Inversion method and system based on depth domain seismic data, electronic equipment and storage medium

Abstract

The invention provides an inversion method and system based on depth domain seismic data, electronic equipment and a storage medium. The method comprises the following steps: establishing a pseudo-domain relationship between logging data and depth-domain seismic data, and carrying out well-seismic calibration based on the depth-domain seismic data; according to well seismic calibration, depth domain seismic wavelets are extracted by applying a convolution model theory, and the depth domain seismic wavelets are corrected to obtain target interval wavelets with stable phases; according to well-seismic calibration, applying the depth-domain seismic data to obtain a depth-domain low-frequency model; obtaining a longitudinal wave impedance attribute according to the target stratum segment wavelet; and carrying out proportional fusion on the depth domain low-frequency model and the longitudinal wave impedance attribute to finish phase-controlled inversion to obtain a low-impedance attribute body which not only contains a seismic facies boundary but also reflects a reservoir in post-stack inversion. According to the scheme provided by the invention, the loss of important high-frequency characteristic information of the logging data and the introduction of accumulated errors in the time-depth conversion process can be avoided, and the fine description of the reservoir is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and particularly relates to an inversion method, system, electronic device and storage medium based on deep-domain seismic data. Background Art

[0002] In recent years, great achievements have been made in the ultra-deep exploration of the Tarim Basin. Through carrying out ultra-deep large-scale exploration and development, two oil reserve bases of Kuqa trillion cubic meters of natural gas and 1000 million tons of crude oil in the Cretaceous of the Kuqa foreland area and the Ordovician of the Tahe-Tazhong area have been built. The total proven geological reserves are 1640 million tons, accounting for about 19% of the global ultra-deep oil and gas proven geological reserves, and the exploration results are remarkable. The great breakthrough in the ultra-deep exploration field benefits from the extensive use of deep-domain seismic data. Compared with time-domain seismic data, deep-domain seismic data improves the imaging accuracy, can more truly and intuitively reflect the underground structural form, and has higher resolution for the deep part. Especially for the Ordovician internal carbonate rock reservoirs in the Tahe-Tazhong area, due to the drastic lateral variation of the carbonate rock reservoir velocity and the complex seismic wave field, the prestack time migration based on the underground lateral uniformity theory is no longer applicable, resulting in the imaging of geological bodies in the prestack time migration data being offset. At the same time, small-scale and small-scale fracture-cavity reservoirs also put forward higher requirements for the amplitude fidelity of the data. Compared with time-domain seismic data, deep-domain seismic data has great advantages in aspects such as signal-to-noise ratio, fracture-cavity imaging accuracy, and contact relationship between strata. At present, the prestack depth migration technology has become one of the essential technologies for improving the imaging accuracy of bead-shaped reservoirs, and the coverage area of deep-domain seismic data in the Tahe-Tazhong area has reached more than 90%. With the continuous deepening of exploration and development, the seismic inversion in the time domain often cannot meet the needs of fine reservoir description due to its own limitations. It is an irresistible trend to use deep-domain seismic data for seismic interpretation and reservoir inversion.

[0003] Seismic inversion, as the core technology of reservoir prediction, can combine the advantages of high vertical resolution of well logging and dense horizontal sampling of seismic data, estimate the lateral changes of lithological and physical property characteristics of strata, and has been widely applied to all stages of oil exploration and development. At present, conventional deep-domain seismic inversion usually first converts deep-domain well logging data and seismic data to the time domain according to the time-depth relationship, completes inversion and interpretation, and then converts the results back to the deep domain. Such a process has three main disadvantages: First, important high-frequency characteristic information will be lost during the process of converting each physical property parameter curve of well logging from the deep domain to the time domain; second, cumulative errors will be introduced into the effective data during the conversion process between different domains, which may cause the imaging of geological bodies to be offset; third, multiple domain conversions will greatly increase the working hours and reduce the working efficiency, and cannot meet the needs of current benefit exploration.

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

[0005] The above technical problems need to be solved urgently. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes an inversion method, system, electronic device, and storage medium based on depth-domain seismic data to solve the above technical problems.

