Variable scale distance DAS-VSP data processing method

Abstract

The invention provides a variable scale distance DAS-VSP data processing method, and relates to the field of distributed sound wave sensing data processing. Firstly, a zero well spacing DAS-VSP data set is used for estimating a local optimal scale distance through apparent velocity and bandwidth analysis, so that a single variable scale distance profile based on the local optimal scale distance is defined; afterwards, the same variable gauge length profile is used for processing optical data of all other vibration source positions of the variable well spacing DAS-VSP again, and finally a variable well spacing DAS-VSP profile with higher quality is generated through processing. The overall signal-to-noise ratio can be improved, spectrum distortion is avoided, and subsequent quantitative interpretation is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of distributed acoustic sensing (DAS) data processing, and particularly to a variable gage DAS-VSP data processing method. The method uses variable gages to perform high-fidelity and high signal-to-noise ratio processing on DAS data, and can be widely applied to geophysical exploration fields such as DAS-VSP acquisition and processing. Background Art

[0002] DAS-VSP (Distributed Acoustic Sensing for Vertical Seismic Profile) is an advanced geophysical measurement technology that combines distributed acoustic sensing (DAS) with vertical seismic profiling (VSP).

[0003] In a traditional DAS system, when using an optical fiber as a sensor to acquire acoustic or vibration signals along a wellbore, a gage parameter usually needs to be set. This gage is often fixed as a single value during the acquisition stage in most commercial DAS hardware, and typical values may range from 10 meters to 30 meters. This usually results in sacrificing the signal-to-noise ratio (SNR) to ensure signal fidelity. In the case of fixed gage processing, usually only the minimum value suitable for the entire data set can be selected. However, due to factors such as formation conditions, wellbore structure, and seismic source frequency bandwidth changing with depth, it is difficult for a single gage to balance the signal fidelity and high SNR of the data of the entire well.

[0004] Therefore, how to implement variable gages in the digital domain so that the locally optimal gage value can be selected at each depth or sampling position has become a major difficult point that urgently needs to be broken through in current DAS data processing. Summary of the Invention

[0005] The present invention aims to provide a variable gage DAS-VSP data processing method to maximize the signal-to-noise ratio while maintaining signal integrity and achieve efficient processing of DAS-VSP data. The present invention proposes a new scheme for determining the optimal gage based on local geophysical parameters (such as saturated formation apparent velocity, maximum effective bandwidth, etc.) and implementing variable gage processing with a central difference operator in the digital domain, so as to achieve the following technical objectives:

[0006] 1. Improve the signal-to-noise ratio: Select a larger gage in areas such as high-speed formations to enhance weak signals;

[0007] 2. Maintain signal fidelity: Select a smaller gauge length in low-speed or high-attenuation areas to avoid signal distortion;

[0008] 3. Keep the DAS transfer function flat: By selecting a locally optimal gauge length for different depths, the dependence of the DAS strain output on factors such as formation velocity is reduced, facilitating subsequent data processing and interpretation.

[0009] Apply this variable gauge length technology to DAS-VSP data acquisition and processing, and finally obtain variable offset DAS-VSP results with high signal-to-noise ratio and high resolution.

[0010] "Zero-offset DAS-VSP optical data" refers to the original distributed optical information such as DAS phase or strain rate recorded by an optical fiber deployed along the wellbore when the seismic source is arranged near the wellhead (ideally, the wellhead coincides with the seismic source or the distance is very close). Different from the seismic data obtained by conventional geophones, the DAS system senses the vibration signals of the entire well section through a distributed optical fiber, and the obtained optical data is essentially a sampling of the phase, intensity, or polarization changes during the scattering of light in the optical fiber. By converting these changes into seismic wave vibration information, the "optical seismic record" in the DAS domain is obtained.

[0011] For zero-offset DAS-VSP, since the seismic source is very close to the wellhead, it can be regarded that the vibration signal almost enters the ground in a vertical incidence manner. At this time, the received DAS response is more obvious, which is beneficial for subsequent data processing.

