CN116856895B - Edge calculation data processing method based on high-frequency pressure crack monitoring - Google Patents
Edge calculation data processing method based on high-frequency pressure crack monitoring Download PDFInfo
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Abstract
The invention relates to the technical field of oil reservoir engineering, in particular to a method for processing edge calculation data based on high-frequency pressure crack monitoring, which comprises the steps of installing acquisition equipment and carrying out data acquisition and debugging; data acquisition is carried out, and cloud transmission is carried out on the acquired data; receiving data transmitted by a cloud end, and carrying out data analysis; and (5) according to data analysis, detecting perforation quality, bridge plug leakage and crack initiation identification. The method provides a simple and practical technology for the fracturing effect evaluation, and can detect and calculate the perforation times of the perforation clusters; detecting and confirming whether the bridge plug leaks; and determining the crack opening position and the like according to the liquid inlet point position, thereby providing technical support for development of dense oil gas, shale gas and coalbed methane in the oil field.
Description
Technical Field
The invention relates to the technical field of oil reservoir engineering, in particular to a method for processing edge calculation data based on high-frequency pressure fracture monitoring.
Background
Because large-scale fracturing requires tens of thousands of square liquids and thousands of tons of sand, the cost of each well is tens of millions or even up to hundreds of millions of primordial notes, real-time evaluation of fracturing is helpful for knowing whether each event of fracturing is successful, such as whether perforation is exploded or not, whether bridge plugs are lost or not, whether cracks are opened or not, and the like, so that technical guarantee is improved for improving the success rate of fracturing.
There are various methods for pressure real-time monitoring, wherein microseism monitoring is more common, and the principle is as follows: microseism can occur along the edges of the pressure rise zone due to the rise in formation pressure during fracturing, according to the mole-coulomb criterion. In the hydraulic fracturing process, the formation fracture or crack extension and expansion generate micro-seismic waves, the micro-seismic waves propagate around in the formation in the form of spherical waves, the micro-seismic waves are monitored, and the position of a seismic source is determined, so that the crack profile can be determined.
Microseism monitoring is divided into two modes, namely surface monitoring and well monitoring. The surface monitoring is to arrange a plurality of receiving points on the surface around a monitoring target area (such as a fracturing well) for microseism monitoring. In the well monitoring, receiving arrangement is arranged in one or more adjacent wells around the monitored target area to perform microseism monitoring. Due to formation absorption, complicated propagation paths, etc.; compared with the well monitoring, the data obtained by the ground monitoring has the defects of less microseismic events, low signal to noise ratio, poor inversion reliability and the like.
Microseism monitoring mainly comprises several key steps of data acquisition, data processing (seismic source imaging), fine inversion and the like. Receiving minute seismic events generated or induced by production activities by placing a detector array in the well or at the surface; inversion of the events is used for solving parameters such as microseism focus positions; finally, the production activity is monitored or guided by these parameters.
From the microseism principle and the crack monitoring mode, the following steps are as follows: in the prior art, whether monitoring is performed at the ground or in a well, a large amount of hardware equipment is required to be arranged, data are further processed after being obtained, and finally fracturing monitoring is performed through fine inversion. Therefore, microseism monitoring is large in investment and complex in construction, and the monitored parameters only include the height, the length and the azimuth of the crack.
Disclosure of Invention
In view of the above, the invention aims to provide a method for processing edge calculation data based on high-frequency pressure crack monitoring, which aims to solve the problems of large investment and complex construction of microseism monitoring, and the monitored parameters only comprise the height, length and azimuth of the crack.
Based on the above object, the present invention provides a method for processing edge calculation data based on high-frequency pressure crack monitoring, comprising: installing acquisition equipment and performing data acquisition and debugging; data acquisition is carried out, and cloud transmission is carried out on the acquired data; receiving data transmitted by a cloud end, and carrying out data analysis; and (5) according to data analysis, detecting perforation quality, bridge plug leakage and crack initiation identification.
Optionally, the installing the collecting device and performing data collection and debugging includes: a high-frequency pressure gauge is arranged on the wellhead four-way valve and is in direct contact with fracturing fluid in a pipeline; the power supply of the high-frequency pressure gauge is maintained by installing a battery; the signal cable is connected with the high-frequency pressure gauge, the data acquisition equipment and the computer to finish the installation of the equipment; the system is powered on, a computer is turned on, acquisition software is operated, the pressure in the fracturing pipeline is debugged and measured, and the pressure is acquired to the computer through a signal cable and data acquisition equipment.