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

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

[0009] Step S2: According to the well-seismic calibration, extract the depth-domain seismic wavelet extracted by applying the convolution model theory, and correct the depth-domain seismic wavelet to obtain a wavelet of the target layer segment with stable phase;

[0010] Step S3: According to the well-seismic calibration, apply the depth-domain seismic data to obtain a depth-domain low-frequency model; according to the wavelet of the target layer segment, obtain the longitudinal wave impedance attribute;

[0011] Step S4: Perform proportional fusion of the depth-domain low-frequency model and the longitudinal wave impedance attribute to complete phase-controlled inversion, and obtain a low-impedance attribute body that contains both seismic phase boundaries and reflects the reservoir in post-stack inversion.

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

[0013] Use an equivalent velocity field to transform the depth-domain seismic data into the "pseudo-domain", in which 1 meter in the depth domain is equal to 1 millisecond in the "pseudo-domain";

[0014] By setting a pseudo-acoustic travel time curve with a constant value of 500 us / m, transform the well logging data into 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 the step S1, the method for performing well-seismic calibration based on depth-domain seismic data includes:

[0016] In the coordinates of the "pseudo-domain", perform translational fine-tuning on the well logging curve to complete well-seismic calibration.

[0017] For the method according to the first aspect of the present invention, in the step S2, the method for extracting the seismic wavelet in the depth domain according to the well-seismic calibration and applying the convolution model theory includes:

[0018] Select the overlying stable formation where the well-seismic calibration reaches the predefined effect;

[0019] Apply the convolution model theory to extract the seismic wavelet in the depth domain of the overlying stable formation.

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

[0021] Make a wavelet stretching coefficient by statistically calculating the average velocities of the overlying stable formation segment and the target layer segment on the logging curve, and perform resampling correction on the seismic wavelet in the depth domain of the overlying stable formation, then a wavelet of the target layer segment with good morphology and stable phase can be obtained.

[0022] For the method according to the first aspect of the present invention, in the step S3, the method for obtaining the low-frequency model in the depth domain according to the well-seismic calibration and applying the depth-domain seismic data includes:

[0023] Perform interpretive preprocessing, differential attribute analysis and optimization on the depth-domain seismic data to obtain a seismic attribute volume reflecting sedimentary facies, fracture zones and geological anomalies of different sedimentary units, and then combine it with the initial low-frequency model in the depth domain obtained from the well to obtain a low-frequency model in the depth domain reflecting seismic facies boundaries and having a low-frequency impedance background from the well.

[0024] For the method according to the first aspect of the present invention, in the step S3, the method for obtaining the P-wave impedance attribute according to the wavelet of the target layer segment includes:

[0025] Perform post-stack inversion with well-seismic constraints according to the wavelet of the target layer segment to obtain the P-wave impedance attribute.

[0026] The second aspect of the present invention discloses an inversion system based on depth-domain seismic data, and the system includes:

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

[0028] A second processing module, configured to, according to the well-seismic calibration, apply the seismic wavelet in the depth domain extracted by using the convolution model theory, and correct the seismic wavelet in the depth domain to obtain a wavelet of the target layer segment with stable phase;

[0029] A third processing module, configured to apply depth-domain seismic data according to the well-seismic calibration to obtain a depth-domain low-frequency model; and obtain a longitudinal wave impedance attribute according to the wavelet of the target layer section.

[0030] A fourth processing module, configured to perform proportional fusion on the depth-domain low-frequency model and the longitudinal wave impedance attribute to complete phased inversion, so as to obtain a low-impedance attribute body that contains both seismic facies boundaries and reflects reservoirs in post-stack inversion.

[0031] A third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in any one of the inversion methods based on depth-domain seismic data in the first aspect of the present disclosure are implemented.

[0032] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in any one of the inversion methods based on depth-domain seismic data in the first aspect of the present disclosure are implemented.

