Multi-information fusion phase control low-frequency modeling method and device and storage medium

Abstract

The invention relates to a multi-information fusion phase control low-frequency modeling method and device and a storage medium. According to the method, a fitting relation between a logging longitudinal wave velocity curve and other elastic parameter curves is fitted according to well logging data and an actually measured elastic parameter curve. And converting the seismic velocity into a low-frequency elastic parameter body according to the fitting relationship. The seismic velocity is the longitudinal wave velocity obtained by processing the seismic data. And a drilled well elastic parameter false well curve is extracted from the low-frequency elastic parameter body, and according to the drilled well elastic parameter false well curve and the actually-measured elastic parameter curve, a relative elastic parameter curve of the burial depth removing trend is obtained through calculation. And obtaining a relative elastic parameter model based on the fault, the horizon structure framework and the relative elastic parameter curve. And backfilling the low-frequency elastic parameter body phase control into the relative elastic parameter model to obtain a multi-information fusion phase control low-frequency model. According to the invention, the precision of low-frequency modeling can be improved, and the accuracy and reliability of a subsequent seismic inversion reservoir prediction result can be improved.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration research, and in particular to a multi-information fusion phase-controlled low-frequency modeling method, device and storage medium. Background Art

[0002] In oil and gas exploration research, low-frequency models typically refer to information with frequencies below 10 Hz that can reflect the geological background. They offer advantages such as good penetration and noise immunity. Due to equipment limitations during seismic acquisition, seismic data generally lack low-frequency components below 5 Hz. This necessitates the use of low-frequency models to compensate for this missing low-frequency information, thereby improving the rationality of seismic inversion results. Furthermore, low-frequency models can effectively constrain the direction and speed of inversion convergence, and their accuracy directly impacts the precision of reservoir prediction.

[0003] Low-frequency modeling is the process of improving reservoir prediction accuracy by combining multiple data sources (such as faults, horizons, seismic velocity spectra, seismic facies, and sedimentary facies) to construct more accurate low-frequency models. With the continuous advancement of offshore oil and gas exploration, deep Paleogene exploration has become a major focus for the discovery of large and medium-sized offshore oil and gas fields. Paleogene reservoir sedimentation changes rapidly. Traditional well-controlled interpolation low-frequency modeling methods overly rely on drilled well data and interpolation algorithms. The resulting low-frequency models fail to reflect actual geological changes and are difficult to meet the actual exploration needs of the Paleogene. Therefore, constructing more accurate low-frequency models is particularly critical in the process of high-precision seismic inversion of deep Paleogene formations. Research on low-frequency modeling is of great significance for achieving the goal of efficient deep exploration with fewer wells and promoting the progress of deep oil and gas exploration.

[0004] Related technologies include the use of Kriging interpolation algorithms, which use information fusion algorithms to achieve integrated modeling of seismic, well logging, and geological information. Other approaches utilize lithologic probability volumes obtained through seismic inversion and existing wells to establish sedimentary facies models reflecting the distribution of sandstone deposits, thereby compensating for the shortcomings of seismic inversion caused by insufficient well information. Furthermore, to address the difficulty of low-frequency modeling in deepwater, deep reservoirs with few wells, a high-precision velocity modeling method for deepwater, deep reservoirs has been proposed. By analyzing the main controlling factors of regional velocity using existing wells, regional velocity variation characteristics are obtained, which are then incorporated into the low-frequency modeling process to construct a high-precision seismic velocity model.

[0005] A systematic review of previous research papers, patents, and other research findings reveals that existing low-frequency modeling methods primarily focus on well-controlled interpolation methods, statistical analysis of well-drilled regional characteristics, and utilization of geological sedimentary facies. However, research on low-frequency modeling methods that integrate multiple information, including geological variations, drilled wells, structural faults, and horizons, is limited. Therefore, developing a facies-controlled low-frequency model that reflects geological variations and matches the structural framework and drilled wells is a critical area of ​​research and urgently requires targeted research. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a multi-information fusion phase-controlled low-frequency modeling method, device and storage medium in response to the above-mentioned defects.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a multi-information fusion phase-controlled low-frequency modeling method, comprising the following steps:

