Microseism event identification method and device based on distributed optical fibers

Abstract

The invention provides a micro-seismic event identification method and device based on distributed optical fibers. The micro-seismic event identification method based on the distributed optical fibers comprises the following steps: acquiring energy channel data of distributed optical fiber sound wave data according to the channel number of the distributed optical fiber sound wave data of a target reservoir; determining a microseism event identification threshold according to the energy channel data; and recognizing the micro-seismic event of the target reservoir according to the energy channel data and the micro-seismic event recognition threshold. According to the micro-seismic event identification method based on the distributed optical fibers, the micro-seismic event identification curve can be obtained without channel-by-channel calculation, the micro-seismic event identification efficiency is improved, the storage amount of the acoustic wave data of the distributed optical fibers is reduced, and therefore the requirement for field real-time monitoring during fracturing is met, and the application prospect is wide. Moreover, the calculation process is simple and efficient, and technical support can be provided for microseism monitoring event identification and subsequent positioning during distributed optical fiber fracturing monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development using geophysics, in particular to the technical field of oil and gas field fracturing monitoring, and more particularly to a microseismic event identification method and device based on distributed optical fiber. Background Art

[0002] Unconventional reservoirs have become the main battlefield for exploration and development, playing an important role in the energy substitution process, especially shale gas development, which has achieved great success. The vigorous development of shale gas has benefited from the vigorous development of hydraulic fracturing technology. Hydraulic fracturing has become the main means of efficient development of unconventional reservoirs, and the quality of fracturing is directly related to the recovery rate of the reservoir. Therefore, it is necessary to monitor the fracturing effect in real time to provide a basis for the improvement of the fracturing process, so as to ensure the effectiveness of fracturing to the greatest extent.

[0003] Microseismic monitoring technology is currently the most direct means of fracturing monitoring. Since 2011, this technology has developed rapidly. However, as the depth of the developed reservoir gradually increases, microseismic monitoring faces the following two challenges. First, for ground microseismic monitoring, the deeper the reservoir, the weaker the effective signal energy received by the ground detector, and the more difficult it is to identify microseismic events. Secondly, for microseismic monitoring in wells, the deeper the reservoir, the gradually higher the formation temperature of the target layer. Affected by the formation temperature, the well detector cannot be too close to the fracturing well, but if the detector is far away from the fracturing well, the monitoring capability will be reduced. Summary of the invention

[0004] One purpose of the present invention is to provide a microseismic event identification method based on distributed optical fiber, which aims to solve the following technical pain points in the prior art: First, the conventional microseismic event identification method requires channel-by-channel calculation, which is large in amount of calculation for massive optical fiber data, is not conducive to real-time processing, and cannot meet the needs of optical fiber fracturing microseismic monitoring. Secondly, massive data processing is generally based on machine learning methods for event identification, which has complex calculations, requires sample training, and is cumbersome to operate.

[0005] Another object of the present invention is to provide a microseismic event identification device based on distributed optical fiber. Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above-mentioned microseismic event identification method based on distributed optical fiber when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above-mentioned microseismic event identification method based on distributed optical fiber are implemented.

[0006] In order to achieve the above objectives, the present invention discloses a method for identifying microseismic events based on distributed optical fiber, comprising:

[0007] Acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0008] Determining a microseismic event identification threshold according to the energy channel data;

[0009] The microseismic events of the target reservoir are identified according to the energy trace data and the microseismic event identification threshold.

[0010] In one embodiment, the method for identifying microseismic events based on distributed optical fiber further includes:

[0011] The storage time interval of the distributed optical fiber acoustic wave data is set according to the pore structure parameters of the target reservoir.

[0012] In one embodiment, the step of obtaining energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir includes:

[0013] Segmenting the distributed optical fiber acoustic wave data according to the storage time interval to generate a plurality of segmentation results;

[0014] The energy channel data of each segmentation result is obtained according to the channel data of each segmentation result.

[0015] In one embodiment, the pore structure parameters include: pore throat coordination number, pore type, throat type, sorting parameter and connectivity parameter.

[0016] In one embodiment, determining a microseismic event identification threshold according to the energy trace data includes:

[0017] Determining the quantile of the energy channel data;

[0018] The microseismic event identification threshold is determined according to the quantile.