[0033] In summary, the solution proposed by the present invention can directly perform inversion using depth-domain seismic data to obtain high-resolution inversion results in both vertical and horizontal directions, bypass the cumbersome time-depth conversion process in conventional depth-domain inversion methods, avoid the loss of important high-frequency feature information of well logging data and the introduction of cumulative errors during the time-depth conversion process, realize the fine description of reservoirs, and improve the depth-domain interpretation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

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

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

[0037] Figure 3 It is the depth-domain synthetic seismic record and its corresponding wavelet in a three-layer medium model according to an embodiment of the present invention;

[0038] Figure 4 It is the depth-domain wavelet correction according to an embodiment of the present invention;

[0039] Figure 5 The phased inversion map according to an embodiment of the present invention;

[0040] Figure 6 The comparison of the effects of the conventional method and the new method of depth domain inversion according to an embodiment of the present invention (along the fault zone);

[0041] Figure 7 The comparison of the effects of the conventional method and the new method of depth domain inversion according to an embodiment of the present invention (perpendicular to the fault zone);

[0042] Figure 8 The depth domain seismic data and the depth domain phased inversion profile diagram of the Fuman area according to an embodiment of the present invention;

[0043] Figure 9 The inversion wave impedance attribute map of the Yijianfang Formation of the Ordovician System in the Fuman area according to an embodiment of the present invention;

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

[0045] Figure 11 The structural diagram of an electronic device according to an embodiment of the present invention. Specific embodiments

[0046] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

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

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

[0049] Step S2: According to the well-seismic calibration, extract the depth domain seismic wavelet extracted by applying the convolution model theory, and correct the depth domain seismic wavelet to obtain a phase-stable target layer wavelet;

[0050] Step S3: According to the well-seismic calibration, apply the depth domain seismic data to obtain a depth domain low-frequency model; according to the target layer wavelet, obtain the longitudinal wave impedance attribute;

[0051] Step S4: Proportionally fuse the deep-domain low-frequency model with the P-wave impedance attribute to complete phased inversion, obtaining an impedance attribute body that contains both seismic phase boundaries and reflects reservoirs in post-stack inversion, as Figure 5 shown.

[0052] In step S1, establish a pseudo-domain relationship between well logging data and deep-domain seismic data, and perform well-seismic calibration based on the deep-domain seismic data.

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

[0054] Convert the deep-domain seismic data into the "pseudo-domain" using an equivalent velocity field. In the "pseudo-domain", 1 meter in the deep domain is equal to 1 millisecond in the "pseudo-domain", that is, the data coordinates are different but the numerical values are the same;

[0055] By setting a pseudo-acoustic travel-time curve with a constant value of 500 μs / m, convert the well logging data into the "pseudo-domain". At this time, both the well logging data and the deep-domain seismic data are in the deep domain and the coordinates are unified. Perform well-seismic calibration and wavelet extraction on the target interval in the "pseudo-domain", as Figure 2 shown.

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

[0057] In the coordinates of the "pseudo-domain", there is no need to compress and stretch the well logging curves as in time-domain calibration. Only simple translation and fine-tuning of the well logging curves are required to complete well-seismic calibration.

[0058] Specifically, for well logging data, when the acoustic travel-time curve is a constant value of 500 μs / m (i.e., the velocity is 2000 m / s), there is:

[0059]

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

[0061] Well-seismic calibration in the deep domain will not cause displacement of the reflection coefficient, the extracted wavelet has a better effect, and the obtained low-impedance model will be better than the low-impedance model obtained in the time domain, which can provide a better wavelet and low-frequency model for seismic data inversion, making the final inversion result more reliable.

[0062] In step S2, according to the well-seismic calibration, extract the deep-domain seismic wavelet using the convolution model theory, and correct the deep-domain seismic wavelet to obtain a wavelet with stable phase for the target interval.

[0063] In some embodiments, in step S2, the method for extracting the seismic wavelet in the depth domain according to the well-seismic calibration and applying the convolution model theory includes:

[0064] Select the overlying stable formation where the well-seismic calibration reaches the predefined effect;

[0065] Apply the convolution model theory to extract the seismic wavelet in the depth domain of the overlying stable formation.

