Method for identifying temporary plugging effect of crack in real time through high-frequency pressure

Abstract

The invention relates to the technical field of mineral resource development, in particular to a high-frequency pressure real-time crack temporary plugging effect recognition method which comprises the steps that a high-frequency pressure meter collects pressure data in the fracturing period and uploads the pressure data to a cloud end; receiving and storing pressure data during fracturing, and filtering the pressure data; performing cepstrum analysis according to the filtered data, and drawing a cepstrum diagram; according to the drawn cepstrum, clustering the cepstrum to obtain a cepstrum after clustering analysis; and according to the cepstrum diagram after clustering analysis, performing cepstrum analysis before and after temporary plugging to determine a temporary plugging effect. Therefore, the high-frequency pressure crack temporary plugging effect can be accurately identified.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral development, and in particular to a method for real-time identification of temporary crack plugging effects using high-frequency pressure. Background Art

[0002] In oil and petroleum engineering, where fracturing operations are involved, fluids containing materials such as proppants are injected into the formation during hydraulic fracturing operations. The goal is to propel the rock open to form cracks and improve the flow of oil and gas resources. However, sometimes cracks may extend excessively, or certain unwanted branching cracks need to be plugged (temporarily plugged) to optimize the fracturing effect. In this case, a temporary plugging agent is injected into the wellbore. The temporary plugging agent will accumulate and solidify at specific locations (such as the cracks to be plugged) to achieve a plugging effect.

[0003] In large-scale volume fracturing, when there are multiple clusters in a single stage, especially when there are more than 4 clusters in a single stage, temporary plugging measures are generally adopted. Currently, the common temporary plugging measures include plugging agents, knots and plugging balls. Figures 1 to 3 As shown, there are three temporary blocking methods: no blocking agent and rope knot, blocking agent and rope knot.

[0004] However, the applicant has found that the prior art has at least the following problems:

[0005] For operations that implement temporary blocking measures, it is necessary to accurately analyze the effects of the temporary blocking measures in order to determine the effectiveness of the temporary blocking. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to propose a method for real-time identification of temporary plugging effect of cracks using high-frequency pressure, so as to solve the problem of accurately analyzing the effect of temporary plugging measures and thus determining the effect of temporary plugging.

[0007] Based on the above objectives, the present invention provides a method for real-time identification of crack temporary plugging effect using high-frequency pressure, comprising:

[0008] High-frequency pressure gauges collect pressure data during fracturing and upload it to the cloud;

[0009] receiving and storing pressure data during fracturing, and filtering the pressure data;

[0010] Perform cepstrum analysis on the filtered data and draw it into a cepstrum graph;

[0011] According to the drawn cepstrum, the cepstrum is clustered to obtain the cepstrum after cluster analysis;

[0012] According to the cepstral diagram after cluster analysis, cepstral analysis is performed before and after temporary plugging to determine the temporary plugging effect.

[0013] Optionally, receiving and storing pressure data during fracturing and filtering the pressure data includes:

[0014] The surface pressure signal y(t) is expressed as the sum of the useful signal x(t) and the noise component e(t):

[0015] y(t)=x(t)+e(t)

[0016] In the preprocessing stage, the surface pressure signal y(t) is filtered to keep the useful signal x(t) within its bandwidth and remove the noise e(t).

[0017] Optionally, the noise component e(t) comprises broadband electronic noise, narrow harmonic peaks caused by hydraulic pump strokes, and a zero-frequency trend component associated with slow process pressure changes;

[0018] The bandwidth of the effective signal x(t) is limited to a frequency range of 0.1 Hz to 15-20 Hz, depending on the pump stop time and the attenuation of the hydraulic signal in the wellbore.