[0012] In view of this, the present invention proposes a new idea: By implementing variable gauge length in the digital domain, locally optimal gauge length values can be adopted at different depth positions, so as to maximize the signal-to-noise ratio while maintaining signal integrity, and make the overall transfer function of the DAS system tend to be flat, more clearly reflecting the true situation underground.

[0013] A variable gauge length DAS-VSP data processing method first uses a zero-offset DAS-VSP data set to estimate the locally optimal gauge length through apparent velocity and bandwidth analysis, thereby defining a single variable gauge length profile based on this locally optimal gauge length. Then, the same variable gauge length profile is reused to process the optical data of all other vibration source positions of variable offset DAS-VSP, and finally a higher-quality variable offset DAS-VSP profile is generated.

[0014] In variable source-offset DAS-VSP, the seismic source is usually placed far from the wellhead, and the offset gradually increases. At this time, the signal bandwidth and noise characteristics in the optical data may change with the source position, but there will be no sudden change in the formation in the well at the same depth. Therefore, the variable gauge profile estimated in the zero source-offset DAS-VSP stage can be "reused", and central difference processing with the same parameter settings can be performed on all variable source-offset DAS-VSP data, so as to obtain better quality DAS optical records at different seismic source positions.

[0015] Specifically, the technical solution of the present invention can be divided into the following steps:

[0016] (1) Data preparation;

[0017] Obtain the DAS-VSP strain data that the DAS demodulator has collected through a fixed gauge;

[0018] Obtain or estimate formation parameters, including the apparent velocity v apparent (z) and the variation relationship of the maximum effective frequency F max (z) with depth;

[0019] (2) Derive the locally optimal gauge based on geophysical parameters;

[0020] Relate the observed optimal gauge to the minimum wavelength, specifically as shown in formula (1)

[0021]

[0022] where, v apparent (z) is the apparent velocity at depth z; F max (z) is the maximum effective frequency that can be recovered at this depth;

[0023] Add a correction coefficient C and derive the following approximate linear tuning formula, as shown in formula (2)

[0024]

[0025] where, GL opt (z) is the optimal gauge at depth z; C is an empirical coefficient obtained by comprehensive correction of high-frequency attenuation, low-frequency enhancement and resolution index;

[0026] In order to ensure a smooth transition of the gauge in the depth direction, an interpolation transition strategy is introduced:

[0027] In the depth interval [z i ,z i+1 , perform cubic spline interpolation on the gauge values and and gradually smooth the transition from to

[0028] In addition, a maximum gradient threshold is set for the gauge length to avoid overly drastic jumps.

[0029] Input the variations of apparent velocity and maximum bandwidth parameters with depth into the above formula to generate a variable gauge length profile.

[0030] If the optimal gauge length formula is continuous, map it to the discrete channel k, as shown in formula (3):

[0031] GL opt (k) ≈ GL opt (z(k)) (3)

[0032] where (z(k)) represents the well depth corresponding to the k-th channel;

[0033] A variable gauge length array that varies with channel k is obtained, and it is called a "profile".

[0034] Plot GL opt (z) or GL opt (k) as a waterfall plot to obtain a two-dimensional waterfall plot with the time variable on the horizontal axis and the gauge length on the vertical axis.

[0035] (3) Implementation of variable gauge length central difference;

[0036] The sampling index corresponding to the difference point is a function Δk(k) that varies with k, and the difference operator changes dynamically with the position.

[0037]

[0038] where y[k] is the strain output after variable gauge length; δ m [k] represents the physical scale after conversion to the metric system; x[·] is the original optical phase or fixed gauge length strain data; δ[k] is the discrete gauge length (number of samples) corresponding to the data sampling point k;

[0039] Through the interpolation method, map GL opt (z) indexed by the wellbore measurement depth z to the discrete sampling index k to obtain δ[k] and δ m [k].

[0040] In this process, fill or delay-compensate the data of the edge sampling points. Define the data within a range of L / 2 distances from the wellhead and the bottom of the well as edge data, where L is the maximum gauge length. For the sampling point k < α(k), if φ[k - α(k)] cannot be used, then use mirror supplementation, as shown in formula (6).