[0019] In one embodiment, identifying the microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold includes:

[0020] Calculating the difference between the energy trace data and the microseismic event identification threshold;

[0021] Determining a scanning time window according to reservoir rock physical property parameters of the target reservoir;

[0022] Microseismic events of the target reservoir are identified according to the energy trace data, the difference and the scanning time window.

[0023] In one embodiment, the energy channel data is average energy channel data of the distributed optical fiber acoustic wave data.

[0024] The present invention also discloses a microseismic event identification device based on distributed optical fiber, comprising:

[0025] An energy channel data acquisition module, used for acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0026] An event recognition threshold determination module, used to determine a microseismic event recognition threshold according to the energy channel data;

[0027] A microseismic event identification module is used to identify microseismic events in the target reservoir according to the energy trace data and the microseismic event identification threshold.

[0028] In one embodiment, the microseismic event identification device based on distributed optical fiber further includes:

[0029] The storage time interval setting module is used to set the storage time interval of the distributed optical fiber acoustic wave data according to the pore structure parameters of the target reservoir.

[0030] In one embodiment, the energy channel data acquisition module includes:

[0031] a segmentation result generating unit, configured to segment the distributed optical fiber acoustic wave data according to the storage time interval to generate a plurality of segmentation results;

[0032] An energy channel data acquisition unit is used to acquire the energy channel data of each segmentation result according to the channel data of each segmentation result.

[0033] In one embodiment, the pore structure parameters include: pore throat coordination number, pore type, throat type, sorting parameter and connectivity parameter.

[0034] In one embodiment, the event recognition threshold determination module includes:

[0035] A quantile determination unit, used to determine the quantile of the energy channel data;

[0036] An event identification threshold determination unit is used to determine the microseismic event identification threshold according to the quantile.

[0037] In one embodiment, the microseismic event identification module includes:

[0038] A difference calculation unit, used for calculating the difference between the energy channel data and the microseismic event identification threshold;

[0039] A scanning time window determination unit, used to determine the scanning time window according to the reservoir petrophysical parameters of the target reservoir;

[0040] A microseismic event identification unit is used to identify microseismic events of the target reservoir according to the energy trace data, the difference and the scanning time window.

[0041] In one embodiment, the energy channel data is average energy channel data of the distributed optical fiber acoustic wave data.

[0042] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0043] When the processor executes the program, the method described above is implemented.

[0044] The present invention also discloses a computer readable medium having a computer program stored thereon.

[0045] When the program is executed by a processor, the method described above is implemented.

[0046] From the above description, it can be seen that the distributed optical fiber-based microseismic event identification method and device provided in the embodiment of the present invention first obtains the energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; then, the microseismic event identification threshold is determined according to the energy channel data; finally, the microseismic events of the target reservoir are identified according to the energy channel data and the microseismic event identification threshold. Compared with the prior art, the present invention has the following technical effects: it does not require channel-by-channel calculation to obtain the microseismic event identification curve, which not only improves the efficiency of microseismic event identification, but also reduces the storage volume of distributed optical fiber acoustic wave data, thereby meeting the requirements of on-site real-time monitoring during fracturing. In addition, the calculation process is concise and efficient, and can provide technical support for the identification and subsequent positioning of microseismic monitoring events during distributed optical fiber fracturing monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic diagram of a process of a microseismic event identification method based on distributed optical fiber in Embodiment 1 of the present invention;

[0049] Figure 2It is another schematic flow chart of the microseismic event identification method based on distributed optical fiber in the first embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the process of step 200 in the method for identifying microseismic events based on distributed optical fiber in the first embodiment of the present invention;

[0051] Figure 4 It is a flow chart of step 300 of the method for identifying microseismic events based on distributed optical fiber in the first embodiment of the present invention;

[0052] Figure 5 It is a flow chart of a method for identifying microseismic events based on distributed optical fiber in the second embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of DAS microseismic recording in Example 2 of the present invention;

[0054] Figure 7 It is a schematic diagram of the average energy trace and microseismic event identification threshold in the second embodiment of the present invention;

[0055] Figure 8 A schematic diagram of the DAS microseismic recording results identified and extracted in the second embodiment of the present invention;

