A multi-information fusion phased low-frequency modeling method and device and a storage medium

Abstract

The present application relates to a kind of multi-information fusion phased low-frequency modeling method, device and storage medium.The method is drilled well logging data and measured elastic parameter curve, fitting out the fitting relationship of logging longitudinal wave velocity curve and other elastic parameter curve.According to fitting relationship, seismic velocity is converted into low-frequency elastic parameter body.Seismic velocity is the longitudinal wave velocity obtained by processing seismic data.From low-frequency elastic parameter body, the elastic parameter pseudo well curve of drilled well is extracted, and according to the elastic parameter pseudo well curve of drilled well and measured elastic parameter curve, the relative elastic parameter curve of depth-trend is calculated.Based on fault, horizon structure framework and relative elastic parameter curve, relative elastic parameter model is obtained.Low-frequency elastic parameter body is phased backfilled into relative elastic parameter model to obtain multi-information fusion phased low-frequency model.The present application can improve the precision of low-frequency modeling, and then improve the accuracy and reliability of subsequent seismic inversion reservoir prediction result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration and research, and particularly relates to a multi-information fusion phase-controlled low-frequency modeling method and device and a storage medium. BACKGROUND

[0002] In the field of oil and gas exploration and research, a low-frequency model generally refers to information reflecting geological background with a frequency lower than 10 Hz, which has advantages such as good penetration ability and noise resistance. Due to equipment limitations in the seismic acquisition process, seismic data generally lacks low-frequency components less than 5 Hz, which requires a low-frequency model to compensate for the lack of low-frequency information in the seismic data, thereby improving the rationality of the seismic inversion result. At the same time, the low-frequency model can effectively constrain the convergence direction and convergence speed of the inversion, and the accuracy of the model directly affects the accuracy of reservoir prediction.

[0003] Low-frequency modeling is a process of constructing a more accurate low-frequency model by combining multiple data sources (such as faults, horizons, seismic velocity spectra, seismic facies, and sedimentary facies), thereby improving the accuracy of reservoir prediction. With the continuous deepening of offshore oil and gas exploration, deep Paleogene exploration has become the main direction of discovering large and medium-sized oil and gas fields in the sea. The Paleogene reservoir has a rapid sedimentary change, and the traditional well-controlled interpolation low-frequency modeling method excessively relies on drilled well data and interpolation algorithms, and the obtained low-frequency model cannot reflect the true geological changes, making it difficult to meet the actual exploration needs of the Paleogene. Therefore, constructing a more accurate low-frequency model is particularly crucial in the process of high-precision seismic inversion of deep Paleogene, and the research on low-frequency modeling has important significance for achieving efficient deep exploration with few wells and promoting the progress of deep oil and gas exploration.

[0004] In related technologies, a Kriging interpolation algorithm is used to realize comprehensive modeling of seismic, logging, and geological information through an information fusion algorithm. A sedimentary facies model reflecting the sedimentary distribution of sand bodies is established by using the lithology probability volume obtained by seismic inversion and drilled wells to make up for the defects brought by the lack of well information in seismic inversion. In addition, a high-precision velocity modeling method for deepwater deep reservoirs is proposed to solve the problem of low-frequency modeling in deepwater deep areas with few wells. The regional velocity variation characteristics are obtained by using drilled wells to analyze the main control factors of regional velocity, and then added to the low-frequency modeling process to realize the construction of a high-precision seismic velocity model.

[0005] Through systematic research on the research results of previous papers and patents, it is found that the existing research on low-frequency modeling methods mainly focuses on well-controlled interpolation methods, drilled well regional feature statistics, and geological sedimentary facies utilization methods, but there is less research on multi-information fusion low-frequency modeling methods considering geological changes, drilled wells, structural faults, and horizons. Therefore, it is necessary to study how to establish a phase-controlled low-frequency model that can reflect geological changes and match the tectonic framework and drilled wells, and it is urgent to carry out targeted research. SUMMARY

[0006] The technical problem solved by the present application is to provide a multi-information fusion phased low-frequency modeling method, device and storage medium to solve the above-mentioned defects.

