Open hole vertical well fracturing crack position identification method based on density clustering

Abstract

The invention discloses an open hole vertical well fracturing crack position identification method based on density clustering, which belongs to the technical field of hydraulic fracturing, and comprises the following steps: collecting a water hammer signal at the moment of pump stop by using a high-frequency pressure monitor; performing filtering processing on the signal by using an FIR filter; carrying out deconvolution processing on the filtered signal by adopting a cepstrum analysis method, and extracting crack reflection characteristics; determining the position of a liquid inlet point by combining an impedance identification technology; and performing dynamic analysis by using a density clustering method according to the determined liquid inlet point position, and identifying the crack number and the main body position based on the liquid inlet point space density. By the adoption of the method, the spatial distribution of the liquid inlet points is obtained through water hammer signal processing, the spatial density of the liquid inlet points is analyzed based on the density clustering analysis method, and therefore crack position recognition in the complex liquid inlet process such as vertical well open hole fracturing is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic fracturing, and in particular to a method for identifying the position of hydraulic fractures in open-hole vertical wells based on density clustering. Background Art

[0002] As an important oil and gas field production enhancement measure, vertical openhole fracturing has been widely used in the development of low-permeability, tight oil and gas reservoirs in recent years. However, due to the lack of casing isolation in openhole wells, fractures in the reservoir exhibit complex behavior with simultaneous fluid inflow at multiple points, making fracture monitoring and fluid inflow point identification extremely challenging. Although currently commonly used water hammer monitoring methods have achieved the identification of fracture opening status in cased wells, in openhole wells, due to the open fluid inflow channels, multiple fractures may be simultaneously fluid-injected, resulting in a complex distribution of fluid inflow points and significant overlap in response signals, making it difficult to accurately distinguish the main location of each fracture and the contribution of fluid inflow.

[0003] Chinese patent publication CN119025840A proposes a method for calculating the depth of the primary fluid inlet point in a fracturing fracture based on wellhead water hammer signals. This method achieves this depth calculation through DC removal, high-frequency noise filtering, windowing, and frequency difference calculation. However, this method has limitations in analyzing complex fluid inlet behavior. It cannot effectively distinguish between fluid inlet events in multiple fractures, making it difficult to identify complex fluid inlet behavior in vertical openhole fracturing. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for identifying crack positions in open-hole vertical well fracturing based on density clustering. The spatial distribution of liquid inlet points is obtained by water hammer signal processing, and the spatial density of the liquid inlet points is analyzed based on the density clustering analysis method, thereby realizing crack position identification in complex liquid inlet processes such as vertical well open-hole fracturing.

[0005] To achieve the above objectives, the present invention provides a method for identifying fracture locations in open hole vertical wells based on density clustering, comprising the following steps:

[0006] S1. Install a high-frequency pressure monitor at the wellhead to collect water hammer signals at the moment the pump stops;

[0007] S2, use FIR filter to filter the signal;

[0008] S3. Deconvolution processing is performed on the filtered signal using the cepstrum analysis method to extract the crack reflection characteristics;

[0009] S4. Combine impedance recognition technology to determine the location of the liquid inlet point;

[0010] S5. Based on the determined locations of the liquid inlet points, a density clustering method is used to perform dynamic analysis, and the number of cracks and the main locations are identified based on the spatial density of the liquid inlet points.