[0056] Fig. 9 It is a structural schematic diagram of a microseismic event identification device based on distributed optical fiber in the third embodiment of the present invention;

[0057] Fig.10 It is a schematic diagram of the structure of an electronic device in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] It should be noted that the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the absence of conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0060] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0061] Embodiment 1

[0062] In this embodiment, Figure 1 As shown, a method for identifying microseismic events based on distributed optical fiber is provided, which includes:

[0063] Step 100: acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0064] In recent years, distributed optical fiber has been widely used in the fields of national power grid, oil and gas well monitoring, oil pipelines, national defense security, etc. Distributed Acoustic Sensing (DAS) is based on the principle of optical time-domain reflectometer (OTDR). It sends laser pulses to the connected detection optical cable through a high-power laser transmitter, and collects and analyzes the Rayleigh scattered light in the backscattered light, thereby realizing distributed perception of vibration or acoustic wave signals. DAS-based in-well fracturing monitoring not only has the advantages of high temperature resistance, high pressure resistance, and corrosion resistance, but also can achieve high-density sampling in space throughout the well section. It is a new type of fracturing monitoring technology. Therefore, distributed optical fiber can sense the acoustic wave signal of underground ruptures in the same way as the detector. At the same time, optical fiber is also resistant to high temperature and high pressure, so it is a revolutionary in-well monitoring technology for fracturing.

[0065] Step 200: determining a microseismic event recognition threshold according to the energy channel data;

[0066] It is understandable that there are significant differences between distributed fiber optic microseismic monitoring and microseismic well monitoring and ground monitoring. In distributed fiber optic microseismic monitoring, the optical fiber is generally set along the entire well section. Distributed optical fiber has the characteristics of high spatial sampling rate and high temporal sampling rate, while microseismic monitoring belongs to long-term dynamic monitoring. Each fracturing period can reach 3-6 hours, or even longer. Therefore, the amount of data from distributed fiber optic monitoring is particularly large.

[0067] The existing microseismic event recognition method requires channel-by-channel calculations, which is computationally intensive for massive fiber optic data and is not conducive to real-time processing, and cannot meet the needs of fiber optic fracturing microseismic monitoring. Massive data processing is often based on machine learning methods for event recognition, which is computationally complex, requires sample training, and is cumbersome to operate.

[0068] Step 300: Identify microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold.

[0069] One of the most obvious characteristics of the microseismic event waveform is that it lasts for a short time, usually only a few tenths of a second. In addition, the amplitude is usually small, which is also an important criterion for distinguishing microseismic event signals. The microseismic waves of microseismic events are no different from seismic waves in physical nature and can be understood as seismic waves with less energy.

[0070] In the process of oil and gas field development, for low permeability oil and gas fields, fracturing technology is an important measure to increase production and injection, and the cracks generated by fracturing (the orientation of the cracks is closely related to the ground stress) and the scale of fracturing are important references for well network deployment. Therefore, in-depth research on the orientation and morphology of the cracks and timely adjustment of the well network have always been an urgent issue to be solved in oil fields. Microseismic fracturing monitoring technology has become an important new technology for increasing production in low permeability oil and gas reservoirs in recent years. Specifically, by deploying detectors in adjacent wells (or on the ground) to monitor the microseismic waves induced by the fracturing wells during the fracturing process, the geometric shape and spatial distribution of the fracture growth during the fracturing process are described. It can provide real-time information on the height, length and azimuth of the cracks generated during the fracturing construction process. This information can be used to optimize the well location design, well network and other development measures. Microseismic event monitoring is used to observe the microseismic signals generated by underground rock fractures, which is an important means to evaluate the fracturing effect of unconventional oil and gas reservoirs.

[0071] From the above description, it can be seen that the distributed optical fiber-based microseismic event identification method provided by the embodiment of the present invention first obtains the energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; then, the microseismic event identification threshold is determined according to the energy channel data; finally, the microseismic events of the target reservoir are identified according to the energy channel data and the microseismic event identification threshold. Compared with the prior art, the present invention has the following technical effects: it does not require channel-by-channel calculation to obtain the microseismic event identification curve, which not only improves the efficiency of microseismic event identification, but also reduces the storage volume of distributed optical fiber acoustic wave data, thereby meeting the requirements of on-site real-time monitoring during fracturing. In addition, the calculation process is concise and efficient, and can provide technical support for the identification and subsequent positioning of microseismic monitoring events during distributed optical fiber fracturing monitoring.

