preprocessing operation is performed on the original array waveform data, so that the quality and the precision of the original array waveform data are improved, and the subsequent calculation and analysis can be better served.

Step S103, determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array.

The present application provides three STC spectra, including: the method comprises the steps of obtaining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum, wherein the weighted STC spectrum is a spectrum pattern graph formed by a plurality of acquisition times corresponding to original array waveform data, the EMA STC spectrum is a spectrum pattern graph formed by a plurality of acquisition depths corresponding to the original array waveform data, the slowness in the initial STC spectrum is obtained through an initial STC algorithm and the acquisition times corresponding to the original array waveform data, the slowness in the weighted STC spectrum is obtained through the weighted STC algorithm and the acquisition times corresponding to the original array waveform data, and the slowness in the EMA STC spectrum is obtained through the EMA STC algorithm and the acquisition depths corresponding to the original array waveform data. Specifically, the weighted STC spectrum calculation has the characteristics of suppressing random noise and highlighting the energy of related waveforms, and the EMA STC spectrum shows the trend of the STC spectrum along with the change of slowness.

Step S104, determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data.

Above-mentioned ground, confirm the credible position of the corresponding first slowness of primitive array waveform data through the comprehensive use of weighted STC spectrum and EMA STC spectrum, first slowness is the important parameter of acoustic logging, is the reciprocal of the propagation speed of primitive array waveform data, confirm through first slowness and improve the measurement accuracy of acoustic logging.

Step S105, determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

According to the method, final calibration of the first slowness corresponding to the original array waveform data is achieved through the initial STC spectrum and the first slowness, and the final slowness calibrated through a calibration mechanism is obtained.

By the method, various adverse factors faced by actual acoustic logging are effectively considered and overcome, and slowness is reliably extracted from the original array waveform data in real time, wherein the various adverse factors comprise: the method has the advantages of no distinction between noise and signals, inapplicability to low signal-to-noise ratio data, high calculation cost, long flow, no timeliness and stability of real-time processing, waveform data defects and the like, and meanwhile, the method provided by the application has the greatest characteristic of real-time processing.

A method and apparatus for determining slowness (slowness is the inverse of propagation velocity) by real-time extraction and estimation of the primary wave components (longitudinal and transverse waves, but not limited to longitudinal and transverse waves) from acoustic logging array waveform data. The invention comprises the following steps: acquiring original array waveform data acquired in a target oil-gas well; performing high-efficiency preprocessing operation on the original array waveform data, and improving the quality of the array waveform data; according to the preprocessed array waveform data, respectively calculating an original STC spectrum, a weighted STC spectrum and an EMA STC spectrum; comprehensively analyzing the weighted STC spectrum and the EMA STC spectrum, and determining initial slowness estimation of a target wave component (such as longitudinal wave or transverse wave) in the array waveform data through a fault tolerance peak searching mechanism; and calibrating the slowness estimation value through a re-peak searching mechanism according to the characteristics of the slowness initial estimation on the original STC spectrum, determining the final slowness of the target waveform and outputting the final slowness. The invention provides a brand new thought for real-time difference extraction of acoustic logging, and effectively solves the problems of insufficient precision and low stability of the real-time extraction technology in the prior art system.

Meanwhile, the technology provided by the invention is also suitable for real-time difference estimation of digital acoustic logging, variable density acoustic logging, array acoustic logging and acoustic logging while drilling, and can also be applied to time difference estimation in cased wells.

The application performs preprocessing operation on the original array waveform data to obtain target array waveform data, and comprises the following steps: acquiring the number of time sampling points corresponding to the original array waveform data; performing differential operation on the original array waveform data according to a formula, and obtaining array waveform data after the removal operation, wherein the differential operation is used for representing removal of straight included in the original array waveform dataOperation of the flow component, equation one is:x is the original array waveform data and,in order to remove the array waveform data after operation, T is the number of time sampling points; performing amplitude equalization operation on the array waveform data after the removal operation according to a formula II, and determining the data after the amplitude equalization operation as target array waveform data, wherein the amplitude equalization operation is used for representing the operation of correcting the array waveform data after the removal operation, and the formula II is as follows:x is the original array waveform data, < >>For the array waveform data after differential operation, +.>For target array waveform data, T represents the number of time sampling points.

