A method and device for identifying a pump-off water hammer peak

Abstract

The application provides a pump-off water hammer peak identification method and device, and relates to the field of oil and gas exploitation.The method comprises the following steps: performing sliding translation analysis on the obtained pump-off water hammer to obtain an asymmetry parameter of a wave peak-valley pair contained in the pump-off water hammer; determining an asymmetry influence factor of the wave peak-valley pair according to a predetermined asymmetry weight, an asymmetry sensitivity adjustment factor and the asymmetry parameter; performing dynamic false peak screening on the wave peak-valley pair based on a predetermined wave peak representative amplitude and the asymmetry influence factor to obtain a real wave peak sequence.The application can realize automatic false peak elimination and stable real wave peak identification by introducing the asymmetry sensitivity adjustment factor to perform weighted evaluation on the asymmetry degree of the adjacent wave valley difference of the wave peak.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction, specifically a method and apparatus for identifying water hammer peaks after pump shutdown based on peak-valley asymmetry and sensitivity adjustment factors. Background Technology

[0002] Hydraulic fracturing is a core technology for the efficient development of unconventional oil and gas reservoirs, and the accurate characterization of fracture morphology is directly related to the accuracy of fracturing effect evaluation, bridge plug sealing verification, and reservoir parameter inversion. Currently, mainstream fracture diagnosis methods include microseismic monitoring, inclinometer monitoring, and distributed temperature sensing (DTS). While microseismic monitoring can accurately characterize fracture geometry, it suffers from high equipment costs, poor noise resistance, and complex deployment. Inclinometer and DTS technologies are limited by topographic conditions, construction interference, and data interpretation difficulties, making them unsuitable for rapid on-site diagnosis. In contrast, fracture diagnosis methods based on the pump shutdown water hammer effect only require wellhead pressure data, offering significant advantages such as low cost, ease of operation, and real-time performance, making them a research hotspot in recent years.

[0003] The water hammer effect refers to the phenomenon where the kinetic energy of fracturing fluid is converted into pressure energy when the flow of fracturing fluid is suddenly interrupted, forming periodic pressure fluctuations (water hammer waves) in the wellbore-fracture system. The amplitude decay rate and frequency of these waves are closely related to parameters such as fracture length and conductivity; therefore, accurate identification of the peaks is a prerequisite for subsequent quantitative analysis. However, existing peak identification methods have significant limitations: ① Manual interpretation, relying on engineers' experience to manually mark peak positions, is easily influenced by subjective judgment, with different personnel yielding interpretations of the same data differing by 10%-15%, and is inefficient, making it difficult to handle massive amounts of fracturing data; ② Thresholding methods, using fixed amplitude or slope thresholds to screen peaks, but the dynamic changes in amplitude during water hammer wave decay can lead to missed peaks in the early stages or misjudgments of noise in the later stages; ③ Peak detection algorithms, such as local extremum methods and wavelet transform methods, while capable of automatic identification, have poor adaptability to the asymmetric decay characteristics of water hammer waves (such as abrupt changes in the peak-to-trough amplitude ratio and waveform distortion), and are prone to misidentifying noise oscillations or pressure rebounds as false peaks.

[0004] For example, CN111550230A discloses a system and method for fracturing diagnosis based on water hammer pressure wave signals. It collects water hammer signals from pump shutdown using a high-frequency pressure detection device and uses spectral analysis to determine the fracture reflection time and fracture location. CN113987972A proposes to determine the optimal wavelet basis function based on the frequency of the pressure value sequence, thereby calculating the water hammer pressure wave velocity and improving the accuracy of fracture depth identification. CN119558153B establishes the water hammer continuity equation, motion equation, and fluid elasticity equation, and couples the wellhead flow boundary with the bottom hole fracture boundary conditions to invert fracture-related parameters.

[0005] However, the aforementioned studies mostly focus on the overall filtering, spectrum identification, wave velocity correction, or fracture parameter inversion of water hammer signals, primarily addressing the issues of "how to reduce signal noise" and "how to interpret parameters," while paying insufficient attention to the automatic, stable, and accurate identification of the water hammer peaks themselves. Especially in actual fracturing pump shutdown data, water hammer signals often exhibit characteristics such as strong non-stationary attenuation, asymmetric peak-to-trough amplitudes, local waveform distortion, and the mixing of weak signals with noise in the later stages. If fixed thresholds, conventional local extrema, or single frequency domain criteria are still used, it is easy to cause early strong peaks to be missed, later spurious peaks to be misjudged, and adjacent peaks to be misjudged in time sequence, thereby affecting the subsequent quantitative inversion of fracture length, conductivity, and energy dissipation parameters.

[0006] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0007] To address the problems in the prior art, this application provides a method and apparatus for identifying water hammer peaks after pump shutdown. By introducing an asymmetric sensitivity adjustment factor, the asymmetry of the difference between adjacent peaks and valleys can be weighted and evaluated, thereby achieving automatic removal of false peaks and stable identification of true peaks.

[0008] To solve the above-mentioned technical problems, this application provides the following technical solution: In a first aspect, this application provides a method for identifying water hammer peaks after pump shutdown, including: A sliding translation analysis was performed on the water hammer wave after the pump was stopped to obtain the asymmetric parameters of the peak-valley pairs contained in the water hammer wave after the pump was stopped. The asymmetric influence factor of the peak-trough pair is determined based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and the asymmetric parameter. Based on the predetermined peak representative amplitude and the asymmetric influence factor, the peak-valley pairs are dynamically filtered to remove false peaks, thus obtaining the true peak sequence.