[0072] In one embodiment, see Figure 2 , the microseismic event identification method based on distributed optical fiber also includes:

[0073] Step 400: Setting the storage time interval of the distributed optical fiber acoustic wave data according to the pore structure parameters of the target reservoir.

[0074] Although fracturing monitoring using DAS microseismic records is continuous monitoring, in the actual data collection process, the collected data is often stored as a seismic SGY file in units of preset periods. When step 400 is implemented, the preset period is determined according to the pore-throat coordination number, pore type, throat type, sorting parameters and connectivity parameters of the target reservoir.

[0075] Pore ​​structure parameters are used to characterize the types, sizes, distribution and interconnectedness of pores and throats in reservoir rocks. The pore system of rocks consists of two parts: pores and throats. Pores are the expanded part of the system, and the small part connecting the pores is called the throat. Pores are the basic storage space for fluids in rocks, while throats are important channels that control the seepage of fluids in rocks. When fluids flow in the complex pore system in nature, they must go through a series of alternating pores and throats.

[0076] The pore-throat coordination number refers to the number of throats connecting each pore, and is usually expressed as the average of the statistical results.

[0077] The types of throats include: pore-reducing throats, necking throats, lamellar throats, tubular throats, bundled throats, and cracks.

[0078] Sorting parameters are used to characterize the linearity of the target reservoir, and sorting is used to reflect the degree to which the pore (throat) size deviates from a certain standard value (median or maximum value). The smaller the deviation, the more uniform the distribution; conversely, the larger the deviation, the more uneven the distribution. The better the pore throat sorting, the more conducive to fluid seepage.

[0079] The sorting parameters that characterize the sorting of pores or throats mainly include sorting coefficient, relative sorting coefficient and homogeneity coefficient. Among them, sorting coefficient and relative sorting coefficient express the degree to which the pore (throat) size deviates from the pore throat mean, and homogeneity coefficient expresses the degree to which the pore (throat) size deviates from the maximum connected pore throat radius. Connectivity parameters are used to characterize the connectivity of the target reservoir.

[0080] In one embodiment, see Figure 3 , step 200 comprises:

[0081] Step 201: Determine the quantile of the energy channel data;

[0082] Specifically, the probability distribution range of the energy channel data is divided into a plurality of equal numerical points to analyze the trend of the energy channel data.

[0083] Step 202: Determine the microseismic event identification threshold according to the quantile.

[0084] Preferably, in step 201, quantiles of a plurality of energy trace data are obtained, and a microseismic event recognition threshold is determined according to the plurality of quantiles.

[0085] In one embodiment, see Figure 4 , step 300 comprises:

[0086] Step 301: Calculate the difference between the energy trace data and the microseismic event identification threshold;

[0087] The difference between the energy channel data and the microseismic event identification threshold is calculated, and the places where the difference is greater than 0 are determined as valid signals.

[0088] Step 302: determining a scanning time window according to reservoir petrophysical parameters of the target reservoir;

[0089] Step 303: Identify microseismic events of the target reservoir according to the energy trace data, the difference and the scanning time window.

[0090] Specifically, in step 302 and step 303, the scanning time window is determined according to the porosity, permeability, fracture development and mineral type of the target reservoir. For unconventional reservoirs, the scanning time window is preferably set as follows: for positions where the difference is greater than 0, a time window is opened before and after, 0.25 s in the front and 1 s in the back, and the records of 1.25 s before and after are intercepted and stored as DAS microseismic records representing microseismic events.

[0091] Porosity refers to the ratio of the sum of the volumes of all pore spaces in a rock sample to the volume of the rock sample, which is called the total porosity of the rock and is expressed as a percentage. The larger the total porosity of the reservoir, the larger the pore space in the rock. From a practical point of view, only those pores that are interconnected are of practical significance, because they can not only store oil and gas, but also allow oil and gas to permeate through them. Therefore, in production practice, the concept of effective porosity is proposed. Effective porosity refers to the ratio of the sum of the volumes of those pores that are interconnected and allow fluid to flow through them under normal pressure conditions to the total volume of the rock sample, expressed as a percentage. Obviously, the effective porosity of the same rock is less than its total porosity.