[0019] Optionally, filtering the surface pressure signal y(t) so that the useful signal x(t) remains within its bandwidth and removing the noise e(t) includes:

[0020] Perform Fourier transform on the input signal x(t) to obtain its frequency domain distribution (spectrum):

[0021]

[0022] Assume that the filter transfer function is H(ω), let Y(ω) = H(ω)X(ω), and get the filtered spectrum;

[0023] Perform inverse Fourier transform on Y(ω) to obtain the filtered signal:

[0024]

[0025] For the classical filtering method, the difference between different filters lies in the different transfer functions H(ω). For the Butterworth filter, its transfer function is expressed as:

[0026]

[0027] Where: c is the cutoff frequency, n is the filter order;

[0028] The ideal low-pass filter transfer function is:

[0029]

[0030] Optionally, performing cepstrum analysis on the filtered data and drawing the cepstrum into a cepstrum graph includes:

[0031] The filtered useful signal x(t) is expressed as the convolution of the source pressure pulse s(t) excited by the water hammer and the wellbore reflectivity w(t):

[0032] x(t)=s(t)*w(t)(2)

[0033] The wellbore reflectivity w(t) is an unknown parameter in the convolution equation (2):

[0034]

[0035] Where 0<|a|<1, δ(t) is the unit pulse:

[0036]

[0037] The reflection coefficients are determined by the change in hydraulic impedance at the reflecting boundary; they are negative for hydraulic fractures and positive for artificial well bottoms or bridge plugs;

[0038] The inverse Fourier transform of the logarithm of the signal's estimated spectrum is the result of the inverse Fourier transform:

[0039]

[0040] In (4), the useful signal x(t) is divided into two parts in the cepstrum domain:

[0041]

[0042] in and is the cepstrum of the source signal and the bridge plug reflectivity. The cepstrum of the reflectivity in the wellbore defined in equation (3)

[0043]

[0044] Fracturing is a pressure pulse caused by the shut-off of a pump It has a smooth spectrum, and its inverse spectrum is located near the low frequency value. Separate it out, There are non-zero peaks only at (T, 2T, 3T...) cepstrum.

[0045] Optionally, performing cepstrum analysis on the filtered data and drawing the cepstrum into a cepstrum graph includes:

[0046] The strong negative peaks on the cepstrum are judged to be the wellbore pressure oscillations caused by the tube wave reflection of the hydraulic fracture at the corresponding frequency; the strong positive peaks on the cepstrum are judged to be the wellbore pressure oscillations caused by the tube wave reflection due to the wellbore restriction.

[0047] Optionally, clustering the cepstral graph according to the drawn cepstral graph to obtain the cepstral graph after cluster analysis includes:

[0048] Based on multiple observation indicators of a batch of samples, we find some statistics that can measure the similarity between samples or indicators, and use these statistics as the basis for classification: aggregate some samples with a greater degree of similarity into one category, and aggregate another part of samples with a greater degree of similarity into another category, until all samples are aggregated.

[0049] Beneficial effects of the present invention: The present invention provides a method for real-time identification of the temporary plugging effect of cracks using high-frequency pressure. Since microseismicity is used to capture seismic events after rock fracture, microseismicity cannot monitor the temporary plugging of cracks; distributed optical fibers can judge temporary plugging based on the liquid inflow of each cluster before and after temporary plugging, but using distributed optical fibers to judge the liquid inflow of each cluster requires pre-buried optical fibers during drilling, which is not only costly, but also easy to damage the pre-buried optical fibers during the perforation period before fracturing, resulting in the inability to monitor during fracturing; and tracer wells are only suitable for monitoring during flowback and cannot be applied to monitoring during fracturing. This method collects pressure data during fracturing through a high-frequency pressure gauge and uploads it to the cloud; receives and stores pressure data during fracturing, and filters the pressure data; performs inverse spectrum analysis on the filtered data and draws it into a inverse spectrum graph; clusters the inverse spectrum graph based on the drawn inverse spectrum graph to obtain a inverse spectrum graph after cluster analysis; and performs inverse spectrum analysis on the inverse spectrum graph after cluster analysis before and after temporary plugging to determine the temporary plugging effect. The cost is low and the judgment method is simple and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 This is a schematic diagram of a temporary blocking measure without a blocking agent and a rope ball in the prior art;