[0009] Furthermore, the sliding translation analysis of the acquired water hammer wave after pump shutdown yields the asymmetric parameters of the peak-trough pairs contained in the water hammer wave after pump shutdown, including: A sliding window detection method is used to detect the water hammer wave after the pump stops, and the peak-valley pairs contained in the water hammer wave after the pump stops are obtained. Calculate the pressure difference between the crest of the wave and the adjacent trough on the left and right sides of the wave crest-trough pair; The asymmetric parameters of the peak-trough pair are determined based on the pressure difference.

[0010] Furthermore, the step of performing sliding window detection on the water hammer wave after pump shutdown to obtain the peak-valley pairs contained in the water hammer wave after pump shutdown includes: Based on the time sequence traversal of the water hammer wave after the pump is stopped, local maxima that satisfy the first preset condition are searched as candidate wave peaks, and local minima that satisfy the second preset condition are searched as candidate wave troughs. Based on the sliding window, the nearest candidate valley to the candidate peak is searched forward and backward on the time axis, respectively, and used as the left and right boundary valleys of the candidate peak. The candidate peak, the left boundary valley, and the right boundary valley are combined to form the peak-valley pair.

[0011] Further, determining the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weights, asymmetric sensitivity adjustment factors, and the asymmetric parameters includes: The asymmetric weights are determined based on the geological characteristics of the fracturing block; Calculate the product of the asymmetric weight and the asymmetric parameter; The sum of the product and the asymmetric sensitivity adjustment factor is determined as the asymmetric influence factor.

[0012] Furthermore, the method for identifying water hammer peaks after pump shutdown also includes: Determine whether the values ​​of the asymmetric weight and the asymmetric sensitivity adjustment factor match the current fracturing conditions; If so, determine whether the asymmetric influence factor is within a reasonable range based on the current fracturing conditions.

[0013] Furthermore, the step of pre-determining the amplitude represented by the wave peak includes: Obtain the left pressure value corresponding to the left boundary trough and the right pressure value corresponding to the right boundary trough; The average pressure value is determined based on the left pressure value and the right pressure value. The difference between the peak pressure value corresponding to the candidate wave peak and the average pressure value is determined as the bulge height of the candidate wave peak relative to the baseline of the water hammer wave after the pump is stopped. The amplitude represented by the peak corresponding to the candidate peak is determined based on the height of the protrusion.

[0014] Furthermore, the dynamic filtering of peak-trough pairs based on pre-determined peak representative amplitudes and the asymmetric influence factor to obtain the true peak sequence includes: The quotient of the peak representative amplitude and the asymmetric influence factor is determined as the peak evaluation value of the peak-trough pair. Based on the peak evaluation value and the preset false-removal threshold, the peak-valley pairs are initially screened to obtain the first true peak and the doubtful peak. Selectively remove the questionable peaks to obtain the second true peak; A true peak sequence containing the first true peak and the second true peak is generated based on their respective time series.

[0015] Furthermore, the selective removal of the questionable peaks to obtain the second true peak includes: The questionable peaks are recombined with adjacent troughs to construct new peak-trough pairs; Calculate the new peak evaluation value corresponding to the new peak and trough pair; The evaluation value of the new peak is compared with the evaluation values ​​of the adjacent peaks of the adjacent questionable peaks; Based on the comparison results, the questionable peaks are filtered to obtain the second true peak.

[0016] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for identifying water hammer peaks after pump shutdown.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for identifying water hammer peaks after pump shutdown.

[0018] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the method for identifying water hammer peaks after pump shutdown.

[0019] To address the problems in the prior art, the method and apparatus for identifying water hammer peaks after pump shutdown provided in this application can, by introducing an asymmetric sensitivity adjustment factor, perform a weighted evaluation of the asymmetry of the difference between adjacent troughs of the peak, thereby achieving automatic elimination of false peaks and stable identification of true peaks, significantly improving the accuracy and reliability of water hammer peak identification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the method for identifying water hammer peaks after pump shutdown in this application embodiment; Figure 2 This is a flowchart illustrating the asymmetric parameters obtained in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the calculation of the pressure difference between the wave crest and the adjacent wave trough in an embodiment of this application; Figure 4 This is a flowchart illustrating the process of obtaining peak-valley pairs in the embodiments of this application; Figure 5 This is a typical pseudo-peak shape in the embodiments of this application; Figure 6 This is a normal water hammer wave crest shape in the embodiments of this application; Figure 7 The "trough-crest-trough" structural unit is used in the embodiments of this application; Figure 8 This is a flowchart illustrating the determination of asymmetric influence factors in the embodiments of this application; Figure 9 This is a flowchart of parameter calibration in the embodiments of this application; Figure 10 This is a flowchart illustrating the determination of the peak representative amplitude in the embodiments of this application; Figure 11 This is a flowchart illustrating the process of obtaining the true peak sequence in the embodiments of this application; Figure 12 This is a flowchart illustrating the process of obtaining the second true peak in this embodiment of the application; Figure 13 This is a structural diagram of the water hammer peak identification device after pump shutdown in an embodiment of this application; Figure 14 This is a schematic diagram of the structure of the electronic device in the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0023] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0024] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0025] In one embodiment, see Figure 1 In order to automatically eliminate false peaks and stably identify true peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry in the difference between adjacent peaks and troughs, this application provides a method for identifying water hammer peaks after pump shutdown, including: S101: Perform sliding translation analysis on the obtained water hammer wave after pump shutdown to obtain the asymmetric parameters of the wave crest-valley pairs contained in the water hammer wave after pump shutdown. S102: Determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and the asymmetric parameter; S103: Based on the predetermined peak representative amplitude and the asymmetric influence factor, the peak-valley pairs are dynamically filtered to remove false signals, thereby obtaining the true peak sequence.

