Distributed optical fiber based fracturing low-frequency strain response signal extraction method and device

Abstract

The application provides a distributed optical fiber-based fracturing low-frequency strain response signal extraction method and device, and a corresponding method comprises the following steps: generating a time domain filter of a target reservoir according to reservoir rock physical property parameters of the target reservoir; generating an expected signal corresponding to distributed optical fiber acoustic wave data of the target reservoir according to the distributed optical fiber acoustic wave data of the target reservoir obtained in advance and the time domain filter; wherein the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir; and performing down-sampling on the expected signal according to a micro-fracture effective opening of the target reservoir to extract a fracturing low-frequency strain response signal of the target reservoir. The application greatly reduces the calculation amount in the process of extracting the fracturing low-frequency strain response signal, additionally, reduces the data storage cost while improving the signal-to-noise ratio of the data, and the calculation process is simple and efficient, which can provide effective technical support for the fracture propagation development process of optical fiber fracturing monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development using geophysics, in particular to the technical field of oil and gas field fracturing monitoring, and more particularly to a fracturing low-frequency strain response signal extraction method and device based on distributed optical fibers. BACKGROUND

[0002] Conventional reservoirs are developed earlier and more mature in the oil and gas field due to the development advantages of shallow burial depth and easy excavation, so the remaining reserves of such reservoirs are relatively small. The proven reserves of unconventional reservoirs are large, and with the progress of exploration and development technology, the existing technology has the ability to explore and develop unconventional reservoirs, and unconventional reservoirs have gradually become the target of mainstream exploration and development. The economic and efficient development of unconventional reservoirs is inevitably inseparable from the hydraulic fracturing technology. Fracturing technology is related to the productivity of unconventional reservoirs, so it is urgent to evaluate the fracturing effect.

[0003] The commonly used fracturing monitoring technologies at present include microseismic, tracer, inclinometer, electromagnetic method, etc. Distributed optical fiber has the advantage of multi-physical quantity sensing, which can realize vibration, temperature and strain sensing. Unlike previous monitoring methods, based on distributed optical fiber vibration monitoring, the formation strain response monitoring can be realized through appropriate processing.

[0004] The principle of distributed optical fiber based on strain monitoring is to directly evaluate the change of formation stress state in the fracturing process; instead of indirectly evaluating the fracturing effect through the position of underground rupture and other information. Therefore, the research on distributed optical fiber strain response extraction is of great significance for the efficient development of unconventional reservoirs. However, the distributed optical fiber fracturing monitoring technology is still in the exploratory stage.

[0005] Because the monitoring signal frequency band of distributed optical fiber is wide, the minimum frequency can be close to 0Hz, and the maximum frequency can reach 4KHz. Therefore, the polarity of the DAS signal collected by the optical fiber needs to be processed to obtain the data for evaluating the formation strain response. On the other hand, fracturing monitoring generally needs to show the monitoring results in real time, and the timeliness requirement of data processing and result display is high, which generally cannot be delayed for more than 30 seconds, that is, the current monitoring results need to be displayed within 30 seconds. In summary, the existing technology about distributed optical fiber fracturing low-frequency strain response signal extraction cannot process the huge amount of optical fiber signals in time, making the processing and display of optical fiber signals extremely difficult. SUMMARY

[0006] The present application aims to provide a distributed optical fiber-based fracturing low-frequency strain response signal extraction method, which aims to reduce the storage cost of distributed optical fiber-based fracturing low-frequency strain response signals, improve the signal-to-noise ratio of data, and make the calculation process simple and efficient, thereby providing favorable technical support for the development process of optical fiber fracturing monitoring.

[0007] The present application aims to provide a distributed optical fiber-based fracturing low-frequency strain response signal extraction method, which aims to reduce the storage cost of distributed optical fiber-based fracturing low-frequency strain response signals, improve the signal-to-noise ratio of data, and make the calculation process simple and efficient, thereby providing favorable technical support for the development process of optical fiber fracturing monitoring.

[0008] In order to achieve the above-mentioned purposes, the present application discloses a distributed optical fiber-based fracturing low-frequency strain response signal extraction method, comprising:

[0009] Generating a time-domain filter of the target reservoir according to the reservoir rock physical parameters of the target reservoir;

[0010] Generating an expected signal corresponding to the distributed optical fiber acoustic data of the target reservoir according to the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter; wherein the distributed optical fiber acoustic data is generated by fracturing the target reservoir;

[0011] According to the effective opening of the micro-cracks of the target reservoir, the expected signal is down-sampled to extract the fracturing low-frequency strain response signal of the target reservoir.

[0012] In some embodiments of the present application, the time-domain filter of the target reservoir is generated according to the reservoir rock physical parameters of the target reservoir, comprising:

[0013] Setting a low-pass filter corresponding to the target reservoir according to the reservoir rock physical parameters of the target reservoir;

[0014] Performing inverse Fourier transform on the low-pass filter to generate the time-domain filter.

[0015] In some embodiments of the present application, the expected signal corresponding to the distributed optical fiber acoustic data of the target reservoir is generated according to the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter, comprising:

[0016] Extracting strain rate data in the distributed optical fiber acoustic data;

[0017] The strain rate data is convolved with the time-domain filter to generate the desired signal.