[0052] Figure 2 This is a schematic diagram of a temporary blocking measure using a blocking agent in the prior art;

[0053] Figure 3 This is a schematic diagram of a temporary blocking measure using a knot in the prior art;

[0054] Figure 4 This is a flow chart of a method for real-time identification of temporary crack plugging effect using high-frequency pressure according to the present invention;

[0055] Figure 5This is a homomorphic graph of a method for real-time identification of temporary crack plugging effect using high-frequency pressure according to the present invention;

[0056] Figure 6 This is a picture obtained after cluster analysis of the inverted spectrum using a method for real-time identification of temporary plugging effects of cracks using high-frequency pressure according to the present invention. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0058] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0059] like Figures 1 to 5 As shown, a specific embodiment of the present invention provides a method for real-time identification of temporary crack plugging effect using high-frequency pressure, comprising:

[0060] S101: The high-frequency pressure meter collects pressure data during fracturing and uploads it to the cloud;

[0061] S201: receiving and storing pressure data during fracturing, and filtering the pressure data;

[0062] S301: Perform cepstrum analysis on the filtered data and draw it into a cepstrum graph;

[0063] S401: clustering the cepstral graph according to the drawn cepstral graph to obtain a cepstral graph after cluster analysis;

[0064] S501: Based on the cepstrum graph after cluster analysis, a cepstrum analysis is performed before and after temporary blocking to determine the temporary blocking effect.

[0065] In some optional specific embodiments, receiving and storing pressure data during fracturing and filtering the pressure data includes:

[0066] The surface pressure signal y(t) is expressed as the sum of the useful signal x(t) and the noise component e(t):

[0067] y(t)=x(t)+e(t)

[0068] In the preprocessing stage, the surface pressure signal y(t) is filtered to keep the useful signal x(t) within its bandwidth and remove the noise e(t).

[0069] The noise component e(t) consists of broadband electronic noise, narrow harmonic peaks caused by the hydraulic pump stroke, and a zero-frequency trend component associated with slow process pressure changes:

[0070] The bandwidth of the useful signal x(t) is limited to a frequency range of approximately 0.1 Hz to 15-20 Hz, which depends on the pump off time and the attenuation of the hydraulic signal in the wellbore:

[0071] In some optional specific embodiments, filtering the surface pressure signal y(t) to keep the useful signal x(t) within its bandwidth and remove the noise e(t) includes:

[0072] Perform Fourier transform on the input signal x(t) to obtain its frequency domain distribution (spectrum):

[0073]

[0074] Assume that the filter transfer function is H(ω), let Y(ω) = H(ω)X(ω), and get the filtered spectrum;

[0075] Then perform inverse Fourier transform on Y(ω) to obtain the filtered signal:

[0076]

[0077] For the classical filtering method, the difference between different filters lies in the different transfer functions H(ω). For the Butterworth filter, its transfer function is expressed as:

[0078]

[0079] Where: c is the cutoff frequency, n is the filter order;

[0080] The FIR filter is an approximation algorithm for the classic ideal low-pass filter. The transfer function of the ideal low-pass filter is:

[0081]

[0082] Advantages of FIR filter: ① The entire curve is smooth and without fluctuations; ② No feedback operation, and the error is small.

[0083] Limitations of the FIR filtering method: The filter order is relatively high; there is generally no analytical design formula, and it must be completed through computer-aided design.

[0084] In some optional specific embodiments, performing cepstrum analysis on the filtered data and drawing the cepstrum into a cepstrum graph includes:

[0085] The filtered useful signal x(t) is expressed as the convolution of the source pressure pulse s(t) excited by the water hammer and the wellbore reflectivity w(t):

[0086] x(t)=s(t)*w(t)(2)

[0087] The wellbore reflectivity w(t) is an unknown parameter in the convolution equation (2). It is usually a sequence of attenuated minimum phase echo pulses delayed by the pressure pulse oscillation period t. The pulse amplitude a depends on the corresponding reflection coefficient and the attenuation of the wave in the wellbore:

[0088]

[0089] Where 0<|a|<1, δ(t) is the unit pulse:

[0090]

[0091] Reflection coefficients are determined by the change in hydraulic impedance at reflecting boundaries. They are negative for hydraulic fractures and positive for artificial bottom holes or bridge plugs.