[0026] Understandably, existing water hammer peak identification methods rely on manual interpretation, are inefficient, and struggle to adapt to the asymmetric attenuation characteristics of water hammer waves. This application proposes a post-pump shutdown water hammer peak identification method based on peak-valley asymmetry and a sensitivity adjustment factor. This method is designed to address the asymmetric attenuation characteristics of water hammer pressure waves within the wellbore-fracture coupling system after pump shutdown. By introducing an asymmetric sensitivity adjustment factor, it quantifies the asymmetry of the peak-valley amplitude difference, performs a weighted evaluation of the difference between adjacent peaks and valleys, and dynamically adjusts the sensitivity threshold during the attenuation stage to achieve automatic removal of false peaks and accurate and stable identification of true peaks. This method can provide high-precision data support for fracture parameter inversion, thereby improving the accuracy and reliability of fracturing effect evaluation.

[0027] Specifically, firstly, a sliding translation analysis is performed on the collected water hammer pressure wave signal after pump shutdown. This method moves segment by segment along the pressure sequence within a fixed time window, automatically detecting local maxima (peaks) and adjacent local minima (troughs) within each window, forming several peak-trough pairs. By calculating the proportional relationship between the amplitude difference and the time span of each peak and its preceding and following troughs, parameters describing the asymmetry of the waveform are quantified, including the slope ratio of the rising and falling segments and the difference in amplitude change rate. Secondly, based on the pre-set asymmetry weights and dynamically adjustable asymmetry sensitivity adjustment factors, combined with the asymmetry parameters obtained in the previous step, the asymmetry influence factor of each peak-trough pair is calculated. The asymmetry weights reflect the importance of waveform asymmetry features at different locations for overall identification; the sensitivity adjustment factor can adaptively change according to the current attenuation stage (early, middle, or late), enabling the algorithm to distinguish the inherent asymmetric attenuation pattern of real water hammer peaks from the random noise fluctuations of pseudo-peaks. Finally, a representative amplitude (such as peak pressure) for each wave peak is extracted and fused with an asymmetric influence factor to form a dynamic discrimination index for spurious wave removal. Only when this index exceeds a preset threshold and the time interval between adjacent wave peaks meets physical constraints is the wave peak retained as a true water hammer wave peak; otherwise, it is discarded. Through this process, the time series of true wave peaks can be stably extracted from the original pressure sequence, providing a high-quality data foundation for subsequent fracture parameter inversion.

[0028] As can be seen from the above description, the water hammer wave peak identification method provided in this application can automatically eliminate false wave peaks and stably identify real wave peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry of the difference between adjacent wave peaks and valleys, thereby significantly improving the accuracy and reliability of water hammer wave peak identification.

[0029] In one embodiment, see Figure 2 The sliding translation analysis of the acquired water hammer wave after pump shutdown yields the asymmetric parameters of the peak-trough pairs contained in the water hammer wave after pump shutdown, including: S201: Perform sliding window detection on the water hammer wave after the pump stops to obtain the peak-valley pairs contained in the water hammer wave after the pump stops; S202: Calculate the pressure difference between the crest of the wave peak and the adjacent trough on the left and right sides of the wave peak and trough pair; S203: Determine the asymmetry parameter (also known as the asymmetry index S) of the peak-trough pair based on the pressure difference. k ).

[0030] Understandably, the first step is to identify the peaks in the water hammer signal after the pump stops. and its adjacent troughs and Construct a "trough-crest-trough" structural unit. For each crest, calculate the pressure difference between the crest and its left and right adjacent troughs. .

[0031] For each "trough-crest-trough" structural unit established in the previous step, calculate the pressure value at the crest. With the pressure value of the trough on the left Right side trough pressure value The pressure difference between them, see Figure 3 As shown. The calculation formula includes: .

[0032] In the ideal frictionless, filtration-free water hammer theoretical model, the reflection of the pressure wave at the crack end should be a symmetrical waveform. However, during actual fracturing and pump shutdown, fracturing fluid continuously leaks into the formation, the fracture walls tend to close, and frictional dissipation occurs between the fluid inside the wellbore and the pipe wall. These factors collectively cause the water hammer wave energy to decay exponentially during propagation, and the decay process is not perfectly symmetrical on either side of the wave crest—specifically, the trailing edge of the wave crest (corresponding to the pressure drop during fracture closure) is often steeper or gentler than the leading edge of the wave crest (corresponding to the pressure buildup during fracture opening), resulting in a significant pressure difference between the left and right sides. and Differences arise.

[0033] Conventional thresholding or one-sided amplitude methods rely solely on The absolute value or one-sided slope of the curve completely ignores the difference in energy dissipation between the left and right sides. This is the fundamental reason why existing methods are susceptible to noise interference in the weak signal stage and misidentify asymmetric spurious peaks as true peaks. The embodiments of this application calculate separately... and This explicitly exposes the difference in energy distribution on both sides of the wave peak, providing a crucial two-way comparison basis for the subsequent introduction of asymmetric quantification indicators, and also laying the foundation for calculating a more robust representative amplitude.