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

[0008] S1. According to the well logging data of the drilled well in the work area and the measured elastic parameter curve, the fitting relationship between the logging longitudinal wave velocity curve and other elastic parameter curves is fitted;

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

[0010] S3. The drilled well elastic parameter pseudo-well curve is extracted from the low-frequency elastic parameter volume, and the relative elastic parameter curve with depth trend is calculated according to the drilled well elastic parameter pseudo-well curve and the measured elastic parameter curve;

[0011] S4. Based on the fault, horizon structure framework and the relative elastic parameter curve, a relative elastic parameter model is obtained;

[0012] S5. The low-frequency elastic parameter volume is phased backfilled into the relative elastic parameter model to obtain a multi-information fusion phased low-frequency model.

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

[0014] Further, in the multi-information fusion phased low-frequency modeling method of the present application, the other elastic parameter is longitudinal wave impedance, and correspondingly, the multi-information fusion phased low-frequency model is a multi-information fusion phased longitudinal wave impedance low-frequency model.

[0015] Further, in the multi-information fusion phased low-frequency modeling method of the present application, the low-frequency elastic parameter volume is a longitudinal wave impedance low-frequency model, and the relative elastic parameter model is a relative longitudinal wave impedance model, and in step S5, the phased backfill expression is:

[0016] Merg_Pimp_trend=Pimp_trend+delta_Pimp_trend

[0017] In the formula, Merg_Pimp_trend is the multi-information fusion phased 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] Further, in the multi-information fusion phased low-frequency modeling method, the other elastic parameter is the density, and correspondingly, the multi-information fusion phased low-frequency model is a multi-information fusion phased density low-frequency model.

[0019] Further, in the multi-information fusion phased low-frequency modeling method, the other elastic parameter is the density, and correspondingly, the multi-information fusion phased low-frequency model is a multi-information fusion phased density low-frequency model.

[0020] Further, in the multi-information fusion phased low-frequency modeling method, the step S3 includes:

[0021] The measured elastic parameter curve and the drilled well elastic parameter pseudo well curve are subtracted to obtain the relative elastic parameter curve with the de-burial trend.

[0022] Further, in the multi-information fusion phased low-frequency modeling method, the step S4 includes:

[0023] Based on the fault, horizon structure framework and the relative elastic parameter curve, the well-controlled interpolation modeling method is used to obtain the relative elastic parameter model.

[0024] In addition, the application also provides a multi-information fusion phased low-frequency modeling device, which comprises:

[0025] A curve fitting unit is configured to fit a fitting relationship between a logging longitudinal wave velocity curve and other elastic parameter curves according to drilled well logging data in a work area and a measured elastic parameter curve.

[0026] A low-frequency conversion unit is configured to convert seismic velocity into a low-frequency elastic parameter volume according to the fitting relationship, wherein the seismic velocity is a longitudinal wave velocity obtained by processing seismic data.

[0027] A relative curve calculation unit is configured to extract a drilled well elastic parameter pseudo well curve from the low-frequency elastic parameter volume, and calculate a relative elastic parameter curve with a de-burial trend according to the drilled well elastic parameter pseudo well curve and the measured elastic parameter curve.

[0028] A relative model generation unit is configured to generate a relative elastic parameter model based on the faults, the stratigraphic framework and the relative elastic parameter curve;

[0029] A multi-information fusion phase-controlled low-frequency model generation unit is configured to backfill the low-frequency elastic parameter volume into the relative elastic parameter model to obtain a multi-information fusion phase-controlled low-frequency model.

[0030] In addition, the application further provides a computer readable storage medium storing a computer program, and the computer program is adapted to be loaded by a processor to execute the steps of the multi-information fusion phase-controlled low-frequency modeling method.

