Method for evaluating coal seam roof fracturing effect by utilizing dynamic attributes of micro-seismic event

Abstract

The invention provides a method for evaluating a coal seam roof fracturing effect by utilizing dynamic attributes of a micro-seismic event, which comprises the following steps of: calculating an epicenter position and occurrence time of the micro-seismic event by utilizing an arrival time difference and a wave velocity model recorded by a detector network; analyzing the amplitude and frequency of the micro-seismic event, and knowing the energy release and fracture characteristics of the seismic source; calculating the magnitude of each microseismic event according to the amplitude and distance of the seismic waves; moment tensor inversion is carried out through seismic waveform data recorded by the three-component broadband intrinsic safety type detector; calculating a seismic moment tensor of each seismic event, estimating a seismic source mechanism and a seismic moment of each seismic event, and finally obtaining micro-seismic radiation energy and a seismic moment; clustering events related to a plurality of fractures and a plurality of crack surface sliding generated in the hydraulic fracturing process; and carrying out attribute estimation on the microseismic event cluster to analyze the change of the macroscopic property of the system. The fracturing progress and effect of the coal seam roof can be known, and whether the crack develops according to the expected direction or not is judged.

Description

Technical Field

[0001] The invention provides a method for evaluating the fracturing effect of a coal seam roof by utilizing the dynamic attributes of microseismic events, and belongs to the technical field of microseismic monitoring. Background Art

[0002] Microseismicity is essentially a dissipative process that emits seismic waves that contain information about the deformation at the site. The amplitude and frequency of the seismic waves radiated by a seismic event depend on the strength and stress state of the rock, the size of the seismic radiation source, the magnitude, and the speed at which the rock deforms during the fracture process.

[0003] In the prior art, the peak mining stress of the coal body in the surrounding rock of the tunnel to be drilled for anti-blowout construction is obtained and applied in the coal seam to determine the critical conditions of the borehole impact and the quantitative relationship between the critical conditions of the borehole impact and the start of the tunnel impact ground pressure; finally, the construction parameters of the anti-blowout drilling are quantitatively determined based on the critical softening depth of the surrounding rock for the start of the tunnel impact ground pressure, the critical plastic softening zone radius of the borehole impact and the critical mining stress index. This technology has the following problems: (1) The peak mining stress is calculated based on the parameters of rock mechanics, the calculation parameters are single and the application range is small. (2) The rock parameters need to be obtained before the calculation, and the implementation cost is high. Summary of the invention

[0004] In view of the above technical problems, the present invention provides a method for evaluating the fracturing effect of the coal seam roof by using the dynamic attributes of microseismic events. The fracturing effect of the coal seam roof is evaluated by analyzing the dynamic attributes of the microseismic events.

[0005] Microseismicity is essentially a dissipative process that emits seismic waves that contain information about in-situ deformation. The amplitude and frequency of the seismic waves radiated by a seismic event depend on the strength and stress state of the rock, the size of the seismic radiation source, the magnitude, and the speed at which the rock deforms during the fracture process. Although each microseismic event contains this valuable information, it is not enough to study only a single event, because a single event only involves a single fracture on a single fracture surface. To fully evaluate the fracture and deformation processes, a large number of events related to multiple fractures and sliding on multiple fracture surfaces need to be clustered and analyzed in a temporal and spatial context. This method can better describe the response of the rock mass to hydraulic fracturing and thus evaluate the fracturing effect of the coal seam roof.

[0006] The specific technical solution is:

[0007] A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events comprises the following steps:

[0008] S1. Microseismic data acquisition. During the hydraulic fracturing process of the coal seam roof, microseismic data acquisition requires the deployment of a three-dimensional geophone network. According to the spatial characteristics of the coal mine, geophones are deployed underground and in tunnels. The seismic waveform data captured by the geophone is recorded, including amplitude, frequency and time information. The collected seismic waveform data is filtered, de-noised and event identified to extract effective microseismic events.