[0018] In some embodiments of the invention, before downsampling the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, the method further includes:

[0019] The desired signal is divided into blocks to generate multiple block data;

[0020] Dividing the desired signal into blocks includes:

[0021] The sampling window is determined based on the length of the desired signal;

[0022] Calculate the average value of the desired signal within the sampling window;

[0023] The number of sampling points in each block of data is determined based on the average value.

[0024] The desired signal is divided into blocks based on the number of sampling points in each block of data.

[0025] In some embodiments of the invention, the desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, including:

[0026] The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir.

[0027] The desired signal is downsampled according to the sampling frequency to extract the low-frequency strain response signal of the target reservoir.

[0028] In some embodiments of the invention, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers further includes:

[0029] Determine the highest frequency of the desired signal;

[0030] The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir and the highest frequency.

[0031] The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, and the method further includes:

[0032] The sampling time for downsampling is determined based on the sampling frequency;

[0033] The desired signal is downsampled according to the sampling time to extract the low-frequency strain response signal of the target reservoir.

[0034] In some embodiments of the invention, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers further includes:

[0035] Determine the weight of the influence of different throats on permeability in the target reservoir;

[0036] The effective permeability of the target reservoir is determined based on the influence weights.

[0037] The effective opening of the microcracks is determined based on the effective permeability.

[0038] This invention also discloses a device for extracting low-frequency strain response signals from fracturing based on distributed optical fibers, comprising:

[0039] A time-domain filter generation module is used to generate a time-domain filter for the target reservoir based on the reservoir rock physical property parameters of the target reservoir.

[0040] The desired signal generation module is used to generate a desired signal corresponding to the distributed optical fiber acoustic wave data based on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir and the time domain filter; wherein, the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir.

[0041] The desired signal downsampling module is used to downsample the desired signal based on the effective aperture of the microfractures in the target reservoir in order to extract the low-frequency strain response signal of the fracturing in the target reservoir.

[0042] In some embodiments of the invention, the time-domain filter generation module includes:

[0043] A low-pass filter setting unit is used to set the low-pass filter corresponding to the target reservoir according to the reservoir rock physical property parameters of the target reservoir.

[0044] A time-domain filter generation unit is used to perform an inverse Fourier transform on the low-pass filter to generate the time-domain filter.

[0045] In some embodiments of the invention, the desired signal generation module includes:

[0046] A strain rate data extraction unit is used to extract strain rate data from the distributed optical fiber acoustic data;

[0047] The desired signal generation unit is used to convolve the strain rate data with the time domain filter to generate the desired signal.

[0048] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0049] The desired signal segmentation module is used to segment the desired signal into blocks to generate multiple block data.

[0050] The desired signal segmentation module includes:

[0051] A sampling time window determination unit is used to determine the sampling time window based on the length of the desired signal;

[0052] An average value calculation unit is used to calculate the average value of the desired signal within the sampling window;

[0053] A sampling point determination unit is used to determine the number of sampling points in each block of data based on the average value.

[0054] The desired signal segmentation unit segments the desired signal into blocks based on the number of sampling points in each block of data.

[0055] In some embodiments of the invention, the desired signal downsampling module includes:

[0056] A sampling frequency determination unit is used to determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir.

[0057] The desired signal downsampling unit is used to downsample the desired signal according to the sampling frequency in order to extract the fracturing low-frequency strain response signal of the target reservoir.

[0058] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0059] The highest frequency determination module is used to determine the highest frequency of the desired signal;

[0060] The sampling frequency determination module is used to determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir and the highest frequency.

[0061] The desired signal downsampling module also includes:

[0062] A sampling time determination unit is used to determine the sampling time of the downsampling based on the sampling frequency;

[0063] The strain response signal extraction unit is used to downsample the desired signal according to the sampling time in order to extract the fracturing low-frequency strain response signal of the target reservoir.

[0064] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0065] The influence weight determination module is used to determine the influence weight of different throats in the target reservoir on permeability.

[0066] An effective permeability determination module is used to determine the effective permeability of the target reservoir based on the influence weights.

[0067] An effective aperture determination module is used to determine the effective aperture of the microcrack based on the effective permeability.

[0068] 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.

[0069] When the processor executes the program, it implements the method described above.

[0070] The present invention also discloses a computer-readable medium having a computer program stored thereon.

[0071] When the program is executed by the processor, it implements the method described above.

[0072] As described above, the method and apparatus for extracting low-frequency strain response signals from fracturing based on distributed optical fibers provided in this embodiment of the invention first generates a time-domain filter for the target reservoir based on the reservoir rock properties parameters; then, it generates a desired signal corresponding to the distributed optical fiber acoustic data based on the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter; wherein, the distributed optical fiber acoustic data is generated by fracturing the target reservoir; finally, it downsamples the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal from the target reservoir.

