Reservoir rock strain monitoring method and device based on distributed optical fibers

Abstract

The invention provides a reservoir rock strain monitoring method and device based on distributed optical fibers, and the method comprises the steps: carrying out the low-pass filtering of the pre-obtained distributed optical fiber sound wave data of a target reservoir according to the physical parameters of the reservoir rock of the target reservoir, so as to generate a filtering result; reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters so as to generate a down-sampling result; and monitoring the reservoir rock strain of the target reservoir according to the downsampling result. Based on the distributed optical fiber sound wave data, the rock strain data can be accurately calculated, meanwhile, the data volume is reduced, the data storage cost is reduced, the calculation process is simple and efficient, and technical support can be provided for the crack propagation and development process for optical fiber fracturing monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development and production, in particular to the technical field of oil and gas field fracturing monitoring, and in particular to a reservoir rock strain monitoring method and device based on distributed optical fiber. Background Art

[0002] Unconventional oil and gas resources are an important part of energy. Due to the characteristics of low porosity, low permeability and density of unconventional oil and gas resources, it is impossible to achieve economic exploitation of oil and gas resources by conventional means. In order to improve the recovery rate of unconventional reservoirs, hydraulic fracturing technology is often used in the development stage, that is, by injecting high-pressure fluid and proppants of different particle sizes into the formation to artificially create cracks in the formation, forming channels for the flow of reservoir fluids. The effect of artificial fractures is directly related to the recovery rate of subsequent reservoirs, so there is an urgent need to evaluate the effect of artificial fractures.

[0003] In the prior art, there are many methods for evaluating artificial fractures in hydraulic fracturing, including microseismic, tracer, inclinometer, electromagnetic method, etc. Among them, microseismic is currently recognized as the most effective means of monitoring fractures, which can provide the most comprehensive fracture parameters and can evaluate the fracture effect in real time; inclinometer, tracer, and electromagnetic method cannot monitor the fracture effect in real time, so they cannot evaluate the development process of artificial fractures. Microseismic is a method of locating fracture microseisms by monitoring the acoustic wave signals of underground fractures. However, with the continuous deepening of the development of deep shale oil and gas, microseismic monitoring faces many difficulties. The deep burial depth of the reservoir leads to weak ground microseismic monitoring signals, difficulty in event identification, and difficulty in data processing; the high temperature of the deep formation makes it impossible for the microseismic monitoring detector in the well to reach the depth of the target layer, resulting in a long observation distance, affecting the observation angle and positioning accuracy.

[0004] During the fracturing process, formation rupture not only generates acoustic signals, but also inevitably accompanies changes in the underground strain field. The frequency band of strain signals is in the range of 0-1Hz, and seismic detectors cannot respond to signals in this frequency band. Distributed optical fiber has the advantage of full-band acquisition, which can realize strain signal monitoring. However, since distributed optical fiber is full-band acquisition, its acquisition signal frequency band range is wide. At the same time, the amount of optical fiber signal data is large, and real-time display has high requirements for graphics cards and computer memory. These factors restrict the widespread application of distributed optical fiber logging data acquisition. Summary of the invention

[0005] One object of the present invention is to provide a reservoir rock strain monitoring method based on distributed optical fiber, which determines the frequency range of the low-pass filter and the filter through the reservoir rock physical property parameters; then uses the low-pass filter to filter the original record; secondly, determines the time sampling interval of downsampling; finally, uses the new time sampling interval to downsample the filtered record, thereby achieving the purpose of monitoring strain response from distributed acoustic waves.

[0006] Another object of the present invention is to provide a reservoir rock strain monitoring 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 reservoir rock strain monitoring 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 reservoir rock strain monitoring method based on distributed optical fiber are implemented.

[0007] In order to achieve the above objectives, the present invention discloses a reservoir rock strain monitoring method based on distributed optical fiber, comprising:

[0008] Performing low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0009] Reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a down-sampling result;

[0010] The reservoir rock strain of the target reservoir is monitored according to the downsampling result.

