Method for identifying liquid inlet hydraulic fracture by using microseismic event spectrum ratio

Abstract

The invention provides a method for identifying a liquid inlet hydraulic fracture by using a microseismic event spectrum ratio. The method comprises the following steps: S1, data acquisition; s2, performing Fourier transform on the obtained P wave and S wave, converting the P wave and S wave into a frequency domain representation mode, and then calculating to obtain a displacement amplitude spectrum; s3, calculating an average value of spectrum data in a low-frequency range as a zero-frequency level omega0; s4, calculating Brune stress drop, performing parameterization by using an integral of ground velocity square, and defining the integral of the ground velocity square as J which is a second moment of a power spectrum in a stress drop calculation process; s5, calculating the corner frequency of the S wave; the calculated S-wave corner frequencies are used for classifying and dividing micro-seismic events, the low S-wave corner frequency is a tensional event, and the high S-wave corner frequency is a shearing event. According to the invention, the distribution condition of the fracturing fluid can be evaluated and the stress distribution and change of the stratum can be known.

Description

Technical Field

[0001] The invention provides a method for identifying hydraulic fractures with fluid inflow by utilizing microseismic event spectrum ratios, and belongs to the technical field of coal mine microseismic event monitoring. Background Art

[0002] The source mechanisms of microseismic events recorded during hydraulic fracturing can be roughly divided into shear events and tensile events. Since both types of mechanisms play an important role in reservoir deformation, distinguishing events with different types of rupture mechanisms is important for understanding how hydraulic fractures and the corresponding stress fields evolve over time.

[0003] In existing methods, focal mechanisms are usually determined by moment tensor inversion (MTI), which is a computationally expensive and time-consuming process. In order to obtain a well-resolved moment tensor (MT) solution, the focal mechanism sphere of the event needs to be fully sampled. During hydraulic fracturing, when downhole monitoring equipment is used, most of them can only sample a very limited part of the focal mechanism sphere, making it difficult to distinguish between shear events and tensile events. Summary of the invention

[0004] In view of the above technical problems, the present invention provides a method for identifying hydraulic fractures with fluid inflow by using the spectrum ratio of microseismic events. By performing spectrum analysis on the seismic characteristics of microseismic events, hydraulic fractures with fluid inflow and non-hydraulic fractures can be identified, the hydraulic fracturing effect can be evaluated, and the fracturing construction parameters can be optimized.

[0005] The present invention can distinguish between events generated by tensile and shear mechanisms through spectrum analysis of microseismic events and calculation of corner frequencies. For example, compared with shear events, the spectrum of tensile events has lower frequency components, and the spectrum drops faster in the high-frequency band, with a higher S / P spectrum amplitude ratio and a lower S / P corner frequency ratio. These spectral features may be a reflection of reduced rupture velocity and lower seismic efficiency. Therefore, spectrum analysis may provide a faster and more reliable method to identify tensile sources than traditional focal mechanism inversion.

[0006] The specific technical solution is:

[0007] A method for identifying hydraulic fractures with fluid inflow using microseismic event spectrum ratios comprises the following steps:

[0008] S1. In terms of data collection, the microseismic events during the hydraulic fracturing process are monitored. Downhole three-component broadband geophones are deployed in monitoring wells near the fracturing wells to monitor the hydraulic fracturing activities of the fracturing wells.

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

[0010]

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

[0012]

[0013] 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, and Im(U(ω)) is the imaginary part of the complex number.

[0014] S3, select a low-frequency range in the spectrum, and calculate the average value of the spectrum data in the 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 in the selected low-frequency range, A(f i ) is the displacement amplitude at the ith frequency point.

[0017] S4. Brune stress drop is parameterized using the integral of the square of the ground velocity. In the calculation of stress drop, 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, |ωF(ω) 2 is the power spectral density of the displacement amplitude spectrum;

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

[0021]

[0022] Among them, v1 and v2 represent the lower and upper limits of the frequency band used to calculate J, that is, the starting and ending frequencies of the integration, v1 refers to the upper frequency line of the zero-frequency domain, v2 includes the upper frequency limit of the microseismic signal, ω = 2πv, 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 frequency is used to classify microseismic events. Low S-wave corner frequency is a tension event, and high S-wave corner frequency is a shear event. Ultimately, it is determined whether the microseismic event is a tension event (wet event, hydraulic fracture with fluid inflow) or a shear event (dry event, hydraulic fracture without fluid inflow).

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

[0027] 1. By identifying tensile events, it can be determined whether the fracture has been effectively injected with fracturing fluid, which helps to evaluate 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, cracks formed due to stress changes can be determined, which helps to understand the stress distribution and changes in the formation, optimize the fracturing design, avoid stress concentration areas, and improve fracturing efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0030] Figure 2 Top view of the results of using spectral ratios to differentiate between wet and dry events. DETAILED DESCRIPTION

[0031] The specific technical solution of the present invention is explained in conjunction with embodiments.

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

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

[0034]

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

[0036]

[0037] The result of FFT is a complex number 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 number, and Im(U(ω)) is the imaginary part of the complex number.

[0038] Select a low-frequency range in the spectrum, for example, from 0 Hz to 1 Hz, and calculate the average value of the spectrum data in 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 in the selected low-frequency range, A(f i ) is the displacement amplitude at the ith frequency point.