[0034] Furthermore, define the asymmetric index. It is used to quantitatively characterize the degree of pressure asymmetry between the peak and the left and right troughs, providing a quantitative basis for distinguishing between true and false peaks and judging the state of the crack system.

[0035] Based on the left and right pressure differences obtained in the previous steps, the peak is defined. Asymmetric indicators The absolute value of the difference between the two:

[0036] This indicator It has a clear physical meaning—it directly quantifies the difference in the magnitude of the "energy drop" on both sides of the wave crest—that is, the difference between the pressure attenuation on the left side of the wave crest from the crest to the trough and the pressure attenuation on the right side from the crest to the trough.

[0037] According to S k The magnitude and stability of the value can clearly distinguish between real water hammer peaks and pseudo-peaks, specifically in two scenarios: First, the actual water hammer wave crest (dominated by the fracture system): because the system damping remains relatively stable within a certain period, the pressure difference on both sides of the wave crest is small. The value is in a low and stable range.

[0038] Second, false peaks (caused by interference factors): When peaks or troughs are caused by interference factors such as pressure gauge vibration, local fluid eddies, or electronic noise, their rising and falling edges often exhibit abnormal characteristics of steep rise and slow fall or slow rise and steep fall, leading to... and The difference is so great that it leads to The value has increased abnormally.

[0039] Extended application of the indicator—fracture system condition diagnosis: In hydraulic fracturing operations, bridge plug seal failure or abnormal fracture closure directly alters the energy dissipation path of water hammer waves, reflected in the pressure curve as a sudden change in peak shape. Therefore, The index can not only be used to distinguish between true and false peaks, but also serve as a sensitive indicator of changes in the state of a cracked system. For example, when A sudden, systematic increase in the value after a certain period may indicate incomplete crack closure or the presence of additional fluid leakage channels.

[0040] S k The core advantage of the indicator lies in the fact that it is not a simple mathematical feature quantity, but an engineering diagnostic quantity closely coupled with the fracturing physical process. This is significantly different from the "symmetry parameter" used only for mathematical discrimination in general signal processing patents. It can more accurately reflect the actual working conditions at the fracturing site and improve the reliability of diagnosis and engineering practicality.

[0041] As can be seen from the above description, the method for identifying water hammer peaks after pump shutdown provided in this application can perform sliding translation analysis on the acquired water hammer after pump shutdown to obtain the asymmetric parameters of the peak-valley pairs contained in the water hammer after pump shutdown.

[0042] In one embodiment, see Figure 4 The step of performing sliding window detection on the water hammer wave after pump shutdown to obtain the peak-valley pairs contained in the water hammer wave after pump shutdown includes: S401: Based on the time sequence traversal of the water hammer wave after the pump is stopped, search for local maxima that satisfy the first preset condition as candidate peaks, and search for local minima that satisfy the second preset condition as candidate troughs. S402: Based on the sliding window, search forward and backward on the time axis for the candidate valleys closest to the candidate peak, and use them as the left and right boundary valleys of the candidate peak; S403: Combine the candidate peak, the left boundary valley, and the right boundary valley into the peak-valley pair.

[0043] It is understandable that identifying the peak in the signal after the pump stops is important. and its adjacent troughs and The specific steps for constructing a "trough-crest-trough" structural unit are as follows: In traditional engineering signal processing methods, extreme points are typically detected using a sliding window, and adjacent maxima and minima are simply paired into "peak-trough" combinations. However, in actual pressure monitoring data after hydraulic fracturing pump shutdown, the pressure curve often exhibits local high-frequency oscillations due to interference from multiple factors such as fluid pulsation, mechanical vibration, and electronic noise. This results in a large number of "pseudo-peaks" that lack physical meaning. A typical example of this can be found in [link to relevant documentation]. Figure 5 As shown. The essential difference between this type of pseudo-peak and water hammer wave lies in the waveform symmetry: real water hammer waves, originating from crack opening and closing and fluid compressibility, exhibit pressure fluctuations in a quasi-symmetrical damped oscillation pattern, see [reference needed]. Figure 6 As shown; while the pseudo-peaks caused by noise usually appear as deformed protrusions that are severely asymmetrical on one or both sides.

[0044] Existing general-purpose peak detection algorithms (such as those based on wavelet transform or local extremum search) often fail to constrain waveform symmetry, leading to the misidentification of pressure rebound or noise oscillations as water hammer peaks during the decay stage, which seriously affects the accuracy of subsequent crack parameter inversion based on amplitude decay.

[0045] Therefore, this embodiment first employs a translational sliding window technique with amplitude constraints to scan the entire pressure time series, extracting all local maxima and local minima. Subsequently, instead of using simple "peak-trough" pairing, a ternary structural unit centered on the peak, consisting of "trough-peak-trough," is constructed (see [reference]). Figure 7 As shown. The specific construction strategy is as follows: First, for the wellhead pressure data, search through the local maxima that meet the conditions and mark them as candidate peaks; similarly, find the local minima and mark them as candidate troughs.

[0046] Second, for each candidate peak, search forward and backward along the time axis for the nearest candidate valley, which will be used as its left and right boundary valleys. If no valley is found within the specified search window, the candidate peak is discarded.

[0047] Third, the three feature points are combined into an indivisible structural unit, and all subsequent discrimination and calculations are based on this unit.

[0048] As can be seen from the above description, the method for identifying water hammer peaks after pump shutdown provided in this application can perform sliding window detection on the water hammer after pump shutdown to obtain the peak-valley pairs contained in the water hammer after pump shutdown.

