Distributed fiber based microseismic event identification method and apparatus

Abstract

The application provides a microseismic event identification method and device based on distributed optical fiber, and the method comprises the following steps: obtaining energy channel data of distributed optical fiber acoustic wave data of a target reservoir according to the number of channels of the distributed optical fiber acoustic wave data; determining a microseismic event identification threshold according to the energy channel data; and identifying microseismic events of the target reservoir according to the energy channel data and the microseismic event identification threshold. The microseismic event identification method based on distributed optical fiber provided by the application does not need to calculate channel by channel to obtain a microseismic event identification curve, which improves the microseismic event identification efficiency, reduces the storage amount of distributed optical fiber acoustic wave data, meets the requirement of real-time monitoring on site during fracturing, and the calculation process is simple and efficient, which provides technical support for microseismic monitoring event identification and subsequent positioning during distributed optical fiber fracturing monitoring.

Description

Technical Field

[0001] This invention relates to the technical field of oil and gas field development using geophysics, particularly the technical field of oil and gas field fracturing monitoring, and especially to a method and device for identifying microseismic events based on distributed optical fibers. Background Technology

[0002] Unconventional reservoirs have become the main battleground for exploration and development, playing a crucial role in energy substitution, especially in shale gas development, which has already achieved significant success. The vigorous development of shale gas is attributed to the rapid advancement of hydraulic fracturing technology. Hydraulic fracturing has become the primary means of efficient development of unconventional reservoirs, and the effectiveness of fracturing directly affects the reservoir recovery rate. Therefore, real-time monitoring of fracturing effects is necessary to provide a basis for improving fracturing processes and to maximize fracturing effectiveness.

[0003] Microseismic monitoring technology is currently the most direct means of monitoring fracturing, and its development has been rapid since 2011. However, as the depth of developed reservoirs gradually increases, microseismic monitoring faces the following two challenges. First, for surface microseismic monitoring, the deeper the reservoir, the weaker the effective signal energy received by surface geophones, making microseismic event identification increasingly difficult. Second, for well-drilled microseismic monitoring, the deeper the reservoir, the higher the formation temperature of the target layer. Due to the influence of formation temperature, well-drilled geophones cannot be placed too close to the fracturing well, but a greater distance between the geophone and the fracturing well reduces monitoring capability. Summary of the Invention

[0004] One objective of this invention is to provide a microseismic event identification method based on distributed optical fibers. This method aims to address the following technical pain points in existing technologies: First, conventional microseismic event identification methods require sequential calculations, which is computationally intensive for massive optical fiber data, hindering real-time processing and failing to meet the needs of optical fiber fracturing microseismic monitoring. Second, massive data processing is generally based on machine learning methods for event identification, which is computationally complex, requires training on samples, and is cumbersome.

[0005] Another object of the present invention is to provide a microseismic event identification device based on distributed optical fiber. A further object of the present invention is to provide a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the steps of the aforementioned microseismic event identification method based on distributed optical fiber. A further object of the present invention is to provide a readable medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned microseismic event identification method based on distributed optical fiber.

[0006] To achieve the above objectives, this invention discloses a method for identifying microseismic events based on distributed optical fibers, comprising:

[0007] The energy channel data of the distributed fiber optic acoustic data is obtained based on the number of channels of the distributed fiber optic acoustic data of the target reservoir.

[0008] The threshold for identifying microseismic events is determined based on the energy channel data.

[0009] Microseismic events of the target reservoir are identified based on the energy channel data and the microseismic event identification threshold.

[0010] In one embodiment, the microseismic event identification method based on distributed optical fiber further includes:

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

[0012] In one embodiment, obtaining the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the target reservoir's distributed optical fiber acoustic data includes:

[0013] The distributed optical fiber acoustic data is segmented according to the storage time interval to generate multiple segmentation results;

[0014] The energy channel data of each segmentation result is obtained based on 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 parameters, and connectivity parameters.

[0016] In one embodiment, determining the microseismic event identification threshold based on the energy channel data includes:

[0017] Determine the quantiles of the energy channel data;

[0018] The microseismic event identification threshold is determined based on the quantile.