[0011] In one embodiment, the reservoir rock strain monitoring method based on distributed optical fiber further includes:

[0012] Determining the type of the collected physical quantity based on the distributed fiber optic acoustic wave data; the type of the physical quantity includes: strain and strain rate; and

[0013] The reservoir rock physical property parameters include porosity, permeability, fracture development and mineral type.

[0014] In one embodiment, the determining the type of the collected physical quantity based on the distributed optical fiber acoustic wave data includes:

[0015] Reading a label value corresponding to a data unit of the distributed optical fiber acoustic wave data;

[0016] The type corresponding to the physical quantity is determined according to the tag value.

[0017] In one embodiment, the pre-acquired distributed optical fiber acoustic wave data of the target reservoir are low-pass filtered according to the reservoir petrophysical parameters of the target reservoir to generate a filtering result, including:

[0018] Determining the cutoff frequency according to the porosity, permeability, fracture development and mineral type of the reservoir rock;

[0019] determining a high cutoff frequency according to the cutoff frequency;

[0020] Determine the frequency range corresponding to the low-pass filtering according to the cut-off frequency and the high-cut-off frequency;

[0021] The distributed optical fiber acoustic wave data is low-pass filtered according to the frequency range.

[0022] In one embodiment, low-pass filtering the distributed optical fiber acoustic wave data according to the frequency range includes:

[0023] Establishing a low pass filter according to the frequency range;

[0024] The distributed optical fiber acoustic wave data is low-pass filtered according to the low-pass filter.

[0025] In one embodiment, when the type corresponding to the physical quantity is the strain, it further includes:

[0026] performing time differentiation on the distributed fiber optic acoustic wave data to generate the strain rate;

[0027] When the type corresponding to the physical quantity is the strain rate, low-pass filtering is performed on the distributed optical fiber acoustic wave data according to the frequency range, including:

[0028] Performing a time-domain Fourier transform on the distributed optical fiber acoustic wave data to obtain a frequency-domain signal of the distributed optical fiber acoustic wave data;

[0029] The distributed optical fiber acoustic wave data is low-pass filtered according to the frequency domain signal and the low-pass filter.

[0030] In one embodiment, reducing the sampling rate of the filtering result according to the reservoir petrophysical parameters to generate a down-sampling result includes:

[0031] Determining the sampling frequency according to the reservoir rock physical property parameters;

[0032] Determining a sampling interval according to the sampling frequency;

[0033] The sampling rate of the filtering result is reduced according to the sampling interval to generate a down-sampling result.

[0034] The present invention also discloses a reservoir rock strain monitoring device based on distributed optical fiber, comprising:

[0035] A filtering result generating module is used to perform low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0036] A downsampling result generating module, used for reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a downsampling result;

[0037] A reservoir rock strain monitoring module is used to monitor the reservoir rock strain of the target reservoir according to the downsampling result.

[0038] In one embodiment, the reservoir rock strain monitoring device based on distributed optical fiber further includes:

[0039] A physical quantity type determination module, used to determine the type of the collected physical quantity based on the distributed optical fiber acoustic wave data; the type of the physical quantity includes: strain and strain rate; and

[0040] The reservoir rock physical property parameters include porosity, permeability, fracture development and mineral type.

[0041] In one embodiment, the physical quantity type determination module includes:

[0042] A label value reading unit, used to read a label value corresponding to a data unit of the distributed optical fiber acoustic wave data;

[0043] A physical quantity type determination unit is used to determine the type corresponding to the physical quantity according to the label value.

[0044] In one embodiment, the filtering result generating module includes:

[0045] A cut-off frequency determination unit, used to determine the cut-off frequency according to the porosity, permeability, fracture development and mineral type of the reservoir rock;

[0046] a high cutoff frequency determination unit, configured to determine the high cutoff frequency according to the cutoff frequency;

[0047] A frequency range determining unit, configured to determine a frequency range corresponding to the low-pass filtering according to the cut-off frequency and the high-cut-off frequency;

[0048] A data low-pass filtering unit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the frequency range.

