An inversion method and system based on deep domain seismic data, an electronic device and a storage medium

Abstract

The application provides an inversion method and system based on depth domain seismic data, an electronic device and a storage medium. The method comprises the following steps: establishing a pseudo domain relationship between logging data and depth domain seismic data, and performing well-seismic calibration based on the depth domain seismic data; according to the well-seismic calibration, a depth domain seismic wavelet extracted by using a convolution model theory is corrected to obtain a phase-stable target layer wavelet; according to the well-seismic calibration, a depth domain low-frequency model is obtained by using the depth domain seismic data; according to the target layer wavelet, a P-wave impedance attribute is obtained; the depth domain low-frequency model and the P-wave impedance attribute are proportionally fused to complete phase-controlled inversion, and a low impedance attribute volume reflecting a reservoir is obtained, which contains both a seismic facies boundary and a post-stack inversion. The scheme provided by the application can avoid the loss of important high-frequency characteristic information of the logging data and the introduction of cumulative errors in the time-depth conversion process, and can realize fine description of the reservoir.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration and development technology, and particularly relates to an inversion method, system, electronic device and storage medium based on depth domain seismic data. Background Technology

[0002] In recent years, significant progress has been made in ultra-deep exploration in the Tarim Basin. Through large-scale ultra-deep exploration and development, two major oil reserve bases have been established, focusing on the Cretaceous strata in the Kuqa foreland area and the Ordovician strata in the basin area: one trillion cubic meters of natural gas in Kuqa and 10 × 10⁸ tons of crude oil in the northern and central Tarim Basin. The total proven geological reserves are 16.4 × 10⁸ tons, accounting for approximately 19% of the global proven geological reserves of ultra-deep oil and gas. These exploration achievements are remarkable. This significant breakthrough in ultra-deep exploration is attributed to the widespread use of depth-domain seismic data. Compared to time-domain seismic data, depth-domain seismic data improves imaging accuracy, provides a more realistic and intuitive reflection of subsurface structural morphology, and offers higher resolution for deeper layers. Especially for the Ordovician internal carbonate reservoirs in the northern and central Tarim Basin, the drastic lateral velocity variations and complex seismic wavefields of carbonate reservoirs render pre-stack time migration based on the theory of lateral homogeneity in the subsurface inapplicable, causing shifts in the geological body imaging of pre-stack time migration data. Simultaneously, small-scale fracture-vuggy reservoirs place higher demands on data amplitude fidelity. Compared to time-domain seismic data, depth-domain seismic data has significant advantages in signal-to-noise ratio, fracture-vuggy imaging accuracy, and inter-stratum contact relationships. Currently, pre-stack depth migration technology has become one of the essential techniques for improving the imaging accuracy of beaded reservoirs, with depth-domain seismic data covering over 90% of the northern and central Tarim Basin. With the continuous deepening of exploration and development, time-domain seismic inversion often fails to meet the needs of detailed reservoir description due to its inherent limitations; therefore, utilizing depth-domain seismic data for seismic interpretation and reservoir inversion has become an inevitable trend.

[0003] Seismic inversion, as a core technology for reservoir prediction, combines the advantages of high-resolution vertical logging and dense horizontal seismic sampling to estimate the lateral variations in lithological and physical property characteristics of formations. It has been widely applied in various stages of oil exploration and development. Currently, conventional depth-domain seismic inversion typically involves first converting depth-domain logging and seismic data to the time domain based on their time-depth relationship, completing the inversion and interpretation, and then converting the results back to the depth domain. This process has three main drawbacks: first, important high-frequency characteristic information is lost during the conversion of logging parameters from the depth domain to the time domain; second, cumulative errors are introduced into the effective data during the conversion between different domains, potentially causing shifts in geological body imaging; and third, multiple domain conversions significantly increase working time and reduce efficiency, failing to meet the current demands for cost-effective exploration.

[0004] Therefore, conventional depth-domain seismic inversion and interpretation processes cannot fully utilize the effective information in depth-domain seismic data that reflects reservoir structure, lithology, and fluid conditions. This is not conducive to exploring the application value and potential of depth-domain seismic data, nor to achieving a detailed description of the reservoir.