[0019] In one embodiment, identifying microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold includes:

[0020] Calculate the difference between the energy channel data and the microseismic event identification threshold;

[0021] The scanning window is determined based on the reservoir rock properties of the target reservoir.

[0022] Microseismic events of the target reservoir are identified based on the energy channel data, the difference, and the scan window.

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

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

[0025] The energy channel data acquisition module is used to acquire the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir.

[0026] An event identification threshold determination module is used to determine a microseismic event identification threshold based on the energy channel data.

[0027] The microseismic event identification module is used to identify microseismic events in the target reservoir based on the energy channel 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 data according to the pore structure parameters of the target reservoir.

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

[0031] The segmentation result generation unit is used to segment the distributed optical fiber acoustic data according to the storage time interval to generate multiple segmentation results;

[0032] An energy channel data acquisition unit is used to acquire energy channel data for each segmentation result based on 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 parameters, and connectivity parameters.

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

[0035] Quantile determination unit, used to determine the quantiles of the energy channel data;

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

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

[0038] The difference calculation unit is used to calculate the difference between the energy channel data and the microseismic event identification threshold;

[0039] The scanning time window determination unit is used to determine the scanning time window based on the reservoir rock physical property parameters of the target reservoir.

[0040] The microseismic event identification unit is used to identify microseismic events of the target reservoir based on the energy channel data, the difference, and the scanning time window.

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

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

[0043] When the processor executes the program, it implements the method described above.

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

[0045] When the program is executed by the processor, it implements the method described above.

[0046] As described above, the microseismic event identification method and apparatus based on distributed optical fiber provided in this invention first obtains the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the target reservoir's distributed optical fiber acoustic data; then, it determines the microseismic event identification threshold based on the energy channel data; finally, it identifies the microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold. Compared with existing technologies, this invention has the following technical advantages: it eliminates the need for channel-by-channel calculations to obtain the microseismic event identification curve, thus improving the efficiency of microseismic event identification and reducing the storage volume of distributed optical fiber acoustic data, thereby meeting the requirements for real-time on-site monitoring during fracturing. Furthermore, the calculation process is simple and efficient, providing technical support for microseismic event identification and subsequent location during distributed optical fiber fracturing monitoring. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating the microseismic event identification method based on distributed optical fiber in Embodiment 1 of the present invention.

[0049] Figure 2This is another flowchart illustrating the microseismic event identification method based on distributed optical fiber in Embodiment 1 of the present invention;

[0050] Figure 3 This is a flowchart illustrating step 200 in the microseismic event identification method based on distributed optical fiber in Embodiment 1 of the present invention.

[0051] Figure 4 This is a flowchart illustrating step 300 of the microseismic event identification method based on distributed optical fiber in Embodiment 1 of the present invention.

[0052] Figure 5 This is a flowchart illustrating the microseismic event identification method based on distributed optical fiber in Embodiment 2 of the present invention.

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

[0054] Figure 7 This is a schematic diagram of the average energy channel and microseismic event identification threshold in Embodiment 2 of the present invention;

[0055] Figure 8 This is a schematic diagram of the DAS microseismic record results identified and extracted in Embodiment 2 of the present invention;

[0056] Figure 9 This is a schematic diagram of the microseismic event identification device based on distributed optical fiber in Embodiment 3 of the present invention;

[0057] Figure 10 This is a schematic diagram of the electronic device in Embodiment 4 of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] It should be noted that the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Without conflict, the embodiments and features in the embodiments of this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

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

[0061] Example 1

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

[0063] Step 100: Obtain the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir;

[0064] In recent years, distributed optical fibers have been widely used in fields such as State Grid power, oil and gas well monitoring, oil pipelines, and national defense security. Distributed optical fiber acoustic sensing (DAS) is based on the principle of optical time-domain reflection (OTDR). It sends laser pulses to the connected detection optical cable through a high-power laser transmitter, while simultaneously collecting and analyzing Rayleigh scattering light in the backscattered light, thereby achieving distributed sensing of vibration or acoustic signals. DAS-based well fracturing monitoring not only has the advantages of high temperature resistance, high pressure resistance, and corrosion resistance, but also enables high-density spatial sampling throughout the entire well section, making it a novel fracturing monitoring technology. Therefore, distributed optical fibers can sense the acoustic signals of underground ruptures, just like geophones. Furthermore, optical fibers are resistant to high temperatures and high pressures, making it a revolutionary technology for well fracturing monitoring.