[0073] This invention addresses the scenario of distributed optical fiber strain response monitoring during hydraulic fracturing, proposing a method and apparatus for extracting low-frequency strain response signals based on distributed optical fibers, grounded in time-domain convolution and block processing. This method and apparatus extract strain response signals from distributed optical fiber acoustic sensing data. On one hand, the method extracts strain response signals using a time-domain convolution operator, avoiding sequential forward and inverse Fourier transforms and reducing computational load. On the other hand, block processing reduces data volume, lowering data storage costs while improving the signal-to-noise ratio. Furthermore, the computation process is simple and efficient, providing effective technical support for monitoring the crack propagation and development process in optical fiber fracturing. Attached Figure Description

[0074] 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.

[0075] Figure 1 This is a flowchart illustrating a method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers in Embodiment 1 of the present invention.

[0076] Figure 2 This is a flowchart illustrating step 100 in the method for extracting low-frequency strain response signals from fracturing based on distributed optical fiber in Embodiment 1 of the present invention.

[0077] Figure 3 This is a flowchart illustrating step 200 in the method for extracting low-frequency strain response signals from fracturing based on distributed optical fiber in Embodiment 1 of the present invention.

[0078] Figure 4 This is another flowchart illustrating the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers in Embodiment 1 of the present invention.

[0079] Figure 5 This is a flowchart illustrating step 400 in the method for extracting low-frequency strain response signals from fracturing based on distributed optical fiber in Embodiment 1 of the present invention.

[0080] Figure 6 This is a flowchart illustrating step 300 of the method for extracting low-frequency strain response signals from fracturing based on distributed optical fiber in Embodiment 1 of the present invention.

[0081] Figure 7 This is a schematic diagram of the third process of the low-frequency strain response signal extraction method for fracturing based on distributed optical fiber in Embodiment 1 of the present invention;

[0082] Figure 8 This is another flowchart illustrating step 300 in the method for extracting low-frequency strain response signals from fracturing based on distributed optical fiber in Embodiment 1 of the present invention.

[0083] Figure 9 This is a schematic diagram of the fourth process of the low-frequency strain response signal extraction method for fracturing based on distributed optical fiber in Embodiment 1 of the present invention;

[0084] Figure 10 This is a flowchart illustrating the low-frequency strain response signal extraction method for fracturing based on distributed optical fiber in Embodiment 2 of the present invention.

[0085] Figure 11 This is a schematic diagram of the raw DAS data in Embodiment 2 of the present invention;

[0086] Figure 12 This is a schematic diagram of the frequency domain filter in Embodiment 2 of the present invention;

[0087] Figure 13 This is a schematic diagram of the time-domain filtering operator in Embodiment 2 of the present invention;

[0088] Figure 14 This is a schematic diagram of the high signal-to-noise ratio extraction result of low-frequency strain response in Embodiment 2 of the present invention;

[0089] Figure 15 This is a schematic diagram of the structure of the low-frequency strain response signal extraction device for fracturing based on distributed optical fiber in Embodiment 3 of the present invention;

[0090] Figure 16 This is a schematic diagram of the electronic device in Embodiment 4 of the present invention. Detailed Implementation

[0091] 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.

[0092] 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.

[0093] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0094] Example 1

[0095] In this embodiment, as Figure 1 As shown, a method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers is provided, which includes:

[0096] Step 100: Generate a time-domain filter for the target reservoir based on the reservoir rock properties parameters of the target reservoir;

[0097] Specifically, the amplitude and phase of the low-frequency strain response signal during fracturing are adjusted according to the reservoir rock properties of the target reservoir to control different frequency components. Typically, a set of filter coefficients or filter functions is used to filter the input low-frequency strain response signal, outputting the filtered signal. Common time-domain filters include low-pass filters, high-pass filters, band-pass filters, and band-stop filters. Preferably, the time-domain filter generated in step 100 is a band-stop filter.

[0098] It is important to note that time-domain filters are linear time-invariant (LTI) systems, and their filtering effectiveness is influenced by the signal's frequency response, the filter's characteristics, and the filter parameters. Therefore, when selecting and designing time-domain filters, it is necessary to make reasonable choices and adjustments based on the reservoir rock properties of the target reservoir.

[0099] Preferably, the reservoir rock physical properties in step 100 include: porosity, permeability, saturation, porosity distribution, pore structure, saturation distribution, rock density, and rock elastic modulus, specifically:

[0100] Porosity refers to the proportion of pore space in reservoir rocks, usually expressed as a percentage. The higher the porosity, the stronger the reservoir's storage capacity.

[0101] Permeability measures the ability of a rock to allow fluid to flow through it, describing the degree of pore connectivity within a reservoir rock. Higher permeability indicates greater fluid flow capacity.

[0102] Saturation refers to the degree to which the pore space in reservoir rocks is filled by fluid, and is usually expressed as water saturation, oil saturation, gas saturation, etc.

[0103] Porosity distribution describes the spatial variation of reservoir porosity. Different porosity distributions have different effects on fluid flowability.

[0104] Pore ​​structure is used to describe the shape, size, connectivity and other characteristics of reservoir pores, which affect the flow pattern of fluids in the reservoir.

[0105] Saturation distribution is used to describe the spatial distribution of different fluid saturations in a reservoir, and is of great significance for reservoir development and evaluation.