[0005] The above technical issues urgently need to be resolved. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes an inversion method, system, electronic device, and storage medium based on depth domain seismic data.

[0007] The first aspect of this invention discloses an inversion method based on depth-domain seismic data, the method comprising:

[0008] Step S1: Establish the pseudo-domain relationship between well logging data and depth domain seismic data, and perform well-seismic calibration based on depth domain seismic data;

[0009] Step S2: Based on the well seismic calibration, the depth domain seismic wavelet extracted by the convolution model theory is corrected to obtain the phase-stable target layer wavelet.

[0010] Step S3: Based on the well-seismic calibration, apply the depth-domain seismic data to obtain the depth-domain low-frequency model; obtain the P-wave impedance properties based on the target layer wavelet.

[0011] Step S4: The depth domain low-frequency model and the P-wave impedance attribute are proportionally fused to complete the phase-controlled inversion and obtain a low-impedance attribute body that includes both seismic phase boundaries and reservoir low-impedance attributes reflected in the post-stack inversion.

[0012] According to the method of the first aspect of the present invention, in step S1, the method for establishing the pseudo-domain relationship between well logging data and depth-domain seismic data includes:

[0013] A pseudo-domain is used to transform depth-domain seismic data into a pseudo-domain, where 1 meter in the depth domain is equal to 1 millisecond in the pseudo-domain.

[0014] By setting a pseudo-sonic transit time curve with a constant value of 500 μs / m, the well logging data is converted to the "pseudo-domain". At this time, both the well logging data and the depth-domain seismic data are in the depth domain and the coordinates are unified.

[0015] According to the method of the first aspect of the present invention, in step S1, the method for performing well-seismic calibration based on depth-domain seismic data includes:

[0016] Within the coordinates of the "pseudo-domain", the well logging curve is translated and finely adjusted to complete the well-vibration calibration.

[0017] According to the method of the first aspect of the present invention, in step S2, the method of extracting the depth-domain seismic wavelet based on the well seismic calibration using the convolution model theory includes:

[0018] Select a stable overlying formation that has achieved the predefined effect through well-seismic calibration;

[0019] The depth domain seismic wavelet of the overlying stable strata is extracted by applying the convolution model theory.

[0020] According to a first aspect of the method of the present invention, in step S2, the method of correcting the depth-domain seismic wavelet to obtain a phase-stable target segment wavelet includes:

[0021] By generating wavelet stretching coefficients from the average velocities of the overlying stable strata and the target strata in the statistical logging curves, and resampling and correcting the depth-domain seismic wavelet of the overlying stable strata, a wavelet of the target strata with good morphology and stable phase can be obtained.

[0022] According to the method of the first aspect of the present invention, in step S3, the method of obtaining a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration includes:

[0023] Interpretive preprocessing, differential attribute analysis, and optimization of depth-domain seismic data are performed to obtain seismic attribute bodies that reflect sedimentary facies, fault fracture zones, and geological anomalies in different sedimentary units. Combined with the initial depth-domain low-frequency model obtained from wellbore data, a depth-domain low-frequency model that reflects seismic facies boundaries and has a low-frequency impedance background from wellbore data is obtained.

[0024] According to the method of the first aspect of the present invention, in step S3, the method for obtaining the longitudinal wave impedance properties based on the target layer segment wavelet includes:

[0025] Based on the wavelet of the target layer segment, a post-stack inversion with well-seismic constraints is performed to obtain the longitudinal wave impedance properties.

[0026] A second aspect of this invention discloses an inversion system based on depth-domain seismic data, the system comprising:

[0027] The first processing module is configured to establish a pseudo-domain relationship between well logging data and depth domain seismic data, and to perform well-seismic calibration based on depth domain seismic data.

[0028] The second processing module is configured to, based on the well seismic calibration, apply the depth domain seismic wavelet extracted by the convolution model theory, and then correct the depth domain seismic wavelet to obtain a phase-stable target layer wavelet.