[0065] Step 200: Determine the microseismic event identification threshold based on the energy channel data;

[0066] Understandably, distributed fiber optic microseismic monitoring differs significantly from in-well and surface microseismic monitoring. In distributed fiber optic microseismic monitoring, the fiber optic cable is typically installed along the entire well section. Distributed fiber optics are characterized by high spatial and temporal sampling rates, while microseismic monitoring is a long-term dynamic process, with each fracturing segment lasting 3-6 hours or even longer. Therefore, the data volume of distributed fiber optic monitoring is exceptionally large.

[0067] Existing microseismic event identification methods require step-by-step calculations, which is computationally intensive for massive amounts of fiber optic data, hindering real-time processing and failing to meet the needs of fiber optic fracturing microseismic monitoring. Furthermore, massive data processing often relies on machine learning methods for event identification, which are computationally complex, require sample training, and are cumbersome to operate.

[0068] Step 300: Identify the microseismic events of the target reservoir based on the energy channel data and the microseismic event identification threshold.

[0069] The most obvious characteristics of microseismic event waveforms are their short duration, typically only a few tenths of a second. Additionally, their usually small amplitude is also an important criterion for distinguishing microseismic event signals. The microwaves of microseismic events are physically no different from seismic waves and can be understood as low-energy seismic waves.

[0070] In oil and gas field development, fracturing is a crucial measure for increasing production and injection in low-permeability oil and gas fields. The fractures generated by fracturing (whose orientation is closely related to geostress) and their scale are important references for well network deployment. Therefore, in-depth research into fracture orientation and morphology, and timely adjustments to the well network, has always been a pressing issue for 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, it involves deploying geophones in adjacent wells (or on the surface) to monitor the microseismic waves induced during fracturing to describe the geometry and spatial distribution of fracture growth. It can provide real-time information on the height, length, and azimuth of fractures generated during fracturing operations, which can be used to optimize well location design and well network development measures. Microseismic event monitoring is used to observe microseismic signals generated by underground rock fracturing and is an important means of evaluating the fracturing effect of unconventional oil and gas reservoirs.

[0071] As described above, the microseismic event identification method based on distributed optical fiber provided in this invention first obtains the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the target reservoir; then, it determines the microseismic event identification threshold based on the energy channel data; finally, it identifies the microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold. Compared with existing technologies, this invention has the following technical advantages: it eliminates the need for channel-by-channel calculations to obtain the microseismic event identification curve, thus improving the efficiency of microseismic event identification and reducing the storage requirements of distributed optical fiber acoustic data, thereby meeting the requirements of real-time on-site monitoring during fracturing. Furthermore, the calculation process is simple and efficient, providing technical support for microseismic event identification and subsequent location 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: Set the storage time interval of the distributed optical fiber acoustic data according to the pore structure parameters of the target reservoir.

[0074] Although fracturing monitoring using DAS microseismic records is continuous, in actual data acquisition, the acquired data is often saved as a single seismic SGY file in preset periods. Step 400, during implementation, determines the preset period based on 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 type, size, distribution, and interconnections of pores and throats within reservoir rocks. The pore system of a rock consists of two parts: pores and throats. Pores are the expanded parts of the system, and the small portions connecting the pores are called throats. Pores are the basic storage spaces for fluids in rocks, while throats are important channels controlling the seepage of fluids within the rock. When fluids flow through the complex pore systems of nature, they pass 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 statistical results.

[0077] Types of larynx include: narrowed larynx, constricted larynx, sheet-like larynx, tubular larynx, bundle-like larynx, and fissure.

[0078] Sorting parameters are used to characterize the linearity of the target reservoir. Sorting property reflects 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 less uniform the distribution. Better pore and throat sorting property is more conducive to fluid seepage.

[0079] The sorting parameters characterizing pore or throat sorting properties mainly include the sorting coefficient, relative sorting coefficient, and homogeneity coefficient. The sorting coefficient and relative sorting coefficient express the degree to which the pore (throat) size deviates from the average pore-throat value, while the homogeneity coefficient expresses the degree to which the pore (throat) size deviates from the radius of the largest connected pore-throat. Connectivity parameters are used to characterize the connectivity of the target reservoir.