[0092] To estimate the unknown wellbore reflectivity w(t) from the convolution equation (Eq. 2), the cepstrum algorithm is applied. Cepstrum is a nonlinear signal processing technique. It was originally used to describe seismic echoes from earthquakes and bomb blasts. They also introduced a term: "cepstrum" is the first four letters of "spectrum" reversed. Similarly, the independent variable τ is also called "quantum frequency", which has a time dimension;

[0093] The inverse Fourier transform of the logarithm of the signal's estimated spectrum is the result of the inverse Fourier transform:

[0094]

[0095] In (4), the presence of the logarithm allows the useful signal x(t) to be split into two parts in the cepstrum domain:

[0096]

[0097] in and is the cepstrum of the source signal and the bridge plug reflectivity. The cepstrum of the reflectivity in the wellbore defined in equation (3) is a train of delayed echoes and will also be a train of delayed echoes:

[0098]

[0099] Fracturing is a pressure pulse caused by the shut-off of a pump It has a smooth spectrum, so its inverse spectrum is located near the low frequency value, which can be easily obtained from the borehole reflectivity response in the inverse spectrum domain. Separate it out,

[0100] There are non-zero peaks only at (T, 2T, 3T...) cepstrum.

[0101] Based on this method, a homomorphic spectrum is calculated; a homomorphic spectrum is a visual representation of the time-varying cepstrum of pressure data. Wellbore pressure oscillations caused by tube wave reflections from hydraulic fractures appear as strong negative peaks on the cepstrum at the corresponding frequencies. Similarly, wellbore pressure oscillations caused by tube wave reflections due to wellbore confinement produce strong positive peaks on the cepstrum.

[0102] In some optional embodiments, because the collected water hammer waves contain various noises, there are varying degrees of similarity (closeness—measured by the distance between samples) at the cracks and bridge plugs. Therefore, based on multiple observed indicators of a batch of samples, specific statistics are identified that can measure the similarity between samples or indicators, and these statistics are used as the basis for classification. Some samples (or indicators) with high similarity are clustered into one category, and other samples (or indicators) with high similarity are clustered into another category, until all samples (or indicators) are clustered.

[0103] In some specific embodiments, as shown in the figure:

[0104] During fracturing, high-frequency pressure fluctuation information was collected at the wellhead, and the crack initiation situation was analyzed based on the high-frequency pressure wave cepstrum:

[0105] Before temporary plugging, there were three strong liquid inflow points: located at well depths of 4507m, 4497m, and 4479m (bridge plug position 4522m);

[0106] There are 6 clusters of perforations, 3 clusters of fractures are strongly opened, corresponding to the 2nd, 3rd and 5th clusters of fractures, and 3 clusters of fractures are weakly opened, corresponding to the 1st, 4th and 6th clusters of fractures;

[0107] As shown in the figure: During fracturing, high-frequency pressure fluctuation information was collected at the wellhead, and the crack initiation situation was analyzed based on the high-frequency pressure wave cepstrum:

[0108] After temporary plugging, there are four strong liquid inflow points: located at well depths of 4507m, 4497m, 4479m, and 4470m (bridge plug position 4522m);

[0109] Six clusters of perforations were perforated, and four clusters of fractures were strongly opened, corresponding to the second, third, fifth, and sixth clusters of fractures, and two clusters of fractures were weakly opened, corresponding to the first and fourth clusters of fractures, respectively.