[0049] In one embodiment, the data low-pass filtering unit includes:

[0050] A low-pass filter establishing unit, used for establishing a low-pass filter according to the frequency range;

[0051] The data low-pass filtering subunit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the low-pass filter.

[0052] In one embodiment, when the type corresponding to the physical quantity is the strain, the reservoir rock strain monitoring device based on distributed optical fiber includes:

[0053] A strain time difference module, used for performing time difference on the distributed optical fiber acoustic wave data to generate the strain rate;

[0054] When the type corresponding to the physical quantity is the strain rate, the data low-pass filtering unit includes:

[0055] A frequency domain signal acquisition unit, used for performing a time domain Fourier transform on the distributed optical fiber acoustic wave data to acquire a frequency domain signal of the distributed optical fiber acoustic wave data;

[0056] A data filtering unit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the frequency domain signal and the low-pass filter.

[0057] In one embodiment, the downsampling result generating module includes:

[0058] A sampling frequency determination unit, used to determine the sampling frequency according to the reservoir rock physical property parameters;

[0059] A sampling interval determining unit, used to determine the sampling interval according to the sampling frequency;

[0060] The downsampling result generating unit is used to reduce the sampling rate of the filtering result according to the sampling interval to generate a downsampling result.

[0061] 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.

[0062] When the processor executes the program, the method described above is implemented.

[0063] The present invention also discloses a computer readable medium having a computer program stored thereon.

[0064] When the program is executed by a processor, the method described above is implemented.

[0065] From the above description, it can be seen that the reservoir rock strain monitoring method and device based on distributed optical fiber provided in the embodiment of the present invention firstly performs low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; then, the sampling rate of the filtering result is reduced according to the reservoir rock physical property parameters to generate a downsampling result; finally, the reservoir rock strain of the target reservoir is monitored according to the sampling result.

[0066] The reservoir rock strain monitoring method and device based on distributed optical fiber proposed in the embodiment of the present invention is aimed at the distributed optical fiber strain monitoring scenario during hydraulic fracturing, and proposes a method for extracting data that can characterize rock strain from distributed optical fiber acoustic sensing data based on filtering and downsampling. This method can accurately calculate rock strain data, while reducing the amount of data and data storage costs, and the calculation process is simple and efficient, which can provide the crack extension and development process for optical fiber fracturing monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] 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.

[0068] Figure 1 It is a schematic diagram of a process of a reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0069] Figure 2 It is another schematic diagram of the process of the reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0070] Figure 3 4 is a flow chart of step 400 in the reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0071] Figure 4 1 is a flow chart of step 100 of a reservoir rock strain monitoring method based on distributed optical fiber in Embodiment 1 of the present invention;

[0072] Figure 5 It is a flow chart of step 104 of the reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0073] Figure 6 This is a third flow chart of the reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0074] Figure 7 Another schematic flow chart of step 104 of the reservoir rock strain monitoring method based on distributed optical fiber in the first embodiment of the present invention;

[0075] Figure 8 This is a flow chart of step 200 of a reservoir rock strain monitoring method based on distributed optical fiber in Embodiment 1 of the present invention;

[0076] Fig. 9 It is a schematic diagram of the flow of a reservoir rock strain monitoring method based on distributed optical fiber in the second embodiment of the present invention;

[0077] Fig.10 Schematic diagram of DAS data attribute label in the second embodiment of the present invention;

[0078] Fig.11 This is a schematic diagram of DAS raw data in Embodiment 2 of the present invention;

[0079] Fig.12 Schematic diagram of a low-pass filter in Embodiment 2 of the present invention

[0080] Fig.13 is a schematic diagram of DAS data after filtering in Embodiment 2 of the present invention;

[0081] Fig.14 This is a schematic diagram of ultra-low frequency DAS data after downsampling in Embodiment 2 of the present invention;

[0082] Fig.15 This is a schematic structural diagram of a reservoir rock strain monitoring device based on distributed optical fiber in Embodiment 3 of the present invention;

[0083] Fig.16 It is a schematic diagram of the structure of an electronic device in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0084] 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.