Optionally, the parameters of the high-frequency manometer are: withstand voltage 140MPa and acid resistance 20%; pressure range: 0-120 MPa; pressure resolution: 0.1%o MPa; a power supply section: 24VDC, outputting 0-10 VDC; a connecting part: m18×1.5 cone sealing; and (3) electric appliance connection: waterproof aviation plug-in connection; signal cable: PVC polyvinyl fluoride shielded cable, waterproof aviation plug-in type, BNC data acquisition adapter wire terminal and 4 cable adapters; the high-frequency pressure gauge is electrically connected with the HC7804A data collector and the notebook computer, and the sampling frequency is set to be 1000HZ.
Optionally, the performing data acquisition includes: the normal collection of a high-frequency pressure gauge is kept before fracturing, the pressure in a pipeline is measured at a millisecond level during sampling, and data are directly transmitted to a computer through an output line and a collection card to complete data collection; and storing by adopting an HTF-5 data format.
Optionally, the cloud transmission of the collected data includes that the collected high-frequency pressure data is sent to an interpretation center through a cloud; and (3) through establishing TCP long-state connection, sending the data to a cloud server in the form of an http message, and storing the database.
Optionally, the receiving the data transmitted by the cloud includes: the cloud data reception adopts TCP long state connection, and the fastest update interval is set to be 1 second.
Optionally, the receiving the data transmitted by the cloud includes high-frequency pressure data processing:
Low pass filtering of data: noise treatment is carried out by adopting low-pass FIR filtering; assuming that the input signal of the FIR low-pass filter is a convolution of x (N) and b (N), where the response of unit samples b (N) is an n+1-point finite length sequence, N is the filter order, and thus the output is:
；
Wherein y (n) is the output through the filter; x (n) is an input signal; b (n) is the response of the unit sample;
The transfer function B (z) of the filter can be expressed as:
。
optionally, the performing data analysis includes:
Spectrum analysis of the collected data;
cepstrum analysis of the collected data:
Assuming that S (N) is a high frequency pressure signal of length N, according to convolution theory, S (N) is a convolution of wavelet x (N) and reflection coefficient h (N):
（1.1）；
Wherein: s (n) represents the original pressure signal; x (n) represents the signal generated by reflection at the crack; x represents a convolution symbol; n represents a time domain sampling point; h (n) is an unknown parameter in the convolution equation, the sampling point n is rewritten as a continuous function, typically a sequence of attenuated minimum phase echo pulses delayed by the pressure pulse oscillation period t, the pulse amplitude a being dependent on the corresponding reflection coefficient and attenuation of the wave in the wellbore;
（1.2）；
0< |a| <1, delta (t) is the unit pulse;
（1.3）；
For the water hammer, the ratio of the water hammer pressure to the oscillating flow is called the impedance, which is a complex number defined by amplitude, frequency and phase:
（1.4）；
Wherein: z-impedance in s/m 2; h-water head, the unit is m; -flow in m 3/s; t-time, in s; omega-angular frequency in rad/s; phi-the phase angle of the water head and the flow, the unit is rad; g-gravitational acceleration in m/s 2;
The characteristic impedance of equation (1.4) represents the situation that the pressure and the flow movement direction are the same, and the characteristic impedance can be simplified by assuming that the friction in the sleeve in which the pressure and the flow oscillate is constant and the phase angle is equal to 0 or pi/omega: z c = （1.5）；
Wherein: c is the wave velocity, and the unit is m/s; a is the area corresponding to the cross section of the pressure pipeline, and the unit is m 2;
estimating unknown shaft reflectivity x (t) from a convolution equation (1.1), and applying a cepstrum algorithm, wherein the cepstrum calculation process is as follows: first fourier transforming the above signals:
（1.6）；
Wherein: Is a fourier transform;
carrying out logarithmic transformation on the product signal to obtain an addition signal;
（1.7）；
(1.7) transforming the product signal into an addition signal, and reconverting the signal back into the time domain:
（1.8）；
Wherein: Is an inverse fourier transform.