[0009] The collection of microseismic waveform data requires the deployment of an observation system to monitor microseismic events during the hydraulic fracturing of the coal mine roof. Three-component broadband intrinsically safe geophones are deployed in monitoring wells near the fracturing wells and in the tunnels on both sides of the working face to monitor the hydraulic fracturing activities of the fracturing wells.

[0010] S2. Use the arrival time difference and wave velocity model recorded by the detector network to calculate the epicenter location and occurrence time of the microseismic event. Analyze the amplitude and frequency of the microseismic event to understand the energy release and rupture characteristics of the earthquake source. Calculate the magnitude of each microseismic event based on the amplitude and distance of the seismic wave.

[0011] The moment tensor inversion is performed by using the seismic waveform data recorded by the three-component broadband intrinsically safe geophone. The seismic moment tensor of each seismic event is calculated, its focal mechanism and seismic moment are estimated, and finally the microseismic radiation energy E is obtained. r and seismic moment M 0 .

[0012] S3, clustering the events related to multiple fractures and multiple fracture surface sliding generated during hydraulic fracturing;

[0013] In the clustering process, time and space changes are taken into account, events are dynamically clustered, and the dynamic programming algorithm (Dynamic TimeWarping) is applied to calculate the minimum cumulative distance between two sequences to find the best matching path.

[0014] Set the microseismic starting point D(1,1)=d(x 1 ,y 1 ), for each element D(i,j) in the matrix, calculate the minimum cumulative distance, as shown in Formula 1:

[0015] D(i,j)=d(x i ,y i )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)) (1)

[0016] From the end point D(m,n) of the matrix, trace back along the minimum cumulative path to the starting point D(1,1) to perform cluster matching on the microseismic events.

[0017] S4. Estimate the properties of microseismic event clusters to analyze the changes in the macroscopic properties of the system.

[0018] Microseismic events are small earthquakes caused by the breaking of underground rocks. By estimating the properties of microseismic event clusters, we can understand the statistical characteristics of coseismic deformation and the related strain rate changes, stress and rheological properties. The specific properties estimated include: the time interval between events, the distance between events, the time span, the radiated energy, the seismic moment (indicating the energy released by the earthquake), and the density.

[0019] The stress index (SI) is calculated based on the clustering results, as shown in Equation 2. This parameter is the ratio of the elastic force to the viscous force that produces the monitored microseismic activity. It measures the relationship between the energy budget of the monitored microseismic activity and the spatial concentration of seismic activity within a specific volume and time range.

[0020]

[0021] Among them, η c is the cumulative seismic efficiency of clustered events, c is a coefficient for different lithology, time and volume;

[0022] The seismic efficiency of a single microseism is the apparent stress σ a and static stress drop Δσ d The ratio of is shown in formula 3:

[0023]

[0024] The apparent stress σ a and static stress drop Δσ d The two parameters are also calculated based on the microseismic attributes, see Equations 4 and 5:

[0025] Calculate the apparent stress σ a ,as follows:

[0026]

[0027] Where, μ is the shear modulus of the target rock layer, E r is the total energy released by the microseismic event, M 0 is the seismic moment, which can be obtained by moment tensor inversion.

[0028] Calculation of static stress drop Δσ d

[0029]

[0030] Among them, M 0 is the earthquake moment, and r is the focal radius.

[0031] Before comparing SI values, the changes in stress field before and after hydraulic fracturing are analyzed through geological model and stress field simulation; the lower and higher SI values ​​are determined by comparing the values;

[0032] According to the calculation method of stress index, it is obtained that: low SI value means that stress conduction is unstable and limited to a small range of reservoirs, usually microseismic events driven by fluids with increased pore pressure, with small energy release, and low SI indicates that seismic activities are close in space and time. Local stress changes from nearby seismic activities lead to rapidly evolving rupture processes, which indicates that the stress field evolves rapidly and is complex; large SI value means that microseismic events release large energy, the stress field is more stable, and the final fracturing influence range is larger, usually cracks or faults with the direction of maximum principal stress are triggered by stress microseismic events, and finally evaluate the fracturing effect of coal seam roof.