[0041] φ[-x] = φ[x] (6)

[0042] During the processing, it should be noted that the central difference operator is a zero-phase operator, which can avoid delay errors;

[0043] Because δ m [k] varies at different depths. After differentiation, division normalization is required to maintain the dimensionality of the dependent variable.

[0044] (4) Final output:

[0045] Zero-offset DAS-VSP data processed with variable gauge; variable-offset DAS-VSP full-well data processed with variable gauge; the two complement each other and are used for subsequent comprehensive interpretation of velocity analysis, imaging, and Q-value inversion.

[0046] The technical effects of the present invention are:

[0047] 1. Improve the overall signal-to-noise ratio: For well sections with high velocity or low attenuation, the gauge can be appropriately increased to enhance the effective signal.

[0048] 2. Avoid spectral distortion: When low velocity or strong attenuation bands appear, the gauge is automatically reduced to reduce high-frequency loss and double-peak distortion.

[0049] 3. Facilitate subsequent quantitative interpretation: After selecting an appropriate gauge, the DAS transfer function is flatter, and the results of Q-value estimation, velocity analysis, etc. are more accurate. Brief Description of the Drawings

[0050] Figure 1 It is a flowchart of variable-offset DAS-VSP processing supporting variable gauge for the present invention. Detailed Embodiment

[0051] The variable-gauge processing flow proposed by the present invention can be applied to variable-offset DAS-VSP data processing. As Figure 1 shown, first, a zero-offset DAS-VSP data set is used to estimate the local optimal gauge through apparent velocity and bandwidth analysis, thereby defining a single variable-gauge profile based on this local optimal gauge. Then, the same variable-gauge profile is reused to process the optical data at all other vibration source positions of variable-offset DAS-VSP, and finally, a higher-quality variable-offset DAS-VSP profile is generated through processing.

[0052] Specifically, the technical solution of the present invention can be divided into the following steps:

[0053] 1. Data preparation;

[0054] Obtain the DAS-VSP strain data collected by the DAS demodulator through a fixed gauge;

[0055] At different depths, the formation velocity and frequency bandwidth (energy distribution) change, which affects the response of DAS. For high-velocity sections, the high-frequency attenuation of DAS signals is not significant, and the gauge length can be appropriately enlarged; while in low-velocity sections, in order to avoid further attenuation of high-frequency components, the gauge length needs to be appropriately reduced.

[0056] The larger the gauge length, the more energy the signal can "accumulate" to a certain extent, enhancing the signal-to-noise ratio; but some high-frequency details will be lost. Therefore, the so-called "optimal gauge length" is to find a balance between the improvement of SNR and the fidelity of the signal.

[0057] Obtain (or estimate) formation parameters, including the apparent velocity v apparent (z) and the maximum effective frequency F max (z) as a function of depth; it can be estimated based on existing well logging data or seismic data.

[0058] 2. Derive the local optimal gauge length based on geophysical parameters;

[0059] Through a large number of actual DAS measurements and analyses, it can be seen that as long as the high-frequency attenuation, low-frequency enhancement, and resolution loss are all within the acceptable thresholds, the larger the gauge length can generally improve the signal-to-noise ratio and enhance the overall energy to a certain extent. Based on this, the observed optimal gauge length is related to the minimum wavelength, as shown in formula (1)

[0060]

[0061] where

[0062] v apparent (z) is the apparent velocity at depth z;

[0063] F max (z) is the maximum effective frequency that can be recovered at this depth;

[0064] Adding a correction coefficient C, the following approximate linear tuning formula is derived, as shown in formula (2)

[0065]

[0066] where

[0067] GL opt (z) is the optimal gauge length at depth z;

[0068] C is an empirical coefficient obtained by comprehensive correction of indicators such as high-frequency attenuation, low-frequency enhancement, and resolution, and usually C is taken as 0.47.