[0029] The third processing module is configured to obtain a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration; and to obtain the P-wave impedance properties based on the target layer wavelet.

[0030] The fourth processing module is configured to proportionally fuse the depth domain low-frequency model with the longitudinal wave impedance attribute to complete phase-controlled inversion and obtain a low-impedance attribute body that includes both seismic phase boundaries and reservoir low-impedance attributes reflected in post-stack inversion.

[0031] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the inversion method based on depth domain seismic data according to any one of the first aspects of this disclosure.

[0032] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an inversion method based on depth-domain seismic data according to any one of the first aspects of this disclosure.

[0033] In summary, the proposed solution can directly utilize depth-domain seismic data for inversion to obtain high-resolution vertical and horizontal inversion results, bypassing the cumbersome time-depth conversion process in conventional depth-domain inversion methods. This avoids the loss of important high-frequency characteristic information of well logging data and the introduction of cumulative errors during the time-depth conversion process, thereby achieving a detailed description of the reservoir and improving the depth-domain interpretation process. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a flowchart of an inversion method based on depth domain seismic data according to an embodiment of the present invention;

[0036] Figure 2 This is a calibration map of the depth-domain seismic synthetic record according to an embodiment of the present invention;

[0037] Figure 3 This refers to the depth-domain synthesized seismic record and its corresponding wavelet in a three-layer medium model according to an embodiment of the present invention.

[0038] Figure 4 For depth domain wavelet correction according to embodiments of the present invention;

[0039] Figure 5 This is a phase inversion diagram according to an embodiment of the present invention;

[0040] Figure 6 A comparison of the effects of conventional and novel depth domain inversion methods according to embodiments of the present invention (along the fracture zone);

[0041] Figure 7 A comparison of the effects of conventional and novel methods for depth domain inversion (vertical fracture zone) according to embodiments of the present invention;

[0042] Figure 8 This is a deep-domain seismic data and a deep-domain facies-controlled inversion profile of the Fuman area according to an embodiment of the present invention;

[0043] Figure 9 This is an inversion wave impedance property diagram of the Yijianfang Formation of the Ordovician system in the Fuman area according to an embodiment of the present invention;

[0044] Figure 10 This is a structural diagram of an inversion system based on depth-domain seismic data according to an embodiment of the present invention;

[0045] Figure 11 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0047] The first aspect of this invention discloses an inversion method based on depth domain seismic data. Figure 1 This is a flowchart of an inversion method based on depth domain seismic data according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0048] Step S1: Establish the pseudo-domain relationship between well logging data and depth domain seismic data, and perform well-seismic calibration based on depth domain seismic data;

[0049] Step S2: Based on the well seismic calibration, the depth domain seismic wavelet extracted by the convolution model theory is corrected to obtain the phase-stable target layer wavelet.

[0050] Step S3: Based on the well-seismic calibration, apply the depth-domain seismic data to obtain the depth-domain low-frequency model; obtain the P-wave impedance properties based on the target layer wavelet.

[0051] Step S4: The depth-domain low-frequency model and the P-wave impedance properties are proportionally fused to complete phase-controlled inversion, resulting in a volume containing both seismic phase boundaries and low-impedance properties reflecting the reservoir in post-stack inversion, such as... Figure 5 As shown.

[0052] In step S1, a pseudo-domain relationship is established between well logging data and depth domain seismic data, and well-seismic calibration based on depth domain seismic data is performed.

[0053] In some embodiments, in step S1, the method for establishing the pseudo-domain relationship between well logging data and depth-domain seismic data includes:

[0054] An equivalent velocity field is used to transform depth-domain seismic data into a "pseudo-domain". In the "pseudo-domain", 1 meter in the depth domain is equal to 1 millisecond in the "pseudo-domain", that is, the data coordinates are different but the values ​​are the same.

[0055] By setting a pseudo-sonic transit time curve with a constant value of 500 μs / m, the well logging data is converted to the "pseudo-domain." At this point, both the well logging data and the depth-domain seismic data are in the depth domain, and their coordinates are unified. Within the "pseudo-domain," well-seismic calibration and wavelet extraction are performed on the target layer. Figure 2 As shown.

