A method for identifying fluid entry hydraulic fractures using microseismic event spectral ratios

Abstract

The present application provides a kind of method for identifying liquid inlet hydraulic fracture using microseismic event spectral ratio, comprising the following steps: S1, data acquisition;S2, Fourier transform is carried out to the P wave and S wave obtained, and then the expression in frequency domain is converted into displacement amplitude spectrum;S3, the average value of spectral data in low frequency range is calculated as zero frequency level ω0;S4, Brune stress drop is calculated using the integral of ground velocity square to parameterize, using in the calculation process of stress drop, the integral of ground velocity square is defined as J, which is the second moment of power spectrum;S5, the corner frequency of S wave is calculated;The calculated S wave corner frequency is classified and divided for microseismic event, and low S wave corner frequency is tension event, and high S wave corner frequency is shear event.The present application is helpful to evaluate the distribution of fracturing fluid and understand the stress distribution and change of formation.

Description

Technical Field

[0001] This invention provides a method for identifying hydraulically infiltrated cracks using the microseismic event spectrum ratio, belonging to the field of coal mine microseismic event monitoring technology. Background Technology

[0002] The source mechanisms of microseismic events recorded during hydraulic fracturing can be broadly classified into shear events and tensional events. Since both types of mechanisms play important roles in reservoir deformation, distinguishing between events with different fracturing mechanisms is crucial for understanding how hydraulic fractures and the corresponding stress fields evolve over time.

[0003] In existing methods, the focal mechanism is typically determined through moment tensor inversion (MTI), a computationally expensive and time-consuming process. To obtain a well-analyzed moment tensor (MTI) solution, a sufficiently large sample of the focal mechanism sphere is required. However, during hydraulic fracturing, downhole monitoring equipment typically only samples a very limited portion of the focal mechanism sphere, making it difficult to distinguish between shear and tensile events. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for identifying hydraulically infiltrated fractures using the spectral ratio of microseismic events. By performing spectral analysis on the seismic data of microseismic events, the method identifies hydraulically infiltrated and non-hydraulic fractures, evaluates the hydraulic fracturing effect, and optimizes fracturing construction parameters.

[0005] This invention distinguishes between events generated by tensional and shear mechanisms through spectral analysis of microseismic events and calculation of corner frequencies. For example, compared to shear events, tensional events have lower frequency components in their spectra, a faster spectral decline in the high-frequency range, a higher S / P amplitude ratio, and a lower S / P corner frequency ratio. These spectral characteristics may reflect reduced rupture velocity and lower seismic efficiency. Therefore, spectral analysis may provide a faster and more reliable method for identifying tensional sources than traditional focal mechanism inversion.

[0006] The specific technical solution is as follows:

[0007] A method for identifying hydraulically infiltrated fractures using microseismic event spectrum ratios includes the following steps:

[0008] S1. In terms of data acquisition, microseismic events during the hydraulic fracturing process are monitored by deploying downhole three-component broadband geophones in monitoring wells near the fracturing well to monitor the hydraulic fracturing activities of the fracturing well.

[0009] S2. From the original waveform file in the time domain monitored, perform Fourier transform on the acquired P wave and S wave to convert them into a frequency domain representation, as shown in formula (1). Calculate the displacement amplitude spectrum from the obtained X[k], as shown in formula (2).

[0010]

[0011] x[n] is the nth sampling point of the time-domain signal, with a value ranging from 0 to N-1, and X[k] is the kth frequency component of the frequency-domain signal. It is a complex exponent, representing the phase information of the frequency components.

[0012]

[0013] The result of FFT is a complex sequence representing the amplitude and phase of the frequency components. The displacement amplitude spectrum |F(ω)| is the modulus of the complex number, Re(U(ω)) is the real part of the complex part, Im(U(ω)) is the imaginary part of the complex part, and ω = 2πv.

[0014] S3. Select the low-frequency range in the spectrum and calculate the average value of the spectrum data in this low-frequency range as the zero frequency level ω0. The specific calculation is as shown in formula (3):

[0015]

[0016] Where N is the number of data points selected in the low-frequency range, A(f i ) is the displacement amplitude at the i-th frequency point.

[0017] S4. The stress drop of Brune is parameterized by the integral of the square of the ground velocity. In the process of stress drop calculation, the integral of the square of the ground velocity is defined as J. According to Parseval's theorem, this is the second moment of the power spectrum, as shown in formula (4).

[0018]

[0019] Where F(ω) is the displacement amplitude in the frequency domain, ωF(ω) is the frequency domain representation of the velocity, and |ωF(ω)| 2 It is the power spectral density of the displacement amplitude spectrum;

[0020] When calculating the integral of J, the integration is performed within a finite frequency band, as shown in formula (5):

[0021]

[0022] Where v1 and v2 represent the lower and upper limits of the frequency band used to calculate J, i.e. the starting and ending frequencies of the integration, v1 refers to the upper frequency limit of the zero frequency domain, v2 includes the upper frequency limit of microseismic signals, ω1 and ω2 represent the upper and lower limits of the angular frequency, and Ω0ω1 represents the far-field displacement amplitude at the zero frequency level.