The preprocessing operation for the original array waveform data comprises the steps of removing the direct current component and equalizing the amplitude, the direct current component contained in the original array waveform data is removed through the formula I, the first-order difference of waveform tracks along the time direction is realized, noise existing in the acquisition process of the original array waveform data is suppressed, the array waveform data after the removal operation is corrected through the formula II, correction is carried out on each waveform track according to the root mean square of the waveform tracks corresponding to the array waveform data, waveform amplitude differences caused by factors such as attenuation are balanced, waveform consistency is improved, and the effects of removing the direct current component in the original array waveform data and equalizing the amplitude of the waveform tracks are achieved through efficient preprocessing operation.

In this application, determining an initial STC spectrum according to the target array waveform data includes: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of second slowness according to the plurality of acquisition times and a preset STC algorithm, wherein the plurality of second slowness corresponds to the plurality of acquisition times one by one. The initial STC algorithm is an algorithm for realizing automatic extraction of slowness, comprehensively considers the consistency of waveform phase and amplitude in the calculation process of a preset STC spectrum, scans and calculates a correlation coefficient according to a time window with a certain length in two dimensions of acquisition time and slowness, and has better anti-noise capability.

Above, determining a plurality of second slowness according to a plurality of acquisition times and a preset STC algorithm includes: acquiring a time window corresponding to original array waveform data, a receiver index and a receiver interval, wherein the time window is a waveform period corresponding to an initial STC spectrum, the receiver index is a constant, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of second slowness by presetting a third formula in the STC algorithm, wherein the third formula is thats ₁ Is the slowness index of the initial STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing. And calculating a plurality of second slowness by a third formula in a preset STC algorithm, wherein the preset STC algorithm is essentially the ratio of the correlation energy to the total energy of the original array waveform data.

In this application, determining a weighted STC spectrum according to the target array waveform data includes: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of third slowness according to the plurality of acquisition times and the weighted STC algorithm, wherein the plurality of third slowness corresponds to the plurality of acquisition times one by one.

Above-mentioned ground, weighted STC algorithm has introduced regularization item mu for predetermine STC algorithm, has the characteristics of suppressing random noise, outstanding relevant waveform energy, has effectively suppressed the adverse effect of environmental noise and other coherent noise.

In an alternative example, a plurality of third slowness is determined based on a plurality of acquisition times and weighted STC algorithms,comprising the following steps: acquiring a time window, a receiver index and a receiver interval corresponding to original array waveform data, wherein the time window is a waveform period corresponding to an initial STC spectrum, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of third slowness by a fourth formula in the weighted STC algorithm, wherein the fourth formula is that CE is->TE iss ₂ For the slowness index of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

The fourth formula in the weighted STC algorithm is a special case where μ=0, where the regularization term μ is added at the denominator of the third formula in the preset STC algorithm. Specifically, the weighted STC has the effect of suppressing random noise, highlighting the energy of the associated waveform.

In an alternative example, determining an EMA STC spectrum from the target array waveform data includes: acquiring a plurality of acquisition times corresponding to the original array waveform data; and determining a plurality of fourth slowness according to the plurality of acquisition times and the EMA STC algorithm, wherein the plurality of fourth slowness corresponds to the plurality of acquisition times one by one. The EMA STC algorithm is an average algorithm for further calculating index sliding on the acquisition depth on the basis of the weighted STC algorithm, original array waveform data acquired at the preset depth of the target oil-gas well correspond to different acquisition depths, the basic trend of the slowness changing along with the acquisition depth is updated in real time by the EMA STC algorithm through an index sliding average strategy, the basic trend is used for assisting automatic picking and correcting of abnormal pick-up points of longitudinal wave time difference, and the change trend of the preset STC spectrum along with the acquisition depth is generated in time by the index sliding average strategy. Specifically, when the EMA STC processes the original array waveform data, it can play a role in assisting in automatic picking up of the longitudinal wave time difference and correcting the abnormal picking point.

In this application, determining a plurality of fourth slowness according to a plurality of acquisition times and EMA STC algorithm includes: acquiring the current depth of the acquired original array waveform data and smoothing control parameters corresponding to the acquired original array waveform data; calculating a plurality of fourth slowness by a fifth formula in the EMA STC algorithm, wherein the fifth formula is thats ₃ And (3) indexing the slowness of the EMA STC spectrum, j is the current depth, and beta is a smoothing control parameter.