[0066] The method for correcting the seismic wavelet in the depth domain to obtain the wavelet of the target layer segment with stable phase includes:

[0067] By making the wavelet stretching coefficient through statistically calculating the average velocities of the overlying stable formation segment and the target layer segment on the logging curve, and performing resampling correction on the seismic wavelet in the depth domain of the overlying stable formation, the wavelet of the target layer segment with good morphology and stable phase can be obtained.

[0068] Specifically, the seismic wavelet in the time domain is a vibration curve formed by the vibration amplitudes of a certain particle at each moment within the wave propagation duration range. The seismic wavelet in the space (depth) domain is a waveform curve formed by the vibration amplitudes of a series of particles within the wave propagation space range at a certain moment. The seismic wavelet can be expressed as a signal composed of a series of harmonic waves (a group of sine curves with different amplitudes, frequencies, and phases), and is respectively expressed in the time domain and the depth domain as:

[0069] ω(t) = ∑ω(f)e i2πft

[0070] ω(d) = ∑ω(k)e ikd

[0071] In the formulas: ω(t) and ω(d) represent the seismic wavelets in the time domain and the depth domain respectively; t and d represent time and depth respectively; ω(f) and ω(k) represent the frequency spectrum of the wavelet in the time domain and the wave number of the wavelet in the depth domain respectively; k and f represent wave number and frequency respectively. The relationship between the wave number and the frequency is:

[0072]

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

[0074] Compared with the time-domain seismic wavelet, the depth-domain seismic wavelet is affected not only by factors such as the characteristics of the seismic source and the absorption and dispersion of the underground medium, but also by the medium velocity. Since the depth-domain seismic wavelet is a function related to the underground medium velocity, its waveform stretches as the medium velocity increases and compresses as the medium velocity decreases, and the waveform at the interface of different medium layers presents an asymmetric characteristic. 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 the depth-domain seismic data, resulting in difficulty in accurately extracting the depth-domain wavelet. However, for relatively thick, relatively homogeneous strata with little velocity variation, ignoring the asymmetry of the depth-domain seismic wavelet waveform at the interface of different media, it can be considered that the convolution model theory is still applicable, and the depth-domain seismic wavelet extracted according to the convolution model theory is relatively stable, as Figure 3 shown.

[0075] Usually, the logging curves in the target interval are relatively short and of relatively low quality, resulting in relatively poor calibration effect in the target interval. The wavelet shape directly extracted from the target interval is unstable, which will affect the accuracy of the final inversion. Therefore, the wavelet of the overlying stable strata with better calibration effect is selected for correction. Denote the wavelet of the overlying stable strata as ω1, the average velocity as v1; the wavelet of the target interval as ω2, and the average velocity as v2. According to the above formula, we have:

[0076]

[0077] It can be seen from the above formula that by making the wavelet stretching coefficient by statistically calculating the average velocities of the overlying stable strata section and the target interval on the logging curve and resampling and correcting the wavelet of the overlying stable strata, a wavelet of the target interval with good shape and stable phase can be obtained, as Figure 4 shown.

[0078] In step S3, according to the well-seismic calibration, applying the depth-domain seismic data to obtain a depth-domain low-frequency model; according to the wavelet of the target interval, obtaining the longitudinal wave impedance attribute.

[0079] In some embodiments, in the step S3, the method for obtaining the depth-domain low-frequency model by applying the depth-domain seismic data according to the well-seismic calibration includes:

[0080] Performing interpretive preprocessing, differential attribute analysis and optimization on the depth-domain seismic data to obtain a seismic attribute volume reflecting the sedimentary facies, fault fracture zones and geological anomalies of different sedimentary units, and then combining with the initial depth-domain low-frequency model obtained from the well to obtain a depth-domain low-frequency model reflecting the seismic facies boundary and having the low-frequency impedance background on the well.

[0081] The method for obtaining the longitudinal wave impedance attribute according to the wavelet of the target interval includes:

[0082] Perform post-stack inversion with well-seismic constraint according to the wavelet of the target layer segment to obtain the P-wave impedance attribute.