[0085] 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.

[0086] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0087] Embodiment 1

[0088] In this embodiment, Figure 1 As shown, a reservoir rock strain monitoring method based on distributed optical fiber is provided, which includes:

[0089] Step 100: low-pass filtering the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0090] 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 spatial sampling in the entire well section. It is a new type of fracturing monitoring technology.

[0091] It can be understood that low-pass filtering is a filtering method, the rule is that low-frequency signals can pass normally, while high-frequency signals exceeding the set threshold are blocked and weakened. However, the degree of blocking and weakening will vary according to different frequencies and different filtering procedures (purposes).

[0092] When step 100 is implemented, it can be performed by the following formula:

[0093] Low-pass filtering result = a × current sampling value + (1-a) × last filtering result; (1)

[0094] Where a is the correlation coefficient, which is obtained by fitting the historical data of reservoir rock physical properties and reservoir rock strain.

[0095] Step 200: reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a down-sampling result;

[0096] It is understandable that the amount of logging data signals collected by distributed optical fiber is large, and real-time display requires high graphics card and computer memory. Therefore, when extracting low-frequency strain signals, it is necessary to downsample the signals to improve the efficiency of fracturing monitoring.

[0097] Specifically, a certain number of sampling points are skipped to reduce the sampling rate. For example, if the sampling rate of the original signal is Fs, and the sampling rate is to be reduced to Fs / N, then only one sampling point can be selected for retention every N sampling points.

[0098] Step 300: Monitor the reservoir rock strain of the target reservoir according to the downsampling result.

[0099] It is understandable that there is a certain functional relationship between the distributed fiber optic acoustic wave data and the reservoir rock strain. By determining the functional relationship, the rock strain of the target reservoir can be predicted based on the distributed fiber optic acoustic wave data. The reservoir rock strain here refers to the relative change in length or volume of the reservoir rock under stress (fracturing). It means the ratio of the changed shape or volume to the original.

[0100] From the above description, it can be seen that the reservoir rock strain monitoring method based on distributed optical fiber provided in an embodiment of the present invention first performs low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; then, the sampling rate of the filtering result is reduced according to the reservoir rock physical property parameters to generate a downsampling result; finally, the reservoir rock strain of the target reservoir is monitored according to the sampling result.

[0101] The present invention extracts the characterization data of strain information from DAS data and performs downsampling processing at the same time, which not only ensures the acquisition of effective signals, but also reduces the amount of data, meeting the requirements of on-site real-time monitoring during fracturing. The calculation process is simple and efficient. The present invention can provide a basis for using strain to describe crack extension characteristics in real time during distributed optical fiber fracturing monitoring.

[0102] In one embodiment, see Figure 2 , the reservoir rock strain monitoring method based on distributed optical fiber also includes:

[0103] Step 400: determining the type corresponding to the collected physical quantity based on the distributed optical fiber acoustic wave data;

[0104] Specifically, the type of the physical quantity collected by the distributed optical fiber acoustic wave data can be identified according to a certain feature of the distributed optical fiber acoustic wave data.

[0105] In step 400, the types of physical quantities include: strain and strain rate; reservoir rock physical property parameters include: porosity, permeability, fracture development degree and mineral type.

[0106] 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.

[0107] 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.

[0108] The degree of fracture development can be characterized by fracture density, fracture porosity, fracture permeability and fracture inclination.

[0109] The main mineral types include: quartz, illite and chlorite.

[0110] In one embodiment, see Figure 3 , step 400 comprises:

[0111] Step 401: Read the label value corresponding to the data unit of the distributed optical fiber acoustic wave data;

[0112] Step 402: Determine the type corresponding to the physical quantity according to the tag value.