Above, from equation five, we can get that at the current depth jDuring the process of (1), the EMA STC +.>And the current depth weighted STC spectrum +.>Beta is a smoothing control parameter, preferably beta is 0.98.

According to the weighted STC spectrum and the EMA STC spectrum, determining a first slowness corresponding to the waveform data of the target array comprises: acquiring a first slowness peak value, wherein the first slowness peak value is a slowness peak value which appears for the first time in a weighted STC spectrum; acquiring a second slowness peak value, wherein the second slowness peak value is a slowness peak value which appears for the first time in the EMA STC spectrum; calculating a difference value between the first slowness peak value and the second slowness peak value; under the condition that the difference value is larger than or equal to a preset value, determining the slowness corresponding to the first slowness peak value as a first slowness; and under the condition that the difference value is smaller than a preset value, determining the slowness corresponding to the second slowness peak value as the first slowness.

And the first-appearing slowness peak value in the weighted STC spectrum is marked as a first slowness peak value, the first-appearing slowness peak value in the EMA STC spectrum is marked as a second slowness peak value, two sets of pickup results are comprehensively compared, and the credible position of the slowness is determined. In view of the trend of the EMA STC spectrum along with the change of the slowness, the method also compensates for some defects of the single-depth STC spectrum, provides a good reference for searching and judging the position of the STC peak value, and calculates the difference value between the first slowness peak value and the second slowness peak value, wherein the slowness corresponding to the first slowness peak value is determined as the first slowness when the difference value is greater than or equal to a preset value, and the slowness corresponding to the second slowness peak value is determined as the first slowness when the difference value is smaller than the preset value, and preferably the preset value is 25us/ft. Specifically, EMA STC spectra improve the accuracy of finding and discriminating peak positions.

In the present application, determining, by a calibration mechanism, a final slowness corresponding to waveform data of a target array according to an initial STC spectrum and a first slowness includes: determining the corresponding slowness of the first slowness in the initial STC spectrum, and determining the peak value closest to the position corresponding to the slowness as a third slowness peak value; and determining the slowness corresponding to the third slowness peak as a target slowness, and calibrating the target slowness through a calibration mechanism to obtain the final slowness.

As described above, a small range peak finding is performed on the conventional STC spectrum with the first slowness position as a reference, and slowness is further calibrated as the final output of the depth point. The integrated use of the weighted STC spectrum and the EMA STC spectrum can stably determine the trusted position of the first slowness, determine the slowness corresponding to the first slowness in the initial STC spectrum, perform peak searching optimization again in the initial STC spectrum, determine the slowness corresponding to the peak nearest to the position corresponding to the slowness as the target slowness, calibrate the target slowness through a calibration mechanism to obtain the final slowness, so as to realize final calibration of the slowness, and preferably, perform peak searching optimization again in the initial STC spectrum within a small range of [ -5us/ft-5us/ft ].

As shown in FIG. 2, FIG. 2 is another alternative embodiment of the present applicationFig. 2 is a flowchart of a method for determining slowness corresponding to array waveform data in real time according to an embodiment of the present invention, where original array waveform data is obtained, the original array waveform data is efficiently preprocessed, the slowness is calculated by a weighted STC algorithm, the weighted STC algorithm includes a conventional STC spectrum and a weighted STC spectrum, μ of the conventional STC spectrum is 0, μ of the weighted STC spectrum is le ^-4 And (3) calculating an EMA STC spectrum by the weighted STC spectrum, carrying out fault-tolerant peak searching mechanism and correction on the weighted STC spectrum and the EMA STC spectrum to obtain an estimated value of the slowness, carrying out the peak searching and calibration of the slowness again by combining the estimated value of the slowness with the conventional STC spectrum, and determining the slowness after calibration.

Above-mentioned, this application can effectively handle original array waveform data corresponding slowness scope [40us/ft-200us/ft ], can catch and trace the longitudinal wave time difference change law (including soft stratum and gas bearing stratum) of multiple stratum type effectively, has higher original array waveform data corresponding slowness's real-time extraction precision and automation level, has reduced human intervention degree by a wide margin. In addition, the slowness estimation for the casing segments of the present application is [56us/ft-58us/ft ], which is closer to the actual casing slowness value 57us/ft than the threshold estimation, and meets the acceptance criteria. The time difference extraction method has short average time consumption in extracting the time difference of a single depth, and meets timeliness required by real-time processing.