[0008] S1. Based on the well logging data of the wells drilled in the work area and the measured elastic parameter curve, the fitting relationship between the logging P-wave velocity curve and other elastic parameter curves is fitted;

[0009] S2. Converting the seismic velocity into a low-frequency elastic parameter volume according to the fitting relationship; the seismic velocity is the longitudinal wave velocity obtained by processing the seismic data;

[0010] S3, extracting a pseudo-well curve of elastic parameters of a drilled well from the low-frequency elastic parameter volume, and calculating a relative elastic parameter curve for removing the burial depth trend based on the pseudo-well curve of elastic parameters of the drilled well and the measured elastic parameter curve;

[0011] S4. obtaining a relative elastic parameter model based on the fault, the stratigraphic framework, and the relative elastic parameter curve;

[0012] S5. Backfill the low-frequency elastic parameter volume into the relative elastic parameter model in a phase-controlled manner to obtain a multi-information fusion phase-controlled low-frequency model.

[0013] Furthermore, in the multi-information fusion phased-controlled low-frequency modeling method of the present invention, each other elastic parameter corresponds to a multi-information fusion phased-controlled low-frequency model.

[0014] Furthermore, in the multi-information fusion phase-controlled low-frequency modeling method described in the present invention, the other elastic parameter is longitudinal wave impedance. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled longitudinal wave impedance low-frequency model.

[0015] Furthermore, in the multi-information fusion phase-controlled low-frequency modeling method of the present invention, the low-frequency elastic parameter body is a longitudinal wave impedance low-frequency model, and the relative elastic parameter model is a relative longitudinal wave impedance model. In step S5, the phase-controlled backfill expression is:

[0016] Merg_Pimp_trend=Pimp_trend+delta_Pimp_trend

[0017] Wherein, Merg_Pimp_trend is the multi-information fusion phase-controlled longitudinal wave impedance low-frequency model, Pimp_trend is the longitudinal wave impedance low-frequency model, and delta_Pimp_trend is the relative longitudinal wave impedance model.

[0018] Furthermore, in the multi-information fusion phase-controlled low-frequency modeling method described in the present invention, the other elastic parameter is shear wave impedance. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled shear wave impedance low-frequency model.

[0019] Furthermore, in the multi-information fusion phased-controlled low-frequency modeling method of the present invention, the other elastic parameter is density. Accordingly, the multi-information fusion phased-controlled low-frequency model is a multi-information fusion phased-controlled density low-frequency model.

[0020] Furthermore, in the multi-information fusion phase-controlled low-frequency modeling method of the present invention, step S3 of calculating the relative elastic parameter curve without burial depth trend based on the drilled elastic parameter pseudo-well curve and the measured elastic parameter curve includes:

[0021] The measured elastic parameter curve is subtracted from the drilled elastic parameter pseudo-well curve, and the result obtained by calculation is the relative elastic parameter curve of the deburial depth trend.

[0022] Furthermore, in the multi-information fusion phase-controlled low-frequency modeling method of the present invention, step S4 includes:

[0023] Based on the fault, the stratigraphic framework and the relative elastic parameter curve, the relative elastic parameter model is obtained by a well-controlled interpolation modeling method.

[0024] In addition, the present invention also provides a multi-information fusion phase-controlled low-frequency modeling device, comprising:

[0025] The curve fitting unit is used to fit the relationship between the well logging compressional wave velocity curve and other elastic parameter curves based on the well logging data of the drilled wells in the work area and the measured elastic parameter curve;

[0026] A low-frequency conversion unit is used to convert the seismic velocity into a low-frequency elastic parameter body according to the fitting relationship; the seismic velocity is the longitudinal wave velocity obtained by processing the seismic data;

[0027] a relative curve calculation unit, configured to extract a pseudo-well curve of elastic parameters of a drilled well from the low-frequency elastic parameter volume, and calculate a relative elastic parameter curve without burial depth trend based on the pseudo-well curve of elastic parameters of the drilled well and the measured elastic parameter curve;

[0028] A relative model generating unit is used to obtain a relative elastic parameter model based on the fault, the horizon structural framework and the relative elastic parameter curve;

[0029] The multi-information fusion phase-controlled low-frequency model generating unit is used to backfill the low-frequency elastic parameter volume into the relative elastic parameter model in phase control to obtain the multi-information fusion phase-controlled low-frequency model.