[0056] The method for well-seismic calibration based on depth-domain seismic data includes:

[0057] In the coordinates of the "pseudo-domain", there is no need to compress and stretch the logging curves in the same time domain calibration. Only a simple translation and fine adjustment of the logging curves is needed to complete the well-vibration calibration.

[0058] Specifically, for well logging data, when the sonic transit time curve is a constant value of 500 μs / m (i.e., a velocity of 2000 m / s), we have:

[0059]

[0060] In the formula, D and D ′ Let v represent the actual depth and the pseudo-domain depth, respectively, and v be the velocity.

[0061] Depth-domain well-seismic calibration does not cause displacement of the reflection coefficient, and the extracted wavelet effect is better. At the same time, the obtained low-impedance model is better than the low-impedance model obtained in the time domain, which can provide better wavelet and low-frequency models for seismic data inversion, making the final inversion results more reliable.

[0062] In step S2, based on the well-seismic calibration, the depth-domain seismic wavelet extracted using the convolution model theory is corrected to obtain a phase-stable target layer wavelet.

[0063] In some embodiments, in step S2, the method for extracting the depth-domain seismic wavelet based on the well seismic calibration using the convolution model theory includes:

[0064] Select a stable overlying formation that has achieved the predefined effect through well-seismic calibration;

[0065] The depth domain seismic wavelet of the overlying stable strata is extracted by applying the convolution model theory.

[0066] The method for correcting the depth-domain seismic wavelet to obtain a phase-stable target layer wavelet includes:

[0067] By generating wavelet stretching coefficients from the average velocities of the overlying stable strata and the target strata in the statistical logging curves, and resampling and correcting the depth-domain seismic wavelet of the overlying stable strata, a wavelet of the target strata with good morphology and stable phase can be obtained.

[0068] Specifically, a time-domain seismic wavelet is a vibration curve formed by the amplitude of vibration of a certain particle at various moments within the duration of the wave. A spatial (depth)-domain seismic wavelet is a waveform curve formed by the amplitude of vibration of a series of particles within the spatial duration of the wave at a certain moment. A seismic wavelet can be represented as a signal composed of a series of harmonics (a set of sine curves with different amplitudes, frequencies, and phases), expressed in the time and depth domains as follows:

[0069] ω(t)=∑ω(f)e i2πft

[0070] ω(d)=∑ω(k)e ikd

[0071] In the formula: ω(t) and ω(d) represent the seismic wavelets in the time and depth domains, respectively; t and d represent time and depth, respectively; ω(f) and ω(k) represent the spectrum of the time-domain wavelet and the wavenumber of the depth-domain wavelet, respectively; k and f represent the wavenumber and frequency, respectively. The relationship between wavenumber and frequency is as follows:

[0072]

[0073] In the formula: v is the velocity of the underground medium, which varies with depth;

[0074] Compared to time-domain seismic wavelets, depth-domain seismic wavelets are affected not only by earthquake source characteristics, absorption and dispersion of the subsurface medium, but also by the medium velocity. Since depth-domain seismic wavelets are functions related to subsurface medium velocity, their waveforms stretch as the medium velocity increases and compress as the medium velocity decreases, and exhibit asymmetric characteristics at the interfaces of different medium layers. Therefore, the "linear time-invariant" condition does not hold in the depth domain, and the seismic wavelet extraction method based on the convolution model is not directly applicable to depth-domain seismic data, making accurate extraction of depth-domain wavelets difficult. However, for thicker, relatively homogeneous strata with small velocity variations, ignoring the asymmetry of the depth-domain seismic wavelet waveform at the interfaces of different media, the convolution model theory can still be considered applicable, and the depth-domain seismic wavelets extracted according to the convolution model theory are relatively stable. Figure 3 As shown.