[0106] Rock density is a density characteristic of reservoir rocks and can be used to calculate the fluid volume in the reservoir.

[0107] The elastic modulus of rock is used to describe the elastic properties of reservoir rocks and is relevant to the in-situ stress of the reservoir.

[0108] Step 200: Generate the desired signal corresponding to the distributed fiber acoustic wave data based on the pre-acquired target reservoir distributed fiber acoustic wave data and the time domain filter; wherein, the distributed fiber acoustic wave data is generated by fracturing the target reservoir.

[0109] Distributed Acoustic Sensing (DAS) is based on the principle of Optical Time-Domain Reflectometer (OTDR). It uses a high-power laser transmitter to send laser pulses to a connected optical fiber, while simultaneously collecting and analyzing Rayleigh scattering in the backscattered light, thus achieving distributed sensing of vibration or acoustic signals. DAS-based well fracturing monitoring not only offers advantages such as 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. A certain functional relationship exists between distributed optical fiber acoustic data and reservoir rock strain (generated by reservoir fracturing). By determining this functional relationship, the rock strain of the target reservoir can be predicted based on the distributed optical fiber acoustic data. Here, reservoir rock strain is the relative change in length or volume of the reservoir rock under stress (fracturing). It represents the ratio of the changed shape or volume to the original volume.

[0110] In step 200, the distributed fiber acoustic data and the time-domain filter are convolved to generate the desired signal in step 200.

[0111] Step 300: Downsample the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing in the target reservoir.

[0112] As is well known, fiber optic signals generate large amounts of data, thus fiber optic data processing demands high computing power. Simultaneously, displaying the processed results places high demands on the computer's graphics card. In short, in addition to extracting strain data, downsampling processing is necessary to improve computational efficiency and display capabilities. Furthermore, distributed fiber optic monitoring suffers from noise due to environmental noise and internal impurities, resulting in low signal-to-noise ratios in fiber optic measurement data. Therefore, while performing downsampling processing, it is also necessary to improve the signal-to-noise ratio of the extracted low-frequency strain response signal from fracturing.

[0113] As described above, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers provided in this embodiment of the invention first generates a time-domain filter for the target reservoir based on the reservoir rock properties parameters; then, it generates a desired signal corresponding to the distributed optical fiber acoustic data based on the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter; wherein, the distributed optical fiber acoustic data is generated by fracturing the target reservoir; finally, it downsamples the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal from the target reservoir.

[0114] This invention addresses the distributed optical fiber strain response monitoring scenario during hydraulic fracturing, proposing a method for extracting low-frequency strain response signals based on distributed optical fibers, grounded in time-domain convolution and block processing. This method extracts strain response signals from distributed optical fiber acoustic sensing data. On one hand, the method uses a time-domain convolution operator to extract strain response signals, avoiding sequential forward and inverse Fourier transforms and reducing computational load. On the other hand, block processing reduces data volume, lowering data storage costs while improving the signal-to-noise ratio. Furthermore, the computation process is simple and efficient, providing effective technical support for monitoring the crack propagation and development process in optical fiber fracturing.

[0115] In some embodiments of the present invention, see Figure 2 Step 100 includes:

[0116] Step 101: Set the low-pass filter corresponding to the target reservoir according to the reservoir rock properties parameters of the target reservoir;

[0117] First, the cutoff frequency F is determined based on the porosity, permeability, saturation, porosity distribution, pore structure, saturation distribution, rock density, and rock elastic modulus of the target reservoir, so as to filter out the part of the low-frequency strain response signal of fracturing with a frequency higher than F (Hz).

[0118] Step 102: Perform an inverse Fourier transform on the low-pass filter to generate the time-domain filter.

[0119] In step 102, the mathematical expression for the inverse Fourier transform is:

[0120] f(t)=(1 / 2π)∫F(ω)e^(jωt)dω

[0121] Where f(t) is the time domain representation of the function (low-pass filter), F(ω) is the frequency domain representation of the function (low-pass filter), t is time, and ω is the angular frequency.

[0122] The purpose of the inverse Fourier transform is to convert a function in the frequency domain back to the time domain, thereby obtaining the time representation of a low-pass filter. When performing the inverse Fourier transform, it is necessary to consider the signal sampling rate and truncation effect based on the reservoir rock properties, and to rationally select appropriate inverse Fourier transform algorithms and parameters to ensure an accurate time-domain representation.

[0123] In some embodiments of the present invention, see Figure 3 Step 200 includes:

[0124] Step 201: Extract strain rate data from the distributed fiber optic acoustic data;

[0125] First, the MATLAB function H5disp is used to read the information of "RawDataUnit" in "Group' / Acquisition / Raw[0]" of the HDF5 file in the distributed optical fiber acoustic wave data to determine the physical meaning of the stored data. If the value of this tag is "rad", then the type of DAS data is strain rate, and strain rate data is then extracted.

[0126] Additionally, if the label value is "nε", then the DAS data type is strain data. In this case, time differencing is required to obtain strain rate data.

[0127] Step 202: Convolve the strain rate data with the time domain filter to generate the desired signal.