[0033] The technical effects of the technical solution of the present invention are as follows:

[0034] 1. According to the information provided by dynamic parameters, the formation stress state can be provided in real time, the progress and effect of the fracturing of the coal seam roof can be understood, the fracturing method can be adjusted in time, and the fracturing efficiency and safety of the coal seam roof can be improved.

[0035] 2. Based on the crack expansion and development reflected by the stress index, determine whether the cracks develop in the expected direction to ensure the effectiveness of the fracturing operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the process of the present invention;

[0037] Figure 2 Schematic diagram of the layout of a three-component wideband intrinsically safe detector in an embodiment;

[0038] Figure 3 It is a stress index heat map corresponding to the time of occurrence of the last microseismic event of hydraulic fracturing of a certain well in the embodiment. DETAILED DESCRIPTION

[0039] The specific technical solution of the present invention is explained in conjunction with embodiments.

[0040] The technical route of the present invention is as follows Figure 1 shown.

[0041] In terms of microseismic data acquisition, during the hydraulic fracturing of the coal seam roof, a three-dimensional network of detectors is required to collect microseismic data. These detectors will be arranged at different depths and positions to form a three-dimensional monitoring network. According to the characteristics of the spatial location of the coal mine, they are mainly arranged underground and in tunnels. The seismic waveform data captured by the detector is recorded, including information such as amplitude, frequency and time, and the collected seismic waveform data is filtered, denoised and event identified to extract effective microseismic events. In this way, the propagation characteristics and amplitude information of microseismic waves can be obtained more accurately, so as to analyze the formation and expansion process of cracks. This three-dimensionally arranged detector network can provide higher-resolution microseismic data, which helps to more deeply and finely analyze the response mechanism of the coal seam roof during hydraulic fracturing.

[0042] The collection of microseismic waveform data requires the deployment of an observation system to monitor microseismic events during hydraulic fracturing of the coal mine roof. Three-component broadband intrinsically safe geophones are deployed in the monitoring wells near the fracturing wells and in the tunnels on both sides of the working face to monitor the hydraulic fracturing activities of the fracturing wells. The deployment method of the observation system is as follows: Figure 2 As shown, since the coal seam roof is located above the tunnel, the depth of the monitoring well is generally above the depth of the coal seam, which meets the requirements for arranging detectors in three-dimensional space.

[0043] The arrival time difference and wave velocity model recorded by the geophone array are used to calculate the epicenter location and occurrence time of the microseismic event. The amplitude and frequency of the microseismic event are analyzed to understand the energy release and rupture characteristics of the earthquake source. The magnitude of each microseismic event is calculated based on the amplitude and distance of the seismic wave.

[0044] The moment tensor inversion is performed through the seismic waveform data recorded by the three-component geophone. The seismic moment tensor of each earthquake event is calculated to estimate its focal mechanism and seismic moment, and finally the microseismic radiation energy E is obtained. r and seismic moment M 0 .

[0045] Throughout the hydraulic fracturing process, high-pressure fluid and proppant injections generate stress gradients within the reservoir. The rock mass responds to this change through energy, momentum, and mass transfer to reduce and transfer stress. The amplitude and frequency of the seismic waves radiated by microseismic events depend on the strength and stress state of the rock, the size of the seismic radiation source, the magnitude, and the speed at which the rock deforms during the fracture process. Although each microseismic event contains this useful information, studying only a single event attributed to a single fracture on a single fracture surface is not enough to evaluate the entire fracture and deformation process. However, more than a single microseismic event is generated during hydraulic fracturing, so a large number of events related to multiple fractures and sliding on multiple fracture surfaces generated during hydraulic fracturing are clustered and analyzed in a spatiotemporal context.

[0046] In the clustering process, time and space changes are taken into account, events are dynamically clustered, and the dynamic programming algorithm (Dynamic TimeWarping) is applied to calculate the minimum cumulative distance between two sequences to find the best matching path.