[0075] Typically, the logging curves of the target formation are relatively short and of relatively low quality, resulting in poor calibration performance. Directly extracting the wavelet morphology of the target formation is unstable and will affect the accuracy of the final inversion. Therefore, the wavelet from the overlying stable formation, which has better calibration performance, is selected for correction. Let the wavelet of the overlying stable formation be ω1 and its average velocity be v1; let the wavelet of the target formation be ω2 and its average velocity be v2. According to the above formula:

[0076]

[0077] As shown in the above formula, by using the average velocity of the overlying stable formation segment and the target segment from the statistical logging curve to generate the wavelet stretching coefficient, and then resampling and correcting the wavelet of the overlying stable formation, a wavelet with good morphology and stable phase for the target segment can be obtained. Figure 4 As shown.

[0078] In step S3, based on the well-seismic calibration, a depth-domain low-frequency model is obtained by applying depth-domain seismic data; and the P-wave impedance properties are obtained based on the target layer wavelet.

[0079] In some embodiments, in step S3, the method of obtaining a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration includes:

[0080] Interpretive preprocessing, differential attribute analysis, and optimization of depth-domain seismic data are performed to obtain seismic attribute bodies that reflect sedimentary facies, fault fracture zones, and geological anomalies in different sedimentary units. Combined with the initial depth-domain low-frequency model obtained from wellbore data, a depth-domain low-frequency model that reflects seismic facies boundaries and has a low-frequency impedance background from wellbore data is obtained.

[0081] The method for obtaining the longitudinal wave impedance properties based on the target layer segment wavelet includes:

[0082] Based on the wavelet of the target layer segment, a post-stack inversion with well-seismic constraints is performed to obtain the longitudinal wave impedance properties.

[0083] Example 1

[0084] The proposed solution was tested in the X oilfield of the Tarim Basin, and the application results were good. Ordovician fractured-vuggy carbonate reservoirs are widely developed in the X oilfield. Numerous dissolution cavities formed by formation fracturing controlled by strike-slip faults appear as multi-peaked, multi-valley "beaded" reflections or groups on seismic profiles. Along the fault fracture zone, seismic data mainly show long beaded reflections along the fault or chaotic reflections along the fault. After using this invention, the vertical fault fracture zone characteristics are more obvious, the characterization accuracy of beaded reservoirs is improved, and weakly beaded reservoirs are highlighted. Compared with conventional depth-domain inversion methods, such as… Figure 6 and Figure 7 As shown, this significantly improves the accuracy and efficiency of inversion, enabling a detailed description of carbonate fractured-vuggy reservoirs and supporting research on oilfield traps, reserves, well locations, and development plans. Furthermore, the reservoir prediction results and drilling results are well-matched, with the drilling success rate in the X oilfield of the Tarim Basin increasing from 75% to 95%, wells producing 100-ton and 1000-ton wells accounting for 83%, and high-efficiency wells (cumulative production exceeding 50,000 tons) reaching 66.7%.

[0085] Example 2

[0086] The technology was applied in a 3D block of the Fuman Oilfield in the Tarim Basin. Inversion was performed based on depth-domain seismic data. Vertically, the depth-domain inversion results clearly depicted the contours of fracture zones and accurately predicted the spatial locations of fractured-vuggy reservoirs, providing support for well site selection and well trajectory design. Planarly, the reservoir planar distribution patterns under facies-controlled constraints were more pronounced, better matching the strike-slip fault trend and consistent with the fault-controlled heterogeneous reservoir characteristics of the block, aligning with the block's geological understanding. Combined with qualitative depth-domain reservoir description technology, the demonstration of 12 traps and the well location demonstration of 25 wells in the area were completed. Figure 8 and Figure 9 As shown.

[0087] The second aspect of this invention discloses an inversion system based on depth domain seismic data. Figure 10 This is a structural diagram of an inversion system based on depth-domain seismic data according to an embodiment of the present invention; as shown below. Figure 10 As shown, the system 100 includes:

[0088] The first processing module 101 is configured to establish a pseudo-domain relationship between well logging data and depth domain seismic data, and to perform well-seismic calibration based on depth domain seismic data.