[0069] When significant changes in the gauge length are required for certain depth segments (e.g., a jump from 10 m to 20 m), directly making a "jump" in the difference operator may lead to numerical instability or signal distortion. To ensure a smooth transition of the gauge length in the depth direction, an interpolation transition strategy is introduced:

[0070] Within the depth interval [z i , z i+1 , perform cubic spline interpolation on the gauge length values and to gradually transition smoothly from to

[0071] In addition, set a maximum gradient threshold for the gauge length to avoid sharp transitions. This value is generally set to 30 m.

[0072] Input the variations of parameters such as apparent velocity and maximum bandwidth with depth into the above formula to generate a variable gauge length profile.

[0073] DAS data sampling points are often obtained based on a certain spatial (or temporal) sampling interval. For example, there is a sampling channel every 1.02 m. If the optimal gauge length formula is continuous, it needs to be mapped to the discrete channel k, as shown in formula (3):

[0074] GL opt (k) ≈ GL opt (z(k))(3)

[0075] where (z(k)) represents the well depth corresponding to the k-th channel. In this way, a variable gauge length array that varies with the channel k is obtained, which is called a "profile".

[0076] In practical applications, GL opt (z) or GL opt (k) is often plotted as a waterfall plot to obtain a two-dimensional waterfall plot with the time variable on the horizontal axis and the gauge length on the vertical axis.

[0077] 3. Implementation of variable gauge length central difference;

[0078] In the DAS digital domain, a common practice is to use "central difference" to calculate strain or strain rate. The traditional fixed gauge length (such as 10 m) is a fixed difference point distance.

[0079] Now, since the gauge length varies with depth, the sampling index corresponding to the difference point is no longer a fixed value but a function Δk(k) that varies with k. This makes the difference operator change dynamically with the position.

[0080] In the implementation of the DAS system digital domain, the fixed gauge length often adopts the following fixed gauge length expression of the central difference operator, as shown in formula (4)

[0081]

[0082] Among them,

[0083] y[k] is the strain output after a fixed gauge length;

[0084] δ m is the corresponding physical length after converting δ to the metric system;

[0085] x[·] is the original phase (or preliminarily processed strain) data;

[0086] δ is the corresponding value of the fixed gauge length in terms of the number of discrete sampling points.

[0087] To generalize to a variable gauge length, δ needs to be changed to a function δ[k] that varies dynamically with the sampling position k. Therefore, formula (4) can be derived into formula (5)

[0088]

[0089] Among them,

[0090] y[k] is the strain output after a variable gauge length;

[0091] δ m [k] represents the physical scale after conversion to the metric system;

[0092] x[·] is the original optical phase or fixed gauge length strain data;

[0093] δ[k] is the discrete gauge length (number of sample points) corresponding to the data sampling point k;

[0094] Through the interpolation method, the GL opt (z) indexed by the wellbore measured depth z can be mapped to the discrete sampling index k, so as to obtain δ[k] and δ m [k]. In this process, the data of the edge sampling points (the shorter head and tail) should be appropriately filled or delayed compensated. The data within a range of L / 2 distances (L is the maximum gauge length) from the wellhead and the bottom of the well is defined as the edge data. To ensure the integrity of the processing interval, for the sampling point k < α(k), φ[k - α(k)] cannot be used, then mirror supplementation is used, as shown in formula (6).

[0095] φ[-x] = φ[x] (6)

[0096] Using mirror supplementation for edge data is suitable for retaining the extension trend in wave field data and improving the data processing quality.

[0097] During the processing, it should be noted that the central difference operator is a zero-phase operator, which can avoid delay errors; for variable offsets, forward or backward differences should not be used, otherwise additional delays will be superimposed at different depths.

[0098] Because δ m [k] varies at different depths. After differentiation, division normalization is required to maintain the dimension of the dependent variable.

[0099] Edge padding and phase delay compensation are performed on the head and tail parts to minimize the invalid area.