Optionally: the perforation quality, bridge plug leakage and crack initiation identification are checked according to the data analysis, and the method comprises the following steps:
Determination of wave velocity: the wave velocity expression in the wellbore is:
，/>（1.9）；
Wherein: k eff is the effective bulk modulus in Pa; k is the fluid bulk modulus, pa; ρ is the fluid density; g is the shear modulus of the surrounding stratum, and the unit is Pa; e is the sleeve shear modulus in Pa; d is the diameter of the sleeve, and the unit is m; epsilon is the wall thickness of the sleeve wall and the unit is m;
High frequency manometer perforation diagnosis: the wave speed is determined by filtering the original data and then determining the wave speed by the time between the wave crest and the wave trough generated by perforation: c=2l/t s (1.10);
wherein: l is the position of a perforation hole, and the unit is m; t s is the time interval of two adjacent reflected waves after perforation explosion, and the unit is sec;
determining the bridge plug and crack initiation position: performing cepstrum analysis on the instantaneous data of the fracturing pump-stopping pressure to determine the bridge plug and the crack starting position; the reflection coefficient is defined as follows: （1.11）；
Wherein: z 1 is the impedance of the water hammer before the diameter of the shaft pipeline is not changed, the unit is s/m 2;Z2 is the impedance of the water hammer pipeline after the shape or the area of the water hammer pipeline is changed, and the unit is s/m 2;
The invention has the beneficial effects that: a method for processing edge calculation data based on high-frequency pressure crack monitoring includes the steps of installing acquisition equipment and carrying out data acquisition and debugging; data acquisition is carried out, and cloud transmission is carried out on the acquired data; receiving data transmitted by a cloud end, and carrying out data analysis; and (5) according to data analysis, detecting perforation quality, bridge plug leakage and crack initiation identification. The method provides a simple and practical technology for the fracturing effect evaluation, and can detect and calculate the perforation times of the perforation clusters; detecting and confirming whether the bridge plug leaks; and determining the crack opening position and the like according to the liquid inlet point position, thereby providing technical support for development of dense oil gas, shale gas and coalbed methane in the oil field. The invention has two advantages: the operation is simple and convenient: only one pressure gauge is required to be installed at the wellhead, deployment is simple and easy to implement, only a ground acquisition and interpretation algorithm is relied on, no change or additional steps are required, and the utilized event is a part of fracturing operation; the method can guide the fracturing in real time, and not only evaluate the fracturing effect, but also correct the deviation in the fracturing in time by the provided perforation times, bridge plug conditions, crack initiation positions and the like.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for processing edge calculation data based on high-frequency pressure crack monitoring according to an embodiment of the invention;
FIG. 2 shows a wellhead pressure high-frequency data collected in real time based on a high-frequency pressure crack monitoring edge calculation data processing method according to an embodiment of the present invention;
FIG. 3 is an original acquisition curve of a method for computing data based on high-frequency pressure crack monitoring edges according to an embodiment of the present invention;
FIG. 4 is a graph of a low-pass filtered curve of a method for computing data based on high-frequency pressure crack monitoring edges according to an embodiment of the present invention;
FIG. 5 is a graph of the high frequency pressure before (a) and after (b) filtration during perforation based on a high frequency pressure fracture monitoring edge calculation data processing method according to an embodiment of the present invention;
FIG. 6 is an enlarged view of the high frequency pressure during the hole period before filtering (a) and after filtering (b) based on the high frequency pressure crack monitoring edge calculation data processing method according to the embodiment of the present invention;
FIG. 7 is a pressure pulse cepstrum analysis spectrogram of a fracturing pump based on a high-frequency pressure crack monitoring edge calculation data processing method according to an embodiment of the invention;
FIG. 8 is high frequency wellhead pressure data collected from a well of example 1 of the present invention at 2022, 3, and 10 days;
FIG. 9 is a plot of wellhead pressure during perforation according to example 1 of the present invention;
FIG. 10 is a partial magnified data of a first perforation segment according to example 1 of the present invention;
FIG. 11 is a cepstrum analysis of the fracturing pump down (0:52:57) data of example 1 of the present invention;
fig. 12 is a cepstrum homomorphic spectrum of the present well example of example 1 of the present invention showing 3 cracks.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, an embodiment of the present invention provides a method for processing edge calculation data based on high-frequency pressure crack monitoring, including:
Step 101, installing acquisition equipment and performing data acquisition and debugging;
102, data acquisition is carried out, and cloud transmission is carried out on the acquired data;
step 103, receiving data transmitted by a cloud end, and carrying out data analysis;
and 104, analyzing and checking perforation quality, bridge plug leakage and crack initiation identification according to the data.
In some alternative implementations, the installing the acquisition device and performing the data acquisition commissioning includes: a high-frequency pressure gauge is arranged on the wellhead four-way valve and is in direct contact with fracturing fluid in a pipeline; the power supply of the high-frequency pressure gauge is maintained by installing a battery; the signal cable is connected with the high-frequency pressure gauge, the data acquisition equipment and the computer to finish the installation of the equipment. The system is powered on, the computer is turned on, the acquisition software is operated, the pressure in the fracturing pipeline is debugged and measured, the pressure is acquired to the computer through the signal cable and the data acquisition equipment, and the normal operation of the acquisition and transmission of the pressure gauge and the acquisition software of the computer is ensured.
In some alternative embodiments, the parameters of the high frequency manometer are: withstand voltage 140MPa and acid resistance 20%; pressure range: 0-120 MPa; pressure resolution: 0.1 per mill MPa. A power supply section: 24VDC, outputting 0-10 VDC; a connecting part: m18×1.5 cone sealing; and (3) electric appliance connection: waterproof aviation plug-in connection. Signal cable: PVC polyvinyl fluoride shielded cable, waterproof aviation plug-in type, BNC data acquisition adapter wire terminal and 4 cable adapters; the high-frequency pressure gauge is electrically connected with the HC7804A data collector and the notebook computer, and the sampling frequency is set to be 1000HZ.