[0023] S5. Calculate the corner frequency of the S-wave, as shown in formula (6):

[0024]

[0025] The calculated S-wave corner frequencies were used to classify microseismic events. Low S-wave corner frequencies were classified as tensile events, while high S-wave corner frequencies were classified as shear events. Ultimately, the microseismic events were determined to be either tensile events (wet events, hydraulically infiltrated fractures) or shear events (dry events, non-hydraulically infiltrated fractures).

[0026] The technical effects of the present invention are as follows:

[0027] 1. By identifying tensile events, it can be determined whether fracturing fluid has been effectively injected into the fracture, which helps to assess the distribution of fracturing fluid and ensure that the fracturing fluid can effectively penetrate into the target formation, thereby better evaluating the fracturing effect.

[0028] 2. By identifying shear events, it is possible to determine the fractures formed due to stress changes. This helps to understand the stress distribution and changes in the formation, optimize fracturing design, avoid stress concentration areas, and improve fracturing efficiency and safety. Attached Figure Description

[0029] Figure 1 Schematic diagram of the layout of downhole geophones;

[0030] Figure 2 A top view of the results using spectral ratios to distinguish between wet and dry events. Detailed Implementation

[0031] The specific technical solutions of the present invention will be described with reference to the embodiments.

[0032] In terms of data acquisition, microseismic events during the hydraulic fracturing process are monitored. Downhole three-component broadband geophones are deployed in monitoring wells near the fracturing well to monitor the hydraulic fracturing activity of the well. Figure 1 As shown. The detectors are arranged in 8 stages, with a fixed spacing of 10 meters between each stage.

[0033] From the original time-domain waveform file obtained from the monitoring, the acquired P-wave and S-wave are subjected to Fourier transform and converted into a frequency domain representation, as shown in Equation (1). The displacement amplitude spectrum is then calculated from the obtained X[k], as shown in Equation (2).

[0034]

[0035] x[n] is the nth sampling point of the time-domain signal, with a value ranging from 0 to N-1, and X[k] is the kth frequency component of the frequency-domain signal. It is a complex exponent, representing the phase information of the frequency components.

[0036]

[0037] The result of FFT is a complex sequence representing the amplitude and phase of the frequency components. The displacement amplitude spectrum |F(ω)| is the modulus of these complex numbers, Re(U(ω)) is the real part of the complex part, Im(U(ω)) is the imaginary part of the complex part, and ω = 2πv.

[0038] Select a low-frequency range in the spectrum, such as from 0Hz to 1Hz, and calculate the average value of the spectral data within this frequency range as the zero-frequency level ω0. The specific calculation is as shown in formula (3):

[0039]

[0040] Where N is the number of data points selected in the low-frequency range, A(f i ) is the displacement amplitude at the i-th frequency point.

[0041] The stress drop of Brune is parameterized using the integral of the square of the ground velocity. In the process of stress drop calculation, the integral of the square of the ground velocity is defined as J. According to Parseval's theorem, this is the second moment of the power spectrum, as shown in formula (4). For data from a wide-spectrum detector, J is real and objective.

[0042]

[0043] Where F(ω) is the displacement amplitude in the frequency domain, the integral is multiplied by 2 because the spectrum is symmetrical. For real-valued signals, the spectrum is symmetrical, so in actual calculations, only the integral of the positive frequency part is calculated, and then multiplied by 2 to include the contribution of the negative frequency part; ωF(ω) is the frequency domain representation of velocity, |ωF(ω)| 2 It is the power spectral density of the displacement amplitude spectrum. According to Parseval's theorem, the energy of a signal in the time domain is equal to the energy represented in the frequency domain. Therefore, |ωF(ω)| 2 The second moment of the power spectrum represents the energy of each frequency component in the frequency domain. It is the integral of the power spectral density multiplied by the square of the frequency in the frequency domain, i.e., formula (4). It represents the weighted sum of the signal energy distribution on the square of the frequency. This weighting emphasizes the contribution of high-frequency components. For microseismic signals, the cumulative velocity square integral J can more reliably reflect the high-frequency characteristics of the signal and the source mechanism.

[0044] In practice, when calculating the integral of J, integration is generally performed only within a finite frequency band, as shown in formula (5):

[0045]

[0046] Where v1 and v2 represent the lower and upper limits of the frequency band used to calculate J, i.e. the starting and ending frequencies of the integration. v1 generally refers to the upper frequency limit of the zero frequency domain, and v2 includes the upper frequency limit of microseismic signals. ω1 and ω2 represent the upper and lower limits of the angular frequency, and Ω0ω1 represents the far-field displacement amplitude at the zero frequency level.