[0092] Permeability refers to the ability of rock to allow fluid to pass through under a certain pressure difference. It is a parameter that characterizes the ability of rock itself to conduct liquid. Its size is related to factors such as porosity, the geometry of pores in the direction of liquid penetration, particle size and arrangement direction, but has nothing to do with the properties of the liquid moving in the medium. Permeability (k) is used to indicate the size of permeability. When a multiphase fluid seeps in a porous medium, the permeability of one of the fluids is called the effective permeability of the fluid, also called phase permeability. When a multiphase fluid seeps in a porous medium, the ratio of the permeability coefficient of one of the fluids at the saturation to the saturated permeability coefficient of the medium is called relative permeability, which is a dimensionless quantity.

[0093] The degree of fracture development can be characterized by fracture density, fracture porosity, fracture permeability and fracture inclination.

[0094] The main mineral types include: quartz, illite and chlorite.

[0095] In one embodiment, the energy channel data is average energy channel data of the distributed optical fiber acoustic wave data.

[0096] From the above description, it can be seen that the distributed optical fiber-based microseismic event identification method provided by the embodiment of the present invention first obtains the energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; then, the microseismic event identification threshold is determined according to the energy channel data; finally, the microseismic events of the target reservoir are identified according to the energy channel data and the microseismic event identification threshold. Compared with the prior art, the present invention has the following technical effects: it does not require channel-by-channel calculation to obtain the microseismic event identification curve, which not only improves the efficiency of microseismic event identification, but also reduces the storage volume of distributed optical fiber acoustic wave data, thereby meeting the requirements of on-site real-time monitoring during fracturing. In addition, the calculation process is concise and efficient, and can provide technical support for the identification and subsequent positioning of microseismic monitoring events during distributed optical fiber fracturing monitoring.

[0097] Embodiment 2

[0098] In order to further illustrate the solution, the present invention also provides a specific application example of the microseismic event identification method based on distributed optical fiber, specifically, as follows Figure 5 As shown, the specific application example includes the following steps:

[0099] S1: Obtain the average energy trace of DAS microseismic records.

[0100] Although DAS fracturing monitoring is continuous monitoring, in the actual data collection process, a preset period (preferably 10s or 30s) is often determined according to the pore structure parameters of the target reservoir to store the collected data as a seismic SGY file. For the SGY files, the average energy channel is calculated one by one, and microseismic events are quickly identified based on the average energy channel. Assume that a single SGY file has M channels and N sampling points, and the data is recorded as d m (t i ), where i = 1, 2, ... N, m = 1, 2, ... M, the average energy is calculated as follows:

[0101]

[0102] In the above formula, t is the sampling time.

[0103] S2: Determine the microseismic event identification threshold based on the average energy trace data.

[0104] Since the identification of microseismic events is based on the data response of the average energy trace, a statistical analysis is performed on the average energy trace. Specifically, the average trace data needs to be displaced from small to large so that the data satisfies the following relationship:

[0105]

[0106] For the sorted data, calculate the upper quartile Q1 and the lower quartile Q3. The calculation formula is as follows:

[0107] Q1=1+(n-1)×0.25 (3)

[0108] Q3=1+(n-1)×0.75 (4)

[0109] The calculation formula of the microseismic event identification threshold K is as follows:

[0110] K=2×(Q3-Q1) (5)

[0111] S3: Identification of microseismic events.

[0112] Calculate the difference between the average energy and the microseismic event identification threshold, and convert the difference D(t i ) is greater than 0, which is determined as a valid signal. The D value is calculated as follows:

[0113]

[0114] S4: Extract microseismic events.

[0115] For the position where the difference D(t) ≥ 0 in step S3, a time window is opened, 0.25 s in the front and 1 s in the back, and the records of 1.25 s before and after are intercepted and stored as the DAS microseismic event record S(t j ).