In some alternative embodiments, as shown in fig. 2, the performing data collection includes: the normal collection of a high-frequency pressure gauge is kept before fracturing, the pressure in a pipeline is measured at a millisecond level during sampling, and data are directly transmitted to a computer through an output line and a collection card to complete data collection; the high-frequency manometer has large data volume, and HTF (HydrographicTransferFormat) -5 data format is adopted for data calling and sharing.
In some optional embodiments, the cloud transmitting of the collected data includes the collected high-frequency pressure data being required to be sent to an interpretation center through the cloud. This process includes data upload: after the pressure signal is collected by the local server, the pressure signal is distributed to different systems through data, and the system comprises: the local data analysis system is responsible for storing local data and analyzing real-time data; and the cloud service system is used for transmitting data to the cloud server in an http message form by establishing TCP long-state connection and storing the data in a database. Benefits of long state connections: a persistent connection is established without establishing the connection and interrupting the connection every time, so that the load of a server end is reduced, the time of spending is reduced, and the http request and the response can be ended more quickly.
In some optional embodiments, the receiving the data transmitted by the cloud includes: because the server is loaded more during the high-frequency pressure data acquisition, especially during the multi-path acquisition, the cloud data reception also adopts TCP long-state connection, and the fastest update interval is set to be 1 second. The real-time visualization method not only can ensure the real-time visualization requirement, but also can reduce the cost of part of the servers.
In some optional embodiments, the receiving the data transmitted by the cloud includes: high frequency pressure data processing, is exclusively used in fracturing field service, and fracturing field noise is many, and after the pressure fluctuation data of actual measurement needs noise abatement, carries out spectral analysis etc. again, high frequency pressure data processing includes:
Low pass filtering of data: whether perforation, bridge plug setting, and fracture initiation occur at a location in the wellbore remote from the high frequency manometer, the sensor of the high frequency manometer receives a reflected signal, and thus these signals are low frequency signals. The FIR filter is considered to have good linear phase characteristics, and low-pass FIR filtering is adopted for noise processing. Assuming that the input signal of the FIR low-pass filter is a convolution of x (N) and b (N), where the response of unit samples b (N) is an n+1-point finite length sequence, N is the filter order, and thus the output is: 。
Wherein y (n) is the output through the filter; x (n) is an input signal; b (n) is the response of the unit sample.
The transfer function B (z) of the filter can be expressed as:
。
the Matlab programming language is adopted, and the calculation codes are as follows:
function[b,a]=Buttord_Filter(Wp,Ws,Rp,Rs,N,Fs)
% design of butterworth low pass filter
% Filter design parameters (calculation of normalized angular frequency)
Wp=wp/(Fs/2);% passband cut-off frequency
Ws=Ws/(Fs/2);% stopband onset frequency
% Calculation filter minimum order n and 3dB cut-off frequency Wn
[n,Wn]=buttord(Wp,Ws,Rp,Rs);
% Calculation of the numerator and denominator polynomial coefficients of the system function H (z)
[b,a]=butter(n,Wn);
% Calculation of the amplitude-frequency response of the system function H (z): freqz (b, a), calculating the number of points, sampling rate;
[H,F]=freqz(b,a,N,Fs);
% calculate the phase of the filter
pha=angle(H)×180/pi;
Amplitude-frequency characteristic of% low-pass filter
figure;
subplot(2,1,1);plot(F,20×log10(abs(H)));
Xlabel ('frequency (Hz)'), ylabel ('amplitude (dB)');
axistight;
gridon;
subplot(2,1,2);plot(F,pha);
xlabel ('frequency (Hz)'), ylabel ('phase');
axistight;
gridon;
title ('low pass filter');
end
Fig. 3 and 4 show the comparison of the collected original data curve and the filtered curve, the original collected data has a large amount of noise, and the noise is eliminated after the filtering treatment, but the inherent pump-stopping wave-beating curve form of the fracturing curve still exists.
In some alternative embodiments, the performing data analysis includes:
Spectral analysis of the collected data: pressure data acquired by the high-frequency pressure gauge during fracturing is a time sequence, the vertical axis of the data is a wellhead pressure value, the vertical axis of the data is equivalent to amplitude, the horizontal axis of the data is time, and the real-time data is subjected to spectrum analysis, so that the spectrum data can be obtained by directly using the fft or pwelch function in Matlab.