[0031] The multi-information fusion phase-controlled low-frequency modeling method, device and storage medium have the following beneficial effects: the application adds the faults and the stratigraphic framework interpreted by the structural interpretation and the logging information of the drilled well into the low-frequency modeling process on the basis of the information reflecting the geological background obtained by fully utilizing the high-precision seismic velocity field, finally realizes the phase-controlled low-frequency model which is most matched with the geological changes and the drilled well, and can effectively improve the precision of the low-frequency modeling and further improve the accuracy and reliability of the subsequent seismic inversion reservoir prediction result. BRIEF DESCRIPTION OF DRAWINGS

[0032] The application will be further described below in combination with the drawings and embodiments, and the drawings are as follows:

[0033] Figure 1 FIG. 1 is a flowchart of a multi-information fusion phase-controlled low-frequency modeling method provided by an embodiment of the application;

[0034] Figure 2 FIG. 5 is a fitting relationship diagram of the P-wave velocity and the P-wave impedance of some embodiments;

[0035] Figure 3 FIG. 7 is a comparison diagram of the seismic velocity (left) and the converted P-wave impedance (right) of some embodiments;

[0036] Figure 4 FIG. 9 is a comparison diagram of the P-wave impedance pseudo well (left), the P-wave impedance (middle) and the detrended relative impedance (right) curve of some embodiments;

[0037] Figure 5 FIG. 11 is a comparison diagram of the relative P-wave impedance model (left), the P-wave impedance model of the seismic velocity (middle) and the multi-information fusion phase-controlled P-wave impedance low-frequency model (right) of some embodiments;

[0038] Figure 6 FIG. 13 is a comparison diagram of the model profile of the low-frequency modeling method of the seismic velocity fitting modeling (left) of the related art, the well-controlled interpolation modeling (middle) of the related art and the multi-information fusion phase-controlled modeling (right) of the application; and

[0039] Figure 7 is a comparison chart of seismic inversion effects based on multi-information fusion phase control modeling (left) of the present application and related technology based on well control interpolation modeling (right);

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

[0041] In order to have a clearer understanding of the technical features, objectives and effects of the present application, the specific embodiments of the present application will now be described in detail with reference to the drawings. In the following description, it should be understood that the directions or positional relationships indicated by "front", "back", "upper", "lower", "left", "right", "vertical", "horizontal", "vertical", "horizontal", "top", "bottom", "inner", "outer", "head", "tail" and the like are based on the directions or positional relationships shown in the drawings, constructed and operated in a particular direction, and are only for the convenience of describing the technical solutions, and therefore cannot be understood as indicating that the devices or elements referred to must have a particular direction, thus not limiting the present application.

[0042] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted in order not to obscure the description of the present application with unnecessary details.

[0043] Reference Figure 1 In a preferred embodiment, the multi-information fusion phase control low-frequency modeling method of the embodiments of the present application comprises the following steps:

[0044] S1, according to the well logging data of the drilled well in the work area and the measured elastic parameter curve, the fitting relationship of the logging longitudinal wave velocity curve and other elastic parameter curves is fitted. It should be noted that in the embodiments of the present application, the elastic parameter refers to a parameter used to describe the physical properties of the underground medium in seismic exploration, mainly including longitudinal wave velocity (P wave velocity), transverse wave velocity (S wave velocity), longitudinal wave impedance, transverse wave impedance, density, bulk modulus, shear modulus, Poisson's ratio, longitudinal to transverse wave velocity ratio, etc. These parameters reflect the elastic response of rock when subjected to seismic waves, and are an important basis for building underground velocity models and describing reservoirs.

[0045] S2, according to the fitting relationship, convert the seismic velocity into a low-frequency elastic parameter volume. The seismic velocity is the longitudinal wave velocity obtained by processing the seismic data. It can be understood that in the embodiments of the present application, the low-frequency elastic parameter volume can represent the depth trend of the elastic parameter and reflect the spatial variation of the sedimentary facies.

[0046] S3, extracting the drilled well elastic parameter pseudo well curve from the low frequency elastic parameter volume, and calculating the relative elastic parameter curve without burial trend according to the drilled well elastic parameter pseudo well curve and the measured elastic parameter curve. Specifically, in this step, the measured elastic parameter curve and the drilled well elastic parameter pseudo well curve can be subtracted to obtain the relative elastic parameter curve without burial trend. It should be noted that the subtraction operation in the embodiment of the present application is not limited to the subtracted object and the subtracted object, and can be determined according to actual needs, and if necessary, the absolute value mode conversion processing can be used to obtain the relative elastic parameter curve without burial trend.