[0080] In one embodiment, see Figure 3 Step 200 includes:

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

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

[0083] Step 202: Determine the microseismic event identification threshold based on the quantile.

[0084] Preferably, in step 201, the quantiles of multiple energy channel data are obtained, and the microseismic event identification threshold is determined based on the multiple quantiles.

[0085] In one embodiment, see Figure 4 Step 300 includes:

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

[0087] The difference between the energy channel data and the microseismic event identification threshold is calculated, and areas with a difference greater than 0 are identified as valid signals.

[0088] Step 302: Determine the scanning time window based on the reservoir rock properties of the target reservoir;

[0089] Step 303: Identify the microseismic events of the target reservoir based on the energy channel data, the difference, and the scanning time window.

[0090] Specifically, in steps 302 and 303, the scanning time window is determined based on the porosity, permeability, fracture development, and mineral type of the target reservoir. For unconventional reservoirs, the preferred setting for the scanning time window is to open a time window before and after the position where the difference is greater than 0, with a time window of 0.25s before and 1s after, and to extract and store the records of the first and last 1.25s as records of microseismic events in DAS microseismic records.

[0091] Porosity refers to the ratio of the sum of the volumes of all pore spaces in a rock sample to the total volume of the rock sample, expressed as a percentage. The greater the total porosity of a reservoir, the larger the pore spaces within the rock. From a practical standpoint, only interconnected pores are meaningful, as they not only store oil and gas but also allow for their permeation. Therefore, the concept of effective porosity has been introduced in production practice. Effective porosity is the ratio of the sum of the volumes of interconnected pores that allow fluid flow under normal pressure conditions to the total volume of the rock sample, expressed as a percentage. Clearly, the effective porosity of the same rock is less than its total porosity.

[0092] Permeability refers to the ability of a rock to allow fluids to pass through it under a certain pressure difference; it is a parameter characterizing the rock's ability to conduct liquids. Its magnitude is related to factors such as porosity, the geometry of the pores along the direction of liquid permeation, particle size, and their arrangement, but is independent of the properties of the liquid moving in the medium. Permeability (k) is used to represent the magnitude of permeability. When a multiphase fluid seeps into a porous medium, the permeability of one of the fluid components is called the effective permeability of that fluid component, also known as phase permeability. When a multiphase fluid seeps into a porous medium, the ratio of the permeability coefficient of one fluid component at that saturation level to the saturated permeability coefficient of the medium is called relative permeability, which is a dimensionless quantity.

[0093] The degree of crack development can be characterized by crack density, crack porosity, crack permeability, and crack dip angle.

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

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

[0096] As described above, the microseismic event identification method based on distributed optical fiber provided in this invention first obtains the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the target reservoir; then, it determines the microseismic event identification threshold based on the energy channel data; finally, it identifies the microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold. Compared with existing technologies, this invention has the following technical advantages: it eliminates the need for channel-by-channel calculations to obtain the microseismic event identification curve, thus improving the efficiency of microseismic event identification and reducing the storage requirements of distributed optical fiber acoustic data, thereby meeting the requirements of real-time on-site monitoring during fracturing. Furthermore, the calculation process is simple and efficient, providing technical support for microseismic event identification and subsequent location during distributed optical fiber fracturing monitoring.

[0097] Example 2

[0098] To further illustrate the solution, this invention also provides specific application examples of the microseismic event identification method based on distributed optical fibers, specifically, such as... Figure 5 As shown, this specific application example includes the following steps:

[0099] S1: Calculate the average energy trace of the DAS microseismic record.

[0100] Although DAS fracturing monitoring is continuous, in actual data acquisition, a preset period (preferably 10s or 30s) is often determined based on the target reservoir pore structure parameters before the acquired data is saved as a seismic SGY file. For each SGY file, the average energy trace is calculated, and microseismic events are rapidly identified based on the average energy trace. Let a single SGY file have M traces and N sampling points, and the data be denoted as d. m (t i ), where i = 1, 2, ... N, m = 1, 2, ... M, and the average energy channel 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 channel data.