Cepstrum analysis of the collected data: during large hydraulic fracturing, a number of events occur, such as perforation, bridge plug setting, fracture initiation and pump shut-in operations, all of which are activated and then transmitted to the wellhead through the aqueous medium in the wellbore to be monitored by the high frequency manometer. Taking the water hammer generated during pump shut down as an example, the pump shut down action will excite a source pressure pulse S (t), which is a sparse wave that will propagate in the pipeline aqueous medium and reflect when a crack or bridge plug wave is encountered, the reflected wave being monitored by the wellhead high frequency manometer. According to the related research, the acoustic model in the processing of the voice signal is similar to the fracturing water hammer wave convolution model, and the fracturing reflection coefficient information can be extracted by referring to the processing method of the voice signal.
Assuming that S (N) is a high frequency pressure signal of length N, according to convolution theory, S (N) is a convolution of wavelet x (N) and reflection coefficient h (N):
（1.1）；
Wherein: s (n) represents the original pressure signal; x (n) represents the signal generated by reflection at the crack; x represents a convolution symbol; n represents a time domain sampling point; h (n) is an unknown parameter in the convolution equation, the sampling point n is rewritten as a continuous function, typically a sequence of attenuated minimum phase echo pulses delayed by the pressure pulse oscillation period t, the pulse amplitude a being dependent on the corresponding reflection coefficient and attenuation of the wave in the wellbore;
（1.2）；
0< |a| <1, delta (t) is the unit pulse;
（1.3）；
For the water hammer, the ratio of the water hammer pressure to the oscillating flow is called the impedance, which is a complex number defined by amplitude, frequency and phase:
（1.4）；
Wherein: z-impedance in s/m 2; h-water head, the unit is m; -flow in m 3/s; t-time, in s; omega-angular frequency in rad/s; phi-the phase angle of the water head and the flow, the unit is rad; g-gravitational acceleration in m/s 2;
The characteristic impedance of equation (1.4) represents the situation that the pressure and the flow movement direction are the same, and the characteristic impedance can be simplified by assuming that the friction in the sleeve in which the pressure and the flow oscillate is constant and the phase angle is equal to 0 or pi/omega: z c = （1.5）；
Wherein: c is the wave velocity, and the unit is m/s; a is the area corresponding to the cross section of the pressure pipeline, and the unit is m 2.
Considering the reflection of the pressure wave at the fracture, the reflection coefficient R at this time is negative as the fracture increases the cross-sectional area of the wellbore. And (3) injection: to facilitate the expression of the function, the following equation is to change the equation sampling points to a continuous function form.
Estimating unknown shaft reflectivity x (t) from a convolution equation (1.1), applying a cepstrum algorithm, wherein the cepstrum is obtained by carrying out Fourier inverse transformation on the logarithm of a signal estimated spectrum, and is a nonlinear signal processing technology, and the cepstrum is calculated by the following steps: first fourier transforming the above signals:
（1.6）；
Wherein: Is a fourier transform;
fourier transforms are capable of transforming a convolved signal into a product signal, but product domain signals still have difficulty distinguishing between two different signals, and therefore logarithmically transforming the product signal into an additive signal.
（1.7）；
(1.7) Transforming the product signal into an additive signal, the transformation at this time still being in the frequency domain, requiring a reconversion of the signal back into the time domain.
（1.8）；
Wherein: Is inverse fourier transform;
The cepstrum transformation is to perform the above series of processing on the signal, and the result obtained by the transformation is still in the time domain, so that the argument is called scrambling in order to distinguish from the original time. Cepstrum transformation has been widely used in seismic signal processing and acoustic signal processing (1.8).
The pressure pulse x (T) caused by the pump shut-off has a smooth spectrum, so that its cepstrum is located near low frequency values, and can be easily separated from the wellbore reflectivity response h (T) in the cepstrum domain, which has non-zero peaks only at the (T, 2T, 3T …) cepstrum. The wellbore pressure oscillations caused by the reflection of the hydraulic fracture tube waves appear as strong negative peaks on the inverted spectrum at the corresponding frequencies. Also, wellbore pressure oscillations due to reflection of pipe waves due to wellbore restriction can result in Jiang Zhengfeng on the cepstrum, as shown in fig. 7.
In some alternative embodiments, verifying perforation quality, bridge plug loss, and fracture initiation identification based on data analysis includes:
Determination of wave velocity: the cepstrum transformation gives the time of occurrence of the event, if the location must give the velocity of the wave in the pipe, according to the theory of propagation of the water hammer wave in the pipe, the wave velocity expression in the wellbore is:
，/>（1.9）；
Wherein: k eff is the effective bulk modulus in Pa; k is the fluid bulk modulus, pa; ρ is the fluid density; g is the shear modulus of the surrounding stratum, and the unit is Pa; e is the sleeve shear modulus in Pa; d is the diameter of the sleeve, and the unit is m; epsilon is the wall thickness of the sleeve wall in m.