[0047] Set the microseismic starting point D(1,1)=d(x 1 ,y 1 ), for each element D(i,j) in the matrix, calculate the minimum cumulative distance, as shown in Formula 1:

[0048] D(i,j)=d(x i ,y i )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)) (1)

[0049] From the end point D(m,n) of the matrix, trace back along the minimum cumulative path to the starting point D(1,1) to perform cluster matching on the microseismic events.

[0050] The properties of microseismic event clusters are estimated to analyze the changes in the macroscopic properties of the system. Microseismic events are small earthquakes caused by the rupture of underground rocks. By estimating the properties of these event clusters, we can understand the statistical characteristics of co-seismic deformation and the related strain rate changes, stress and rheological properties. The specific properties estimated include: the time interval between events, the distance between events, the time span, the radiated energy, the seismic moment (indicating the energy released by the earthquake), and the density.

[0051] The stress index (SI) is calculated based on the clustering results, as shown in Equation 2. This parameter is the ratio of the elastic force to the viscous force that produces the monitored microseismic activity. It measures the relationship between the energy budget of the monitored microseismic activity and the spatial concentration of seismic activity within a specific volume and time range.

[0052]

[0053] Among them, η c is the cumulative seismic efficiency of clustered events, c is a coefficient for different lithology, time and volume;

[0054] The seismic efficiency of a single microseism is the apparent stress σ a and static stress drop Δσ d The ratio of is shown in formula 3:

[0055]

[0056] The apparent stress σ a and static stress drop Δσ d The two parameters can also be calculated based on microseismic attributes, see Equations 4 and 5:

[0057] Calculate the apparent stress σ a ,as follows:

[0058]

[0059] Where, μ is the shear modulus of the target rock layer, E r is the total energy released by the microseismic event, M 0 is the seismic moment, which can be obtained by moment tensor inversion.

[0060] Apparent stress can be used to describe the difficulty of the rupture process, because the value of apparent stress is related to the total energy released by the microseismic event. When the apparent stress value is low, it means that relatively little radiated energy is released during the hydraulic fracturing process. This indicates that the microseismic event mainly slides along existing faults or cracks without generating new fracture surfaces. When the apparent stress value is high, it means that more radiated energy is released during the hydraulic fracturing process. This usually indicates that the seismic event involves not only the sliding of pre-existing cracks, but also the formation and expansion of new cracks.

[0061] Calculation of static stress drop Δσ d

[0062]

[0063] Among them, M 0 is the earthquake moment, and r is the focal radius.

[0064] According to the calculation method of stress index mentioned above, it can be obtained that: low SI value means that stress conduction is unstable and limited to a small range of reservoirs, usually microseismic events driven by fluids with increased pore pressure, with small energy release, and low SI indicates that seismic activities are close in space and time. Local stress changes from nearby seismic activities lead to rapidly evolving rupture processes, which indicates that the stress field evolves rapidly and is complex; large SI value means that microseismic events release large energy, the stress field is more stable, and the final fracturing influence range is larger, usually cracks or faults with the direction of maximum principal stress are triggered by stress microseismic events, and finally evaluate the fracturing effect of coal seam roof.

[0065] The method proposed in the present invention is used to carry out microseismic monitoring of hydraulic fracturing in a directional well, and a stress index thermodynamic map and a fracturing curve map are drawn from the monitored data to evaluate the fracturing effect. Figure 3 .