[0116]

[0117] Results verification: DAS microseismic records monitored during fracturing are as follows: Figure 6 As shown in the figure, the record has 1321 channels, the receiving time is 10 seconds, and the time sampling interval is 0.5 milliseconds, so 20,000 sampling points are collected for each channel. Figure 6 Assuming that each sample point is a floating point number and occupies 4 bytes, the file size is 1321×20000×4 / (1024×1024)=100.78MB, which shows that the amount of DAS microseismic record data is very large. For such a large amount of data, conventional microseismic event identification methods cannot meet the needs of real-time monitoring.

[0118] See also Figure 6 , indicating that there is a microseismic event in the record around 1 second. For this DAS microseismic record, the average energy trace is calculated using formula (1), as follows: Figure 7 As shown in the black curve, the curve shows an obvious amplitude (energy) peak. The threshold curve for microseismic event recognition is calculated using formulas (3)-(5) as follows: Figure 7 Indicated by the dashed line. Figure 7 The two curves in the figure are used to calculate the difference between the average energy trace and the microseismic event recognition threshold curve using formula (6), determine the amplitude peak value greater than the microseismic event recognition threshold, and calculate the position where the difference is the largest. Using formula (7), a time window is opened before and after the maximum difference, and the extracted DAS microseismic waveform is as follows: Figure 8 shown.

[0119] From the above description, it can be seen that the distributed optical fiber-based microseismic event identification method provided in an embodiment of the present invention first obtains energy channel data of distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; then, the microseismic event identification threshold is determined according to the energy channel data; finally, the microseismic events of the target reservoir are identified according to the energy channel data and the microseismic event identification threshold.

[0120] Specifically, the distributed optical fiber-based microseismic event identification method provided by the embodiment of the present invention first calculates a single record file to obtain its average energy channel; secondly, based on a statistical method, a microseismic event identification threshold is set; then, the area on the average energy channel that is greater than the threshold is set as a valid event, and a certain time window is selected to intercept the valid signal for subsequent microseismic positioning processing. After the above processing, only DAS microseismic records with valid signals can be retained, avoiding the preservation of all collected data and reducing data storage pressure.

[0121] The present invention is aimed at the distributed optical fiber microseismic monitoring scenario during hydraulic fracturing, and proposes a method for rapid identification and extraction of microseismic events to extract optical fiber microseismic events from massive distributed optical fiber microseismic recording data. This method avoids the disadvantages of the prior art of one-by-one calculation, and can quickly extract microseismic event data, while reducing the amount of subsequent processing data, reducing data storage and calculation costs, and the calculation process is concise and efficient, which better meets the needs of real-time monitoring of distributed optical fiber fracturing microseismic.

[0122] Embodiment 3

[0123] Based on the same principle, this embodiment also discloses a microseismic event identification device based on distributed optical fiber. Fig. 9 As shown, the device comprises:

[0124] An energy channel data acquisition module 10 is used to acquire energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0125] An event identification threshold determination module 20, used to determine a microseismic event identification threshold according to the energy trace data;

[0126] The microseismic event identification module 30 is used to identify the microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold.

[0127] In one embodiment, the microseismic event identification device based on distributed optical fiber further includes:

[0128] The storage time interval setting module is used to set the storage time interval of the distributed optical fiber acoustic wave data according to the pore structure parameters of the target reservoir.

[0129] In one embodiment, the energy channel data acquisition module includes:

[0130] a segmentation result generating unit, configured to segment the distributed optical fiber acoustic wave data according to the storage time interval to generate a plurality of segmentation results;

[0131] An energy channel data acquisition unit is used to acquire the energy channel data of each segmentation result according to the channel data of each segmentation result.

[0132] In one embodiment, the pore structure parameters include: pore throat coordination number, pore type, throat type, sorting parameter and connectivity parameter.

[0133] In one embodiment, the event recognition threshold determination module includes:

[0134] A quantile determination unit, used to determine the quantile of the energy channel data;

[0135] An event identification threshold determination unit is used to determine the microseismic event identification threshold according to the quantile.

[0136] In one embodiment, the microseismic event identification module includes:

[0137] A difference calculation unit, used for calculating the difference between the energy channel data and the microseismic event identification threshold;

[0138] A scanning time window determination unit, used to determine the scanning time window according to the reservoir petrophysical parameters of the target reservoir;

[0139] A microseismic event identification unit is used to identify microseismic events of the target reservoir according to the energy trace data, the difference and the scanning time window.