Fig. 3 is an application effect diagram of a method for determining slowness corresponding to array waveform data in real time according to an embodiment of the present invention, where (a) is a conventional STC spectrum of original array waveform data, (b) is a conventional STC spectrum of array waveform data after being preprocessed, and (c) is a picked-up result of EMA STC spectrum and slowness corresponding to array waveform data thereof; (d) For the extraction result of slowness corresponding to the finally output array waveform data, the ordinate in fig. 3 is the acquisition depth, the unit of the acquisition depth is feet, the abscissa is the slowness, the unit of the slowness is microseconds/feet, and comparison of (a) and (b) finds that the preprocessing operation of the method improves the quality of the array waveform data, so that the STC characteristics of the array waveform data with relatively weak upper part become more prominent, and the method is the basis of subsequent STC spectrum analysis and slowness extraction. It should be noted that, in this embodiment, there are two depth segments of low snr data, namely the depth segments of [270-320] and [370-400] in fig. 3, the corresponding STC features are not obvious or even disappear, but the EMA STC spectrum ((c)) provided in this application realizes the grasp of the overall trend, makes up the defect of the actual waveform data, and can extract the slowness baseline corresponding to the array waveform data through the fault-tolerant peak-seeking mechanism, thus providing an important reference for the slowness extraction, command and correction of the low snr segment. (d) The final slowness extraction result in (1) proves that the uncertainty caused by the defects of waveform data on real-time pickup can be well overcome by introducing EMA STC spectrum, and the method is a key of slowness extraction stability in the application.

Fig. 4 is a schematic diagram of a slowness-extraction result corresponding to array waveform data of a digital acoustic logging applied in a southwest oilfield according to an embodiment of the present invention, where (a) is an STC spectrum of original array waveform data; (b) is a weighted STC spectrum; (c) EMA STC spectrum and slowness pickup result P1; (d) is a weighted STC spectrum and its slowness pickup result P2; (e) For the final output slowness pickup result, the ordinate in fig. 4 is the acquisition depth, the unit of the acquisition depth is feet, the abscissa is slowness, the unit of the slowness is microseconds/feet, obvious relevant energy exists on the whole background of the conventional STC spectrum ((a)) and is mainly caused by random noise in waveform data, and the weighted STC spectrum ((b)) provided by the invention can suppress the influence of noise and improve the whole signal-to-noise ratio of the STC spectrum. The use of EMA STC spectrum effectively describes the gradual change trend ((c)) of slowness corresponding to array waveform data along with depth measurement, outputs a high-quality slowness baseline, can automatically correct slowness outliers existing between depths [5000-5400] in (d) by taking the slowness baseline as a reference, and finally achieves real-time pickup and stable tracking of slowness corresponding to array waveform data of the whole depth section.

Fig. 5 is an application effect diagram of a real-time determination method of slowness corresponding to array waveform data in a stratum with a larger slowness according to another alternative embodiment provided in the present application, where (a) is a petroleum well 1; (b) is a petroleum well 2; (c) is a petroleum well 3; (d) For the petroleum well 4, the application of the technology is verified in four petroleum wells in northeast China, the four examples are stratum with larger slowness, the slowness range corresponding to the array waveform data of part of stratum reaches more than 150us/ft, and the phenomenon of abrupt change of the slowness corresponding to the array waveform data is commonly existed under the influence of the sand-shale interbedded in the region. The application results of the four examples show that the method can effectively cope with the stratum with large time difference, and real-time continuous and stable extraction of slowness corresponding to the array waveform data is realized. In addition, the slowness corresponding to the array waveform data extracted from the casing section is within [56us/ft-58us/ft ], and is close to the theoretical slowness value 57us/ft of the casing, so that the practical acceptance standard is achieved.