[0049] In one embodiment, see Figure 8 The step of determining the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weights, asymmetric sensitivity adjustment factors, and the asymmetric parameters includes: S801: Determine the asymmetric weights based on the geological characteristics of the fracturing block; S802: Calculate the product of the asymmetric weight and the asymmetric parameter; S803: The sum of the product and the asymmetric sensitivity adjustment factor is determined as the asymmetric influence factor.

[0050] Understandably, given asymmetric weights With asymmetric sensitivity modulation factor Calculate the asymmetric influence factor .

[0051] (1) Parameter preparation before calculation: In calculating the asymmetric influence factor D k Before proceeding, it is necessary to confirm, calibrate, and verify the basic data of all parameters to provide accurate and reliable input for subsequent calculations and avoid distortion of calculation results due to parameter deviations. The specific steps are as follows: First, the asymmetry weight ω is determined and calibrated: Based on the current fracturing conditions, the fracturing fluid system is used as the core reference, while also considering the geological characteristics of the fracturing block (such as reservoir permeability), to determine the specific value of ω. The core principle of "larger values ​​for low-viscosity systems and smaller values ​​for high-viscosity systems" is clearly followed to ensure that ω is used to normalize and compensate for waveform benchmark differences under different construction parameters. The reasons are as follows: For slickwater fracturing (low viscosity), the water hammer wave has good symmetry, so a larger ω can be set to amplify small asymmetry differences and improve recognition sensitivity; for gel fracturing (high viscosity), the fluid internal dissipation is strong, and the waveform itself has a certain degree of natural asymmetry. To avoid misjudging true peaks as false peaks, the symmetry requirement should be relaxed by appropriately reducing the ω value. This reflects the algorithm's engineering adaptability to differences in fracturing fluid systems. If the current conditions differ from historical similar conditions (such as changes in fluid viscosity in different construction sections of the same block), ω needs to be fine-tuned to ensure it can effectively normalize and compensate for waveform benchmark differences in different systems, avoiding D due to improper ω values. k Calculate the deviation.

[0052] Second, the value of the sensitivity adjustment factor λ is confirmed and adjusted: λ acts as a bias term to control the standardization of the recognition system. When λ is large, The overall increase in λ relaxes the evaluation criteria, making it suitable for peak capture in the early stages of large amplitude and high signal-to-noise ratio; when λ is small... right The changes are more sensitive, making it suitable for fine-grained identification in the later stages of weak signal and high-noise background. In specific implementation, it is recommended to use λ=10 as the initial calculation input; adjust according to the stage of fracturing construction. In the early stage of fracturing (0~3 minutes after pump shutdown, pressure wave amplitude is large and signal-to-noise ratio is high), λ can be adjusted to 8~12 to relax the identification criteria; in the later stage of fracturing (3 minutes after pump shutdown, pressure signal is weak and noise is high), λ can be adjusted to 3~7 to improve the identification sensitivity; if there is strong interference on site (such as background noise), the value of λ can be temporarily increased (12~15) to avoid false peak misjudgment.

[0053] (2) Clarify D k The calculation principle and formula: Asymmetric influence factor D k Its core function is to transform the original asymmetric index S k Standardization eliminates benchmark shifts caused by different fracturing conditions (blocks, fluid systems, construction stages), ensuring D k It can accurately reflect the true characteristics of peak asymmetry, and its calculation is based on the asymmetry weight ω, the sensitivity adjustment factor λ, and the original index S. k The specific principles and formulas are as follows: The original index S is adjusted using asymmetric weight ω. kNormalization compensation is performed to offset the natural waveform asymmetry caused by differences in geology and fluid systems in different fracturing blocks. A sensitivity adjustment factor λ is used as a bias term to flexibly adjust the sensitivity and judgment criteria of the identification system, avoiding misjudgments caused by changes in operating conditions, ultimately resulting in S... k Converted to standardized asymmetric impact factor D k To unify the criteria for judgment.

[0054] The calculation formula is:

[0055] As can be seen from the above description, the method for identifying water hammer peaks after pump shutdown provided in this application can determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor and the asymmetric parameter.

[0056] In one embodiment, see Figure 9 The method for identifying water hammer peaks after pump shutdown further includes: S901: Determine whether the values ​​of the asymmetric weight and the asymmetric sensitivity adjustment factor match the current fracturing conditions; S902: If so, determine whether the asymmetric influence factor is within a reasonable range based on the current fracturing conditions.

[0057] Understandably, further verification of the reasonableness of the calculation results is necessary: ​​complete D. k After calculation, the results need to be verified for reasonableness to avoid subsequent diagnostic biases due to improper parameter values ​​or calculation errors. The specific verification method is as follows: (1) Parameter correlation verification: Check whether the values ​​of ω and λ match the current fracturing conditions (fluid system, construction stage). If the value of ω is too large in the high viscosity system and the value of λ is too large in the later construction stage, the parameters need to be readjusted and recalculated.

[0058] (2) Numerical range verification: Based on the current working conditions, determine D k Is the value within a reasonable range—D corresponding to the actual water hammer peak? k The value should be relatively stable, without any abnormally high (exceeding the normal range for similar operating conditions) or low. If a significant deviation occurs, S needs to be checked. k Is there an error in the calculation or parameter value?

[0059] As can be seen from the above description, the method for identifying water hammer peaks after pump shutdown provided in this application can verify the rationality of the calculation results.