[0030] In addition, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor to execute the steps of the multi-information fusion phase-controlled low-frequency modeling method as described above.

[0031] The implementation of the multi-information fusion phase-controlled low-frequency modeling method, device and storage medium of the present invention has the following beneficial effects: based on the full use of high-precision seismic velocity field to obtain information reflecting the geological background, the present invention incorporates structurally interpreted faults, layers and logging information of drilled wells into the low-frequency modeling process, and ultimately realizes a phase-controlled low-frequency model that matches the geological changes and drilled wells to the greatest extent. It can effectively improve the accuracy of low-frequency modeling, and thereby enhance the accuracy and reliability of subsequent seismic inversion reservoir prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0033] Figure 1 1 is a flow chart of a multi-information fusion phase-controlled low-frequency modeling method provided by an embodiment of the present invention;

[0034] Figure 2 is a fitting relationship diagram between longitudinal wave velocity and longitudinal wave impedance in some embodiments;

[0035] Figure 3 1 is a cross-sectional comparison diagram of seismic velocity (left) and converted longitudinal wave impedance (right) according to some embodiments;

[0036] Figure 4 1. Comparison of longitudinal wave impedance pseudo-well (left), longitudinal wave impedance (center), and detrended relative impedance (right) curves of some embodiments;

[0037] Figure 5 1. Comparison diagram of the relative P-wave impedance model (left), the P-wave impedance model of seismic velocity (center), and the multi-information fusion phase-controlled P-wave impedance low-frequency model (right) of some embodiments;

[0038] Figure 6 This is a comparison diagram of the model cross sections of the low-frequency modeling method of the related art seismic velocity fitting modeling (left), the related art well control interpolation modeling (center), and the multi-information fusion phase control modeling (right) of the present invention;

[0039] Figure 7 A comparison of seismic inversion effects based on the multi-information fusion phase-controlled modeling of the present invention (left) and the related technology based on well-controlled interpolation modeling (right);

[0040] Figure 8 It is a structural diagram of a multi-information fusion phase-controlled low-frequency modeling device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific embodiments of the present invention are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the directions or positional relationships indicated by "front", "back", "up", "down", "left", "right", "longitudinal", "horizontal", "vertical", "horizontal", "top", "bottom", "inside", "outside", "head", "tail", etc. are based on the directions or positional relationships shown in the accompanying drawings and are constructed and operated in specific directions. They are only for the convenience of describing the technical solution and do not indicate that the devices or components referred to must have specific directions. Therefore, they should not be understood as limiting the present invention.

[0042] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0043] refer to Figure 1 In a preferred embodiment, the multi-information fusion phase-controlled low-frequency modeling method of the embodiment of the present invention includes the following steps:

[0044] S1. Based on the well logging data of the drilled wells in the work area and the measured elastic parameter curve, the fitting relationship between the well logging P-wave velocity curve and other elastic parameter curves is fitted. It should be noted that in the embodiments of the present invention, elastic parameters refer to parameters used to describe the physical properties of underground media in seismic exploration, mainly including P-wave velocity (P-wave velocity), S-wave velocity (S-wave velocity), P-wave impedance, S-wave impedance, density, bulk modulus, shear modulus, Poisson's ratio, P-wave velocity ratio, etc. These parameters reflect the elastic response of rock when subjected to seismic waves and are an important basis for constructing underground velocity models and performing reservoir descriptions.

[0045] S2. Convert the seismic velocity into a low-frequency elastic parameter volume based on the fitting relationship. Seismic velocity is the P-wave velocity obtained by processing seismic data. It will be appreciated that in this embodiment of the present invention, this low-frequency elastic parameter volume can both represent the depth trend of the elastic parameter and reflect the spatial variation of the sedimentary facies.