The embodiment of the invention provides a real-time determination method of slowness corresponding to array waveform data, which is implemented by acquiring original array waveform data acquired at a preset depth of a target oil-gas well, wherein the original array waveform data is any one of the following: longitudinal wave waveform data and transverse wave waveform data; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; according to the initial STC spectrum and the first slowness, the final slowness corresponding to the waveform data of the target array is determined through a calibration mechanism, the problems of insufficient precision and low stability of the slowness real-time extraction technology of the sound waves in the related technology are solved, and the effects of real-time accurate identification and automatic stable extraction of the slowness are achieved.

It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.

The embodiment of the invention also provides a real-time determining device for the slowness corresponding to the array waveform data, and the real-time determining device for the slowness corresponding to the array waveform data can be used for executing the real-time determining method for the slowness corresponding to the array waveform data. The following describes a real-time determining device for slowness corresponding to array waveform data provided by the embodiment of the invention.

Fig. 6 is a schematic diagram of a real-time determining apparatus for determining slowness corresponding to array waveform data according to an embodiment of the present invention. As shown in fig. 6, the apparatus includes: a first obtaining unit 601, configured to obtain raw array waveform data collected at a target oil-gas well, where the raw array waveform data at least includes longitudinal waveform information and transverse waveform information; a pre-operation unit 602, configured to perform a pre-processing operation on the original array waveform data to obtain target array waveform data; a first determining unit 603, configured to determine an initial STC spectrum, a weighted STC spectrum, and an EMA STC spectrum according to the target array waveform data; a second determining unit 604, configured to determine a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, where the first slowness is an inverse of a propagation speed of the original array waveform data; a third determining unit 605 is configured to determine a final slowness corresponding to the target array waveform data according to the initial STC spectrum and the first slowness.

In an alternative example, the pre-operation unit 602 includes: the first acquisition subunit is used for acquiring the number of time sampling points corresponding to the original array waveform data; the first operation unit is configured to perform a differential operation on the original array waveform data according to a formula, and obtain array waveform data after the removal operation, where the differential operation is used to characterize an operation of removing a dc component included in the original array waveform data, and the formula one is:x is the original array waveform data, and +.>The array waveform data after the removal operation is obtained, wherein T is the number of the time sampling points; the second operation unit is configured to perform an amplitude equalization operation on the array waveform data after the removal operation according to a formula ii, and determine the data after the amplitude equalization operation as the target array waveform data, where the amplitude equalization operation is used to characterize an operation of correcting the array waveform data after the removal operation, and the formula ii is: />X is the original array waveform data, and +.>For the array waveform data after the above differential operation, < >>For the target array waveform data, T represents the number of time sampling points.

In an alternative example, the first determining unit 603 includes: the second acquisition subunit is used for acquiring a plurality of acquisition times corresponding to the original array waveform data; the first determining subunit is configured to determine a plurality of second slowness according to the plurality of acquisition times and a preset STC algorithm, where the plurality of second slowness corresponds to the plurality of acquisition times one to one.

In an alternative example, the first determining subunit includes: a third obtaining subunit, configured to obtain a time window corresponding to the original array waveform data, a receiver index, and a receiver interval, where the time window is a waveform period corresponding to the initial STC spectrum, the receiver index is a constant, and the receiver interval is a interval between every two original array waveform data received by the data receiver; a first calculating subunit, configured to calculate a plurality of the second slowness through a third formula in the preset STC algorithm, where the third formula iss ₁ For the slowness index of the initial STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

In an alternative example, the first determining unit 603 includes: a fourth acquisition subunit, configured to acquire a plurality of acquisition times corresponding to the original array waveform data; and the fifth acquisition subunit is used for determining a plurality of third slowness according to the acquisition times and the weighted STC algorithm, wherein the third slowness corresponds to the acquisition times one by one.

In an alternative example, the second acquisition subunit includes: the first acquisition module is used for acquiring a time window, a receiver index and a receiver interval corresponding to the original array waveform data, wherein the time window is a waveform period corresponding to the initial STC spectrum, and the receiver interval is a interval between every two original array waveform data received by a data receiver; a first calculation module configured to calculate a plurality of the third slowness by using a fourth formula in the weighted STC algorithm, where the fourth formula isCE is->TE is +.>s ₂ For the slowness index of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

In an alternative example, the first determining unit 603 includes: a sixth acquisition subunit, configured to acquire a plurality of acquisition times corresponding to the original array waveform data; and the second determining subunit is used for determining a plurality of fourth slowness according to the acquisition times and the EMA STC algorithm, wherein the fourth slowness corresponds to the acquisition times one by one.