[0089] The second processing module 102 is configured to, based on the well seismic calibration, apply the depth domain seismic wavelet extracted by the convolution model theory, and then correct the depth domain seismic wavelet to obtain a phase-stable target layer wavelet.

[0090] The third processing module 103 is configured to obtain a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration; and to obtain the P-wave impedance properties based on the target layer wavelet.

[0091] The fourth processing module 104 is configured to proportionally fuse the depth domain low-frequency model with the longitudinal wave impedance attribute to complete phase-controlled inversion and obtain a low-impedance attribute body that includes both seismic phase boundaries and low-impedance attributes reflecting the reservoir in post-stack inversion.

[0092] According to a system of a second aspect of the present invention, the first processing module 101 is specifically configured such that the method for establishing a pseudo-domain relationship between well logging data and depth-domain seismic data includes:

[0093] An equivalent velocity field is used to transform depth-domain seismic data into a "pseudo-domain". In the "pseudo-domain", 1 meter in the depth domain is equal to 1 millisecond in the "pseudo-domain", that is, the data coordinates are different but the values ​​are the same.

[0094] By setting a pseudo-sonic transit time curve with a constant value of 500 μs / m, the well logging data is converted to the "pseudo-domain." At this point, both the well logging data and the depth-domain seismic data are in the depth domain, and their coordinates are unified. Within the "pseudo-domain," well-seismic calibration and wavelet extraction are performed on the target layer. Figure 2 As shown.

[0095] The method for well-seismic calibration based on depth-domain seismic data includes:

[0096] In the coordinates of the "pseudo-domain", there is no need to compress and stretch the logging curves in the same time domain calibration. Only a simple translation and fine adjustment of the logging curves is needed to complete the well-vibration calibration.

[0097] Specifically, for well logging data, when the sonic transit time curve is a constant value of 500 μs / m (i.e., a velocity of 2000 m / s), we have:

[0098]

[0099] In the formula, D and D ′ Let v represent the actual depth and the pseudo-domain depth, respectively, and v be the velocity.

[0100] Depth-domain well-seismic calibration does not cause displacement of the reflection coefficient, and the extracted wavelet effect is better. At the same time, the obtained low-impedance model is better than the low-impedance model obtained in the time domain, which can provide better wavelet and low-frequency models for seismic data inversion, making the final inversion results more reliable.

[0101] According to a system of a second aspect of the present invention, the second processing module 102 is specifically configured such that the method for extracting the depth-domain seismic wavelet based on the well seismic calibration using convolution model theory includes:

[0102] Select a stable overlying formation that achieves the predefined effect through well-seismic calibration;

[0103] The depth domain seismic wavelet of the overlying stable strata is extracted by applying the convolution model theory.

[0104] The method for correcting the depth-domain seismic wavelet to obtain a phase-stable target layer wavelet includes:

[0105] By generating wavelet stretching coefficients from the average velocities of the overlying stable strata and the target strata in the statistical logging curves, and resampling and correcting the depth-domain seismic wavelet of the overlying stable strata, a wavelet of the target strata with good morphology and stable phase can be obtained.

[0106] Specifically, a time-domain seismic wavelet is a vibration curve formed by the amplitude of vibration of a certain particle at various moments within the duration of the wave. A spatial (depth)-domain seismic wavelet is a waveform curve formed by the amplitude of vibration of a series of particles within the spatial duration of the wave at a certain moment. A seismic wavelet can be represented as a signal composed of a series of harmonics (a set of sine curves with different amplitudes, frequencies, and phases), expressed in the time and depth domains as follows:

[0107] ω(t)=∑ω(f)e i2πft

[0108] ω(d)=∑ω(k)e ikd

[0109] In the formula: ω(t) and ω(d) represent the seismic wavelets in the time and depth domains, respectively; t and d represent time and depth, respectively; ω(f) and ω(k) represent the spectrum of the time-domain wavelet and the wavenumber of the depth-domain wavelet, respectively; k and f represent the wavenumber and frequency, respectively. The relationship between wavenumber and frequency is as follows:

[0110]

[0111] In the formula: v is the velocity of the underground medium, which varies with depth;

