A method for waveform inversion of near-surface quality factors

Abstract

The present invention relates to the technical field of oil and gas field seismic exploration, and in particular to a near-surface quality factor waveform inversion method. The method comprises the following steps: collecting micro-logging data; picking up micro-logging first arrival waves; correcting the time difference of micro-logging first arrival waves; initializing a near-surface quality factor model group and correction values; compensating for first arrival wave absorption attenuation; calculating the similarity between each first arrival waveform; and determining the similarity or the number of iterations to obtain the best near-surface quality factor model. The method of the present invention is based on waveform characteristics and uses a nonlinear iterative method to perform quality factor inversion, thereby improving the accuracy and stability of the obtained results and eliminating the local instability of the two-point spectrum ratio in the traditional method.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas field seismic exploration, and in particular to a near-surface quality factor waveform inversion method. Background Art

[0002] The near-surface medium absorption attenuation compensation processing has become the key to the success of high-resolution seismic exploration. Its core is to build an accurate near-surface quality factor model, and the reasonable selection of near-surface quality factor estimation methods is the basis for building near-surface quality factor models. At present, there is no obvious innovation in the algorithm theory of near-surface quality factor estimation. Basically, the existing relatively mature algorithms are used, with the classic spectral ratio method being the most common. The basic ways to obtain near-surface quality factors mainly include rock sample determination and direct estimation or inversion imaging of field measured seismic data such as micro-well logging, cannon first arrival, surface wave exploration and small refraction. The classic spectral ratio method for estimating quality factors uses the spectrum difference between two adjacent seismic signals. Based on this principle, many similar methods have been developed. For example, Chinese patent application CN110261904A discloses a near-surface Q value inversion and classification evaluation method based on generalized S transform. This method selects two adjacent data, applies generalized S transform to extract the corresponding instantaneous amplitude spectrum of the first arrival signal, and applies the logarithmic spectral ratio method to invert and fit the quality factor between the two channels.

[0003] Chinese invention patent CN107544087B discloses a method for measuring the quality factor of near-surface formations. The method determines the quality factor of near-surface formations based on the frequency spectrum of seismic waves detected by a first detector, a second detector, and a plurality of third detectors. Using dual-well micro-logging data to estimate the near-surface quality factor can improve the stability of the results to a certain extent. For example, Chinese patent application CN109425903A discloses a method for obtaining the quality factor of near-surface formations. The method obtains the logarithmic spectrum of the seismic records received by the wellhead detector and the bottom hole detector for the same shot point in the excitation well, and obtains the formation quality factor based on the logarithmic spectrum and the travel time difference. However, the cost of dual-well micro-logging is relatively high, and large-scale application will significantly increase the exploration cost.

[0004] The existing methods are affected by factors such as signal-to-noise ratio and waveform consistency. They can only obtain the quality factor between two adjacent points at a time and only consider the impact of the quality factor on the amplitude spectrum. Therefore, the estimation results have large errors and instability. Summary of the invention

[0005] The main purpose of the present invention is to provide a near-surface quality factor waveform inversion method. The method of the present invention is based on waveform characteristics and uses a nonlinear iterative method to invert the quality factor, thereby improving the accuracy and stability of the result and overcoming the shortcomings of the prior art.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a near-surface quality factor waveform inversion method, which comprises the following steps:

[0008] Step 1. Collect micro-logging data;

[0009] Step 2. Picking up the first arrival wave of micro-logging;

[0010] Step 3. Micro-logging first arrival time difference correction;

[0011] Step 4. Initialize the near-surface quality factor model group and correction value;

[0012] Step 5. First arrival wave absorption attenuation compensation;

[0013] Step 6. Calculate the similarity between the first arrival waveforms;

[0014] Step 7. Determine the similarity or number of iterations to obtain the best near-surface quality factor model.

[0015] Furthermore, in step 1, micro-logging data is collected within the near-surface depth range by arranging detectors at different depths in the well and exciting seismic waves at the wellhead, or arranging detectors at the wellhead and exciting seismic waves at different depths in the well to perform micro-logging and collect data.

[0016] Furthermore, in step 2, the first arrival time and waveform of each micro-logging signal are picked up.