In an alternative example, the second determining subunit includes: the second acquisition module is used for acquiring the current depth of the original array waveform data and the smoothing control parameters corresponding to the original array waveform data; a second calculating module, configured to calculate a plurality of the fourth slowness by using a fifth formula in the EMA STC algorithm, where the fifth formula iss ₃ And j is the current depth, and beta is the smoothing control parameter.

In an alternative example, the second determining unit 604 includes: a seventh obtaining subunit, configured to obtain a first slowness peak, where the first slowness peak is a slowness peak that first appears in the weighted STC spectrum; an eighth obtaining subunit, configured to obtain a second slowness peak, where the second slowness peak is a slowness peak that first appears in the EMA STC spectrum; a second calculating subunit, configured to calculate a difference between the first slowness and the second slowness; a third determining subunit, configured to determine, when the difference value is greater than a preset value, the slowness corresponding to the first slowness peak value as the first slowness; and a fourth determining subunit, configured to determine, when the difference is smaller than the preset value, the slowness corresponding to the second slowness peak as the first slowness.

In an alternative example, the third determining unit 605 includes: a fifth determining subunit, configured to determine a slowness corresponding to the first slowness in the initial STC spectrum, and determine a peak nearest to a location corresponding to the slowness as a third slowness peak; and a sixth determining subunit configured to determine a slowness corresponding to the third slowness peak as the final slowness.

The real-time determining device for slowness corresponding to array waveform data is used for acquiring original array waveform data acquired in a target oil-gas well through a first acquiring unit 601, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; a pre-operation unit 602, configured to perform a pre-processing operation on the original array waveform data to obtain target array waveform data; a first determining unit 603, configured to determine an initial STC spectrum, a weighted STC spectrum, and an EMA STC spectrum according to the target array waveform data; a second determining unit 604, configured to determine a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, where the first slowness is an inverse of a propagation speed of the original array waveform data; the third determining unit 605 is configured to determine the final slowness corresponding to the waveform data of the target array according to the initial STC spectrum and the first slowness, thereby solving the problems of insufficient precision and low stability of the slowness real-time extraction technology of the acoustic wave in the related art, and further achieving the effects of real-time accurate identification and automatic stable extraction of the slowness.

The real-time determining device for determining the slowness corresponding to the array waveform data comprises a processor and a memory, wherein the first obtaining unit 601 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The inner core can be provided with one or more than one, and the problems of insufficient precision and low stability of the slowness real-time extraction technology of the sound wave in the related technology are solved by adjusting the parameters of the inner core.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the invention provides a computer readable storage medium, wherein a program is stored on the computer readable storage medium, and the program is executed by a processor to realize the real-time determination method of the slowness corresponding to the array waveform data.

The embodiment of the invention provides a processor which is used for running a program, wherein the program runs to execute a real-time determination method of slowness corresponding to array waveform data.

The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes the following steps when executing the program: acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; and determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

Further, the preprocessing operation is performed on the original array waveform data to obtain target array waveform data, including: acquiring the number of time sampling points corresponding to the original array waveform data; performing differential operation on the original array waveform data according to a formula I, and obtaining array waveform data after the removal operation, wherein the differential operation is used for representing the operation of removing the direct current component contained in the original array waveform data, and the formula I is as follows: X is the original array waveform data, < >>In order to remove the array waveform data after operation, T is the number of time sampling points; performing amplitude equalization operation on the array waveform data after the removal operation according to a formula II, and determining the data after the amplitude equalization operation as target array waveform data, wherein the amplitude equalization operation is used for representing the operation of correcting the array waveform data after the removal operationThe formula II is:x is the original array waveform data, < >>For the array waveform data after differential operation, +.>For target array waveform data, T represents the number of time sampling points.