[0113] In step 401 and step 402, HDF5 is generally a common format for distributed fiber acoustic sensing monitoring data. Generally, the attribute tags of the raw data of the distributed fiber acoustic wave data are stored in the HDF5 file. First, the matlab function H5disp is used to read the information of "RawDataUnit" in "Group' / Acquisition / Raw[0]" in the HDF5 file to determine the physical meaning of the stored data. If the value of the tag is "rad", the type of DAS data is strain rate; if the tag value is "nε", the type of DAS data is strain.

[0114] In one embodiment, see Figure 4 , step 100 comprises:

[0115] Step 101: determining a cutoff frequency according to the porosity, permeability, fracture development and mineral type of the reservoir rock;

[0116] Specifically, the corresponding cutoff frequency is selected according to the porosity, permeability, fracture development, mineral type of the rock reservoir and a mapping relationship library between the pre-generated cutoff frequency and the rock reservoir physical property parameters.

[0117] Step 102: determining a high cutoff frequency according to the cutoff frequency;

[0118] Preferably, the high cutoff frequency is 0.9 times the cutoff frequency.

[0119] Step 103: determining a frequency range corresponding to the low-pass filter according to the cut-off frequency and the high-cut-off frequency;

[0120] Step 104: low-pass filtering the distributed optical fiber acoustic wave data according to the frequency range.

[0121] The cutoff frequency can filter out the component of the distributed optical fiber acoustic wave data with a frequency higher than the cutoff frequency. In order to avoid the Gibbs phenomenon caused by filtering, the high cutoff frequency is set. Through the coordination of the cutoff frequency and the high cutoff frequency, the distributed optical fiber data is low-pass filtered.

[0122] In one embodiment, see Figure 5 , step 104 comprises:

[0123] Step 1041: establishing a low-pass filter according to the frequency range;

[0124] The mathematical expression of the low-pass filter H(f) is:

[0125]

[0126] Where F is the cutoff frequency; f h is the high cutoff frequency.

[0127] Step 1042: low-pass filtering the distributed optical fiber acoustic wave data according to the low-pass filter.

[0128] There are various types and designs of low-pass filters, including Butterworth filters, Chebyshev filters, and elliptic filters, etc. Preferably, the embodiment of the present application selects a Chebyshev filter as the low-pass filter.

[0129] In one embodiment, see Figure 6 When the type corresponding to the physical quantity is the strain, the reservoir rock strain monitoring method based on distributed optical fiber also includes:

[0130] Step 500: performing time differentiation on the distributed optical fiber acoustic wave data to generate the strain rate;

[0131] If the data type stored in DAS is strain, it is necessary to perform time difference calculation to obtain strain rate data. The specific difference formula is as follows:

[0132]

[0133] Where ε(t,x) is the strain, is the strain rate.

[0134] In one embodiment, see Figure 7 , when the type corresponding to the physical quantity is the strain rate, step 104 includes:

[0135] Step 1043: performing a time domain Fourier transform on the distributed optical fiber acoustic wave data to obtain a frequency domain signal of the distributed optical fiber acoustic wave data;

[0136] It should be noted that step 1043 is implemented on the basis of step 1042, and the strain rate data Perform time domain Fourier transform to obtain its frequency domain signal The transformation expression is as follows.

[0137]

[0138] in, Represents t n At the moment, the space is located at x m The strain rate measurement at .

[0139] Step 1044: low-pass filtering the distributed optical fiber acoustic wave data according to the frequency domain signal and the low-pass filter.

[0140] Specifically, the low-pass filter H(f) mentioned above is multiplied by the frequency domain signal. Then multiply the result Perform inverse Fourier transform to obtain the filtered time domain strain rate signal

[0141]

[0142]

[0143] In one embodiment, see Figure 8 Step 200 of the reservoir rock strain monitoring method based on distributed optical fiber includes:

[0144] Step 201: Determine the sampling frequency according to the reservoir rock physical property parameters;

[0145] Step 202: Determine a sampling interval according to the sampling frequency;

[0146] For step 201 and step 202, based on the sampling theorem, the sampling frequency F c Must satisfy the following formula

[0147] F c ≥2×F (7)

[0148] Where F is the highest frequency in the target signal frequency band.