[0017] Further, in step 4, the near-surface model group is a group of near-surface quality factor models Q i (h j ), i = 1, 2, 3, ..., N, j = 1, 2, 3, ..., M; N is the number of models set, M is the number of depth grids of the near-surface quality factor model set;

[0018] For each model Q i (h j ) has a value for iterative modification, using M i (h j ) indicates that it corresponds to the value updated by the i-th model in the current iteration, and its initial value is set to M i (h j )=0, i=1, 2, 3,..., N, j=1, 2, 3,..., M.

[0019] Furthermore, in step 5, first arrival wave absorption attenuation compensation is performed according to each near-surface quality factor model. First, the first arrival waveform of each signal is Fourier transformed, and then near-surface absorption attenuation compensation is performed according to the following formula:

[0020]

[0021] In the formula, U(f) is the frequency domain representation of the first arrival signal at the receiving (excitation) depth h, V(f) is the data of U(f) after near-surface absorption attenuation compensation, f is the frequency, f0 is the reference frequency, n is the number of model depth grids contained in the depth h, Q k is the Q value of the kth model grid, Δt k is the quotient of the model grid size and the kth model grid velocity.

[0022] Furthermore, in step 6, the similarity between the first arrival waveforms of each signal is calculated using the following formula:

[0023]

[0024] In the formula, A is the similarity of the first arrival waveform of each signal, X j (i) represents the i-th sampling point of the j-th signal; N is the number of sampling points of the initial arrival waveform of each signal; M is the number of channels.

[0025] Furthermore, in step 7, if the similarity meets the preset threshold or the number of iterations meets the preset value, the best near-surface quality factor model is obtained; if the similarity does not meet the preset threshold or the number of iterations does not meet the preset value, the near-surface quality factor model group is updated, and steps 5-7 are repeated until the similarity meets the preset threshold or the number of iterations meets the preset value, and the best near-surface quality factor model is obtained.

[0026] Furthermore, the near-surface quality factor model group is updated according to the following formula:

[0027]

[0028]

[0029] In the formula, the number of iterations k = 1, 2, 3, ..., I, I is the maximum number of iterations; is the correction value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; is the value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; ω, c1, and c2 are all constants; Q is the model value of the ith model in the near-surface quality factor model group that obtains the maximum similarity in all iterations; best (h j ) is the model value with the maximum similarity among all models in the near-surface quality factor model group in all iterations.

[0030] Furthermore, the optimal near-surface quality factor model is Qbest (h j ).

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] The method of the present invention is based on waveform characteristics and utilizes a nonlinear iterative method to perform quality factor inversion, thereby improving the accuracy and stability of the result and eliminating the local instability of the two-point spectrum ratio in the traditional method. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0034] Figure 1 This is a flow chart of the near-surface quality factor waveform inversion method described in Example 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of a micro-well logging structure in the A work area according to Example 2 of the present invention;

[0036] Figure 3 The first arrival waveform of a micro-well picked up in the A work area described in Example 2 of the present invention;

[0037] Figure 4 The objective function value variation curve of the near-surface quality factor inversion process of a micro-logging well in the A work area described in Example 2 of the present invention;

[0038] Figure 5 The near-surface quality factor model obtained by micro-logging a well in the A work area described in Example 2 of the present invention;

[0039] Figure 6 This is a schematic diagram of a micro-well logging structure in the B work area described in Example 3 of the present invention;

[0040] Figure 7 The first arrival waveform of a micro-well in the B work area described in Example 3 of the present invention;

[0041] Figure 8 This is a curve showing the change of the objective function value during the inversion of the near-surface quality factor of a micro-logging well in the B work area described in Example 3 of the present invention;

[0042] Fig. 9 This is the near-surface quality factor model obtained from a micro-well logging in the B work area described in Example 3 of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0045] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0046] Example 1

[0047] like Figure 1 As shown, the near-surface quality factor waveform inversion method comprises the following steps:

[0048] Step 1. Collect micro-logging data within the near-surface depth range by arranging detectors at different depths in the well and exciting seismic waves at the wellhead, or arranging detectors at the wellhead and exciting seismic waves at different depths in the well to perform micro-logging and collect data.

[0049] Step 2. Pick up the first arrival wave of micro-logging: Pick up the first arrival time and waveform of each micro-logging signal.

[0050] Step 3. Micro-logging first arrival time difference correction.