Further, determining a plurality of second slowness according to the plurality of acquisition times and the preset STC algorithm includes: acquiring a time window corresponding to original array waveform data, a receiver index and a receiver interval, wherein the time window is a waveform period corresponding to an initial STC spectrum, the receiver index is a constant, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of second slowness by presetting a third formula in the STC algorithm, wherein the third formula is that s ₁ Is the slowness index of the initial STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

Further, based on the multiple acquisition times and the weighted STC calculationA method of determining a plurality of third slowness comprising: acquiring a time window, a receiver index and a receiver interval corresponding to original array waveform data, wherein the time window is a waveform period corresponding to an initial STC spectrum, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of third slowness by a fourth formula in the weighted STC algorithm, wherein the fourth formula is thatCE is->TE iss ₂ For the slowness index of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

The device herein may be a server, PC, PAD, cell phone, etc.

The invention also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information; preprocessing the original array waveform data to obtain target array waveform data; determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the waveform data of the target array; determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data; and determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

Further, the preprocessing operation is performed on the original array waveform data to obtain target array waveform data, including: acquiring the number of time sampling points corresponding to the original array waveform data; performing differential operation on the original array waveform data according to a formula, and obtaining array waveform data after the removal operation, wherein the differential operation is used for representing removal of straight included in the original array waveform data Operation of the flow component, equation one is:x is the original array waveform data, < >>In order to remove the array waveform data after operation, T is the number of time sampling points; performing amplitude equalization operation on the array waveform data after the removal operation according to a formula II, and determining the data after the amplitude equalization operation as target array waveform data, wherein the amplitude equalization operation is used for representing the operation of correcting the array waveform data after the removal operation, and the formula II is as follows:x is the original array waveform data, < >>For the array waveform data after differential operation, +.>For target array waveform data, T represents the number of time sampling points.

Further, determining a plurality of third slowness according to the plurality of acquisition times and the weighted STC algorithm includes: acquiring a time window, a receiver index and a receiver interval corresponding to original array waveform data, wherein the time window is a waveform period corresponding to an initial STC spectrum, and the receiver interval is the interval between every two original array waveform data received by a data receiver; calculating a plurality of third slowness by a fourth formula in the weighted STC algorithm, wherein the fourth formula is thatCE is->TE iss ₂ For the slowness index of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

Further, determining a plurality of fourth slowness according to the plurality of acquisition times and the EMA STC algorithm includes: acquiring and collecting original array waveThe current depth of the shape data and the smoothing control parameters corresponding to the waveform data of the acquired original array; calculating a plurality of fourth slowness by a fifth formula in the EMA STC algorithm, wherein the fifth formula is thats ₃ And (3) indexing the slowness of the EMA STC spectrum, j is the current depth, and beta is a smoothing control parameter.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.

Claims

1. The real-time determination method of the slowness corresponding to the array waveform data is characterized by comprising the following steps of:

acquiring original array waveform data acquired in a target oil-gas well, wherein the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information;

preprocessing the original array waveform data to obtain target array waveform data;

Determining an initial STC spectrum, a weighted STC spectrum and an EMASC spectrum according to the target array waveform data;

determining a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, wherein the first slowness is the reciprocal of the propagation speed of the original array waveform data;

and determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

2. The method of claim 1, wherein preprocessing the raw array waveform data to obtain target array waveform data comprises:

acquiring the number of time sampling points corresponding to the original array waveform data;

performing differential operation on the original array waveform data according to a formula I, and obtaining array waveform data after the removal operation, wherein the differential operation is used for representing the operation of removing the direct current component contained in the original array waveform data, and the formula I is as follows:x is the original array waveform data, +.>For the array waveform data after the removal operation, T is the number of the time sampling points;

performing amplitude equalization operation on the array waveform data after the removal operation according to a formula II, and determining the data after the amplitude equalization operation as the target array waveform data, wherein the amplitude equalization operation is used for representing the operation of correcting the array waveform data after the removal operation, and the formula II is as follows: X is the original array waveform data, +.>For the array waveform data after the differential operation, < >>For the target array waveform data, T represents the number of time sampling points.

3. The method of claim 1 wherein determining the initial STC spectrum from the target array waveform data comprises:

acquiring a plurality of acquisition times corresponding to the original array waveform data;

and determining a plurality of second slowness according to the acquisition times and a preset STC algorithm, wherein the second slowness corresponds to the acquisition times one by one.