[0060] In one embodiment, see Figure 10 The step of pre-determining the amplitude represented by the wave peak includes: S1001: Obtain the left pressure value corresponding to the left boundary valley and the right pressure value corresponding to the right boundary valley; S1002: Determine the average pressure value based on the left pressure value and the right pressure value; S1003: The difference between the peak pressure value corresponding to the candidate wave peak and the average pressure value is determined as the bulge height of the candidate wave peak relative to the baseline of the water hammer wave after the pump is stopped. S1004: Determine the peak representative amplitude corresponding to the candidate peak based on the height of the protrusion.

[0061] Understandably, the representative amplitude of the wave crest is calculated. The steps are as follows: Conventional amplitude calculation methods often use the "difference between the peak and the trough on one side". This method has a significant drawback: when the pressure waveform is asymmetrical, the selection of the trough on one side will cause a serious directional deviation in the amplitude calculation, and cannot truly reflect the actual degree of peak protrusion.

[0062] This application's embodiment uses the "two-sided trough average method" to calculate A. k The core principle is as follows: using the average pressure value of the adjacent troughs on both sides of the target peak as the waveform baseline, and defining half of the difference between the peak pressure value and the baseline pressure value as the representative amplitude of the peak. This method can effectively reduce the interference of waveform asymmetry on amplitude calculation, and further eliminate the influence of factors such as baseline tilt and local trend terms on amplitude assessment. The calculation results are more consistent with the actual characteristics of the pressure waveform at the fracturing site, and the accuracy is higher.

[0063] (1) Calculate the average pressure value of both troughs: the pressure of the left trough With right-side trough pressure Add them together, then divide by 2 to obtain the average pressure value of both troughs. The calculation formula is as follows: ; (2) Calculate the difference between the peak and the baseline: use the peak pressure value P of the target peak. k Subtract the average pressure value of the two-sided troughs calculated in step 1 to obtain the total bulge height of the peak relative to the baseline.

[0064] (3) Calculate the representative amplitude A k Divide the total bulge height obtained in step 2 by 2 to obtain the representative amplitude A of the wave crest. k Complete the calculation.

[0065] ) / 2

[0066] As can be seen from the above description, the method for identifying water hammer peaks after pump shutdown provided in this application can predetermine the representative amplitude of the peak.

[0067] In one embodiment, see Figure 11 The step of dynamically filtering peak-trough pairs based on a predetermined peak representative amplitude and the asymmetric influence factor to obtain the true peak sequence includes: S1101: The quotient of the peak representative amplitude and the asymmetric influence factor is determined as the peak evaluation value of the peak-trough pair; S1102: Based on the peak evaluation value and the preset false-removal threshold, the peak-valley pairs are initially screened to obtain the first true peak and the doubtful peak. S1103: Selectively remove the questionable peaks to obtain the second true peak; specifically, see Figure 12 The step of selectively removing the questionable peaks to obtain the second true peaks includes: recombining the questionable peaks with adjacent valleys to construct new peak-valley pairs (S1201); calculating the new peak evaluation value corresponding to the new peak-valley pair (S1202); comparing the new peak evaluation value with the adjacent peak evaluation values ​​of adjacent questionable peaks (S1203); and filtering the questionable peaks according to the comparison results to obtain the second true peaks (S1204). S1104: Generate a peak real sequence containing the first real peak and the second real peak based on their respective time series.

[0068] Understandably, the process of generating the true peak sequence is as follows: (1) Calculate the evaluation value This unifies amplitude and symmetry into a single discriminant.

[0069] To achieve a comprehensive evaluation of the authenticity and reliability of candidate peaks, amplitude and symmetry characteristics are unified into a single discriminant index, and an evaluation value is defined. .

[0070]

[0071] The authenticity of the wave crest and its amplitude Proportional to its asymmetric influence factor Inversely proportional.

[0072] Real water hammer crests have large amplitude and high symmetry. The characteristics of small, therefore The value is relatively large; noise spurious peaks are either very small in amplitude or severely distorted, leading to... The value is too small. This ratio model seamlessly integrates the two core physical dimensions of peak identification (energy intensity and morphological regularity), and because... and The units have been adapted through the aforementioned steps and can be directly compared without additional normalization.

[0073] (2) Set dynamic threshold Then, iterative elimination is performed to determine the true peak sequence.

[0074] ① Threshold and Iteration Parameter Preparation: Because the amplitude of water hammer waves decays exponentially with the period, using a single fixed threshold $T$ makes it difficult to simultaneously account for the beginning and end peaks. This invention employs a discrimination strategy combining an adaptive threshold and iterative optimization: a. Set dynamic base threshold The recommended initial value is T=0.05, which can be adjusted according to the noise level at the site. b. Set the maximum number of iterations Recommended value =50 is used to control the iteration termination condition and avoid infinite loops; c. Load the comprehensive evaluation value of all candidate peaks calculated in the previous step. sequence.

[0075] ② Initial threshold determination: Based on comprehensive evaluation value Initial screening is completed using the baseline threshold T: a. If It is directly identified as a true peak and retained; b. If These are marked as suspicious candidate peaks and will not be removed for the time being, but will enter the subsequent iteration competition stage.

[0076] ③ Iterative competitive elimination mechanism: Perform iterative optimization and elimination on all candidate peaks marked as suspicious: a. For each suspected peak, recombine it with the adjacent trough to construct a new structural unit; b. Recalculate the revised valuation based on the new combination. ; c. Compare the magnitudes of the evaluation values ​​before and after the correction: like > This indicates that the original peak-trough pairing is not optimal, and the original peak is determined to be a false peak and is therefore removed. like ≤ The current peak structure is retained, and the process moves on to the next candidate point.