[0149] Based on the following formula, it can be inferred that the maximum time sampling interval △t is:

[0150] △t=1 / F c ≤2×F (8)

[0151] Step 203: reducing the sampling rate of the filtering result according to the sampling interval to generate a down-sampling result.

[0152] By downsampling the filtered distributed fiber acoustic wave data using the above sampling interval, the purpose of efficiently extracting strain from DAS records can be achieved. Assuming that the time sampling interval of the original DAS record is △tt, and the time sampling interval of the downsampling is △t, the downsampling rate is

[0153]

[0154] For the filtered records, one sample point is selected from every K data in the time domain as the downsampled sample point. The specific expression is (10), and the downsampled signal can be obtained:

[0155]

[0156] From the above description, it can be seen that the reservoir rock strain monitoring method based on distributed optical fiber provided in an embodiment of the present invention first performs low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; then, the sampling rate of the filtering result is reduced according to the reservoir rock physical property parameters to generate a downsampling result; finally, the reservoir rock strain of the target reservoir is monitored according to the sampling result.

[0157] Considering that when using distributed acoustic sensing signals DAS to characterize the strain information of underground formations, it is necessary to process the full-band DAS signals. Based on low-pass filtering and downsampling, the present invention proposes a concise and efficient distributed optical fiber fracturing monitoring strain response extraction method. The method has a concise and efficient calculation process and can provide a basis for using strain to describe crack extension characteristics in real time during distributed optical fiber fracturing monitoring.

[0158] Embodiment 2

[0159] In order to further illustrate the solution, the present invention also provides a specific application example of the reservoir rock strain monitoring method based on distributed optical fiber, specifically, Fig. 9 As shown, the specific application example includes the following steps:

[0160] S1: Read the label value corresponding to the data unit of the distributed optical fiber acoustic wave data to determine the type corresponding to the physical quantity.

[0161] Get the DAS signal monitored during fracturing and read its HDF5 attribute values, such as Fig.10 As shown, it is shown that the DAS data is strain rate data, that is, no time difference is required.

[0162] S2: construct a low-pass filter and perform low-pass filtering on the distributed fiber acoustic wave data.

[0163] For DAS data within 11 minutes (such as Fig.11 The DAS recording time sampling interval is 2KHz. First, a low-pass filter high cutoff frequency f is set. h , set up as follows:

[0164] f h =0.9×F (11)

[0165] Next, use formulas (2) and (11) to determine the low-pass filter. Here, F is selected as 1 Hz. The spectrum of the filter is as follows: Fig.12 Finally, the filtered ultra-low frequency DAS record is obtained using formulas (5) and (6), as shown in Fig.13 shown.

[0166] S3: reducing the sampling rate of the filtering result according to the sampling interval to generate a down-sampling result.

[0167] First, the time sampling interval after downsampling is calculated using formula (8) as 0.5ms. Then, the downsampling rate K is determined to be 1000 using formula (9). The downsampling of the ultra-low frequency DAS is completed using formula (10). The data after downsampling is as follows: Fig.14 shown.

[0168] From the above description, it can be seen that the reservoir rock strain monitoring method based on distributed optical fiber provided in an embodiment of the present invention first performs low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; then, the sampling rate of the filtering result is reduced according to the reservoir rock physical property parameters to generate a downsampling result; finally, the reservoir rock strain of the target reservoir is monitored according to the sampling result.

[0169] Specifically, the present invention first analyzes the meaning of the physical quantity of the distributed fiber optic acoustic sensing fracturing monitoring data. If the measured physical quantity is strain, it is necessary to perform time difference to obtain strain rate information; if the measured physical quantity is strain rate, then directly perform subsequent processing. Next, determine the frequency range of the low-pass filter and construct a low-pass filter; then use the low-pass filter to filter the original record; secondly, determine the time sampling interval of downsampling; finally, use the new time sampling interval to downsample the filtered record, so as to achieve the purpose of extracting characterization data of rock strain response from distributed acoustic wave monitoring.