[0046] S3. Extract a pseudo-well curve of the drilled elastic parameters from the low-frequency elastic parameter volume, and calculate a relative elastic parameter curve without the burial depth trend based on the pseudo-well curve of the drilled elastic parameters and the measured elastic parameter curve. Specifically, in this step, the measured elastic parameter curve and the pseudo-well curve of the drilled elastic parameters can be subtracted, and the calculated result is the relative elastic parameter curve without the burial depth trend. It should be noted that the subtraction operation in the embodiment of the present invention is not limited to the subtraction object and the subtracted object, and can be determined according to actual needs. If necessary, the absolute value method can be used to convert and process the relative elastic parameter curve without the burial depth trend.

[0047] S4. Obtaining a relative elastic parameter model based on the faults, the stratigraphic framework, and the relative elastic parameter curve. Specifically, in this step, the faults, the stratigraphic framework, and the relative elastic parameter curve can be processed using a well-controlled interpolation modeling method to obtain a relative elastic parameter model.

[0048] It should also be noted that the fault and stratigraphic structural framework refers to a model used to describe the spatial distribution and structural morphology of strata by combining seismic data and well-seismic data in three-dimensional geological modeling. It mainly includes fault models and stratigraphic models. The fault model is used to characterize key elements such as the spatial strike, dip, and inclination of the fault, while the stratigraphic model is used to describe the contact relationship and sedimentary characteristics between different strata. Through these models, the structural morphology can be explained more accurately, and a basis can be provided for subsequent reservoir prediction and oil and gas development. In the embodiments of the present invention, the specific model construction process of the fault and stratigraphic structural framework and the well-controlled interpolation modeling method can refer to the existing technology and will not be repeated here.

[0049] S5. Backfill the low-frequency elastic parameter volume into the relative elastic parameter model in phase control to obtain a multi-information fusion phase-controlled low-frequency model.

[0050] This embodiment fully utilizes the high-precision seismic velocity field to obtain information reflecting the geological background. It incorporates structurally interpreted faults, horizons, and logging information from existing wells into the low-frequency modeling process, ultimately achieving a phase-controlled low-frequency model that best matches the geological changes and existing wells. This can effectively improve the accuracy of low-frequency modeling, thereby enhancing the accuracy and reliability of subsequent seismic inversion reservoir prediction results.

[0051] It can be understood that each other elastic parameter corresponds to a multi-information fusion phase-controlled low-frequency model. In other words, the phase-controlled modeling process requires the establishment of phase-controlled low-frequency models of elastic parameters such as longitudinal wave impedance, shear wave impedance and density, and the modeling process of each elastic parameter can refer to Figure 1The process shown in FIG1 realizes multi-information fusion phase-controlled modeling of different elastic parameters (longitudinal wave impedance, shear wave impedance, and density) in sequence.

[0052] In some embodiments, the other elastic parameter is longitudinal wave impedance, and accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled longitudinal wave impedance low-frequency model. Specifically, this embodiment includes:

[0053] ① Fitting the relationship between wells: Usually, the P-wave velocity has a good correlation with other elastic parameters (P-wave impedance, S-wave impedance and density). Therefore, the fitting relationship f(Vp) between the well logging P-wave velocity curve and the P-wave impedance curve can be fitted based on the logging data of the wells drilled in the work area and the measured P-wave impedance curve.

[0054] ② Elastic parameter body conversion: According to the fitting relationship f(Vp) between the logging P-wave velocity curve and the P-wave impedance curve, the seismic velocity can be converted into a P-wave impedance low-frequency model.

[0055] ③ Calculate the relative elastic parameter curve of the drilled well: Based on the converted low-frequency P-wave impedance model, the P-wave impedance pseudo-well curve of the drilled well is extracted. After subtracting the P-wave impedance pseudo-well curve of the drilled well from the measured P-wave impedance curve, the relative P-wave impedance curve without burial depth trend is obtained.

[0056] ④Relative elastic parameter well-controlled interpolation modeling: Based on the fault, stratigraphic framework and relative P-wave impedance curve, a relative P-wave impedance model is obtained.