The propagation velocity of waves in a wellbore depends on many parameters, such as fluid properties, wellbore geometry and elastic properties, and formation parameters, even the bulk modulus and density of the fluid are primarily dependent on pressure, temperature, presence of air and frequency of the waves. Calculating the wave velocity using the formula entirely produces calculation errors, resulting in uncertainty in depth.
High frequency manometer perforation diagnosis: in order to solve the calculated error, the high-frequency pressure is adopted to monitor the perforation, the perforation energy is strong because the position of each perforation is determined, the interference between a shaft and the ground during the perforation is relatively small, the original data is only filtered, and the wave speed is determined by the time between the wave crest and the wave trough generated by the perforation, as shown in fig. 5 and 6.
Fig. 5 (a) shows pressure data of waves generated by perforation before fracturing and monitored by a wellhead high-frequency pressure gauge, and it can be seen that wellhead pressure data has obvious pressure fluctuation, but due to noise of the data, the fluctuation characteristics and the noise are sometimes difficult to distinguish, and perforation characteristics can be made more obvious through data filtering, as shown in fig. 5 (b). FIG. 6 is an enlarged graph of perforation pressure, FIG. 6 (a) is difficult to distinguish perforation, FIG. 6 (b) perforation features are quite apparent, and the wave velocity c can be calculated using FIG. 6 (b):
c=2L/ts（1.10）；
wherein: l is the position of a perforation hole, and the unit is m; t s is the time interval of two adjacent reflected waves after perforation explosion, and the unit is sec;
Determining the bridge plug and crack initiation position: and carrying out cepstrum analysis on the instantaneous data of the pressure of the fracturing pump to determine the positions of the bridge plug and the fracture. In hydraulic fracturing, when the water hammer wave after stopping the pump propagates in the wellbore, due to the change of the wellbore geometry, including the change of the wellbore radius or the existence of bridge plugs, and the change of the wellbore integrity, including cracks and leakage, the water hammer pressure can change at the positions, and the reflection coefficient can be defined as follows: （1.11）；
Wherein: z 1 is the impedance of the water hammer before the diameter of the well shaft pipeline is not changed, the unit is s/m 2;Z2 is the impedance of the water hammer pipeline after the shape or the area of the water hammer pipeline is changed, and the unit is s/m 2.
From the above equation (1.11), the pressure pulse propagates down the wellbore, the result of each interaction being a change in pressure wave (reflection) back to the surface and monitored by a high frequency manometer. Since the impedance Z k=ρc/Ak is inversely proportional to the effective cross-sectional area of the wellbore (a k), Z k is the impedance in the wellbore before the reflection point (k=1) and after the reflection point (k=2). It can be seen that the communication fracture effectively increases the cross-section of the well, with a negative reflection coefficient. Thus, the shape of the pressure pulse returning to the surface will be opposite to the shape of the pressure pulse sent downhole, and the reflectance value at the bridge plug will be positive, so the fracture initiation location is determined by the cepstrum of the high frequency pressure, as shown in FIG. 7, with a more pronounced negative peak around the cepstrum time of 2.06s and a positive peak at 4.12 s.
The working principle of the invention is as follows: the invention provides a method for processing edge calculation data based on high-frequency pressure crack monitoring, which comprises the steps of installing acquisition equipment and carrying out data acquisition and debugging; data acquisition is carried out, and cloud transmission is carried out on the acquired data; receiving data transmitted by a cloud end, and carrying out data analysis; and (5) according to data analysis, detecting perforation quality, bridge plug leakage and crack initiation identification. The method provides a simple and practical technology for the fracturing effect evaluation, and can detect and calculate the perforation times of the perforation clusters; detecting and confirming whether the bridge plug leaks; and determining the crack opening position and the like according to the liquid inlet point position, thereby providing technical support for development of dense oil gas, shale gas and coalbed methane in the oil field. The invention has two advantages: the operation is simple and convenient: only one pressure gauge is required to be installed at the wellhead, deployment is simple and easy to implement, only a ground acquisition and interpretation algorithm is relied on, no change or additional steps are required, and the utilized event is a part of fracturing operation; the method can guide the fracturing in real time, and not only evaluate the fracturing effect, but also correct the deviation in the fracturing in time by the provided perforation times, bridge plug conditions, crack initiation positions and the like.
For further explanation of the above technical solutions, a specific embodiment of the present invention is provided as a specific example 1 for explanation. This example is derived from a shale gas well with a full depth 4390m, a vertical depth 2317m, a total horizontal displacement 1750m, and a total pressure of 19 stages. The well analysis is that the 19 th section, bridge plug position 2708m and the first shower hole position 2624.5m of the 19 th section.