[0112] Compared to time-domain seismic wavelets, depth-domain seismic wavelets are affected not only by earthquake source characteristics, absorption and dispersion of the subsurface medium, but also by the medium velocity. Since depth-domain seismic wavelets are functions related to subsurface medium velocity, their waveforms stretch as the medium velocity increases and compress as the medium velocity decreases, and exhibit asymmetric characteristics at the interfaces of different medium layers. Therefore, the "linear time-invariant" condition does not hold in the depth domain, and the seismic wavelet extraction method based on the convolution model is not directly applicable to depth-domain seismic data, making accurate extraction of depth-domain wavelets difficult. However, for thicker, relatively homogeneous strata with small velocity variations, ignoring the asymmetry of the depth-domain seismic wavelet waveform at the interfaces of different media, the convolution model theory can still be considered applicable, and the depth-domain seismic wavelets extracted according to the convolution model theory are relatively stable. Figure 3 As shown.

[0113] Typically, the logging curves of the target formation are relatively short and of relatively low quality, resulting in poor calibration performance. Directly extracting the wavelet morphology of the target formation is unstable and will affect the accuracy of the final inversion. Therefore, the wavelet from the overlying stable formation, which has better calibration performance, is selected for correction. Let the wavelet of the overlying stable formation be ω1 and its average velocity be v1; let the wavelet of the target formation be ω2 and its average velocity be v2. According to the above formula:

[0114]

[0115] As shown in the above formula, by using the average velocity of the overlying stable formation segment and the target segment from the statistical logging curve to generate the wavelet stretching coefficient, and then resampling and correcting the wavelet of the overlying stable formation, a wavelet with good morphology and stable phase for the target segment can be obtained. Figure 4 As shown.

[0116] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured such that the method for obtaining a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration includes:

[0117] Interpretive preprocessing, differential attribute analysis, and optimization of depth-domain seismic data are performed to obtain seismic attribute bodies that reflect sedimentary facies, fault fracture zones, and geological anomalies in different sedimentary units. Combined with the initial depth-domain low-frequency model obtained from wellbore data, a depth-domain low-frequency model that reflects seismic facies boundaries and has a low-frequency impedance background from wellbore data is obtained.

[0118] The method for obtaining the longitudinal wave impedance properties based on the target layer segment wavelet includes:

[0119] Based on the wavelet of the target layer segment, a post-stack inversion with well-seismic constraints is performed to obtain the longitudinal wave impedance properties.

[0120] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the inversion method based on depth-domain seismic data according to any one of the first aspects of this invention.

[0121] Figure 11 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 11 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0122] Those skilled in the art will understand that Figure 11 The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0123] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the inversion method based on depth-domain seismic data according to any one of the first aspects of this invention.

[0124] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0125] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An inversion method based on depth-domain seismic data, characterized in that, The method includes: Step S1: Establish the pseudo-domain relationship between well logging data and depth domain seismic data, and perform well-seismic calibration based on depth domain seismic data; The method for establishing the pseudo-domain relationship between well logging data and depth-domain seismic data includes: A pseudo-domain is used to transform depth-domain seismic data into a pseudo-domain using an equivalent velocity field, in which 1 meter in the depth domain is equal to 1 millisecond in the pseudo-domain. By setting a pseudo-sonic transit time curve with a constant value of 500 μs / m, the well logging data is converted to the "pseudo-domain". At this time, both the well logging data and the depth-domain seismic data are in the depth domain and the coordinates are unified. The method for well-seismic calibration based on depth-domain seismic data includes: Within the coordinates of the "pseudo-domain", the well logging curve is translated and finely adjusted to complete the well-vibration calibration; Step S2: Based on the well seismic calibration, the depth domain seismic wavelet extracted by the convolution model theory is corrected to obtain the phase-stable target layer wavelet. The method for extracting depth-domain seismic wavelets using convolution model theory based on the well seismic calibration includes: Select a stable overlying formation that has achieved the predefined effect through well-seismic calibration; The depth-domain seismic wavelet of the overlying stable strata is extracted using the convolution model theory. The method for correcting the depth-domain seismic wavelet to obtain a phase-stable target layer wavelet includes: By generating the wavelet stretching coefficient from the average velocity of the overlying stable stratum and the target stratum in the statistical logging curve, and resampling and correcting the depth domain seismic wavelet of the overlying stable stratum, a wavelet of the target stratum with good shape and stable phase can be obtained. Step S3: Based on the well-seismic calibration, apply the depth-domain seismic data to obtain the depth-domain low-frequency model; obtain the P-wave impedance properties based on the target layer wavelet. Step S4: The depth domain low-frequency model and the P-wave impedance attribute are proportionally fused to complete the phase-controlled inversion and obtain a low-impedance attribute body that includes both seismic phase boundaries and reservoir low-impedance attributes reflected in the post-stack inversion.