[0011] Preferably, the sampling frequency of the high-frequency pressure monitor in step S1 is not less than 1000 Hz to ensure that the high-frequency characteristics of the water hammer wave can be captured. The water hammer signal is continuously reflected in the wellbore and fractures, and the water hammer signal exhibits a convolution characteristic, and the formula is:

[0012] y(t)=s(t)*w(t);

[0013] In the formula, y(t) is the output signal, that is, the convolution signal; s(t) is the input signal, that is, the original water hammer signal; w(t) is the impulse response of the system, that is, the signal generated by the reflection at the crack;

[0014] Preferably, the principle of the FIR filter filtering the signal in step S2 is:

[0015]

[0016] Where x(n) is the output signal of the FIR filter, that is, the filtered water hammer signal; t is the time length; i is the summation index variable, which is used to traverse each term in the finite convolution and calculation process; h is the unit impulse response sequence representing the filter; y(t) is the input signal, that is, the convolution sequence of the original water hammer signal and the crack reflection signal;

[0017] After filtering, the convolution signal becomes:

[0018] x(n)=s(n)*w(n);

[0019] x(n) is the filtered water hammer signal; s(n) is the filtered original water hammer signal; w(n) is the filtered crack reflection signal.

[0020] Preferably, step S3 includes:

[0021] S31. Perform Fourier transform to transform the convolution signal into a product signal. The formula is:

[0022]

[0023] Where, It is a Fourier transform operation that converts the time domain signal into the frequency domain for analysis; is the crack reflection signal after Fourier transform; is the original water hammer signal after Fourier transform;

[0024] S32. Perform logarithmic transformation on the product signal. The formula is:

[0025]

[0026] S33, perform inverse Fourier transform to obtain the cepstrum signal, the formula is:

[0027]

[0028] Where, is the cepstrum signal; are the cepstrum of the initial water hammer signal and the crack reflection signal respectively;

[0029] S34. Obtain the reflection time according to the peak value of the cepstrum signal.

[0030] Preferably, step S4 includes:

[0031] Impedance is defined as the ratio of oscillating pressure to oscillating flow, and the formula is:

[0032]

[0033] Where Z is impedance, s / m 2 ; H is the water head, m; Q is the wellbore flow, m 3 / s; t is time, s; ω is angular frequency, rad / s; φ is the phase angle between head and flow, rad; e is the base of the natural logarithm function, approximately equal to 2.71828; i is the imaginary unit, satisfying i 2 =-1;

[0034] Simplifying the impedance formula, assuming that the friction coefficient is a constant, the phase angle is equal to 0 or π / ω, and the simplified formula is:

[0035] Z c =ρc / A;

[0036] Where Z c is the simplified impedance, s / m 2 ; ρ is the medium density, kg / m 3 ; c is the wave velocity, m / s; A is the cross-sectional area of the fluid flow, m 2 ;

[0037] Calculate the reflection coefficient of the water hammer wave at the crack. If the reflection coefficient is negative, it indicates that this is the liquid inlet point. The reflection coefficient calculation formula is:

[0038]

[0039] Where R is the reflection coefficient; is the impedance of the pressure wave before it passes through the catheter, s / m 2 ; is the impedance of the pressure wave after passing through the catheter, s / m 2 ;

[0040] Based on the reflection coefficient, it is determined whether the reflection time obtained by the cepstrum analysis corresponds to the liquid entry point. After confirmation, the corresponding depth position is calculated according to the reflection time of the liquid entry point. The formula is:

[0041] x L =ct p / 2;

[0042] Where x L is the position of the liquid inlet point, m; c is the wave speed, m / s; t p is the pressure wave reflection time in cepstrum analysis, s.

[0043] Preferably, step S5 includes:

[0044] The determined inlet point position is used as input data to obtain a depth data set of the inlet point in the wellbore, wherein the position of each inlet point corresponds to a depth coordinate;

[0045] Identify the core points. The core points depend on the calculation of the neighborhood density of each point. For point p, calculate the number of points N in the neighborhood. ε (p), the formula is:

[0046] N ε (p)={q∈X|dist(p,q)≤ε};

[0047] Where X is the depth dataset of the inlet point, dist(p,q) is the Euclidean distance between point p and point q, ε is the neighborhood radius;

[0048] After the core points are identified, the density clustering algorithm classifies the core points and their adjacent boundary points into a cluster. By analyzing the depth position, number of points within the cluster, and spatial distribution characteristics of each cluster, the opening position and dominance of the cracks it represents are determined. Clusters with dense points and concentrated ranges correspond to major controlling cracks, while small clusters or clusters with unclear boundaries correspond to secondary cracks or interfering cracks.