[0100] 4. Final output:

[0101] Zero-offset DAS-VSP data processed with variable offset;

[0102] Variable-offset DAS-VSP full-well data processed with variable offset;

[0103] The two complement each other, which is beneficial for subsequent comprehensive interpretations such as velocity analysis, imaging, and Q-value inversion.

Claims

1. A variable gauge DAS-VSP data processing method, characterized in that It includes the following processes: First, use the zero-offset DAS-VSP dataset to estimate the local optimal gauge through apparent velocity and bandwidth analysis, thereby defining a single variable gauge profile based on this local optimal gauge; then, the same variable gauge profile is reused to process the optical data of all other vibration source positions of the variable-offset DAS-VSP, and finally, a higher-quality variable-offset DAS-VSP profile is generated through processing.

2. The variable gauge DAS-VSP data processing method according to claim 1, wherein It includes the following steps: (1) Data preparation; Obtain the DAS-VSP strain data that the DAS demodulator has collected through a fixed gauge; Obtain or estimate formation parameters, including apparent velocity v apparent (z) and maximum effective frequency F max (z) as a function of depth; (2) Derive the local optimal gauge based on geophysical parameters; Relate the observed optimal gauge to the minimum wavelength, specifically as shown in Equation (1) where v apparent (z) is the apparent velocity at depth z; F max (z) is the maximum effective frequency recoverable at that depth; Add the correction coefficient C and derive the following approximate linear tuning formula, as shown in Equation (2) Among them, GL opt (z) is the optimal gauge length at depth z; C is an empirical coefficient obtained by comprehensively correcting high-frequency attenuation, low-frequency enhancement, and resolution index; To ensure a smooth transition of the gauge in the depth direction, introduce an interpolation transition strategy: Within the depth interval [z i , z i+1 , for the gauge values and , perform cubic spline interpolation, and gradually transition smoothly from to In addition, set a maximum gradient threshold for the gauge to avoid sharp jumps during the transition; Input the variations of the apparent velocity and maximum bandwidth parameters with depth into the above formula to generate a variable gauge profile; If the optimal gauge formula is continuous, map it to the discrete channel k, as shown in Equation (3): GL opt (k)≈GL opt (z(k))(3) where (z(k)) represents the well depth corresponding to the k-th channel; Obtain a variable gauge array that varies with the channel k, and call it a "profile"; Plot GL opt (z) or GL opt (k) as a waterfall plot to obtain a two-dimensional waterfall plot with the time variable on the horizontal axis and the gauge length on the vertical axis; (3) Implementation of variable gauge central difference; The sampling index corresponding to the difference point is a function △k(k) that varies with k, and the difference operator changes dynamically with the position; where y[k] is the strain output after variable gauge length; δ m [k] represents the physical scale after conversion to the metric system; x[·] is the original optical phase or fixed gauge length strain data; δ[k] is the discrete gauge length corresponding to the data sampling point k; By means of an interpolation method, map GL opt (z) indexed by the wellbore measured depth z to the discrete sampling index k to obtain δ[k] and δ(z) indexed by the wellbore measured depth z to the discrete sampling index k to obtain δ[k] and δ m [k]; (4) Final output: The zero-offset DAS-VSP data processed with variable gauge; The variable-offset DAS-VSP full-well data processed with variable gauge; The two complement each other and are used for subsequent comprehensive interpretation of velocity analysis, imaging, and Q-value inversion.

3. A variable gauge DAS-VSP data processing method according to claim 2, wherein In step (3), fill or delay-compensate the data of the edge sampling points. Define the data within a range of L / 2 distances from the wellhead and the bottom of the well outside the edge as edge data, where L is the maximum value of the gauge; for the sampling point k < α(k), if φ[k - α(k)] cannot be used, then use mirror supplementation, as shown in Equation (6); φ[-x] = φ[x] (6).

4. A variable gauge DAS-VSP data processing method according to claim 2, characterized in that, In step (3), the central difference operator is a zero-phase operator.

5. A variable gauge DAS-VSP data processing method according to claim 2, characterized in that In step (3), δ m [k] varies at different depths and needs to be normalized by division after differentiation.

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