2. The inversion method based on depth-domain seismic data according to claim 1, characterized in that, In step S3, the method for obtaining a depth-domain low-frequency model based on the well seismic calibration and applying depth-domain seismic data includes: Interpretive preprocessing, differential attribute analysis, and optimization of depth-domain seismic data are performed to obtain seismic attribute bodies that reflect sedimentary facies, fault fracture zones, and geological anomalies in different sedimentary units. Combined with the initial depth-domain low-frequency model obtained from wellbore data, a depth-domain low-frequency model that reflects seismic facies boundaries and has a low-frequency impedance background from wellbore data is obtained.

3. The inversion method based on depth-domain seismic data according to claim 1, characterized in that, In step S3, the method for obtaining the longitudinal wave impedance properties based on the target layer segment wavelet includes: Based on the wavelet of the target layer segment, a post-stack inversion with well-seismic constraints is performed to obtain the longitudinal wave impedance properties.

4. An inversion system for depth-domain seismic data, characterized in that, The system includes: The first processing module is configured to establish a pseudo-domain relationship between well logging data and depth domain seismic data, and to perform well-seismic calibration based on depth domain seismic data. The establishment of the pseudo-domain relationship between well logging data and depth-domain seismic data includes: A pseudo-domain is used to transform depth-domain seismic data into a pseudo-domain, where 1 meter in the depth domain is equal to 1 millisecond in the pseudo-domain. By setting a pseudo-sonic transit time curve with a constant value of 500 μs / m, the well logging data is converted to the "pseudo-domain". At this time, both the well logging data and the depth-domain seismic data are in the depth domain and the coordinates are unified. The well-seismic calibration based on depth-domain seismic data includes: Within the coordinates of the "pseudo-domain", the well logging curve is translated and finely adjusted to complete the well-vibration calibration; The second processing module is configured to, based on the well seismic calibration, apply the depth domain seismic wavelet extracted by the convolution model theory, and then correct the depth domain seismic wavelet to obtain a phase-stable target layer wavelet. The depth-domain seismic wavelet extracted using the convolution model theory based on the well seismic calibration includes: Select a stable overlying formation that has achieved the predefined effect through well-seismic calibration; The depth-domain seismic wavelet of the overlying stable strata is extracted using the convolution model theory. The correction of the depth-domain seismic wavelet to obtain the phase-stable target segment wavelet includes: By generating the wavelet stretching coefficient from the average velocity of the overlying stable stratum and the target stratum in the statistical logging curve, and resampling and correcting the depth domain seismic wavelet of the overlying stable stratum, a wavelet of the target stratum with good shape and stable phase can be obtained. The third processing module is configured to obtain a depth-domain low-frequency model by applying depth-domain seismic data based on the well seismic calibration; and to obtain the P-wave impedance properties based on the target layer wavelet. The fourth processing module is configured to proportionally fuse the depth domain low-frequency model with the longitudinal wave impedance attribute to complete phase-controlled inversion and obtain a low-impedance attribute body that includes both seismic phase boundaries and reservoir low-impedance attributes reflected in post-stack inversion.

5. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the inversion method based on depth domain seismic data according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the inversion method based on depth domain seismic data according to any one of claims 1 to 3.

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