[0104] Since microseismic event identification is based on the data response of the average energy channel, statistical analysis of the average energy channel is performed. Specifically, the average channel data first needs to be sorted from smallest to largest to ensure that the data satisfy the following relationship:

[0105]

[0106] For the sorted data, calculate its upper quartile Q1 and lower quartile Q3 using the following formulas:

[0107] Q1=1+(n-1)×0.25 (3)

[0108] Q3=1+(n-1)×0.75 (4)

[0109] The formula for calculating the microseismic event identification threshold K is as follows:

[0110] K = 2 × (Q3 - Q1) (5)

[0111] S3: Identify microseismic events.

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

[0113]

[0114] S4: Extract microseismic events.

[0115] For the positions in step S3 where the difference D(t) ≥ 0, time windows are opened before and after the point, with a time window of 0.25s before and 1s after. The records of the first and last 1.25s are extracted 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, the record contains 1321 channels, with a reception duration of 10 seconds and a time sampling interval of 0.5 milliseconds, therefore each channel collects 20,000 sampling points. For Figure 6 The file shown, assuming each sample point is a floating-point number occupying 4 bytes, has a file size of 1321 × 20000 × 4 / (1024 × 1024) = 100.78 MB. This demonstrates the extremely large volume of DAS microseismic records. For such a large data volume, conventional microseismic event identification methods are certainly insufficient to meet the demands of real-time monitoring.

[0118] See Figure 6 This indicates that there are microseismic events in the record around 1 second. For this DAS microseismic record, its average energy channel is calculated using formula (1), as follows: Figure 7 As shown by the black curve in the middle, this curve clearly shows the peak amplitude (energy). The microseismic event identification threshold curve is calculated using formulas (3)-(5), as shown below. Figure 7 As shown by the dashed line. (Comparison) Figure 7 The difference between the average energy channel and the microseismic event identification threshold curve is calculated using formula (6) to determine that the peak amplitude is greater than the microseismic event identification threshold. Simultaneously, the location with the largest difference is calculated. Using formula (7), time windows are opened before and after the location with the largest difference, and the extracted DAS microseismic waveform is shown below. Figure 8 As shown.

[0119] As can be seen from the above description, the microseismic event identification method based on distributed optical fiber provided in this embodiment of the invention first obtains the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir; then, it determines the microseismic event identification threshold based on the energy channel data; and finally, it identifies the microseismic events of the target reservoir based on the energy channel data and the microseismic event identification threshold.

[0120] Specifically, the microseismic event identification method based on distributed optical fiber provided in this embodiment of the invention first calculates the average energy channel of a single record file; second, based on statistical methods, a microseismic event identification threshold is set; then, regions on the average energy channel exceeding the threshold are designated as valid events, and a certain time window is selected to extract the valid signals for subsequent microseismic location processing. After the above processing, only DAS microseismic records with valid signals can be retained, avoiding the need to save all acquired data and reducing data storage pressure.

[0121] This invention addresses the distributed fiber optic microseismic monitoring scenario during hydraulic fracturing, proposing a rapid method for identifying and extracting microseismic events from massive distributed fiber optic microseismic records. This method avoids the drawbacks of sequential calculations in existing technologies, while rapidly extracting microseismic event data, reducing the amount of data processed subsequently, decreasing data storage and computation costs, and offering a simple and efficient calculation process, thus effectively meeting the needs of real-time distributed fiber optic fracturing microseismic monitoring.

[0122] Example 3

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

[0124] The energy channel data acquisition module 10 is used to acquire the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir.

[0125] The event identification threshold determination module 20 is used to determine the microseismic event identification threshold based on the energy channel data.

[0126] The microseismic event identification module 30 is used to identify microseismic events of the target reservoir based on the energy channel 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 data according to the pore structure parameters of the target reservoir.

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

[0130] The segmentation result generation unit is used to segment the distributed optical fiber acoustic data according to the storage time interval to generate multiple segmentation results;

[0131] An energy channel data acquisition unit is used to acquire energy channel data for each segmentation result based on 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 parameters, and connectivity parameters.