[0047] S4, obtaining the relative elastic parameter model based on the fault, horizon structure framework and the relative elastic parameter curve. Based on the fault, horizon structure framework and the relative elastic parameter curve, specifically, in this step, the fault, horizon structure framework and the relative elastic parameter curve can be processed by the well control interpolation modeling method to obtain the relative elastic parameter model.

[0048] It should be further noted that the fault, horizon structure framework refers to a model for describing the spatial distribution and structure form of the stratum, which is established by the method of combining seismic data and well-to-seismic in three-dimensional geological modeling. It mainly includes a fault model and a horizon model. The fault model is used to depict the key elements such as the spatial trend, tendency and dip angle of the fault, and the horizon model is used to describe the contact relationship and deposition characteristics between different strata. Through these models, the structure form can be more accurately explained, and the foundation for subsequent reservoir prediction and oil and gas development can be provided. In the embodiment of the present application, the specific model construction process of the fault, horizon structure framework and the well control interpolation modeling method can be referred to the prior art, which will not be described here.

[0049] S5, the low frequency elastic parameter volume is backfilled into the relative elastic parameter model 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, adds the fault, horizon and well logging information of the drilled well into the low frequency modeling process, and finally realizes the phase-controlled low frequency model which is most matched with the geological changes and the drilled well, which can effectively improve the precision of low frequency modeling, and further improve the accuracy and reliability of the 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. That is, the phase-controlled modeling process needs to establish the phase-controlled low frequency models of the longitudinal wave impedance, the transverse wave impedance and the density, and the modeling process of each elastic parameter can be referred to the above description. Figure 1The shown flow realizes the multi-information fusion phase-controlled modeling of different elastic parameters (longitudinal wave impedance, transverse wave impedance and density) in turn.

[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 Drilled well relationship fitting: Longitudinal wave velocity usually has good correlation with other elastic parameters (longitudinal wave impedance, transverse wave impedance and density), so the fitting relationship f(Vp) of the logging longitudinal wave velocity curve and the longitudinal wave impedance curve can be fitted according to the logging data of the drilled wells in the work area and the measured longitudinal wave impedance curve.

[0054] ② Elastic parameter volume conversion: The seismic velocity can be converted into the longitudinal wave impedance low-frequency model according to the fitting relationship f(Vp) of the logging longitudinal wave velocity curve and the longitudinal wave impedance curve.

[0055] ③ Calculation of drilled well relative elastic parameter curve: Based on the converted longitudinal wave impedance low-frequency model, the drilled well longitudinal wave impedance pseudo-well curve is extracted therefrom, and the relative longitudinal wave impedance curve without the burial depth trend is obtained after the drilled well longitudinal wave impedance pseudo-well curve is subtracted from the measured longitudinal wave impedance curve.

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

[0057] ⑤ Phase-controlled backfilling: The longitudinal wave impedance low-frequency model is phase-controlled backfilled into the relative longitudinal wave impedance model, so as to obtain a more detailed multi-information fusion phase-controlled longitudinal wave impedance low-frequency model. A more accurate phase-controlled longitudinal wave impedance low-frequency model is provided for subsequent inversion.

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

[0059] Merg_Pimp_trend = Pimp_trend + delta_Pimp_trend

[0060] In the formula, 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 wave impedance, and accordingly, the multi-information fusion phase-controlled low-frequency model is a multi-information fusion phase-controlled shear wave impedance low-frequency model. Specifically, this embodiment fits a fitting relationship between the logging P-wave velocity curve and the shear wave impedance curve according to the logging data of the drilled well in the work area and the measured shear wave impedance curve. The seismic velocity is converted into a shear wave impedance low-frequency model according to the fitting relationship between the logging P-wave velocity curve and the shear wave impedance curve. The drilled well shear wave impedance pseudo-well curve is extracted from the shear wave impedance low-frequency model, and the relative shear wave impedance curve without the burial depth trend is calculated according to the drilled well shear wave impedance pseudo-well curve and the measured shear wave impedance curve. The relative shear wave impedance model is obtained based on the faults, the stratigraphic framework and the relative shear wave impedance curve. Finally, the shear wave impedance low-frequency model is phase-controlled and backfilled into the relative shear wave impedance model, so as to obtain a more refined multi-information fusion phase-controlled shear wave impedance low-frequency model. A more accurate phase-controlled shear wave impedance low-frequency model is provided for subsequent inversion.