[0083] Example 1

[0084] The solution proposed by the present invention was tested in Oilfield X in the Tarim Basin, and the application effect was good. The Ordovician fracture-vuggy carbonate reservoirs in Oilfield X are generally well developed. A large number of dissolution pores and caves formed by the fracture of the formation controlled by strike-slip faults are shown as "bead"-shaped reflection individuals or groups with multiple peaks and valleys on the seismic profile, and mainly show long beads along the fault or chaotic reflection along the fault on the seismic profile along the fracture zone. After using the present invention, the characteristics of the fracture zone are obvious vertically, the characterization accuracy of the bead-shaped reservoir is improved, and at the same time, the weak bead-shaped reservoir is highlighted. Compared with the conventional depth-domain inversion method, as Figure 6 and Figure 7 shown, the inversion accuracy and efficiency are greatly improved, the fine description of the fracture-vuggy carbonate reservoir is realized, and the research on oilfield traps, reserves, well locations and development plans is supported. At the same time, the reservoir prediction results are matched with the drilling results. The drilling success rate in Oilfield X in the Tarim Basin has increased from 75% to 95%, the proportion of wells with a production of over 100 tons and 1000 tons accounts for 83%, and the proportion of high-efficiency wells (cumulative production exceeding 50,000 tons) reaches 66.7%.

[0085] Example 2

[0086] The application was carried out in a 3D block in Fuman Oilfield in the Tarim Basin. Inversion was carried out based on depth-domain seismic data. Vertically, the inversion results in the depth domain can clearly depict the contour of the fracture zone and accurately predict the spatial position of the fracture-vuggy reservoir, providing support for well point optimization and well trajectory design; horizontally, the planar distribution law of the reservoir in the inversion results under facies control is more obvious, more consistent with the strike of the strike-slip fault, and consistent with the characteristics of the fault-controlled heterogeneous reservoir in the block, which conforms to the geological understanding of the block. Combining with the qualitative depth-domain reservoir description technology, the demonstration of 12 traps and the well location demonstration of 25 wells in this area were completed, as Figure 8 and Figure 9 shown.

[0087] The second aspect of the present invention discloses an inversion system based on depth-domain seismic data. Figure 10 It is a structural diagram of an inversion system based on depth-domain seismic data according to an embodiment of the present invention; as Figure 10 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 perform well-seismic calibration based on the depth-domain seismic data;

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

[0090] The third processing module 103 is configured to, according to the well-seismic calibration, apply the depth domain seismic data to obtain a depth domain low-frequency model; and obtain the longitudinal wave impedance attribute according to the target layer segment wavelet.

[0091] The fourth processing module 104 is configured to perform proportional fusion of the depth domain low-frequency model and the longitudinal wave impedance attribute, complete phase-controlled inversion, and obtain a low-impedance attribute body that includes both seismic phase boundaries and reflects reservoirs in post-stack inversion.

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

[0093] Using an equivalent velocity field to convert the depth domain seismic data into the "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 numerical values are the same.

[0094] By setting a pseudo-acoustic wave time difference curve with a constant value of 500 us / m, the well logging data is converted into 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. Well-seismic calibration and wavelet extraction and other work are carried out on the target layer segment in the "pseudo-domain", as Figure 2 shown.

[0095] The method for performing 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 well logging curve as in time domain calibration. Only simple translation and fine-tuning of the well logging curve are required to complete well-seismic calibration.

[0097] Specifically, for well logging data, when the acoustic wave time difference curve is a constant value of 500 us / m (i.e., the velocity is 2000 m / s), there is:

[0098]

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

[0100] Well-seismic calibration in the depth domain will not cause displacement of the reflection coefficient. The extracted wavelet has a good effect. At the same time, the obtained low-impedance model will be better than the low-impedance model obtained in the time domain, and can provide a better wavelet and low-frequency model for seismic data inversion, making the final inversion result more reliable.

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

[0102] Select the overlying stable formation where the well-seismic calibration reaches the predefined effect;

[0103] Apply the convolution model theory to extract the depth-domain seismic wavelet of the overlying stable formation.

