Mine up-down combined monitoring hydraulic fracturing microseism event identification method

Abstract

The invention discloses a mine up-and-down combined monitoring hydraulic fracturing microseismic event identification method, which comprises the following steps of: respectively arranging microseismic sensor arrays on a ground working surface and an underground working surface, synchronously acquiring microseismic events up and down, selecting the microseismic events in the same time period from up-and-down continuous microseismic records, and identifying the microseismic events in the same time period according to the selected microseismic events. Respectively carrying out empirical mode decomposition (EMD) on the two selected microseism event waveforms by utilizing Hilbert transformation to obtain corresponding intrinsic mode functions (IMF), further identifying effective microseism events through waveform similarity of IMF components by adopting a waveform cross-correlation algorithm, and recombining to form effective hydrofracture microseism events. The method has a good elimination effect on the noise of the microseismic data, can effectively avoid the situation that the signal-to-noise ratio of the original data in the microseismic record is too low, so that the main seismic event is missed, and achieves the efficient and precise recognition of the hydraulic fracturing microseismic event.

Description

Technical Field

[0001] The present invention belongs to the technical field of microseismic monitoring, and particularly relates to a method for identifying microseismic events in hydraulic fracturing by combined surface and underground monitoring in a mine. Background Art

[0002] Hydraulic fracturing is one of the key technologies to increase the gas permeability of coal seams and improve the gas extraction effect. Microseismic monitoring technology is one of the best methods to explain the complex fracture behavior of coal and rock at present, with the advantages of high resolution, low cost, wide coverage, etc. By monitoring the microseismic events generated during the process of coal seam hydraulic fracture propagation, the fracture orientation and propagation range can be accurately judged, and the permeability enhancement area of hydraulic fracturing can be evaluated. Microseismic source location is one of the core technologies of microseismic monitoring. Accurately detecting the range of coal seam hydraulic fracturing is of great significance for scientifically evaluating the permeability enhancement effect of hydraulic fracturing and improving the efficiency of coal seam gas extraction.

[0003] At present, there are mainly two ideas for using microseismic monitoring technology to monitor the range of coal seam hydraulic fracturing: surface monitoring and underground monitoring. The surface monitoring sensors have a wide coverage range and a large monitoring area, and can fully collect microseismic signals. However, the surface monitoring has defects such as low signal-to-noise ratio, poor vertical resolution ability, and low positioning accuracy. Underground monitoring mainly arranges microseismic sensors in the working face roadway. Compared with surface monitoring, underground monitoring can receive the full-wavefield microseismic signals of coal seam hydraulic fracturing, and there are more effective microseismic events. However, due to the limited underground space, it is difficult to arrange sensors, and there are many underground noise signals, the signal-to-noise ratio of monitoring data is low, and it is difficult to identify effective microseismic events, resulting in low microseismic positioning accuracy. To solve this technical problem, it is necessary to develop a method for identifying effective microseismic events in coal seam hydraulic fracturing by combined surface and underground monitoring with higher accuracy. Summary of the Invention

[0004] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a method for identifying microseismic events in hydraulic fracturing by combined surface and underground monitoring in a mine. By the way of combined surface and underground monitoring of microseismic events in coal seam hydraulic fracturing, it can efficiently and accurately obtain microseismic events in hydraulic fracturing.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a method for identifying microseismic events in hydraulic fracturing by combined surface and underground monitoring in a mine, comprising the following steps:

[0006] Step 1: Respectively arrange microseismic sensor arrays on the ground and in the underground working face, and synchronize the acquisition time through the acquisition equipment so that the microseismic sensor arrays on the ground and in the underground working face synchronously acquire microseismic events in coal seam hydraulic fracturing or surface well hydraulic fracturing; in order to avoid the influence of loose formation on the coupling effect of microseismic sensors, bury the sensors on the bedrock at a certain depth on the ground to ensure the signal acquisition effect.