[0077] d. After completing one round of judgment on all suspicious peaks, traverse the remaining candidate points again and repeat the above combination, calculation and comparison process.

[0078] e. Iteration termination condition: No new peaks are removed during continuous iterations; Or the number of iterations reaches the preset limit. .

[0079] Physical Significance and Mechanism Explanation: This iterative competition mechanism simulates the process by which experts repeatedly refine the peak boundaries: among multiple neighboring peak candidates, only the one with the most symmetrical shape and the most significant amplitude can "win" in the iteration, while the rest are eliminated as spurious peaks. This step ensures that the true water hammer peak sequence can still be stably extracted in the peak-dense region or at the end of the decay period, providing a basis for subsequent pressure decay characteristic analysis, casing deformation, and other engineering anomaly diagnosis.

[0080] The specific technical effects of the method provided in this application are as follows: (1) Improve the accuracy of peak identification. By quantifying the asymmetric characteristics of peak-trough, the true peak is effectively distinguished from noise interference, which solves the problem that traditional methods are easily affected by subjective judgment and have large identification errors.

[0081] (2) Enhance the adaptability of the algorithm. A dynamic sensitivity adjustment mechanism is adopted to automatically adjust the recognition threshold according to the attenuation characteristics of water hammer waves. This ensures the complete capture of early large amplitude wave peaks and avoids misjudgment of weak signals in the later stages, enabling the algorithm to adapt to water hammer wave signals under different working conditions.

[0082] (3) Improve data processing efficiency. The automatic identification of water hammer peaks is realized, and the processing speed is significantly improved compared with manual interpretation. It can meet the real-time analysis needs of large-scale fracturing data and provide timely support for on-site decision-making.

[0083] (4) Optimize the evaluation of fracturing effect. By stably identifying the true wave peak, a reliable data basis is provided for subsequent amplitude attenuation analysis and fracture parameter inversion, which helps to accurately evaluate the fracturing effect and guide the optimization of construction plan.

[0084] Based on the same inventive concept, this application also provides a device for identifying water hammer peaks after pump shutdown, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of the device for identifying water hammer peaks after pump shutdown is similar to the method for identifying water hammer peaks after pump shutdown, the implementation of the device can refer to the implementation of the method based on software performance benchmarks, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0085] In one embodiment, see Figure 13In order to automatically eliminate false peaks and stably identify true peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry in the difference between adjacent peaks and troughs, this application provides a device for identifying water hammer peaks after pump shutdown, comprising: The asymmetric parameter determination unit 1301 is used to perform sliding translation analysis on the acquired water hammer wave after pump shutdown to obtain the asymmetric parameters of the wave crest and trough pairs contained in the water hammer wave after pump shutdown. The influence factor determination unit 1302 is used to determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor and the asymmetric parameter. The real sequence generation unit 1303 is used to dynamically filter the peak-valley pairs based on the pre-determined peak representative amplitude and the asymmetric influence factor to obtain the real peak sequence.

[0086] From a hardware perspective, in order to automatically eliminate false peaks and stably identify true peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry in the difference between adjacent peaks and troughs, this application provides an embodiment of an electronic device for implementing all or part of the method for identifying water hammer peaks after pump shutdown. The electronic device specifically includes the following components: The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to transmit information between the water hammer peak identification device after pump shutdown and core business systems, user terminals, and related databases; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the water hammer peak identification method and the water hammer peak identification device after pump shutdown in the embodiments, the contents of which are incorporated herein, and repeated details will not be described again.

[0087] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0088] In practical applications, the identification method for water hammer peaks after pump shutdown can be partially executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0089] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0090] Figure 14 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 14 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 14 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0091] In one embodiment, the function for identifying water hammer peaks after pump shutdown can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: S101: Perform sliding translation analysis on the obtained water hammer wave after pump shutdown to obtain the asymmetric parameters of the wave crest-valley pairs contained in the water hammer wave after pump shutdown. S102: Determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and the asymmetric parameter; S103: Based on the predetermined peak representative amplitude and the asymmetric influence factor, the peak-valley pairs are dynamically filtered to remove false signals, thereby obtaining the true peak sequence.

[0092] As can be seen from the above description, the water hammer wave peak identification method provided in this application can automatically eliminate false wave peaks and stably identify real wave peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry of the difference between adjacent wave peaks and valleys, thereby significantly improving the accuracy and reliability of water hammer wave peak identification.

[0093] In another embodiment, the water hammer peak identification device after pump shutdown can be configured separately from the central processing unit 9100. For example, the water hammer peak identification device after pump shutdown can be configured as a chip connected to the central processing unit 9100, and the function of the water hammer peak identification method after pump shutdown can be realized through the control of the central processing unit.

[0094] like Figure 14 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 14 All components shown; in addition, the electronic device 9600 may also include Figure 14 For components not shown, please refer to existing technology.

[0095] like Figure 14 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0096] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0097] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0098] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0099] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0100] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0101] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0102] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the method for identifying water hammer peaks after pump shutdown, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the method for identifying water hammer peaks after pump shutdown, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: S101: Perform sliding translation analysis on the obtained water hammer wave after pump shutdown to obtain the asymmetric parameters of the wave crest-valley pairs contained in the water hammer wave after pump shutdown. S102: Determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and the asymmetric parameter; S103: Based on the predetermined peak representative amplitude and the asymmetric influence factor, the peak-valley pairs are dynamically filtered to remove false signals, thereby obtaining the true peak sequence.