[0057] ⑤ Phase-controlled backfill: The P-wave impedance low-frequency model is phase-controlled backfilled into the relative P-wave impedance model to obtain a more refined multi-information fusion phase-controlled P-wave impedance low-frequency model. This provides a more accurate phase-controlled P-wave impedance low-frequency model for subsequent inversion.

[0058] Specifically, in this embodiment, the phase-controlled backfill expression is:

[0059] Merg_Pimp_trend=Pimp_trend+delta_Pimp_trend

[0060] Where Merg_Pimp_trend is the multi-information fusion phase-controlled longitudinal wave impedance low-frequency model, Pimp_trend is the longitudinal wave impedance low-frequency model, and delta_Pimp_trend is the relative longitudinal wave impedance model.

[0061] In some embodiments, the other elastic parameter is shear impedance. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled shear impedance low-frequency model. Specifically, this embodiment uses well logging data from existing wells in the work area and the measured shear impedance curve to fit the well logging P-wave velocity curve and the shear impedance curve. Based on the fitting relationship between the well logging P-wave velocity curve and the shear impedance curve, the seismic velocity is converted into a shear impedance low-frequency model. A pseudo-well shear impedance curve for the existing well is extracted from the shear impedance low-frequency model. Based on the pseudo-well shear impedance curve for the existing well and the measured shear impedance curve, a relative shear impedance curve is calculated to remove the burial depth trend. A relative shear impedance model is obtained based on the fault, stratigraphic framework, and the relative shear impedance curve. Finally, the shear impedance low-frequency model is phase-controlled backfilled into the relative shear impedance model to obtain a more refined multi-information fusion phase-controlled shear impedance low-frequency model. This provides a more accurate phase-controlled shear impedance low-frequency model for subsequent inversion.

[0062] In some embodiments, the other elastic parameter is density. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled density low-frequency model. Specifically, this embodiment fits the fitting relationship between the well logging P-wave velocity curve and the density curve based on the well logging data of the work area and the measured density curve. And according to the fitting relationship between the well logging P-wave velocity curve and the density curve, the seismic velocity is converted into a density low-frequency model. The drilled density pseudo-well curve is extracted from the density low-frequency model, and the relative density curve without burial depth trend is calculated based on the drilled density pseudo-well curve and the measured density curve. Based on the fault, stratigraphic structural framework and relative density curve, a relative density model is obtained. Finally, the density low-frequency model is phase-controlled backfilled into the relative density model to obtain a more refined multi-information fusion phase-controlled density low-frequency model. A more accurate phase-controlled density low-frequency model is provided for subsequent inversion.

[0063] Figures 2 to 7 The application of this solution in the low-frequency modeling of the Enping Formation in a certain depression is shown. The entire technical process of the present invention is further described in detail below using the low-frequency modeling of longitudinal wave impedance as an example.

[0064] Drilled well relationship fitting: Figure 2 As shown in the figure, the correlation coefficient of the quadratic fitting relationship between the longitudinal wave velocity and the longitudinal wave impedance in this work area can reach 0.98. The specific expression is:

[0065] Pimpedance=f(Vp)=1.74157×10 6 +1201.75Vp+0.208098Vp 2

[0066] Where Pimpedance is the longitudinal wave impedance and Vp is the longitudinal wave velocity.

[0067] Elastic parameter body conversion: such as Figure 3 As shown in the figure, the seismic velocity increases with increasing burial depth from shallow to deep vertically, and decreases gradually from left to right horizontally, which is consistent with the sedimentary facies change from the fan root to the middle of the fan in the steep slope fan sand body of the Enping Formation in this work area. The converted low-frequency model Pimp_trend is generally consistent with the change characteristics of seismic velocity. These information representing sedimentary changes are crucial in the subsequent phase-controlled modeling process.

[0068] Calculate the relative elastic parameter curve of the drilled well: Figure 4 As shown in Figure 3, the drilled pseudo-well curve extracted based on the converted P-wave impedance body generally shows a low-frequency variation trend in which the P-wave impedance increases with increasing depth. By subtracting this trend from the measured impedance curve of the drilled well, the relative impedance curve without burial depth variation can be obtained.