[0007] Step 2: Set the target time period, and select the microseismic events within this target time period from the microseismic data collected on the ground in Step 1 and the microseismic data collected downhole respectively, to form an on-well microseismic event set and a downhole microseismic event set. Then divide each set of on-well and downhole microseismic events into multiple single microseismic events;

[0008] Step 3: First, arbitrarily select a single microseismic event at the same time node on the well and downhole from Step 2, and perform empirical mode decomposition on the waveforms of the two selected microseismic events respectively;

[0009] Step 4: Obtain a finite number of IMF components through empirical mode decomposition, and then use the Hilbert transform to obtain the Hilbert spectrum and instantaneous frequency parameters of the IMF components. The Hilbert spectrum is a weighted three-dimensional display of joint time, frequency, and amplitude, which can more clearly depict the local information of the signal;

[0010] Step 5: Use the waveform cross-correlation algorithm to calculate the cross-correlation function values between the IMF components of the single microseismic events on the well and downhole, and finally obtain several similar mode components of the single microseismic events on the well and downhole;

[0011] Step 6: Reconstruct the signals of several similar mode components of the single microseismic events on the well and downhole to obtain an effective hydraulic fracturing microseismic event; Repeat Steps 3 to 6 multiple times to obtain all effective hydraulic fracturing microseismic events.

[0012] Furthermore, the downhole microseismic sensor array is composed of multiple microseismic sensors arranged at different heights and different planes.

[0013] Furthermore, the ground microseismic sensor array is composed of multiple microseismic sensors arranged in a star-shaped radial pattern on the ground.

[0014] Furthermore, the specific process of Step 3 is as follows: First, find the maximum and minimum values of the selected microseismic event waveform, and perform interpolation on all extreme points through cubic spline fitting to obtain the upper envelope curve and lower envelope curve of the microseismic event waveform. Then calculate the average value at each point of the upper and lower envelope curves to obtain the mean envelope line; Subtract the value of the mean envelope line from the selected microseismic event waveform to get an approximate IMF component. When this component meets the conditions required for the IMF, determine it as the first-order IMF component. If it does not meet the conditions, repeat the above steps until the screening termination condition is met; Then subtract the first-order IMF component from the selected microseismic event waveform to get the remaining waveform signal, and take the remaining waveform signal as a new original sequence. Repeat this step to extract the second-order, third-order,... n-order IMF components in turn until the obtained IMF component is a monotonic function and cannot be further decomposed, which is the residual of the original signal.

[0015] Further, the cross-correlation function in the fifth step is defined as:

[0016]

[0017] where: x i (n) and x j (n) respectively represent two selected IMF waveform data, and n is the number of sampling points;

[0018] When the cross-correlation function value of two IMFs exceeds 0.8, it proves that there is a high similarity between the two IMF components. Then, combining with the instantaneous frequency parameters of the IMFs, the two IMF components are defined as a group of similar mode components; repeating the fifth step, several similar mode components of a single microseismic event above and below the well are finally obtained.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] 1. The present invention respectively arranges microseismic sensor arrays on the ground and underground working faces, synchronously collects microseismic events through the well and the ground, then selects microseismic events within the same time period from the continuous microseismic records above and below the well, and respectively performs empirical mode decomposition on the waveforms of the two selected microseismic events to obtain the corresponding IMF components. Furthermore, the waveform cross-correlation algorithm is used to identify effective microseismic events through the waveform similarity of the IMF components, and reorganize them to form effective hydraulic fracturing microseismic events, which has a good effect on removing the noise of the microseismic data, can effectively avoid the situation that the main shock event is missed due to too low signal-to-noise ratio of the original data in the microseismic record, can better protect the effective signal, and significantly improve the signal-to-noise ratio of the microseismic data.