[0049] Therefore, the present invention adopts the above-mentioned method for identifying the location of hydraulic fractures in open hole vertical wells based on density clustering, which has the following effects:

[0050] (1) The spatial distribution of the inlet points is obtained by water hammer signal processing, and the spatial density of the inlet points is analyzed based on the density cluster analysis method. This enables the accurate identification of fracture locations and inlet point location information in complex inlet processes such as vertical well open hole fracturing, providing strong support for the evaluation of fracturing effects and subsequent production optimization.

[0051] (2) The location of the fluid injection point is determined based on the method of the present invention, which can optimize the fracturing design, reduce ineffective fluid injection, improve the efficiency of vertical well open hole fracturing, and significantly improve the development effect.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0054] Figure 2 This is a waveform diagram of a water hammer signal according to an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of water hammer signal before and after filtering processing according to an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of cepstrum transformation and impedance analysis according to an embodiment of the present invention;

[0057] Figure 5 Schematic diagram of density cluster analysis according to an embodiment of the present invention;

[0058] Figure 6 A schematic diagram of the number of cracks and main body positions in an embodiment of the present invention;

[0059] Figure 7 This is a microseismic monitoring diagram of the corresponding section of the well according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "upper", "lower", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or is the orientation or position relationship in which the product of the invention is usually placed when in use. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0061] Example

[0062] like Figure 1 As shown, the present invention provides a method for identifying the location of hydraulic fractures in open hole vertical wells based on density clustering, the steps comprising:

[0063] S1. Install a monitoring meter at the wellhead to collect the water hammer waveform curve at the moment the pump stops, i.e., the water hammer signal. The sampling frequency of the high-frequency pressure monitoring meter should be no less than 1000Hz to ensure that the high-frequency characteristics of the water hammer wave can be captured. The water hammer signal is continuously reflected in the wellbore and fractures, and the water hammer signal exhibits a convolution characteristic, as shown in the formula:

[0064] y(t)=s(t)*w(t);

[0065] Where, y(t) is the output signal, i.e., the convolution signal; s(t) is the input signal, i.e., the original water hammer signal; w(t) is the impulse response of the system, i.e., the signal generated by the reflection at the crack.

[0066] S2. Use an FIR filter to filter the signal and remove noise interference. The FIR filter has a strict linear phase characteristic, which can effectively preserve the time domain characteristics of the water hammer signal and selectively filter out noise in different frequency bands. For the input sequence y(t), the output of the FIR is given by a finite convolution sum, as follows:

[0067]

[0068] Where x(n) is the output signal of the FIR filter, that is, the filtered water hammer signal; t is the time length; i is the summation index variable, which is used to traverse each term in the finite convolution and calculation process; h is the unit impulse response sequence representing the filter; y(t) is the input signal, that is, the convolution sequence of the original water hammer signal and the crack reflection signal;

[0069] After filtering, the convolution signal becomes:

[0070] x(n)=s(n)*w(n);

[0071] Where x(n) is the filtered water hammer signal; s(n) is the filtered original water hammer signal; and w(n) is the filtered crack reflection signal.

[0072] S3. Use the cepstrum analysis method to deconvolve the filtered signal x(n), separate the reflected signal from the water hammer signal, and extract the crack reflection characteristics.

[0073] First, perform Fourier transform. Fourier transform can convert the water hammer signal into the frequency domain, turning the originally complex convolution into a simple product. The convolution signal is transformed into a product signal. The formula is:

[0074]

[0075] Where, It is a Fourier transform operation that converts the time domain signal into the frequency domain for analysis; is the crack reflection signal after Fourier transform; is the original water hammer signal after Fourier transform.