[0170] Embodiment 3

[0171] Based on the same principle, this embodiment also discloses a reservoir rock strain monitoring device based on distributed optical fiber. Fig.15 As shown, the device comprises:

[0172] A filtering result generating module 10 is used to perform low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0173] A downsampling result generating module 20, used to reduce the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a downsampling result;

[0174] The reservoir rock strain monitoring module 30 is used to monitor the reservoir rock strain of the target reservoir according to the downsampling result.

[0175] In one embodiment, the reservoir rock strain monitoring device based on distributed optical fiber further includes:

[0176] A physical quantity type determination module, used to determine the type of the collected physical quantity based on the distributed optical fiber acoustic wave data; the type of the physical quantity includes: strain and strain rate; and

[0177] The reservoir rock physical property parameters include porosity, permeability, fracture development and mineral type.

[0178] In one embodiment, the physical quantity type determination module includes:

[0179] A label value reading unit, used to read a label value corresponding to a data unit of the distributed optical fiber acoustic wave data;

[0180] A physical quantity type determination unit is used to determine the type corresponding to the physical quantity according to the label value.

[0181] In one embodiment, the filtering result generating module includes:

[0182] A cut-off frequency determination unit, used to determine the cut-off frequency according to the porosity, permeability, fracture development and mineral type of the reservoir rock;

[0183] a high cutoff frequency determination unit, configured to determine the high cutoff frequency according to the cutoff frequency;

[0184] A frequency range determining unit, configured to determine a frequency range corresponding to the low-pass filtering according to the cut-off frequency and the high-cut-off frequency;

[0185] A data low-pass filtering unit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the frequency range.

[0186] In one embodiment, the data low-pass filtering unit includes:

[0187] A low-pass filter establishing unit, used for establishing a low-pass filter according to the frequency range;

[0188] The data low-pass filtering subunit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the low-pass filter.

[0189] In one embodiment, when the type corresponding to the physical quantity is the strain, the reservoir rock strain monitoring device based on distributed optical fiber includes:

[0190] A strain time difference module, used for performing time difference on the distributed optical fiber acoustic wave data to generate the strain rate;

[0191] When the type corresponding to the physical quantity is the strain rate, the data low-pass filtering unit includes:

[0192] A frequency domain signal acquisition unit, used for performing a time domain Fourier transform on the distributed optical fiber acoustic wave data to acquire a frequency domain signal of the distributed optical fiber acoustic wave data;

[0193] A data filtering unit is used to perform low-pass filtering on the distributed optical fiber acoustic wave data according to the frequency domain signal and the low-pass filter.

[0194] In one embodiment, the downsampling result generating module includes:

[0195] A sampling frequency determination unit, used to determine the sampling frequency according to the reservoir rock physical property parameters;

[0196] A sampling interval determining unit, used to determine the sampling interval according to the sampling frequency;

[0197] The downsampling result generating unit is used to reduce the sampling rate of the filtering result according to the sampling interval to generate a downsampling result.

[0198] From the above description, it can be seen that the reservoir rock strain monitoring device based on distributed optical fiber provided in an embodiment of the present invention first performs low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; then, the sampling rate of the filtering result is reduced according to the reservoir rock physical property parameters to generate a downsampling result; finally, the reservoir rock strain of the target reservoir is monitored according to the sampling result.

[0199] The reservoir rock strain monitoring device based on distributed optical fiber proposed in the embodiment of the present invention is aimed at the distributed optical fiber strain monitoring scenario during hydraulic fracturing, and proposes a method for extracting data that can characterize rock strain from distributed optical fiber acoustic sensing data based on filtering and downsampling. This method can accurately calculate rock strain data, while reducing the amount of data and data storage costs, and the calculation process is simple and efficient, which can provide the crack extension and development process for optical fiber fracturing monitoring.