Firstly, a high-frequency pressure gauge is installed at a wellhead, data acquisition is realized by compiling data acquisition software, and the acquired wellhead pressure raw data are shown in fig. 8. There is a period of pump down time 22:37:34 in FIG. 8, and again after 00:50:54.
Determining the propagation velocity of the water-jet wave in the water from the perforation signal: FIG. 8 is wellhead pressure data for stage 19 fracturing, since perforation was initiated 1 hour after stage 18 fracturing was completed (i.e., 2022, 3, 9, 15: 26), if the time span of FIG. 8 is too large considering perforation data, the fracture construction curve is severely compressed. To this end, the example depicts the wellhead when perforating alone.
Pressure curve, fig. 9 (a) is pressure data of waves generated by perforation before fracturing and monitored by a wellhead high-frequency pressure gauge, fig. 9 (b) is a wellhead pressure curve after data filtering, and perforation characteristics are more obvious. Fig. 10 (a) is locally enlarged pre-fracture perforation high frequency pressure data, which is a partial enlargement of the first perforation segment, and it is difficult to distinguish between peaks and troughs. Fig. 10 (b) is a partial amplified pressure data after filtering, the amplified portion has only 1 peak and 2 valleys, it is obvious that the times between the valleys and the peaks are t s =4389 ms, and the time between the two peaks is just 2t s =8778 ms, and the wave speed is according to the formula:
（m/s）
Determination of crack initiation position: and carrying out cepstrum analysis on the pump stopping pressure, wherein a negative peak of the pump stopping pressure shows the position of a liquid inlet point (namely a crack starting position), and carrying out cepstrum analysis on wellhead pressure data by programming (ceps (x) in Matlab can realize cepstrum transformation) according to a cepstrum analysis theory. In this example, a piece of data after 0:52:57 is taken for cepstrum analysis, and in the data of a blue shadow part in fig. 11, cepstrum transformation is performed in 5 windows, the cepstrum amplitude of the data is from-215.16 to 204.367, the first window is most intense, and a significant negative peak exists near the cepstrum time of 4.4s, although the data is filtered, due to the fact that the noise during fracturing is high, a plurality of cracks often occur, each crack can be repeatedly reflected in a shaft, but the negative peak is still many, and the accurate position of each crack cannot be determined. For this reason, the homomorphic spectrogram is used to identify the crack initiation position and increase the crack identification probability, fig. 12 shows the cepstrum homomorphic spectrogram of the present well example, in which it can be seen that there are 3 distinct negative peak bands, the corresponding cepstrum times are 4.4203, 4.432 and 4.445s, and the crack initiation correspondence positions are respectively calculated according to the current well wave velocity 1195.94 (m/s): 2643.21, 2650.2 and 2658m.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (6)
1. The edge calculation data processing method based on high-frequency pressure crack monitoring is characterized by comprising the following steps of:
Installing acquisition equipment and performing data acquisition and debugging;
data acquisition is carried out, and cloud transmission is carried out on the acquired data;
receiving data transmitted by a cloud end, and carrying out data analysis;
according to the data analysis, perforation quality, bridge plug leakage and crack initiation identification are checked;
The receiving of the cloud transmitted data includes high frequency pressure data processing:
Low pass filtering of data: noise treatment is carried out by adopting low-pass FIR filtering; assuming that the input signal of the FIR low-pass filter is a convolution of x (N) and b (N), where the response of unit samples b (N) is an n+1-point finite length sequence, N is the filter order, and thus the output is:
；
Wherein y (n) is the output through the filter; x (n) is an input signal; b (n) is the response of the unit sample;
The transfer function B (z) of the filter can be expressed as:
；
the data analysis comprises:
Spectrum analysis of the collected data;
cepstrum analysis of the collected data:
Assuming that S (N) is a high frequency pressure signal of length N, according to convolution theory, S (N) is a convolution of wavelet x (N) and reflection coefficient h (N):
（1.1）；
Wherein: s (n) represents the original pressure signal; x (n) represents the signal generated by reflection at the crack; x represents a convolution symbol; n represents a time domain sampling point; h (n) is an unknown parameter in the convolution equation (1.1), the sampling point n is rewritten as a continuous function, typically a decaying minimum phase echo pulse sequence delayed by the pressure pulse oscillation period t, the pulse amplitude a depends on the corresponding reflection coefficient and the decay of the wave in the wellbore;
（1.2）；
0< |a| <1, delta (t) is the unit pulse;
（1.3）；
For the water hammer, the ratio of the water hammer pressure to the oscillating flow is called the impedance, which is a complex number defined by amplitude, frequency and phase:
（1.4）；
Wherein: z-impedance in s/m 2; h-water head, the unit is m; -flow in m 3/s; t-time, in s; omega-angular frequency in rad/s; phi-the phase angle of the water head and the flow, the unit is rad; g-gravitational acceleration in m/s 2;