[0051] Step 4. Initialization of near-surface quality factor model group and correction value: The near-surface model group is a set of near-surface quality factor models Q i (h j ), i = 1, 2, 3, ..., N, j = 1, 2, 3, ..., M; N is the number of models set, M is the number of depth grids of the near-surface quality factor model set;

[0052] For each model Q i (h j ) has a value for iterative modification, using M i (h j ) indicates that it corresponds to the value updated by the i-th model in the current iteration, and its initial value is set to M i (h j )=0, i=1, 2, 3,..., N, j=1, 2, 3,..., M.

[0053] Step 5. First arrival wave absorption attenuation compensation: First arrival wave absorption attenuation compensation is performed according to each near-surface quality factor model. First, the first arrival waveform of each signal is Fourier transformed, and then the near-surface absorption attenuation compensation is performed according to the following formula:

[0054]

[0055] In the formula, U(f) is the frequency domain representation of the first arrival signal at the receiving (excitation) depth h, V(f) is the data of U(f) after near-surface absorption attenuation compensation, f is the frequency, f0 is the reference frequency, n is the number of model depth grids contained in the depth h, Q k is the Q value of the kth model grid, Δt k is the quotient of the model grid size and the kth model grid velocity.

[0056] Step 6. Calculate the similarity between the first arrival waveforms: Use the following formula to calculate the similarity between the first arrival waveforms of each signal:

[0057]

[0058] In the formula, A is the similarity of the first arrival waveform of each signal, X j (i) represents the i-th sampling point of the j-th signal; N is the number of sampling points of the initial arrival waveform of each signal; M is the number of channels.

[0059] Step 7. Determine the similarity or number of iterations to obtain the best near-surface quality factor model:

[0060] If the similarity meets the preset threshold or the number of iterations meets the preset value, the optimal near-surface quality factor model is obtained.

[0061] If the similarity does not meet the preset threshold or the number of iterations does not meet the preset value, the near-surface quality factor model group is updated, and steps 5-7 are repeated until the similarity meets the preset threshold or the number of iterations meets the preset value, and the best near-surface quality factor model is obtained.

[0062] Update the near-surface quality factor model group according to the following formula:

[0063]

[0064]

[0065] In the formula, the number of iterations k = 1, 2, 3, ..., I, I is the maximum number of iterations; is the correction value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; is the value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; ω, c1, and c2 are all constants; Q is the model value of the ith model in the near-surface quality factor model group that obtains the maximum similarity in all iterations; best (h j ) is the model value with the maximum similarity among all models in the near-surface quality factor model group in all iterations.

[0066] The best near-surface quality factor model is Q at the end of the iteration best (h j ).

[0067] Example 2

[0068] Taking the above work area A as an example, the near-surface quality factor waveform inversion method includes the following steps:

[0069] (1) Micro-well logging was carried out in the A work area. The seismic source was located 2 m from the wellhead and the geophones were located in the well. They were arranged at intervals of 1 m from the surface. There were 40 geophones in total. Figure 2 As shown;

[0070] (2) Pick up the arrival time and waveform of the first arrival wave on the micro-logging record, such as Figure 3 As shown;

[0071] (3) Eliminate the time difference of the first arrival waves between the signals of each channel through time difference correction;

[0072] (4) Setting the near-surface quality factor model group and the initial value of the correction value: The near-surface model group is a group of near-surface quality factor models Q i (h j ), i = 1, 2, 3, ..., N, j = 1, 2, 3, ..., M; N is the number of models set, M is the number of depth grids of the near-surface quality factor model set;

[0073] For each model Q i (h j ) has a value for iterative modification, using M i (h j ) indicates that it corresponds to the value updated by the i-th model in the current iteration, and its initial value is set to M i (h j )=0,i=1,2,3,……,N,j=1,2,3,……,M. The number of models in the model group is 50;

[0074] (5) Compensation for first-arrival wave absorption attenuation is performed according to each near-surface quality factor model: First, the first-arrival waveform of each signal is Fourier transformed, and then near-surface absorption attenuation compensation is performed according to the following formula:

[0075]

[0076] In the formula, U(f) is the frequency domain representation of the first arrival signal at the receiving (excitation) depth h, V(f) is the data of U(f) after near-surface absorption attenuation compensation, f is the frequency, f0 is the reference frequency, n is the number of model depth grids contained in the depth h, Q k is the Q value of the kth model grid, Δt k is the quotient of the model grid size and the kth model grid velocity.