[0069] Relative elastic parameter well control interpolation modeling: Figure 5 As shown in (left), based on the control of the horizon and fault framework, a low-frequency model that complies with the drilled well and structural framework is obtained through the well control interpolation method, but this model lacks the addition of geological change information.

[0070] Phase controlled backfill: Figure 5 As shown in , by integrating the trend model representing geological sedimentary changes into the well interpolation model, the obtained low-frequency model can not only reflect the geological changes, but also match the structural framework and drilled wells. Figure 6 As shown in the figure, the effects of different low-frequency modeling methods are compared. Compared with the other two low-frequency modeling methods, the multi-information fusion phase-controlled modeling not only incorporates the sedimentary phase change information reflected by seismic velocity, but also can match the structural framework and drilled wells to the greatest extent. The model is more in line with the laws of geological changes and more refined.

[0071] Application effect: Figure 7 As shown in the figure, compared with the traditional modeling inversion results, the reservoir prediction based on multivariate fusion modeling is more consistent with geological understanding and has better results. This method is suitable for target areas with rapid sedimentary changes and can significantly improve the accuracy of reservoir prediction.

[0072] The key technical point of this invention is to use high-precision seismic velocity fields to obtain information reflecting the geological background, and add structural interpretation of faults, horizons and logging information of drilled wells into the low-frequency modeling process, effectively improving the accuracy of low-frequency modeling, and thus overall improving the accuracy and reliability of Paleogene seismic inversion reservoir prediction results.

[0073] refer to Figure 8 In another preferred embodiment, the information fusion phase-controlled low-frequency modeling device of the embodiment of the present invention includes:

[0074] The curve fitting unit is used to fit the fitting relationship between the logging compressional wave velocity curve and other elastic parameter curves based on the well logging data of the drilled wells in the work area and the measured elastic parameter curve.

[0075] The low-frequency conversion unit is used to convert seismic velocity into a low-frequency elastic parameter volume based on the fitting relationship. Seismic velocity is the longitudinal wave velocity obtained by processing seismic data.

[0076] The relative curve calculation unit is used to extract the drilled elastic parameter pseudo-well curve from the low-frequency elastic parameter body, and calculate the relative elastic parameter curve of the removed burial depth trend based on the drilled elastic parameter pseudo-well curve and the measured elastic parameter curve.

[0077] The relative model generation unit is used to obtain a relative elastic parameter model based on the fault, the stratigraphic framework and the relative elastic parameter curve.

[0078] The multi-information fusion phase-controlled low-frequency model generation unit is used to backfill the low-frequency elastic parameter body into the relative elastic parameter model to obtain the multi-information fusion phase-controlled low-frequency model.

[0079] This embodiment fully utilizes the high-precision seismic velocity field to obtain information reflecting the geological background. It incorporates structurally interpreted faults, horizons, and logging information from existing wells into the low-frequency modeling process, ultimately achieving a phase-controlled low-frequency model that best matches the geological changes and existing wells. This can effectively improve the accuracy of low-frequency modeling, thereby enhancing the accuracy and reliability of subsequent seismic inversion reservoir prediction results.

[0080] In another preferred embodiment, a computer-readable storage medium according to an embodiment of the present invention stores a computer program suitable for loading by a processor to execute the steps of the multi-information fusion phase-controlled low-frequency modeling method described in the above-described embodiment. This embodiment, while fully utilizing high-precision seismic velocity fields to obtain information reflecting the geological background, incorporates structurally interpreted faults, horizons, and logging information from existing wells into the low-frequency modeling process, ultimately achieving a phase-controlled low-frequency model that best matches the geological variations and existing wells. This effectively improves the accuracy of the low-frequency modeling, thereby enhancing the accuracy and reliability of subsequent seismic inversion reservoir prediction results.