the characteristic impedance of equation (1.4) represents the situation that the pressure and the flow movement direction are the same, and the characteristic impedance can be simplified by assuming that the friction in the sleeve in which the pressure and the flow oscillate is constant and the phase angle is equal to 0 or pi/omega: ；
Wherein: c is the wave velocity, and the unit is m/s; a is the area corresponding to the cross section of the pressure pipeline, and the unit is m 2;
estimating unknown shaft reflectivity x (t) from a convolution equation (1.1), and applying a cepstrum algorithm, wherein the cepstrum calculation process is as follows: first fourier transforming the above signals:
（1.6）；
Wherein: Is a fourier transform;
carrying out logarithmic transformation on the product signal to obtain an addition signal;
（1.7）；
(1.7) transforming the product signal into an addition signal, and reconverting the signal back into the time domain:
（1.8）；
Wherein: Is inverse fourier transform;
The perforation quality, bridge plug leakage and crack initiation identification are checked according to the data analysis, and the method comprises the following steps:
Determination of wave velocity: the wave velocity expression in the wellbore is:
（1.9）；
Wherein: k eff is the effective bulk modulus in Pa; k is the fluid bulk modulus, pa; ρ is the fluid density; g is the shear modulus of the surrounding stratum, and the unit is Pa; e is the sleeve shear modulus in Pa; d is the diameter of the sleeve, and the unit is m; epsilon is the wall thickness of the sleeve wall and the unit is m;
high frequency manometer perforation diagnosis: the wave speed is determined by filtering the original data and then determining the wave speed by the time between the wave crest and the wave trough generated by perforation: ；
wherein: l is the position of a perforation hole, and the unit is m; t s is the time interval of two adjacent reflected waves after perforation explosion, and the unit is sec;
determining the bridge plug and crack initiation position: performing cepstrum analysis on the instantaneous data of the fracturing pump-stopping pressure to determine the bridge plug and the crack starting position; the reflection coefficient is defined as follows: （1.11）；
Wherein: z 1 is the impedance of the water hammer before the diameter of the shaft pipeline is not changed, the unit is s/m 2;Z2 is the impedance of the water hammer pipeline after the shape or the area of the water hammer pipeline is changed, and the unit is s/m 2; and determining the crack initiation position through a cepstrum of the high-frequency pressure.
2. The method for processing the edge calculation data based on the high-frequency pressure crack monitoring according to claim 1, wherein the steps of installing the acquisition equipment and performing data acquisition debugging comprise the steps of: a high-frequency pressure gauge is arranged on the wellhead four-way valve and is in direct contact with fracturing fluid in a pipeline; the power supply of the high-frequency pressure gauge is maintained by installing a battery; the signal cable is connected with the high-frequency pressure gauge, the data acquisition equipment and the computer to finish the installation of the equipment; the system is powered on, a computer is turned on, acquisition software is operated, the pressure in the fracturing pipeline is debugged and measured, and the pressure is acquired to the computer through a signal cable and data acquisition equipment.
3. The method for processing the edge calculation data based on the high-frequency pressure crack monitoring according to claim 2, wherein parameters of the high-frequency pressure gauge are as follows: withstand voltage 140MPa and acid resistance 20%; pressure range: 0-120 MPa; pressure resolution: 0.1%o MPa; a power supply section: 24VDC, outputting 0-10 VDC; a connecting part: m18×1.5 cone sealing; and (3) electric appliance connection: waterproof aviation plug-in connection; signal cable: PVC polyvinyl fluoride shielded cable, waterproof aviation plug-in type, BNC data acquisition adapter wire terminal and 4 cable adapters; the high-frequency pressure gauge is electrically connected with the HC7804A data collector and the notebook computer, and the sampling frequency is set to be 1000HZ.
4. The method of claim 1, wherein the performing data collection comprises: the normal collection of a high-frequency pressure gauge is kept before fracturing, the pressure in a pipeline is measured at a millisecond level during sampling, and data are directly transmitted to a computer through an output line and a collection card to complete data collection; and storing by adopting an HTF-5 data format.
5. The method for processing the edge calculation data based on the high-frequency pressure crack monitoring according to claim 1, wherein the step of cloud-transmitting the collected data comprises the steps of sending the collected high-frequency pressure data to an interpretation center through a cloud; and (3) through establishing TCP long-state connection, sending the data to a cloud server in the form of an http message, and storing the database.
6. The method for processing high-frequency pressure crack monitoring edge calculation data according to claim 1, wherein the receiving cloud transmitted data comprises: the cloud data reception adopts TCP long state connection, and the fastest update interval is set to be 1 second.
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