[0076] Fourier transform can transform the convolution signal into a product signal, but it is still difficult to distinguish two different signals in the product domain. Therefore, the product signal is logarithmically transformed to become an addition signal, the formula is:

[0077]

[0078] Perform inverse Fourier transform to obtain the cepstrum signal, the formula is:

[0079]

[0080] Where, is the cepstrum signal; They are the cepstrum of the initial water hammer signal and the crack reflection signal, respectively.

[0081] Finally, the reflection time t is obtained according to the peak position in the cepstrum signal. p .

[0082] S4. Combined with impedance identification technology, determine whether the reflection time obtained by cepstrum analysis corresponds to the actual fluid inlet point. The presence of cracks will cause the fluid impedance to change. By analyzing the change point of the fluid characteristics, the location of the fluid inlet point can be determined.

[0083] In hydraulics, impedance is defined as the ratio of oscillating pressure to oscillating flow, as follows:

[0084]

[0085] Where Z is impedance, s / m 2 ; H is the water head, m; Q is the wellbore flow, m 3 / s; t is time, s; ω is angular frequency, rad / s; φ is the phase angle between head and flow, rad; e is the base of the natural logarithm function, approximately equal to 2.71828; i is the imaginary unit, satisfying i 2 =-1;

[0086] Another important concept in impedance analysis is characteristic impedance, which describes the situation where pressure and flow are moving in the same direction. In a casing where pressure and flow are oscillating, assuming the friction coefficient is constant, the phase angle is equal to 0 or π / ω, and the impedance formula is simplified to:

[0087] Z c =ρc / A;

[0088] Where Z c is the simplified impedance, s / m 2; ρ is the medium density, kg / m 3 ; c is the wave velocity, m / s; A is the cross-sectional area of the fluid flow, m 2 .

[0089] When a water hammer wave propagates, if it encounters a crack (fluid inlet point), the wellbore cross-section suddenly increases, resulting in a sudden change in impedance and partial reflection of the water hammer wave. The reflection coefficient is used to describe the reflection intensity of the pressure wave in different areas. The reflection coefficient of the water hammer wave at the crack is calculated, and the coupling effect between the fluid and the wellbore structure is used to identify the presence of the fluid inlet point. If the reflection coefficient is negative, it indicates that this is the fluid inlet point (the crack has strong conductivity, causing the pressure wave to propagate in the opposite direction). The reflection coefficient calculation formula is:

[0090]

[0091] Where R is the reflection coefficient; is the impedance of the pressure wave before it passes through the catheter, s / m 2 ; is the impedance of the pressure wave after passing through the catheter, s / m 2 Among them, the negative impedance value corresponds to the liquid inlet point, and the impedance size reflects the conductivity of the fracture and can be used to evaluate the effectiveness of the fracture.

[0092] Based on the reflection coefficient, it is determined whether the reflection time obtained by the cepstrum analysis corresponds to the liquid entry point. After confirmation, the corresponding depth position is calculated according to the reflection time of the liquid entry point. The formula is:

[0093] x L =ct p / 2;

[0094] Where x L is the position of the liquid inlet point, m; c is the wave speed, m / s; t p is the pressure wave reflection time in cepstrum analysis, s.

[0095] S5. Based on the determined locations of the inlet points, a density clustering method is used to perform dynamic analysis, and the number of cracks and the main locations are identified based on the spatial density of the inlet points. Specifically:

[0096] Using the determined inlet point locations as input data, a dataset of their depths in the wellbore is generated, where each inlet point corresponds to a depth coordinate. Through multiple experiments or validation based on existing datasets, the density clustering algorithm optimizes two key parameters: the neighborhood radius (ε) and the minimum number of samples (MinPts), ensuring accurate identification of inlet point clusters corresponding to different fractures. The density clustering algorithm divides densely distributed inlet points into clusters based on their spatial density, with each cluster corresponding to a potential fracture. Specifically, a point is defined as a core point if it has at least MinPts points within its ε neighborhood. A point that has fewer than MinPts points within its ε neighborhood but is within the neighborhood of a core point is defined as a boundary point. Points that are neither core nor boundary points are considered noise points. The density clustering algorithm can identify outliers that do not belong to any cluster. In openhole vertical well inlet point identification, these outliers may represent noise or irregular inflow behavior.