[0047] The corner frequency of the S-wave is calculated using formula (6):

[0048]

[0049] The parameters J and ω0 involved have been solved above, and the S-wave corner frequency of each microseismic event can be calculated. The calculated S-wave corner frequencies are used to classify the microseismic events. Low S-wave corner frequencies are mainly tensile events, while high S-wave corner frequencies are mainly shear events. Finally, it is determined whether the microseismic event is a tensile event (wet event, hydraulically infiltrated crack) or a shear event (dry event, non-hydraulically infiltrated crack).

[0050] The method proposed in this invention was used to perform microseismic monitoring on hydraulic fracturing of a directional well. The S-wave corner frequency of each microseismic event was calculated from the monitored data. The monitored microseismic events were then distinguished as either fluid-injecting fractures or non-fluid-injecting fractures. The results of this distinction are as follows: Figure 2 As shown.

[0051] The pentagram represents the wellhead of the fractured well, the black track above it is the well trajectory of the monitoring well, the inverted triangle symbol represents the three-component geophone, and the dots on the diagram represent microseismic events. Events far from the wellbore are shear events, and the darker color indicates that the rupture type of the microseismic event is shear, which is formed by the reopening of pre-existing fractures due to stress disturbance. These are dry events, not fluid-injected fractures. Events close to the wellbore are tensile events, and the lighter color indicates that the rupture type of the microseismic event is tensile, which is caused by fracturing fluid entering the reservoir pore space, leading to rock fracturing. These are wet events.

Claims

1. A method for identifying a fluid-entrained hydraulic fracture using microseismic event spectral ratios, the method comprising: The method comprises the following steps: S1, data acquisition, monitoring microseismic events in the hydraulic fracturing process, selecting a downhole three-component broadband detector in a monitoring well near the fracturing well to monitor the hydraulic fracturing activity of the fracturing well; S2, Fourier transform is performed on the obtained P wave and S wave from the monitored time domain original waveform file to convert it into a frequency domain representation, and then the displacement amplitude spectrum is calculated; S3, select the low frequency range in the spectrum, calculate the average value of the spectrum data in the low frequency range as the zero frequency level ω0; S4, calculate the Brune stress drop using the integral of the ground velocity square for parameterization, and use the integral of the ground velocity square in the calculation of the stress drop as J, according to the Parseval theorem, which is the second moment of the power spectrum; S5, calculate the corner frequency of the S wave; classify and divide the microseismic events according to the calculated S wave corner frequency, low S wave corner frequency is tensile event, high S wave corner frequency is shear event.

2. The method of claim 1, wherein, In S2, the conversion into a frequency domain representation is shown in formula (1), and the displacement amplitude spectrum is calculated as shown in formula (2); x[n] is the nth sample point of the time domain signal, taking values from 0 to N-1, X[k] is the kth frequency component of the frequency domain signal, is the complex exponential, representing the phase information of the frequency component; The result of FFT is a complex number sequence, which represents the amplitude and phase of the frequency component, the displacement amplitude spectrum |F(ω)| is the modulus of the complex number, Re(U(ω)) is the real part of the complex number part, and Im(U(ω)) is the imaginary part of the complex number part, ω=2πv.

3. The method of claim 2, wherein, In S3, the average value of the spectrum data in the low frequency range is calculated as the zero frequency level ω0, and the specific calculation is shown in formula (3): where N is the number of data points in the selected low frequency range, A(f i ) is the displacement amplitude of the i-th frequency point.

4. The method of claim 3, wherein, In S4, the integral of the ground velocity square J is shown in formula (4); where F(ω) is the displacement amplitude in the frequency domain, ωF(ω) is the frequency domain representation of the velocity, and |ωF(ω)| 2 is the power spectral density of the displacement amplitude spectrum.

5. The method of identifying a fluid-filled hydraulic fracture using microseismic event spectral ratios of claim 4, wherein, When calculating the integral J, the integral is performed in a limited frequency band, as shown in formula (5): Wherein, v1 and v2 represent the lower limit and upper limit of the frequency band used to calculate J, that is, the starting and ending frequencies of the integral, v1 refers to the upper limit of the frequency domain, v2 includes the upper limit of the frequency of the microseismic signal, ω1 and ω2 represent the upper and lower limits of the angular frequency, and Ω0ω1 represents the far-field displacement amplitude at the zero frequency level.

6. The method of identifying a fluid-entrained hydraulic fracture using microseismic event spectral ratios of claim 5, wherein, In S5, the corner frequency f of the S wave is calculated c as formula (6) :

7. The method for identifying the liquid-filled hydraulic fracture by using the microseismic event spectral ratio according to claim 1 or 6, characterized in that, The tensile event includes wet event, liquid-filled hydraulic fracture; the shear event includes dry event, non-liquid-filled hydraulic fracture.

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