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

[0105] By making a wavelet stretching coefficient by statistically averaging the velocities of the overlying stable formation section and the target layer section on the logging curve, and resampling and correcting the depth-domain seismic wavelet of the overlying stable formation, a wavelet with good morphology and stable phase for the target layer section can be obtained.

[0106] Specifically, the time-domain seismic wavelet is a vibration curve formed by the vibration amplitudes of a certain particle at each moment within the wave propagation duration range. The spatial (depth)-domain seismic wavelet is a waveform curve formed by the vibration amplitudes of a series of particles within the wave propagation space range at a certain moment. The seismic wavelet can be expressed as a signal composed of a series of harmonics (a group of sine curves with different amplitudes, frequencies, and phases), and is respectively expressed in the time domain and the depth domain as:

[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 domain and the depth domain; t and d represent time and depth respectively; ω(f) and ω(k) represent the spectrum of the time-domain wavelet and the wave number of the depth-domain wavelet respectively; k and f represent the wave number and frequency respectively. The relationship between the wave number and the frequency is:

[0110]

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

[0112] Compared with the time-domain seismic wavelet, the depth-domain seismic wavelet is affected not only by factors such as the characteristics of the seismic source, absorption, and dispersion of the underground medium, but also by the medium velocity. Since the depth-domain seismic wavelet is a function related to the underground medium velocity, its waveform stretches as the medium velocity increases and compresses as the medium velocity decreases, and the waveform at the interface of different medium layers presents an asymmetric characteristic. 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, resulting in difficulty in accurately extracting the depth-domain wavelet. However, for relatively thick, relatively homogeneous, and low-velocity-changing strata, ignoring the asymmetry of the depth-domain seismic wavelet waveform at the interface of different media, it can be considered that the convolution model theory is still applicable, and the depth-domain seismic wavelet extracted according to the convolution model theory is relatively stable, as Figure 3 shown.

[0113] Usually, the logging curves of the target interval are relatively short and of relatively low quality, resulting in relatively poor calibration effect of the target interval. The directly extracted wavelet morphology of the target interval is unstable, which will affect the accuracy of the final inversion. Therefore, the wavelet of the overlying stable formation with better calibration effect is selected for correction. Denote the wavelet of the overlying stable formation as ω1, the average velocity as v1; the wavelet of the target interval as ω2, and the average velocity as v2. According to the above formula, we have:

[0114]

[0115] It can be seen from the above formula that by statistically calculating the average velocities of the overlying stable formation section and the target interval of the logging curve to make the wavelet stretching coefficient, and resampling and correcting the wavelet of the overlying stable formation, a wavelet of the target interval with good morphology and stable phase can be obtained, as Figure 4 shown.

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

[0117] Perform interpretive preprocessing, differential attribute analysis and optimization on the depth-domain seismic data to obtain a seismic attribute volume reflecting sedimentary facies, fracture zones, and geological anomalies of different sedimentary units, and then combine the initial depth-domain low-frequency model obtained from the well to obtain a depth-domain low-frequency model reflecting seismic facies boundaries and having a low-frequency impedance background from the well.

[0118] The method for obtaining the P-wave impedance attribute according to the wavelet of the target interval includes:

[0119] Perform post-stack inversion with well-seismic constraints according to the wavelet of the target interval to obtain the P-wave impedance attribute.

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

[0121] Figure 11 FIG. is a structural diagram of an electronic device according to an embodiment of the present invention. As Figure 11 shown, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

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

[0123] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in an inversion method based on depth-domain seismic data according to any one of the first aspects disclosed in the present invention are implemented.