[0097] The identification of core points depends on the calculation of the neighborhood density of each point. For point p, the number of points N in the neighborhood is calculated. ε (p), the formula is:

[0098] N ε (p)={q∈X|dist(p,q)≤ε};

[0099] Where X is the depth dataset of the inlet point, dist(p,q) is the Euclidean distance between point p and point q, and ε is the neighborhood radius.

[0100] After identifying the core point, a density clustering algorithm groups the core point and its adjacent boundary points into clusters. By analyzing each cluster's depth, number of points within it, and spatial distribution, the algorithm determines the opening location and dominance of the fracture it represents. Clusters with dense points and a concentrated area are likely to correspond to primary controlling fractures, while small clusters or clusters with unclear boundaries may correspond to secondary or interfering fractures. Furthermore, identified noise points can be marked for subsequent analysis of abnormal fluid inflow behavior.

[0101] To verify the effectiveness of the method of the present invention, a vertical open-hole fractured well FY in the Y block of a low-porosity and low-permeability reservoir in a certain area was taken as an example to analyze the 4500-4700 m fracture section. The specific steps are as follows:

[0102] S1, based on the high-frequency pressure monitoring meter installed at the wellhead, collects the water hammer signal at the moment of pump stop. The water hammer signal is shown in the figure Figure 2 The sampling frequency of the high-frequency pressure monitor is 1000 Hz, ensuring that the high-frequency characteristics of water hammer waves can be captured.

[0103] S2. Use FIR filter to filter the collected water hammer signal to remove noise interference. The schematic diagram before and after filtering is as follows: Figure 3 Through filtering, the noise in the signal can be effectively removed, the key features of the water hammer signal can be retained, and a clearer signal basis can be provided for subsequent signal analysis.

[0104] S3. Deconvolve the filtered signal using the cepstrum analysis method. Separate the reflected signal from the water hammer signal and extract the crack reflection characteristics, such as Figure 4 Shown in the enlarged part.

[0105] S4, combined with impedance identification technology, analyze the impedance change of the crack reflection signal and determine the time of liquid entry point, such as Figure 4 As shown in the middle circle, multiple continuous inlet point locations (depths from the wellhead) were calculated based on time, with the results shown in Table 1. By analyzing impedance changes, the inlet point locations were precisely identified, providing accurate input data for subsequent cluster analysis.

[0106] Table 1 Inversion position results

[0107]

[0108]

[0109] S5, such as Figure 5 and Figure 6 As shown in Figure 1, the number and location of the cracks are determined based on the density clustering method. Cluster analysis is performed based on the spatial density of the inlet points calculated in step S4, and four cracks are found with corresponding depths of 4648.84m-4688.84m, 4616.00m-4633.71m, 4553.92m-4580.78m, and 4510.35m-4538.59m.

[0110] like Figure 7 As shown in the figure, the distribution of the inlet points and the main fracture position are consistent with the microseismic monitoring data by more than 90%, which verifies the accuracy and reliability of the method in identifying the inlet points and the main fractures during the vertical well open hole fracturing process.