[0077] (6) Calculate the similarity between the initial arrival waveforms of each signal:

[0078] The similarity between the initial arrival waveforms of each signal is calculated using the following formula:

[0079]

[0080] In the formula, A is the similarity of the first arrival waveform of each signal, X j (i) represents the i-th sampling point of the j-th signal; N is the number of sampling points of the initial arrival waveform of each signal; M is the number of channels.

[0081] (7) The number of iterations is preset to 70, and the near-surface quality factor model group is updated according to the following formula:

[0082]

[0083]

[0084] In the formula, the number of iterations k = 1, 2, 3, ..., I, where I is the maximum number of iterations; is the correction value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; is the value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; ω, c1, and c2 are all constants; Q is the model value of the ith model in the near-surface quality factor model group that obtains the maximum similarity in all iterations; best (h j ) is the model value with the maximum similarity among all models in the near-surface quality factor model group in all iterations.

[0085] Repeat step 5 to perform first arrival wave absorption attenuation compensation and first arrival waveform similarity calculation. Figure 4 is the waveform similarity curve corresponding to the current best near-surface quality model when each iteration is completed;

[0086] (8) When the number of iterations is 70, the best near-surface quality factor model output is obtained. Figure 5The best near-surface quality factor model obtained when the preset number of iterations is reached.

[0087] Example 3

[0088] Taking the above B work area as an example, the near-surface quality factor waveform inversion method includes the following steps:

[0089] (1) Micro-well logging was carried out in the B work area. The geophone was located 1 m from the ground and the wellhead. The seismic source was located in the well. The seismic source was excited at intervals of 1 m from the bottom of the well for 30 times. Figure 6 As shown;

[0090] (2) Pick up the arrival time and waveform of the first arrival wave on the micro-logging record, such as Figure 7 As shown;

[0091] (3) Eliminate the time difference of the first arrival waves between the signals of each channel through time difference correction;

[0092] (4) Setting the near-surface quality factor model group and the initial value of the correction value: The near-surface model group is a group of near-surface quality factor models Q i (h j ), i = 1, 2, 3, ..., N, j = 1, 2, 3, ..., M; N is the number of models set, M is the number of depth grids of the near-surface quality factor model set;

[0093] For each model Q i (h j ) has a value for iterative modification, using M i (h j ) indicates that it corresponds to the value updated by the i-th model in the current iteration, and its initial value is set to M i (h j )=0,i=1,2,3,……,N,j=1,2,3,……,M. The number of models in the model group is 30;

[0094] (5) Compensation for first-arrival wave absorption attenuation is performed according to each near-surface quality factor model: First, the first-arrival waveform of each signal is Fourier transformed, and then near-surface absorption attenuation compensation is performed according to the following formula:

[0095]

[0096] In the formula, U(f) is the frequency domain representation of the first arrival signal at the receiving (excitation) depth h, V(f) is the data of U(f) after near-surface absorption attenuation compensation, f is the frequency, f0 is the reference frequency, n is the number of model depth grids contained in the depth h, Q k is the Q value of the kth model grid, Δt k is the quotient of the model grid size and the kth model grid velocity.

[0097] (6) Calculate the similarity between the initial arrival waveforms of each signal:

[0098] The similarity between the initial arrival waveforms of each signal is calculated using the following formula:

[0099]

[0100] In the formula, A is the similarity of the first arrival waveform of each signal, X j (i) represents the i-th sampling point of the j-th signal; N is the number of sampling points of the initial arrival waveform of each signal; M is the number of channels.

[0101] (7) The number of iterations is preset to 70, and the near-surface quality factor model group is updated according to the following formula:

[0102]

[0103]

[0104] In the formula, the number of iterations k = 1, 2, 3, ..., I, I is the maximum number of iterations; is the correction value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; is the value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; ω, c1, and c2 are all constants; Q is the model value of the ith model in the near-surface quality factor model group that obtains the maximum similarity in all iterations; best (h j ) is the model value with the maximum similarity among all models in the near-surface quality factor model group in all iterations.