Claims

1. A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events. It is characterized in that The following steps are involved: S1. Microseismic data acquisition. During the hydraulic fracturing process of the coal seam roof, microseismic data acquisition requires the deployment of a three-dimensional geophone network. According to the characteristics of the spatial location of the coal mine, the geophones are deployed underground and in the tunnels. Record the seismic waveform data captured by the geophone, including amplitude, frequency and time information, filter and de-noise the collected seismic waveform data and perform event identification to extract effective microseismic events; The collection of microseismic waveform data requires a reasonable deployment of the observation system to monitor microseismic events during the hydraulic fracturing of the coal mine roof. Three-component broadband intrinsically safe geophones are deployed in the monitoring wells near the fracturing wells and in the tunnels on both sides of the working face to monitor the hydraulic fracturing activities of the fracturing wells. S2. Use the arrival time difference and wave velocity model recorded by the detector network to calculate the epicenter location and occurrence time of the microseismic event; analyze the amplitude and frequency of the microseismic event to understand the energy release and rupture characteristics of the earthquake source; calculate the magnitude of each microseismic event based on the amplitude and distance of the seismic wave; The moment tensor inversion is performed through the seismic waveform data recorded by the three-component broadband intrinsically safe geophone; the seismic moment tensor of each seismic event is calculated, its focal mechanism and seismic moment are estimated, and finally the microseismic radiation energy E is obtained. r and seismic moment M 0 ; S3, clustering the events related to multiple fractures and multiple fracture surface sliding generated during hydraulic fracturing; S4. Estimate the attributes of microseismic event clusters to analyze the changes in the macroscopic properties of the system; The stress index SI is calculated based on the clustering results. The stress index SI is the ratio of elastic force to viscous force that produces the monitored microseismic activity; it measures the relationship between the energy budget of the monitored microseismic activity and the spatial concentration of seismic activity within a specific volume and time range.

2. A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events according to claim 1, It is characterized in that The clustering process of S3 considers temporal and spatial changes, dynamically clusters events, and applies a dynamic programming algorithm to calculate the minimum cumulative distance between two sequences to find the best matching path; Set the microseismic starting point D(1,1)=d(x 1 ,y 1 ), for each element D(i,j) in the matrix, calculate the minimum cumulative distance, as shown in Formula 1: D(i,j)=d(x i ,y i )+min(D(i-1,j),D(i,j-1),D(i-1,j-1)) (1) From the end point D(m,n) of the matrix, trace back along the minimum cumulative path to the starting point D(1,1) to perform cluster matching on the microseismic events.

3. A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events according to claim 1, It is characterized in that In S4, microseismic events refer to small earthquakes caused by the rupture of underground rocks; By estimating the properties of microseismic event clusters, we can understand the statistical characteristics of co-seismic deformation and the related strain rate changes, stress and rheological properties; the specific estimated properties include: the time interval between events, the distance between events, the time span, the radiation energy, the seismic moment, and the density.

4. A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events according to claim 1, It is characterized in that In S4, the stress index SI is as follows: Among them, η c is the cumulative seismic efficiency of clustered events, c is a coefficient for different lithology, time and volume; The seismic efficiency of a single microseism is the apparent stress σ a and static stress drop Δσ d The ratio of is shown in formula 3: The apparent stress σ a and static stress drop Δσ d The two parameters are also calculated based on the microseismic attributes, see Equations 4 and 5: Calculate the apparent stress σ a ,as follows: Where, μ is the shear modulus of the target rock layer, E r is the total energy released by the microseismic event, M 0 is the seismic moment, which can be obtained by moment tensor inversion; Calculation of static stress drop Δσ d Among them, M 0 is the earthquake moment, and r is the focal radius.

5. A method for evaluating the effect of coal seam roof fracturing using dynamic attributes of microseismic events according to claim 4, It is characterized in that The SI value is obtained according to the calculation method of stress index: Before comparing SI values, the changes in stress field before and after hydraulic fracturing are analyzed through geological model and stress field simulation; the lower and higher SI values ​​are determined by comparing the values; The lower SI value indicates that the stress conduction is unstable and limited to a small area of ​​the reservoir, which is a microseismic event driven by the fluid with increased pore pressure and releases less energy; Low SI indicates that earthquakes are close in space and time; local stress changes from nearby earthquakes lead to fast-evolving rupture processes, which indicates that the stress field evolves rapidly and is complex; A larger SI value indicates that the microseismic event releases a large amount of energy, the stress field is more stable, and the final fracturing influence range is larger. The microseismic events are triggered by stress in the cracks or faults with the direction of the maximum principal stress, and the fracturing effect of the coal seam roof is finally evaluated.

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