[0124] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as the scope recorded in this specification. The above embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

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

Claims

1. An inversion method based on deep domain seismic data, characterized in that The method includes: Step S1: Establish a pseudo-domain relationship between well logging data and depth-domain seismic data, and perform well-seismic calibration based on the depth-domain seismic data; Step S2: According to the well-seismic calibration, apply the convolutional model theory to extract the depth-domain seismic wavelet, and correct the depth-domain seismic wavelet to obtain a target layer wavelet with stable phase; Step S3: According to the well-seismic calibration, apply the depth-domain seismic data to obtain a depth-domain low-frequency model; according to the target layer wavelet, obtain the P-wave impedance attribute; Step S4: Proportionally fuse the depth-domain low-frequency model and the P-wave impedance attribute to complete phase-controlled inversion, and obtain a low-impedance attribute body that contains both seismic phase boundaries and reflects reservoirs in post-stack inversion.

2. The inversion method based on depth-domain seismic data according to claim 1, wherein In the step S1, the method for establishing a pseudo-domain relationship between well logging data and depth-domain seismic data includes: Using an equivalent velocity field to transform the depth-domain seismic data into the "pseudo-domain", in the "pseudo-domain", 1 meter in the depth domain is equal to 1 millisecond in the "pseudo-domain"; By setting a pseudo-acoustic travel time curve with a constant value of 500 us / m, transform the well logging data into 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.

3. The inversion method based on depth-domain seismic data according to claim 2, wherein In the step S1, the method for performing well-seismic calibration based on the depth-domain seismic data includes: In the coordinates of the "pseudo-domain", perform translational fine-tuning on the well logging curve to complete well-seismic calibration.

4. A seismic inversion method based on deep domain seismic data according to claim 1, characterized in that, In the step S2, the method for applying the convolutional model theory to extract the depth-domain seismic wavelet according to the well-seismic calibration includes: Select the overlying stable formation where the well-seismic calibration reaches the predefined effect; Apply the convolutional model theory to extract the depth-domain seismic wavelet of the overlying stable formation.

5. The inversion method based on depth-domain seismic data according to claim 4, characterized in that, In the step S2, the method for correcting the depth-domain seismic wavelet to obtain a target layer wavelet with stable phase includes: Make a wavelet stretching coefficient by statistically averaging the velocities of the overlying stable formation section and the target layer section of the well logging curve, and perform resampling correction on the depth-domain seismic wavelet of the overlying stable formation, then a target layer wavelet with good morphology and stable phase can be obtained.

6. A seismic data inversion method based on deep domain seismic data according to claim 1, characterized in that, In the step S3, the method for applying the depth-domain seismic data to obtain a depth-domain low-frequency model according to the well-seismic calibration includes: Perform interpretive preprocessing, differential attribute analysis and optimization on the depth-domain seismic data to obtain a seismic attribute body that reflects sedimentary facies, fracture zones and geological anomalies of different sedimentary units, and then combine it with the initial depth-domain low-frequency model obtained from the well to obtain a depth-domain low-frequency model that reflects seismic phase boundaries and has a low-frequency impedance background from the well.

7. A method for inversion based on deep domain seismic data according to claim 1, characterized in that, In the step S3, the method for obtaining the P-wave impedance attribute according to the target layer wavelet includes: Perform post-stack inversion with well-seismic constraints according to the target layer wavelet to obtain the P-wave impedance attribute.

8. An inversion system for seismic data based on the depth domain, characterized in that, The system includes: A first processing module, configured to establish a pseudo-domain relationship between well logging data and depth-domain seismic data, and perform well-seismic calibration based on the depth-domain seismic data; A second processing module, configured to, according to the well-seismic calibration, correct the seismic wavelet in the depth domain extracted by applying the convolution model theory to obtain a target layer segment wavelet with stable phase. A third processing module, configured to, according to the well-seismic calibration, obtain a low-frequency model in the depth domain by applying the depth-domain seismic data; and obtain the longitudinal wave impedance attribute according to the target layer segment wavelet. A fourth processing module, configured to perform proportional fusion of the depth-domain low-frequency model and the longitudinal wave impedance attribute to complete phase-controlled inversion, and obtain a low-impedance attribute body that contains both seismic phase boundaries and reflects reservoirs in post-stack inversion.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in any one of claims 1 to 7 of an inversion method based on depth-domain seismic data are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in any one of claims 1 to 7 of an inversion method based on depth-domain seismic data are implemented.

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