[0110] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0111] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time identification of temporary crack plugging effect using high-frequency pressure, characterized in that: include: High-frequency pressure gauges collect pressure data during fracturing and upload it to the cloud; receiving and storing pressure data during fracturing, and filtering the pressure data; Perform cepstrum analysis on the filtered data and draw it into a cepstrum graph; According to the drawn cepstrum, the cepstrum is clustered to obtain the cepstrum after cluster analysis; According to the cepstral diagram after cluster analysis, cepstral analysis is performed before and after temporary plugging to determine the temporary plugging effect.

2. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 1 is characterized in that: The receiving and storing pressure data during the fracturing period and filtering the pressure data includes: The surface pressure signal y(t) is expressed as the sum of the useful signal x(t) and the noise component e(t): y(t)=x(t)+e(t) In the preprocessing stage, the surface pressure signal y(t) is filtered to keep the useful signal x(t) within its bandwidth and remove the noise e(t).

3. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 1, characterized in that: The noise component e(t) contains broadband electronic noise, narrow harmonic peaks caused by hydraulic pump strokes, and a zero-frequency trend component associated with slow process pressure changes; The bandwidth of the effective signal x(t) is limited to a frequency range of 0.1 Hz to 15-20 Hz, depending on the pump stop time and the attenuation of the hydraulic signal in the wellbore.

4. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 2, characterized in that: The filtering of the surface pressure signal y(t) to keep the useful signal x(t) within its bandwidth and remove the noise e(t) includes: Perform Fourier transform on the input signal x(t) to obtain its frequency domain distribution (spectrum): Assume that the filter transfer function is H(ω), let Y(ω) = H(ω)X(ω), and get the filtered spectrum; Perform inverse Fourier transform on Y(ω) to obtain the filtered signal: For the classical filtering method, the difference between different filters lies in the different transfer functions H(ω). For the Butterworth filter, its transfer function is expressed as: Where: c is the cutoff frequency, n is the filter order; The ideal low-pass filter transfer function is:

5. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 1 is characterized in that: The performing of cepstrum analysis on the filtered data and drawing the cepstrum into a cepstrum graph comprises: The filtered useful signal x(t) is expressed as the convolution of the source pressure pulse s(t) excited by the water hammer and the wellbore reflectivity w(t): x(t)=s(t)*w(t) (2) The wellbore reflectivity w(t) is an unknown parameter in the convolution equation (2): Where 0<|a|<1, δ(t) is the unit pulse: The reflection coefficients are determined by the change in hydraulic impedance at the reflecting boundary; they are negative for hydraulic fractures and positive for artificial well bottoms or bridge plugs; The inverse Fourier transform of the logarithm of the signal's estimated spectrum is the result of the inverse Fourier transform: In (4), the useful signal x(t) is divided into two parts in the cepstrum domain: in and is the cepstrum of the source signal and the bridge plug reflectivity. The cepstrum of the reflectivity in the wellbore defined in equation (3) Fracturing is a pressure pulse caused by the shut-off of a pump It has a smooth spectrum, and its inverse spectrum is located near the low frequency value. Separate it out, There are non-zero peaks only at (T, 2T, 3T...) cepstrum.

6. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 5, characterized in that: The performing of cepstrum analysis on the filtered data and drawing the cepstrum into a cepstrum graph comprises: The strong negative peaks on the cepstrum are judged to be the wellbore pressure oscillations caused by the tube wave reflection of the hydraulic fracture at the corresponding frequency; the strong positive peaks on the cepstrum are judged to be the wellbore pressure oscillations caused by the tube wave reflection due to the wellbore restriction.

7. The method for real-time identification of temporary crack plugging effect using high-frequency pressure according to claim 1, characterized in that: The method of clustering the cepstral graph according to the drawn cepstral graph to obtain the cepstral graph after cluster analysis includes: Based on multiple observation indicators of a batch of samples, we find some statistics that can measure the similarity between samples or indicators, and use these statistics as the basis for classification: aggregate some samples with a greater degree of similarity into one category, and aggregate another part of samples with a greater degree of similarity into another category, until all samples are aggregated.

Images