[0128] Specifically, the strain rate data is slid across a time-domain filter, and a product operation is performed at each location. All product results are then summed. This yields a new function, the desired signal, which describes the interaction between the strain rate data and the time-domain filter.

[0129] Let the time-domain filter be h(t), which is derived from the low-pass filter H(f) through the inverse Fourier transform:

[0130]

[0131] In some embodiments of the present invention, see Figure 4 Before step 300, the method for extracting low-frequency strain response signals in fracturing based on distributed optical fibers also includes:

[0132] Step 400: Divide the desired signal into blocks to generate multiple block data;

[0133] Further, see Figure 5 Step 400 further includes:

[0134] Step 401: Determine the sampling window based on the length of the desired signal;

[0135] Step 402: Calculate the average value of the desired signal within the sampling window;

[0136] Step 403: Determine the number of sampling points in each block of data based on the average value;

[0137] Step 404: Divide the desired signal into blocks according to the number of sampling points in each block of data.

[0138] In steps 401 to 404, the DAS seismic data is divided into N / K blocks, with each block containing K sampling points. The data within the time window determined based on the desired signal length are then averaged.

[0139]

[0140] Next, the average value As the number of sampling points within this time window, N / K sampling points are finally obtained, thus dividing the desired signal into blocks.

[0141] In some embodiments of the present invention, see Figure 6 Step 300 includes:

[0142] Step 301: Determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir;

[0143] Through extensive experimental and actual production data, the applicant discovered that the effective aperture of microfractures in the target reservoir has a very significant indicative effect on the sampling frequency. Therefore, the sampling frequency during the downsampling process of the low-frequency strain response signal of fracturing can be effectively determined based on the effective aperture of microfractures.

[0144] Step 302: Downsample the desired signal according to the sampling frequency to extract the fracturing low-frequency strain response signal of the target reservoir.

[0145] In some embodiments of the present invention, see Figure 7 The method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers also includes:

[0146] Step 500: Determine the highest frequency of the desired signal;

[0147] Step 600: Determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir and the highest frequency;

[0148] Based on the sampling theorem, the sampling frequency F c It must satisfy the following formula

[0149] F c ≥2×F

[0150] Where F is the highest frequency in the target signal's frequency band.

[0151] In some embodiments of the present invention, see Figure 8 Step 300 also includes:

[0152] Step 303: Determine the sampling time for downsampling based on the sampling frequency;

[0153] Based on the following formula, it can be inferred that the maximum time sampling interval Δt is:

[0154] △t=1 / F c ≤2×F

[0155] Step 304: Downsample the desired signal according to the sampling time to extract the fracturing low-frequency strain response signal of the target reservoir.

[0156] By downsampling the filtered desired signal using the aforementioned sampling interval, the goal of efficiently extracting the low-frequency strain response signal from the DAS record can be achieved. Specifically, let the time sampling interval of the original DAS record be Δtt, and the time sampling interval of the downsampling be Δt, then the downsampling rate is...

[0157]

[0158] In some embodiments of the present invention, see Figure 9 The method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers also includes:

[0159] Step 700: Determine the weight of the influence of different throats on permeability in the target reservoir;

[0160] Core analysis is performed on core samples from the target reservoir to obtain its corresponding full-scale throat distribution curve. Then, based on this full-scale throat distribution curve, the relationship between the average throat radius and permeability is determined. Specifically:

[0161] The distribution of the throat at all scales is normalized, and the average throat radius is calculated using the following formula;

[0162]

[0163] In the formula: Indicates the average throat radius;

[0164] r i This represents the radius of the throat corresponding to point i;

[0165] n represents the number of data points;

[0166] α iThis represents the normalized distribution frequency of the throat radius.

[0167] Next, the average throat radius of all samples was obtained; a scatter plot of the correlation between the average throat radius and permeability was plotted, and the correlation function between the average throat radius and permeability was obtained.

[0168] Finally, the influence weight of throats with different radii on permeability is calculated using the following formula;

[0169]

[0170] In the formula: △K i This indicates the weight of the impact of penetration rate.

[0171] Step 800: Determine the effective permeability of the target reservoir based on the influence weights;

[0172] Establish a distribution curve with the throat radius on the x-axis and the cumulative influence weight of permeability on the y-axis. Determine the throat radius at which the sample's permeability contribution reaches 99.99%, which is the lower limit K of the effective throat radius. L .

[0173] Step 900: Determine the effective opening of the microcracks based on the effective permeability.

[0174] Specifically, the maximum microcrack spacing D was obtained by measuring the cast sections and scanning electron microscopes of all samples. MAX Record the corresponding effective permeability; calculate the effective microcrack aperture e using the following formula. L .