[0021] 2. The whole scheme of the present invention is simple to operate, can be automatically recognized through the whole process by software, has high picking accuracy, reduces the workload of manually identifying and extracting microseismic events, and avoids human errors, and finally ensures the efficient and accurate acquisition of hydraulic fracturing microseismic events. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is the overall flow schematic diagram of the present invention;

[0023] Figure 2 is the schematic diagram of the combined underground and surface coal seam hydraulic fracturing microseismic monitoring;

[0024] Figure 3 is the schematic diagram of the combined underground and surface well hydraulic fracturing microseismic monitoring.

[0025] In the figure, 1. Ground monitoring area; 2. Coal seam; 3. Rock stratum; 4. Microseismic sensor; 5. Microseismic wave; 6. Hydraulic fracturing crack; 7. Hydraulic fracturing pipe; 8. Ground well. Detailed implementation manners

[0026] The present invention will be further described below.

[0027] As Figure 1 shown, the present invention includes the following steps:

[0028] Step 1: Micro-seismic sensor arrays are respectively arranged on the ground and the underground working face, and the micro-seismic sensor arrays on the ground and the underground working face are synchronously collected through the acquisition equipment to synchronously collect micro-seismic events of coal seam hydraulic fracturing or surface well hydraulic fracturing; the underground micro-seismic sensor array is composed of a plurality of micro-seismic sensors arranged at different heights and different planes; the ground micro-seismic sensor array is composed of a plurality of micro-seismic sensors arranged in a star-shaped radial pattern on the ground as Figure 2 and 3 shown. In order to avoid the influence of loose strata on the coupling effect of micro-seismic sensors, the sensors are buried on the bedrock at a certain depth below the ground to ensure the signal acquisition effect.

[0029] Step 2: Set a target time period, and select micro-seismic events within the target time period from the micro-seismic data collected on the ground and the micro-seismic data collected underground in Step 1 respectively, and form an on-surface micro-seismic event set and an underground micro-seismic event set respectively. Then, each group of micro-seismic event sets above and below the well is divided into a plurality of single micro-seismic events;

[0030] Step 3: First, randomly select a single micro-seismic event at the same time node above and below the well in Step 2, and perform empirical mode decomposition on the waveforms of the two selected micro-seismic events respectively. The specific process is as follows: First, find the maximum and minimum values of the waveform of the selected micro-seismic event, and interpolate all the extreme points through cubic spline fitting to obtain the upper envelope curve and the lower envelope curve of the micro-seismic event waveform. Then, calculate the average value of the upper and lower envelope curves at each point to obtain the mean envelope line; then subtract the value of the mean envelope line from the waveform of the selected micro-seismic event to obtain an approximate IMF component. When this component meets the conditions required by the IMF, it is determined as the first-order IMF component. If it does not meet the conditions, repeat the above steps until the screening termination condition is met; then subtract the first-order IMF component from the waveform of the selected micro-seismic event to obtain the remaining waveform signal, and use the remaining waveform signal as a new original sequence, repeat this step, and extract the second-order, third-order,... n-order IMF components in turn until the obtained IMF component is a monotonic function and cannot be decomposed any further, and then stop, which is the residual of the original signal.

[0031] Step Four: Obtain a finite number of IMF components through empirical mode decomposition, and then use the Hilbert transform to obtain the Hilbert spectrum and instantaneous frequency parameters of the IMF components. The Hilbert spectrum is a weighted three-dimensional display of joint time, frequency, and amplitude, which can more clearly depict the local information of the signal; Step Three and Step Four are collectively referred to as the Hilbert-Huang transform process.

[0032] Step Five: Use the waveform cross-correlation algorithm to calculate the cross-correlation function values between the IMF components of a single microseismic event above and below the well. The cross-correlation function is defined as:

[0033]

[0034] where: x i (n) and x j (n) represent two selected IMF waveform data respectively, and n is the number of sampling points;

[0035] When the cross-correlation function value of two IMFs exceeds 0.8, it proves that there is a high similarity between the two IMF components. Then, combining with the instantaneous frequency parameters of the IMF, the two IMF components are defined as a group of similar mode components; Repeat Step Five to finally obtain several similar mode components of a single microseismic event above and below the well.