[0103] As can be seen from the above description, the water hammer wave peak identification method provided in this application can automatically eliminate false wave peaks and stably identify real wave peaks by introducing an asymmetric sensitivity adjustment factor to weight the degree of asymmetry of the difference between adjacent wave peaks and valleys, thereby significantly improving the accuracy and reliability of water hammer wave peak identification.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for identifying water hammer peaks after pump shutdown, characterized in that, include: A sliding translation analysis was performed on the water hammer wave after the pump was stopped to obtain the asymmetric parameters of the peak-valley pairs contained in the water hammer wave after the pump was stopped. The asymmetric influence factor of the peak-trough pair is determined based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and the asymmetric parameter. Based on the predetermined peak representative amplitude and the asymmetric influence factor, the peak-valley pairs are dynamically filtered to remove false peaks, thus obtaining the true peak sequence.

2. The method for identifying water hammer peaks after pump shutdown according to claim 1, characterized in that, The sliding translation analysis of the acquired water hammer wave after pump shutdown yields the asymmetric parameters of the wave crest-trough pairs contained in the water hammer wave after pump shutdown, including: A sliding window detection is performed on the water hammer wave after the pump stops to obtain the peak-valley pairs contained in the water hammer wave after the pump stops. Calculate the pressure difference between the crest of the wave and the adjacent trough on the left and right sides of the wave crest-trough pair; The asymmetric parameters of the peak-trough pair are determined based on the pressure difference.

3. The method for identifying water hammer peaks after pump shutdown according to claim 2, characterized in that, The sliding window detection of the water hammer wave after pump shutdown, to obtain the peak-valley pairs contained in the water hammer wave after pump shutdown, includes: Based on the time sequence traversal of the water hammer wave after the pump is stopped, local maxima that satisfy the first preset condition are searched as candidate peaks, and local minima that satisfy the second preset condition are searched as candidate troughs. Based on the sliding window, the nearest candidate valley to the candidate peak is searched forward and backward on the time axis, respectively, and used as the left and right boundary valleys of the candidate peak. The candidate peak, the left boundary valley, and the right boundary valley are combined to form the peak-valley pair.

4. The method for identifying water hammer peaks after pump shutdown according to claim 1, characterized in that, The step of determining the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor, and asymmetric parameter includes: The asymmetric weights are determined based on the geological characteristics of the fracturing block; Calculate the product of the asymmetric weight and the asymmetric parameter; The sum of the product and the asymmetric sensitivity adjustment factor is determined as the asymmetric influence factor.

5. The method for identifying water hammer peaks after pump shutdown according to claim 4, characterized in that, Also includes: Determine whether the values ​​of the asymmetric weight and the asymmetric sensitivity adjustment factor match the current fracturing conditions; If so, determine whether the asymmetric influence factor is within a reasonable range based on the current fracturing conditions.

6. The method for identifying water hammer peaks after pump shutdown according to claim 3, characterized in that, The step of pre-determining the amplitude represented by the wave peak includes: Obtain the left pressure value corresponding to the left boundary trough and the right pressure value corresponding to the right boundary trough; The average pressure value is determined based on the left pressure value and the right pressure value. The difference between the peak pressure value corresponding to the candidate wave peak and the average pressure value is determined as the bulge height of the candidate wave peak relative to the baseline of the water hammer wave after the pump is stopped. The amplitude represented by the peak corresponding to the candidate peak is determined based on the height of the protrusion.

7. The method for identifying water hammer peaks after pump shutdown according to claim 1, characterized in that, The dynamic filtering of peak-trough pairs based on pre-determined peak representative amplitudes and the asymmetric influence factor to obtain the true peak sequence includes: The quotient of the peak representative amplitude and the asymmetric influence factor is determined as the peak evaluation value of the peak-trough pair. Based on the peak evaluation value and the preset false-removal threshold, the peak-valley pairs are initially screened to obtain the first true peak and the doubtful peak. Selectively remove the questionable peaks to obtain the second true peak; A true peak sequence containing the first true peak and the second true peak is generated based on their respective time series.

8. The method for identifying water hammer peaks after pump shutdown according to claim 7, characterized in that, The selective removal of the questionable peaks to obtain the second true peak includes: The questionable peaks are recombined with adjacent troughs to construct new peak-trough pairs; Calculate the new peak evaluation value corresponding to the new peak and trough pair; The evaluation value of the new peak is compared with the evaluation values ​​of the adjacent peaks of the adjacent questionable peaks; Based on the comparison results, the questionable peaks are filtered to obtain the second true peak.

9. A device for identifying water hammer peaks after pump shutdown, characterized in that, include: An asymmetric parameter determination unit is used to perform sliding translation analysis on the acquired water hammer wave after pump shutdown to obtain the asymmetric parameters of the wave crest-valley pairs contained in the water hammer wave after pump shutdown. The influence factor determination unit is used to determine the asymmetric influence factor of the peak-trough pair based on the predetermined asymmetric weight, asymmetric sensitivity adjustment factor and the asymmetric parameter. The true sequence generation unit is used to dynamically filter the peak-valley pairs based on the pre-determined peak representative amplitude and the asymmetric influence factor to obtain the true peak sequence.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for identifying water hammer peaks after pump shutdown as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for identifying water hammer peaks after pump shutdown as described in any one of claims 1 to 8.

12. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the method for identifying water hammer peaks after pump shutdown as described in any one of claims 1 to 8.

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