[0175]

[0176] As described above, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers provided in this invention first generates a time-domain filter for the target reservoir based on the reservoir rock properties parameters; then, it generates a desired signal corresponding to the distributed optical fiber acoustic data based on the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter; wherein, the distributed optical fiber acoustic data is generated by fracturing the target reservoir; finally, it downsamples the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal from the target reservoir. Specifically, this invention has the following beneficial effects:

[0177] This invention extracts strain response signals from DAS data using a time-domain convolution operator, avoiding the need for channel-by-channel forward and inverse Fourier transforms in the frequency domain, thus significantly reducing computational load. Simultaneously, by downsampling and segmenting the desired signal, the signal-to-noise ratio of the strain is improved. This approach ensures the acquisition of effective signals while reducing the amount of data required for computation. In summary, the distributed optical fiber-based low-frequency strain response signal extraction method for fracturing provided by this invention can meet the requirements of real-time on-site monitoring during fracturing while improving the accuracy of the results. Furthermore, the calculation process is simple and efficient. It can provide technical support for using strain to describe crack propagation characteristics in real time during distributed optical fiber fracturing monitoring.

[0178] Example 2

[0179] To further illustrate the solution, this invention also provides specific application examples of the method for extracting low-frequency strain response signals in fracturing based on distributed optical fibers, specifically, as follows: Figure 10 As shown, this specific application example includes the following steps:

[0180] S1: Acquire the DAS signal monitored during fracturing.

[0181] like Figure 11 As shown, the DAS recording time sampling interval is 2KHz.

[0182] S2: Construct a low-pass filter.

[0183] First, a cutoff frequency F needs to be set to filter out components in the signal with frequencies higher than F (Hz). Assuming the original DAS recording time sampling interval is Δt and the number of time sampling points is N, then the frequency sampling interval Δf of the filter is...

[0184]

[0185] Based on the filtering principle, to avoid the Gibbs phenomenon caused by filtering, it is necessary to set a high cutoff frequency f of the low-pass filter. h The settings are as follows:

[0186] f h =0.9×F

[0187] Based on this, the mathematical expression for the low-pass filter H(f) is:

[0188]

[0189] Among them, f k = k × Δf. Here, F is chosen as 1 Hz, and the spectrum of the low-pass filter is as follows: Figure 12 As shown.

[0190] S3: Construct a time-domain filter.

[0191] Let the time-domain filter be h(t), which is derived from H(f) through the inverse Fourier transform.

[0192]

[0193] S4: Generate the desired signal.

[0194] Strain rate data monitored by distributed fiber optic acoustic sensing (DAS) With time-domain filter h(t) n Perform convolution to obtain the desired signal y(t) n ,x m ).

[0195]

[0196] The generated expected signal is as follows Figure 13 As shown.

[0197] S5: Downsample the desired signal.

[0198] First, set the time sampling interval required for downsampling. Based on the sampling theorem, the sampling frequency F... c It must satisfy the following formula

[0199] F c ≥2×F

[0200] Where F is the highest frequency in the target signal's frequency band.

[0201] Based on the following formula, it can be inferred that the maximum time sampling interval Δt is:

[0202] △t=1 / F c ≤2×F

[0203] By utilizing the aforementioned sampling interval (downsampling the filtered desired signal), the goal of efficiently extracting the low-frequency strain response signal from the fracturing data using DAS recordings can be achieved. Let the time sampling interval of the original DAS recording be Δtt, and the time sampling interval of the downsampling be Δt, then the downsampling rate is...

[0204]

[0205] The original signal recording length T is:

[0206] T=△tt×N

[0207] The seismic data is divided into N / K blocks, with each block containing K sampling points. The data within the time window is then averaged.

[0208]

[0209] average These are used as sampling points within the time window, resulting in N / K sampling points for downsampling the desired signal.

[0210] Using the above formula, the downsampling time sampling interval is calculated to be 0.5 ms and the downsampling rate K is 1000. The downsampling of the ultra-low frequency fracturing strain response signal is then completed using the above formula. The downsampling data is as follows: Figure 14 As shown.

[0211] As can be seen from the above description, the method for extracting low-frequency strain response signals for fracturing based on distributed optical fiber provided in this embodiment of the invention first determines the frequency range of the low-pass filter and sets the filter; then, the low-pass filter is subjected to inverse Fourier transform to obtain a time-domain filter; then, the original record is convolved with the time-domain filter; finally, the data within the time window is downsampled using a block processing method, thereby achieving the purpose of extracting high signal-to-noise ratio strain response from distributed acoustic monitoring.

[0212] Example 3

[0213] Based on the same principle, this embodiment also discloses a device for extracting low-frequency strain response signals from fracturing based on distributed optical fibers. For example... Figure 15 As shown, the device includes:

[0214] The time-domain filter generation module 10 is used to generate a time-domain filter for the target reservoir based on the reservoir rock physical property parameters of the target reservoir.

[0215] The desired signal generation module 20 is used to generate a desired signal corresponding to the distributed optical fiber acoustic wave data based on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir and the time domain filter; wherein, the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir.

[0216] The desired signal downsampling module 30 is used to downsample the desired signal according to the effective aperture of the microfractures in the target reservoir in order to extract the low-frequency strain response signal of the fracturing in the target reservoir.

[0217] In some embodiments of the invention, the time-domain filter generation module includes:

[0218] A low-pass filter setting unit is used to set the low-pass filter corresponding to the target reservoir according to the reservoir rock physical property parameters of the target reservoir.