[0036] Step Six: Reconstruct the signal of several similar mode components of a single microseismic event above and below the well to obtain an effective hydraulic fracturing microseismic event; Repeat Steps Three to Six multiple times to obtain all effective hydraulic fracturing microseismic events.

[0037] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for identifying hydraulic fracturing microseismic events by combined monitoring above and below the mine, characterized in that, It includes the following steps: Step 1: Deploy microseismic sensor arrays on the ground and in the underground working face respectively, and synchronize the acquisition time through the acquisition equipment so that the microseismic sensor arrays on the ground and in the underground working face synchronously acquire microseismic events of coal seam hydraulic fracturing; Step 2: Set a target time period, and select microseismic events within the target time period from the microseismic data collected on the ground and underground in Step 1 respectively to form an on-ground microseismic event set and an underground microseismic event set. Then divide each set of microseismic events above and below the well into multiple single microseismic events; Step 3: First, arbitrarily select single microseismic events at the same time node above and below the well in Step 2, and perform empirical mode decomposition on the waveforms of the two selected microseismic events respectively; Step 4: Obtain a finite number of IMF components through empirical mode decomposition, and then use Hilbert transform to obtain the Hilbert spectrum and instantaneous frequency parameters of the IMF components; Step 5: Use the waveform cross-correlation algorithm to calculate the cross-correlation function values between the IMF components of single microseismic events above and below the well, and finally obtain several similar mode components of single microseismic events above and below the well; Step 6: Reconstruct the signals of several similar mode components of single microseismic events above and below the well to obtain an effective microseismic event of hydraulic fracturing; Repeat Steps 3 to 6 multiple times to obtain all effective microseismic events of hydraulic fracturing.

2. The method for identifying hydraulic fracturing microseismic events by combined up-and-down mine monitoring according to claim 1, characterized in that The underground microseismic sensor array is composed of multiple microseismic sensors deployed at different heights and different planes.

3. The method for identifying hydraulic fracturing microseismic events by combined up-and-down mine monitoring according to claim 1, wherein The ground microseismic sensor array is composed of multiple microseismic sensors deployed in a star-shaped radial pattern on the ground.

4. The method for identifying hydraulic fracturing microseismic events by combined up-and-down mine monitoring according to claim 1, wherein The specific process of Step 3 is as follows: First, find the maximum and minimum values of the selected microseismic event waveform, and perform interpolation on all extreme points through cubic spline fitting to obtain the upper envelope curve and lower envelope curve of the microseismic event waveform. Then calculate the average value at each point of the upper and lower envelope curves to obtain the mean envelope line; Subtract the value of the mean envelope line from the selected microseismic event waveform to get an approximate IMF component. When this component meets the conditions required for IMF, it is determined as the first-order IMF component. If it does not meet the conditions, repeat the above steps until the screening termination condition is met; Then subtract the first-order IMF component from the selected microseismic event waveform to get the remaining waveform signal, and take the remaining waveform signal as a new original sequence. Repeat this step to extract the second-order, third-order,... n-order IMF components in turn until the obtained IMF component is a monotonic function and cannot be further decomposed, which is the residual of the original signal.

5. The method for identifying hydraulic fracturing microseismic events by combined up-and-down mine monitoring according to claim 1, wherein The cross-correlation function in Step 5 is defined as: where: x i (n) and x j (n) represent two selected IMF waveform data respectively, and n is the number of sampling points; When the cross-correlation function value of two IMFs exceeds 0.8, it proves that there is a high similarity between the two IMF components. Then, combined with the instantaneous frequency parameters of the IMF, the two IMF components are defined as a group of similar mode components; Repeat Step 5 to finally obtain several similar mode components of single microseismic events above and below the well.

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