[0081] The computer-readable storage medium of the present invention can be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0082] The processor of the present invention is used to provide computing and control capabilities to support the operation of the entire device. It should be understood that in the embodiments of the present application, the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0083] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0084] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0085] It is understandable that the above embodiments only express the preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can be made, all of which fall within the scope of protection of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should fall within the scope of coverage of the claims of the present invention.

Claims

1. A multi-information fusion phase-controlled low-frequency modeling method, characterized in that: The following steps are involved: S1. Based on the well logging data of the drilled wells in the work area and the measured elastic parameter curve, the fitting relationship between the logging P-wave velocity curve and other elastic parameter curves is fitted; S2. Converting the seismic velocity into a low-frequency elastic parameter volume according to the fitting relationship; the seismic velocity is the longitudinal wave velocity obtained by processing the seismic data; S3, extracting a pseudo-well curve of elastic parameters of a drilled well from the low-frequency elastic parameter volume, and calculating a relative elastic parameter curve for removing the burial depth trend based on the pseudo-well curve of elastic parameters of the drilled well and the measured elastic parameter curve; S4. obtaining a relative elastic parameter model based on the fault, the stratigraphic framework, and the relative elastic parameter curve; S5. Backfill the low-frequency elastic parameter volume into the relative elastic parameter model in a phase-controlled manner to obtain a multi-information fusion phase-controlled low-frequency model.

2. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: Each other elastic parameter corresponds to a multi-information fusion phase-controlled low-frequency model.

3. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: The other elastic parameter is longitudinal wave impedance. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled longitudinal wave impedance low-frequency model.

4. The multi-information fusion phase-controlled low-frequency modeling method according to claim 3, characterized in that: The low-frequency elastic parameter body is a low-frequency model of longitudinal wave impedance, and the relative elastic parameter model is a relative longitudinal wave impedance model. In step S5, the phase-controlled backfill expression is: Merg_Pimp_trend=Pimp_trend+delta_Pimp_trend Wherein, Merg_Pimp_trend is the multi-information fusion phase-controlled longitudinal wave impedance low-frequency model, Pimp_trend is the longitudinal wave impedance low-frequency model, and delta_Pimp_trend is the relative longitudinal wave impedance model.

5. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: The other elastic parameter is shear wave impedance. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled shear wave impedance low-frequency model.

6. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: The other elastic parameter is density. Accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled density low-frequency model.

7. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: The step S3 of calculating the relative elastic parameter curve without burial depth trend based on the drilled elastic parameter pseudo-well curve and the measured elastic parameter curve includes: The measured elastic parameter curve is subtracted from the drilled elastic parameter pseudo-well curve, and the result obtained by calculation is the relative elastic parameter curve of the deburial depth trend.

8. The multi-information fusion phase-controlled low-frequency modeling method according to claim 1, characterized in that: Step S4 includes: Based on the fault, the stratigraphic framework and the relative elastic parameter curve, the relative elastic parameter model is obtained by a well-controlled interpolation modeling method.

9. A multi-information fusion phase-controlled low-frequency modeling device, characterized in that: include: The curve fitting unit is used to fit the relationship between the well logging compressional wave velocity curve and other elastic parameter curves based on the well logging data of the drilled wells in the work area and the measured elastic parameter curve; A low-frequency conversion unit is used to convert the seismic velocity into a low-frequency elastic parameter body according to the fitting relationship; the seismic velocity is the longitudinal wave velocity obtained by processing the seismic data; a relative curve calculation unit, configured to extract a pseudo-well curve of elastic parameters of a drilled well from the low-frequency elastic parameter volume, and calculate a relative elastic parameter curve without burial depth trend based on the pseudo-well curve of elastic parameters of the drilled well and the measured elastic parameter curve; A relative model generating unit is used to obtain a relative elastic parameter model based on the fault, the stratigraphic framework and the relative elastic parameter curve; The multi-information fusion phase-controlled low-frequency model generating unit is used to backfill the low-frequency elastic parameter volume into the relative elastic parameter model in phase control to obtain the multi-information fusion phase-controlled low-frequency model.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps of the multi-information fusion phase-controlled low-frequency modeling method according to any one of claims 1 to 8.

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