[0041] The Brune stress drop is parameterized using the integral of the square of the ground velocity. In the stress drop calculation process, 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 the data of the wide spectrum detector, J is real and objective.

[0042]

[0043] Where F(ω) is the displacement amplitude in the frequency domain. The integral sign 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 the velocity, |ωF(ω) 2 is the power spectral density of the displacement amplitude spectrum. By Parseval’s theorem, the energy of a time domain signal is equal to the energy of its frequency domain representation. Therefore, |ωF(ω) 2 Represents the energy of each frequency component in the frequency domain. The second-order moment of the power spectrum refers to the power spectrum density in the frequency domain multiplied by the square integral of the frequency, that is, 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 square integral of velocity J can more reliably reflect the high-frequency characteristics and source mechanism of the signal.

[0044] In fact, when calculating the integral of J, the integral is generally only performed within a limited frequency band, as shown in Formula 5:

[0045]

[0046] Among them, v1 and v2 represent the lower and upper limits of the frequency band used to calculate J, that is, the starting and ending frequencies of the integration, v1 generally refers to the upper frequency line of the zero-frequency domain, v2 includes the upper frequency limit of the microseismic signal, ω=2πv, 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] Calculate the corner frequency of the S wave, as shown in Formula 6:

[0048]

[0049] The parameters J and ω0 involved have been solved in the above text, so the S-wave corner frequency of each microseismic event can be calculated, and the microseismic events can be classified and divided according to the calculated S-wave corner frequency. Low S-wave corner frequency is mainly tensile event, and high S-wave corner frequency is mainly shear event. Finally, it is determined whether the microseismic event is a tensile event (wet event, hydraulic fracture with fluid inflow) or a shear event (dry event, hydraulic fracture without fluid inflow).

[0050] The method proposed in the present invention is used to monitor the hydraulic fracturing of a directional well. The S-wave corner frequency of each microseismic event is calculated from the monitored data. The monitored microseismic event is distinguished as a fluid-injected fracture or a non-fluid-injected fracture. The results of the distinction are as follows: Figure 2 shown.

[0051] The five-pointed star is the wellhead of the fracturing well, the black trajectory above is the well trajectory of the monitoring well, the inverted triangle symbol represents the three-component detector, and the dots on the graph represent microseismic events. Events far away from the wellbore are shear events. Dark colors indicate that the rupture type of the microseismic event is shear, which is caused by the reopening of pre-existing cracks due to stress disturbance. It is a dry event, not a fluid-infused crack. Events close to the wellbore are tension events. Light colors indicate that the rupture type of the microseismic event is tension, which is caused by the entry of fracturing fluid into the pore space of the reservoir, resulting in rock rupture, and is a wet event.

Claims

1. A method for identifying hydraulic fractures with fluid inflow using microseismic event spectrum ratios, characterized in that: The following steps are involved: S1. Data acquisition: monitoring microseismic events during hydraulic fracturing. Three-component broadband geophones are installed in monitoring wells near the fracturing wells to monitor the hydraulic fracturing activities of the fracturing wells. S2. Perform Fourier transform on the acquired P-wave and S-wave from the original waveform file in the monitored time domain, convert them into the representation in the frequency domain, and then calculate the displacement amplitude spectrum; S3, selecting a low-frequency range in the spectrum, and calculating the average value of the spectrum data in the low-frequency range as the zero-frequency level ω0; S4. Brune stress drop is parameterized using the integral of the square of the ground velocity. In the stress drop calculation process, 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. S5. Calculate the corner frequency of the S wave; classify the microseismic events according to the calculated S wave corner frequency, low S wave corner frequency is a tension event, and high S wave corner frequency is a shear event.

2. The method for identifying hydraulic fractures by using microseismic event spectrum ratio according to claim 1, characterized in that: In S2, it is converted into a frequency domain representation, as shown in formula (1), and the displacement amplitude spectrum is calculated for the obtained X(k), as shown in formula (2); x[n] is the nth sampling point of the time domain signal, ranging from 0 to N-1, and X[k] is the kth frequency component of the frequency domain signal. is a complex exponential, indicating 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, and Im(U(ω)) is the imaginary part of the complex number.

3. The method for identifying hydraulic fractures by using microseismic event spectrum ratio according to claim 2, characterized in that: 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 as 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 at the ith frequency point.

4. The method for identifying hydraulic fractures by using microseismic event spectrum ratio according to claim 3, characterized in that: In S4, the integral of the square of the ground speed J is as 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, |ωF(ω) 2 is the power spectral density of the displacement amplitude spectrum.

5. The method for identifying hydraulic fractures using microseismic event spectrum ratios according to claim 4, characterized in that: When calculating the integral of J, the integral is performed within a finite frequency band, as shown in Formula 5: Among them, v1 and v2 represent the lower and upper limits of the frequency band used to calculate J, that is, the starting and ending frequencies of the integration, v1 refers to the upper frequency line of the zero-frequency domain, v2 includes the upper frequency limit of the microseismic signal, ω = 2πv, 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 for identifying hydraulic fractures using microseismic event spectrum ratios according to claim 5, characterized in that: In S5, calculate the corner frequency f of the S wave c , as shown in Formula 6:

7. A method for identifying hydraulic fractures using microseismic event spectrum ratios according to claim 1 or 6, characterized in that: The tensile events include wet events, hydraulic fractures with fluid inflow; the shear events include dry events, hydraulic fractures without fluid inflow.

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