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

[0134] Quantile determination unit, used to determine the quantiles of the energy channel data;

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

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

[0137] The difference calculation unit is used to calculate the difference between the energy channel data and the microseismic event identification threshold;

[0138] The scanning time window determination unit is used to determine the scanning time window based on the reservoir rock physical property parameters of the target reservoir.

[0139] The microseismic event identification unit is used to identify microseismic events of the target reservoir based on the energy channel data, the difference, and the scanning time window.

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

[0141] As can be seen from the above description, the microseismic event identification device based on distributed optical fiber provided in this embodiment of the invention first obtains the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir; then, it determines the microseismic event identification threshold based on the energy channel data; and finally, it identifies the microseismic events of the target reservoir based on the energy channel data and the microseismic event identification threshold.

[0142] This invention addresses the scenario of distributed fiber optic microseismic monitoring during hydraulic fracturing, proposing a rapid microseismic event identification and extraction device to extract fiber optic microseismic events from massive distributed fiber optic microseismic records. This device avoids the drawbacks of sequential calculations in existing technologies, while rapidly extracting microseismic event data, reducing the amount of data processed subsequently, decreasing data storage and computation costs, and offering a simple and efficient calculation process, thus effectively meeting the needs of real-time distributed fiber optic fracturing microseismic monitoring.

[0143] Example 4

[0144] The embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the distributed optical fiber-based microseismic event identification method described in the above embodiments. See [link to relevant documentation]. Figure 10 The electronic devices specifically include the following:

[0145] Processor 1201, memory 1202, communications interface 1203, and bus 1204;

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

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

[0148] Step 100: Obtain the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir;

[0149] Step 200: Determine the microseismic event identification threshold based on the energy channel data;

[0150] Step 300: Identify the microseismic events of the target reservoir based on the energy channel data and the microseismic event identification threshold.

[0151] Example 5

[0152] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the distributed optical fiber-based microseismic event identification method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the distributed optical fiber-based microseismic event identification method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0153] Step 100: Obtain the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir;

[0154] Step 200: Determine the microseismic event identification threshold based on the energy channel data;

[0155] Step 300: Identify the microseismic events of the target reservoir based on the energy channel data and the microseismic event identification threshold.

[0156] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0157] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0158] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for identifying microseismic events based on distributed optical fibers, characterized in that, include: The energy channel data of the distributed fiber optic acoustic data is obtained based on the number of channels of the distributed fiber optic acoustic data of the target reservoir. The threshold for identifying microseismic events is determined based on the energy channel data. Microseismic events of the target reservoir are identified based on the energy channel data and the microseismic event identification threshold. Also includes: The storage time interval of the distributed optical fiber acoustic data is set according to the pore structure parameters of the target reservoir. The step of obtaining the energy channel data of the distributed fiber optic acoustic data based on the number of channels of the target reservoir includes: The distributed optical fiber acoustic data is segmented according to the storage time interval to generate multiple segmentation results; The energy channel data of each segmentation result is obtained based on the channel data of each segmentation result; Determining the microseismic event identification threshold based on the energy channel data includes: Determine the quantiles of the energy channel data; The microseismic event identification threshold is determined based on the quantiles; Identifying microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold includes: Calculate the difference between the energy channel data and the microseismic event identification threshold; The scanning window is determined based on the reservoir rock properties of the target reservoir. Microseismic events of the target reservoir are identified based on the energy channel data, the difference, and the scanning time window.

2. The microseismic event identification method according to claim 1, characterized in that, The pore structure parameters include: pore throat coordination number, pore type, throat type, sorting parameters, and connectivity parameters.

3. The microseismic event identification method according to any one of claims 1 to 2, characterized in that, The energy channel data is the average energy channel data of the distributed optical fiber acoustic data.

4. A distributed optical fiber-based microseismic event identification device for implementing the distributed optical fiber-based microseismic event identification method according to any one of claims 1-3, characterized in that, include: The energy channel data acquisition module is used to acquire the energy channel data of the distributed optical fiber acoustic data based on the number of channels of the distributed optical fiber acoustic data of the target reservoir. An event identification threshold determination module is used to determine a microseismic event identification threshold based on the energy channel data. The microseismic event identification module is used to identify microseismic events in the target reservoir based on the energy channel data and the microseismic event identification threshold.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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