[0105] Repeat step 5 to perform first arrival wave absorption attenuation compensation and first arrival waveform similarity calculation. Figure 8 is the waveform similarity curve corresponding to the current best near-surface quality model when each iteration is completed;

[0106] (8) When the number of iterations is 70, the best near-surface quality factor model output is obtained. Fig. 9 The best near-surface quality factor model obtained when the preset number of iterations is reached.

[0107] Depend on Fig. 9It can be seen that there is an obvious interface near the surface of the area at a depth of 4m, namely the interface between the low-velocity zone and the reduced-velocity zone, and there is an obvious interface near the depth of 20m, namely the interface between the reduced-velocity zone and the high-velocity layer. It can also be seen that the average quality factor of the low-velocity zone is 6, and the quality factor of the reduced-velocity zone gradually increases from 10 to 30 with depth. The above conclusions effectively guide the design of field seismic acquisition construction in the area and provide an important basis for the subsequent processing of seismic data. Figure 8 It can be seen that as the iteration proceeds, the waveform similarity gradually increases and becomes stable after reaching a certain number of times. This proves that the calculation results of the present invention have high accuracy and stability.

[0108] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A near-surface quality factor waveform inversion method, characterized in that: It includes the following steps: Step 1. Collect micro-logging data; Step 2. Picking up the first arrival wave of micro-logging; Step 3. Micro-logging first arrival time difference correction; Step 4. Initialize the near-surface quality factor model group and correction value; Step 5. First arrival wave absorption attenuation compensation; Step 6. Calculate the similarity between the first arrival waveforms; Step 7. Determine the similarity or number of iterations to obtain the best near-surface quality factor model; In step 6, the similarity between the first arrival waveforms of each signal is calculated using the following formula: In the formula, A is the similarity of the first arrival waveform of each signal, X j (i) represents the i-th sampling point of the j-th signal; N is the number of sampling points of the initial arrival waveform of each signal; M is the number of channels; In step 7, if the similarity meets the preset threshold or the number of iterations meets the preset value, the best near-surface quality factor model is obtained; if the similarity does not meet the preset threshold or the number of iterations does not meet the preset value, the near-surface quality factor model group is updated, and steps 5-7 are repeated until the similarity meets the preset threshold or the number of iterations meets the preset value, and the best near-surface quality factor model is obtained; Update the near-surface quality factor model group according to the following formula: In the formula, the number of iterations k = 1, 2, 3, ..., I, I is the maximum number of iterations; is the correction value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; is the value of the i-th model in the near-surface quality factor model group at the completion of the k-th iteration; ω, c1, and c2 are all constants; Q is the model value of the ith model in the near-surface quality factor model group that obtains the maximum similarity in all iterations; best (h j ) is the model value with the maximum similarity among all models in the near-surface quality factor model group in all iterations.

2. The method according to claim 1, characterized in that: In step 1, micro-logging data is collected within the near-surface depth range by arranging detectors at different depths in the well and exciting seismic waves at the wellhead, or arranging detectors at the wellhead and exciting seismic waves at different depths in the well to perform micro-logging and collect data.

3. The method according to claim 1, characterized in that: In step 2, the first arrival time and waveform of each micro-logging signal are picked up.

4. The method according to claim 1, characterized in that: In step 4, the near-surface model group is a group of near-surface quality factor models Q i (h j ), i = 1, 2, 3, ..., N, j = 1, 2, 3, ..., M; N is the number of models set, M is the number of depth grids of the near-surface quality factor model set; For each model Q i (h j ) has a value for iterative modification, using M i (h j ) indicates that it corresponds to the value updated by the i-th model in the current iteration, and its initial value is set to M i (h j )=0, i=1,2,3,...,N, j=1,2,3,...,M.

5. The method according to claim 1, characterized in that: In step 5, the first arrival wave absorption attenuation compensation is performed according to each near-surface quality factor model. First, the first arrival waveform of each signal is Fourier transformed, and then the near-surface absorption attenuation compensation is performed according to the following formula: In the formula, U(f) is the frequency domain representation of the first arrival signal at the receiving depth h, V(f) is the data of U(f) after near-surface absorption attenuation compensation, f is the frequency, f0 is the reference frequency, n is the number of model depth grids contained in the depth h, Q k is the Q value of the kth model grid, Δt k is the quotient of the model grid size and the kth model grid velocity.

6. The method according to claim 1, characterized in that: The best near-surface quality factor model is Q at the end of the iteration best (h j ).

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