[0200] Embodiment 4

[0201] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps in the reservoir rock strain monitoring method based on distributed optical fiber in the above embodiments, see Fig.16 , electronic equipment specifically includes the following:

[0202] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0203] 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.

[0204] 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 reservoir rock strain monitoring 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:

[0205] Step 100: low-pass filtering the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0206] Step 200: reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a down-sampling result;

[0207] Step 300: Monitor the reservoir rock strain of the target reservoir according to the downsampling result.

[0208] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the reservoir rock strain monitoring method based on distributed optical fiber in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the reservoir rock strain monitoring 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:

[0209] Step 100: low-pass filtering the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result;

[0210] Step 200: reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a down-sampling result;

[0211] Step 300: Monitor the reservoir rock strain of the target reservoir according to the downsampling result.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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 including 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.

[0217] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0218] 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 reservoir rock strain monitoring method based on distributed optical fiber, characterized in that: include: Performing low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; Reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a down-sampling result; The reservoir rock strain of the target reservoir is monitored according to the downsampling result.

2. The reservoir rock strain monitoring method according to claim 1, characterized in that: Also includes: Determining the type of the collected physical quantity based on the distributed fiber optic acoustic wave data; The types corresponding to the physical quantities include: strain and strain rate; and The reservoir rock physical property parameters include porosity, permeability, fracture development and mineral type.

3. The reservoir rock strain monitoring method according to claim 2, characterized in that: The determining the type of the collected physical quantity based on the distributed optical fiber acoustic wave data includes: Reading a label value corresponding to a data unit of the distributed optical fiber acoustic wave data; The type corresponding to the physical quantity is determined according to the tag value.

4. The reservoir rock strain monitoring method according to claim 2, characterized in that: The method of low-pass filtering the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir petrophysical parameters of the target reservoir to generate a filtering result includes: Determining the cutoff frequency according to the porosity, permeability, fracture development and mineral type of the reservoir rock; Determining a high cutoff frequency according to the cutoff frequency; Determine the frequency range corresponding to the low-pass filtering according to the cut-off frequency and the high-cut-off frequency; The distributed optical fiber acoustic wave data is low-pass filtered according to the frequency range.

5. The reservoir rock strain monitoring method according to claim 4, characterized in that: Low-pass filtering the distributed optical fiber acoustic wave data according to the frequency range includes: Establishing a low pass filter according to the frequency range; The distributed optical fiber acoustic wave data is low-pass filtered according to the low-pass filter.

6. The reservoir rock strain monitoring method according to claim 5, characterized in that: When the type corresponding to the physical quantity is the strain, it also includes: performing time differentiation on the distributed fiber optic acoustic wave data to generate the strain rate; When the type corresponding to the physical quantity is the strain rate, low-pass filtering is performed on the distributed optical fiber acoustic wave data according to the frequency range, including: Performing a time-domain Fourier transform on the distributed optical fiber acoustic wave data to obtain a frequency-domain signal of the distributed optical fiber acoustic wave data; The distributed optical fiber acoustic wave data is low-pass filtered according to the frequency domain signal and the low-pass filter.

7. The reservoir rock strain monitoring method according to claim 1, characterized in that: Reducing the sampling rate of the filtering result according to the reservoir petrophysical parameters to generate a down-sampling result, including: Determining the sampling frequency according to the reservoir rock physical property parameters; Determining a sampling interval according to the sampling frequency; The sampling rate of the filtering result is reduced according to the sampling interval to generate a down-sampling result.

8. A reservoir rock strain monitoring device based on distributed optical fiber, characterized in that: include: A filtering result generating module is used to perform low-pass filtering on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir according to the reservoir rock physical property parameters of the target reservoir to generate a filtering result; A downsampling result generating module, used for reducing the sampling rate of the filtering result according to the reservoir rock physical property parameters to generate a downsampling result; A reservoir rock strain monitoring module is used to monitor the reservoir rock strain of the target reservoir according to the downsampling result.

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.

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