[0219] A time-domain filter generation unit is used to perform an inverse Fourier transform on the low-pass filter to generate the time-domain filter.

[0220] In some embodiments of the invention, the desired signal generation module includes:

[0221] A strain rate data extraction unit is used to extract strain rate data from the distributed optical fiber acoustic data;

[0222] The desired signal generation unit is used to convolve the strain rate data with the time domain filter to generate the desired signal.

[0223] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0224] The desired signal segmentation module is used to segment the desired signal into blocks to generate multiple block data.

[0225] The desired signal segmentation module includes:

[0226] A sampling time window determination unit is used to determine the sampling time window based on the length of the desired signal;

[0227] An average value calculation unit is used to calculate the average value of the desired signal within the sampling window;

[0228] A sampling point determination unit is used to determine the number of sampling points in each block of data based on the average value.

[0229] The desired signal segmentation unit segments the desired signal into blocks based on the number of sampling points in each block of data.

[0230] In some embodiments of the invention, the desired signal downsampling module includes:

[0231] A sampling frequency determination unit is used to determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir.

[0232] The desired signal downsampling unit is used to downsample the desired signal according to the sampling frequency in order to extract the fracturing low-frequency strain response signal of the target reservoir.

[0233] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0234] The highest frequency determination module is used to determine the highest frequency of the desired signal;

[0235] The sampling frequency determination module is used to determine the sampling frequency corresponding to the downsampling based on the effective aperture of the microfractures in the target reservoir and the highest frequency.

[0236] The desired signal downsampling module also includes:

[0237] A sampling time determination unit is used to determine the sampling time of the downsampling based on the sampling frequency;

[0238] The strain response signal extraction unit is used to downsample the desired signal according to the sampling time in order to extract the fracturing low-frequency strain response signal of the target reservoir.

[0239] In some embodiments of the invention, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber further includes:

[0240] The influence weight determination module is used to determine the influence weight of different throats in the target reservoir on permeability.

[0241] An effective permeability determination module is used to determine the effective permeability of the target reservoir based on the influence weights.

[0242] An effective aperture determination module is used to determine the effective aperture of the microcrack based on the effective permeability.

[0243] As described above, the fracturing low-frequency strain response signal extraction device based on distributed optical fiber provided in this embodiment of the invention first generates a time-domain filter for the target reservoir based on the reservoir rock properties parameters; then, it generates a desired signal corresponding to the distributed optical fiber acoustic data based on the pre-acquired distributed optical fiber acoustic data of the target reservoir and the time-domain filter; wherein, the distributed optical fiber acoustic data is generated by fracturing the target reservoir; finally, it downsamples the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the fracturing low-frequency strain response signal of the target reservoir.

[0244] This invention utilizes distributed acoustic sensing (DAS) signals to describe the strain changes in subsurface formations during fracturing. The raw DAS signals are processed using the method described above. This avoids performing sequential forward and inverse Fourier transforms when obtaining the desired signal, reducing computational load and improving efficiency. Furthermore, based on convolution operations and block processing, it offers advantages such as a simple and clear computational process and a high signal-to-noise ratio in the extracted signal.

[0245] Example 4

[0246] The embodiments of this application also provide a specific implementation of an electronic device capable of performing all steps in the distributed optical fiber-based low-frequency strain response signal extraction method for fracturing described in the above embodiments. See [link to relevant documentation]. Figure 16 The electronic devices specifically include the following:

[0247] Processor 1201, memory 1202, communications interface 1203, and bus 1204;

[0248] 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.

[0249] 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 method for extracting low-frequency strain response signals of fracturing based on distributed optical fiber in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0250] Generate a time-domain filter for the target reservoir based on the reservoir rock properties parameters of the target reservoir;

[0251] The desired signal corresponding to the pre-acquired distributed optical fiber acoustic wave data of the target reservoir and the time domain filter are generated; wherein, the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir.

[0252] The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing in the target reservoir.

[0253] In some embodiments of the invention, generating a time-domain filter for the target reservoir based on reservoir rock property parameters includes:

[0254] Set the low-pass filter corresponding to the target reservoir according to the reservoir rock physical property parameters of the target reservoir;

[0255] The low-pass filter is subjected to an inverse Fourier transform to generate the time-domain filter.

[0256] In some embodiments of the invention, generating the desired signal corresponding to the distributed fiber optic acoustic data based on the pre-acquired target reservoir distributed fiber optic acoustic data and the time-domain filter includes:

[0257] Extract strain rate data from the distributed fiber optic acoustic data;

[0258] The strain rate data is convolved with the time-domain filter to generate the desired signal.

[0259] In some embodiments of the invention, before downsampling the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, the method further includes:

[0260] The desired signal is divided into blocks to generate multiple block data;

[0261] Dividing the desired signal into blocks includes:

[0262] The sampling window is determined based on the length of the desired signal;

[0263] Calculate the average value of the desired signal within the sampling window;

[0264] The number of sampling points in each block of data is determined based on the average value.

[0265] The desired signal is divided into blocks based on the number of sampling points in each block of data.

[0266] In some embodiments of the invention, the desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, including:

[0267] The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir.