[0140] In one embodiment, the energy channel data is average energy channel data of the distributed optical fiber acoustic wave data.

[0141] From the above description, it can be seen that the distributed optical fiber-based microseismic event identification device provided in the embodiment of the present invention first obtains the energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; then, the microseismic event identification threshold is determined according to the energy channel data; finally, the microseismic events of the target reservoir are identified according to the energy channel data and the microseismic event identification threshold.

[0142] The present invention is aimed at the distributed optical fiber microseismic monitoring scenario during hydraulic fracturing, and proposes a device for rapid identification and extraction of microseismic events to extract optical fiber microseismic events from massive distributed optical fiber microseismic recording data. The device avoids the disadvantages of the prior art of one-by-one calculation, and can quickly extract microseismic event data, while reducing the amount of subsequent processing data, reducing data storage and calculation costs, and the calculation process is concise and efficient, which better meets the needs of real-time monitoring of distributed optical fiber fracturing microseismic.

[0143] Embodiment 4

[0144] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps in the microseismic event identification method based on distributed optical fiber in the above embodiments, see Fig.10 , electronic equipment specifically includes the following:

[0145] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0146] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other via the bus 1204; the communication interface 1203 is used to realize information transmission between related devices such as server-side devices, computing units, and client devices.

[0147] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all the steps in the microseismic event identification method based on distributed optical fiber in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0148] Step 100: acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0149] Step 200: determining a microseismic event recognition threshold according to the energy channel data;

[0150] Step 300: Identify microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold.

[0151] Embodiment 5

[0152] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for identifying microseismic events based on distributed optical fibers in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the method for identifying microseismic events based on distributed optical fibers in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0153] Step 100: acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir;

[0154] Step 200: determining a microseismic event recognition threshold according to the energy channel data;

[0155] Step 300: Identify microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold.

[0156] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0157] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0158] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0162] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for identifying microseismic events based on distributed optical fiber, characterized in that: include: Acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; Determining a microseismic event identification threshold according to the energy channel data; The microseismic events of the target reservoir are identified according to the energy trace data and the microseismic event identification threshold.

2. The microseismic event identification method according to claim 1, characterized in that: Also includes: The storage time interval of the distributed optical fiber acoustic wave data is set according to the pore structure parameters of the target reservoir.

3. The microseismic event identification method according to claim 2, characterized in that: The step of obtaining energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir comprises: Segmenting the distributed optical fiber acoustic wave data according to the storage time interval to generate a plurality of segmentation results; The energy channel data of each segmentation result is obtained according to the channel data of each segmentation result.

4. The microseismic event identification method according to claim 2, characterized in that: The pore structure parameters include: pore throat coordination number, pore type, throat type, sorting parameter and connectivity parameter.

5. The microseismic event identification method according to claim 1, characterized in that: Determining the microseismic event identification threshold according to the energy trace data includes: Determining the quantile of the energy channel data; The microseismic event identification threshold is determined according to the quantile.

6. The microseismic event identification method according to claim 1, characterized in that: Identifying the microseismic events of the target reservoir according to the energy trace data and the microseismic event identification threshold includes: Calculating the difference between the energy trace data and the microseismic event identification threshold; Determining a scanning time window according to reservoir rock physical property parameters of the target reservoir; Microseismic events of the target reservoir are identified according to the energy trace data, the difference and the scanning time window.

7. The microseismic event identification method according to any one of claims 1 to 6, characterized in that: The energy channel data is average energy channel data of the distributed optical fiber acoustic wave data.

8. A microseismic event identification device based on distributed optical fiber, characterized in that: include: An energy channel data acquisition module, used for acquiring energy channel data of the distributed optical fiber acoustic wave data according to the number of channels of the distributed optical fiber acoustic wave data of the target reservoir; An event recognition threshold determination module, used to determine a microseismic event recognition threshold according to the energy channel data; A microseismic event identification module is used to identify microseismic events in the target reservoir according to the energy trace data and the microseismic event identification threshold.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images