[0111] Therefore, the present invention adopts the above-mentioned density clustering-based open hole vertical well fracturing crack position identification method, obtains the spatial distribution of the liquid inlet point through water hammer signal processing, and analyzes the spatial density of the liquid inlet point based on the density clustering analysis method, thereby realizing the crack position identification and accurate identification of the liquid inlet point position information in complex liquid inlet processes such as vertical well open hole fracturing.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying fracture locations in open hole vertical wells based on density clustering, characterized in that the steps include: S1. Install a high-frequency pressure monitor at the wellhead to collect water hammer signals at the moment the pump stops; S2, use FIR filter to filter the signal; S3. Deconvolution processing is performed on the filtered signal using the cepstrum analysis method to extract the crack reflection characteristics; S4. Combine impedance recognition technology to determine the location of the liquid inlet point; S5. Based on the determined locations of the liquid inlet points, a density clustering method is used to perform dynamic analysis, and the number of cracks and the main locations are identified based on the spatial density of the liquid inlet points.

2. The method for identifying fracture locations in open-hole vertical wells based on density clustering according to claim 1, characterized in that: In step S1, the water hammer signal is continuously reflected in the wellbore and the fracture, and the water hammer signal exhibits a convolution characteristic.

3. The method for identifying fracture locations in open hole vertical wells based on density clustering according to claim 2, characterized in that: Step S3 includes: S31, performing Fourier transform to transform the convolution signal into a product signal; S32, performing logarithmic transformation on the product signal; S33, performing inverse Fourier transform to obtain a cepstrum signal; S34. Obtain the reflection time according to the peak value of the cepstrum signal.

4. The method for identifying fracture locations in open hole vertical wells based on density clustering according to claim 1, characterized in that: Step S4 includes: Impedance is defined as the ratio of oscillating pressure to oscillating flow, and the formula is: Where Z is impedance, s / m 2 ; H is the water head, m; Q is the wellbore flow, m 3 / s; t is time, s; ω is angular frequency, rad / s; φ is the phase angle between head and flow, rad; e is the base of the natural logarithm function, approximately equal to 2.71828; i is the imaginary unit, satisfying i 2 =-1; Simplifying the impedance formula, assuming that the friction coefficient is a constant, the phase angle is equal to 0 or π / ω, and the simplified formula is: Z c =ρc / A; Where Z c is the simplified impedance, s / m 2 ; ρ is the medium density, kg / m 3 ; c is the wave velocity, m / s; A is the cross-sectional area of the fluid flow, m 2 ; Calculate the reflection coefficient of the water hammer wave at the crack. If the reflection coefficient is negative, it indicates that this is the liquid inlet point. The reflection coefficient calculation formula is: Where R is the reflection coefficient; is the impedance of the pressure wave before it passes through the catheter, s / m 2 ; is the impedance of the pressure wave after passing through the catheter, s / m 2 ; Based on the reflection coefficient, it is determined whether the reflection time obtained by the cepstrum analysis corresponds to the liquid entry point. After confirmation, the corresponding depth position is calculated according to the reflection time of the liquid entry point. The formula is: x L =ct p / 2; Where x L is the position of the liquid inlet point, m; c is the wave speed, m / s; t p is the pressure wave reflection time in cepstrum analysis, s.

5. The method for identifying fracture locations in open hole vertical wells based on density clustering according to claim 1, characterized in that: Step S5 includes: The determined inlet point position is used as input data to obtain a depth data set of the inlet point in the wellbore, wherein the position of each inlet point corresponds to a depth coordinate; Identify core points. Core points rely on calculating the density of the neighborhood of each point. For point p, calculate the number of points N in the neighborhood. ε (p), the formula is: N ε (p)={q∈X|dist(p,q)≤ε}; Where X is the depth dataset of the inlet point, dist(p,q) is the Euclidean distance between point p and point q, ε is the neighborhood radius; After the core point is identified, the density clustering algorithm classifies the core point and its adjacent boundary points into a cluster. The depth position, number of points within the cluster, and spatial distribution characteristics of each cluster are analyzed to determine the opening position and dominance of the crack it represents. Clusters with dense points and concentrated ranges correspond to major controlling cracks, while small clusters or clusters with unclear boundaries correspond to secondary cracks or interfering cracks.

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