[0268] The desired signal is downsampled according to the sampling frequency to extract the low-frequency strain response signal of the target reservoir.

[0269] In some embodiments of the invention, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers further includes:

[0270] Determine the highest frequency of the desired signal;

[0271] The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir and the highest frequency.

[0272] The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, and the method further includes:

[0273] The sampling time for downsampling is determined based on the sampling frequency;

[0274] The desired signal is downsampled according to the sampling time to extract the low-frequency strain response signal of the target reservoir.

[0275] In some embodiments of the invention, the method for extracting low-frequency strain response signals from fracturing based on distributed optical fibers further includes:

[0276] Determine the weight of the influence of different throats on permeability in the target reservoir;

[0277] The effective permeability of the target reservoir is determined based on the influence weights.

[0278] The effective opening of the microcracks is determined based on the effective permeability.

[0279] Example 5

[0280] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the distributed optical fiber-based fracturing low-frequency strain response signal extraction method described 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 fracturing low-frequency strain response signal extraction method described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0281] Step 100: Generate a time-domain filter for the target reservoir based on the reservoir rock properties parameters of the target reservoir;

[0282] Step 200: Generate the desired signal corresponding to the distributed fiber acoustic wave data based on the pre-acquired target reservoir distributed fiber acoustic wave data and the time domain filter; wherein, the distributed fiber acoustic wave data is generated by fracturing the target reservoir.

[0283] Step 300: Downsample the desired signal based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing in the target reservoir.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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 extracting low-frequency strain response signals from fracturing based on distributed optical fibers, characterized in that, include: Generate a time-domain filter for the target reservoir based on the reservoir rock properties parameters of the target reservoir; The desired signal corresponding to the pre-acquired distributed optical fiber acoustic wave data of the target reservoir and the time domain filter are generated; wherein, the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir. The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing in the target reservoir. The steps for generating the desired signal include: Extract strain rate data from the distributed fiber optic acoustic data; The strain rate data is convolved with the time domain filter to generate the desired signal, which is used to describe the interaction between the strain rate data and the time domain filter. Before extracting the low-frequency strain response signal of the target reservoir from fracturing, the method further includes: The sampling window is determined based on the length of the desired signal, and the average value of the desired signal within the sampling window is calculated. The number of sampling points in each block of data is determined based on the average value, and the desired signal is divided into blocks according to the number of sampling points in each block of data to generate multiple blocks of data.

2. The method for extracting low-frequency strain response signals during fracturing according to claim 1, characterized in that, The step of generating a time-domain filter for the target reservoir based on the reservoir rock property parameters includes: Set the low-pass filter corresponding to the target reservoir according to the reservoir rock physical property parameters of the target reservoir; The low-pass filter is subjected to an inverse Fourier transform to generate the time-domain filter.

3. The method for extracting low-frequency strain response signals during fracturing according to claim 1, characterized in that, The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, including: The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir. The desired signal is downsampled according to the sampling frequency to extract the low-frequency strain response signal of the target reservoir.

4. The method for extracting low-frequency strain response signals during fracturing according to claim 3, characterized in that, Also includes: Determine the highest frequency of the desired signal; The sampling frequency corresponding to the downsampling is determined based on the effective aperture of the microfractures in the target reservoir and the highest frequency. The desired signal is downsampled based on the effective aperture of the microfractures in the target reservoir to extract the low-frequency strain response signal of the fracturing reservoir, and the method further includes: The sampling time for downsampling is determined based on the sampling frequency; The desired signal is downsampled according to the sampling time to extract the low-frequency strain response signal of the target reservoir.

5. The method for extracting low-frequency strain response signals during fracturing according to claim 1, characterized in that, Also includes: Determine the weight of the influence of different throats on permeability in the target reservoir; The effective permeability of the target reservoir is determined based on the influence weights. The effective opening of the microcracks is determined based on the effective permeability.

6. A device for extracting low-frequency strain response signals from fracturing based on distributed optical fibers, characterized in that, include: A time-domain filter generation module is used to generate a time-domain filter for the target reservoir based on the reservoir rock physical property parameters of the target reservoir. The desired signal generation module is used to generate a desired signal corresponding to the distributed optical fiber acoustic wave data based on the pre-acquired distributed optical fiber acoustic wave data of the target reservoir and the time domain filter; wherein, the distributed optical fiber acoustic wave data is generated by fracturing the target reservoir. The desired signal downsampling module is used to downsample the desired signal according to the effective aperture of the microfractures in the target reservoir in order to extract the low-frequency strain response signal of the fracturing in the target reservoir. The steps for generating the desired signal include: Extract strain rate data from the distributed fiber optic acoustic data; The strain rate data is convolved with the time domain filter to generate the desired signal, which is used to describe the interaction between the strain rate data and the time domain filter. Before extracting the low-frequency strain response signal of the target reservoir from fracturing, the method further includes: The sampling window is determined based on the length of the desired signal, and the average value of the desired signal within the sampling window is calculated. The number of sampling points in each block of data is determined based on the average value, and the desired signal is divided into blocks according to the number of sampling points in each block of data to generate multiple blocks of data.

7. 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 5.

8. 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 5.

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