4. The method of claim 3 wherein determining a plurality of second slowness according to a plurality of the acquisition times and a preset STC algorithm comprises:

acquiring a time window corresponding to the original array waveform data, a receiver index and a receiver interval, wherein the time window is a waveform period corresponding to the initial STC spectrum, the receiver index is a constant, and the receiver interval is the interval between every two original array waveform data received by a data receiver;

calculating a plurality of the second slowness according to a third formula in the preset STC algorithm, wherein the third formula is that s ₁ Indexing the slowness of the initial STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

5. The method of claim 1 wherein determining a weighted STC spectrum based on the target array waveform data comprises:

and determining a plurality of third slowness according to the acquisition times and the weighted STC algorithm, wherein the third slowness corresponds to the acquisition times one by one.

6. The method of claim 5 wherein determining a plurality of third slowness from a plurality of the acquisition times and weighted STC algorithms comprises:

acquiring a time window, a receiver index and a receiver interval corresponding to the original array waveform data, wherein the time window is a waveform period corresponding to the initial STC spectrum, and the receiver interval is the interval between every two original array waveform data received by a data receiver;

calculating a plurality of the third slowness by a fourth formula in the weighted STC algorithm, wherein the fourth formula is that CE is->TE iss ₂ Indexing the slowness of the weighted STC spectrum, t is the acquisition time, t _w For the time window, m is the receiver index and d is the receiver spacing.

7. The method of claim 1, wherein determining an EMA STC spectrum from the target array waveform data comprises:

and determining a plurality of fourth slowness according to the acquisition times and the EMA STC algorithm, wherein the fourth slowness corresponds to the acquisition times one by one.

8. The method of claim 7, wherein determining a plurality of fourth slowness from a plurality of the acquisition times and EMA STC algorithms comprises:

acquiring the current depth of acquiring the original array waveform data and acquiring smoothing control parameters corresponding to the original array waveform data;

calculating a plurality of fourth slowness according to a fifth formula in the EMA STC algorithm, wherein the fifth formula is thats ₃ And indexing the slowness of the EMA STC spectrum, j is the current depth, and beta is the smoothing control parameter.

9. The method of claim 4 wherein determining the first slowness corresponding to the target array waveform data based on the weighted STC spectrum and the EMA STC spectrum comprises:

Acquiring a first slowness peak value, wherein the first slowness peak value is a slowness peak value which appears for the first time in the weighted STC spectrum;

acquiring a second slowness peak value, wherein the second slowness peak value is a slowness peak value which appears for the first time in the EMA STC spectrum;

calculating a difference between the first slowness and the second slowness;

determining the slowness corresponding to the first slowness peak value as the first slowness under the condition that the difference value is larger than a preset value;

and under the condition that the difference value is smaller than the preset value, determining the slowness corresponding to the second slowness peak value as the first slowness.

10. The method of claim 1 wherein determining a final slowness corresponding to the target array waveform data via a calibration mechanism based on the initial STC spectrum and the first slowness comprises:

determining the corresponding slowness of the first slowness in the initial STC spectrum, and determining the peak value nearest to the position corresponding to the slowness as a third slowness peak value;

and determining the slowness corresponding to the third slowness peak as a target slowness, and calibrating the target slowness through the calibration mechanism to obtain the final slowness.

11. A real-time determination apparatus for slowness corresponding to array waveform data, comprising:

the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring original array waveform data acquired in a target oil-gas well, and the original array waveform data at least comprises longitudinal wave waveform information and transverse wave waveform information;

the pre-operation unit is used for performing pre-processing operation on the original array waveform data to obtain target array waveform data;

the first determining unit is used for determining an initial STC spectrum, a weighted STC spectrum and an EMA STC spectrum according to the target array waveform data;

a second determining unit, configured to determine a first slowness corresponding to the target array waveform data according to the weighted STC spectrum and the EMA STC spectrum, where the first slowness is an inverse of a propagation speed of the original array waveform data;

and the third determining unit is used for determining the final slowness corresponding to the waveform data of the target array through a calibration mechanism according to the initial STC spectrum and the first slowness.

12. A computer readable storage medium, wherein the computer readable storage medium includes a stored program, and wherein the program when executed controls a device in which the computer readable storage medium is located to perform the method for determining slowness corresponding to the array waveform data according to any one of claims 1 to 10.

13. A processor, wherein the processor is configured to run a program, and wherein the program, when run, performs a method for determining a slowness corresponding to the array waveform data as claimed in any one of claims 1 to 10.