[0062] In some embodiments, the other elastic parameter is density, and 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 a fitting relationship between the logging P-wave velocity curve and the density curve according to the logging data of the drilled well in the work area and the measured density curve. The seismic velocity is converted into a density low-frequency model according to the fitting relationship between the logging P-wave velocity curve and the density curve. The drilled well density pseudo-well curve is extracted from the density low-frequency model, and the relative density curve without the burial depth trend is calculated according to the drilled well density pseudo-well curve and the measured density curve. The relative density model is obtained based on the faults, the stratigraphic framework and the relative density curve. Finally, the density low-frequency model is phase-controlled and backfilled into the relative density model, so as 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 is shown in the application of low-frequency modeling of the Enping Formation in a certain sag, and the whole technical process of the application is further described in detail below by taking P-wave impedance low-frequency modeling as an example.

[0064] Drilled well relationship fitting: as shown in FIG. 1, the correlation coefficient of the quadratic fitting relationship between the P-wave velocity and the P-wave impedance in the work area can reach 0.98, and the specific expression is: Figure 2

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

[0066] In the formula, Pimpedance is the P-wave impedance, and Vp is the P-wave velocity.

[0067] ​Elastic parameter volume conversion: as shown in the figure, Figure 3 The seismic velocity shows a trend of increasing with the increase of burial depth from shallow to deep, and a gradually decreasing variation characteristic from the layer velocity of the left to the right, which is consistent with the variation characteristic of the sedimentary facies of the steep slope fan sand body of the Enping group in the work area; the Pimp_trend low-frequency model of the converted P-wave impedance is generally consistent with the variation characteristic of the seismic velocity, and these information representing the sedimentary variation is crucial in the subsequent facies-controlled modeling process.

[0068] Calculate the relative elastic parameter curve of the drilled well: as shown in the figure, Figure 4 Based on the extracted pseudo-well curve of the drilled well, the P-wave impedance shows a low-frequency variation trend of increasing with the increase of depth, and the relative impedance curve without the burial depth variation can be obtained by subtracting the trend from the measured impedance curve of the drilled well.

[0069] Facies-controlled interpolation modeling of relative elastic parameters: as shown in the figure, Figure 5 Based on the horizon and fault framework control, the low-frequency model that conforms to the drilled well and the structural framework is obtained by the well-controlled interpolation method, but the model lacks the addition of geological variation information.

[0070] Facies-controlled backfilling: as shown in the figure, Figure 5 By fusing the trend model representing the geological sedimentary variation into the well interpolation model, the obtained low-frequency model can reflect the geological variation and match the structural framework and the drilled well. As shown in the figure, Figure 6 The effect comparison of different low-frequency modeling methods, compared with the other two low-frequency modeling methods, the facies-controlled modeling of multi-information fusion not only fuses the sedimentary facies variation information represented by the seismic velocity, but also can be matched with the structural framework and the drilled well to the greatest extent, and the model is more consistent with the geological variation law and more fine.

[0071] Application effect: as shown in the figure, Figure 7 Compared with the traditional modeling inversion effect, the inversion predicted reservoir based on the multi-element fusion modeling is more matched with the geological understanding and has better effect. The method is suitable for the target area with fast sedimentary variation, and the effect is obvious and the accuracy of reservoir prediction can be better improved.

[0072] The technical key point of the application is to obtain the information reflecting the geological background by using the high-precision seismic velocity field, to add the logging information of the fault, horizon and drilled well of the structural interpretation into the low-frequency modeling process, to effectively improve the precision of the low-frequency modeling, and to further improve the accuracy and reliability of the result of the paleogene seismic inversion reservoir prediction.

[0073] Reference Figure 8 In another preferred embodiment, the information fusion facies-controlled low-frequency modeling device of the embodiment of the application comprises:

[0074] A curve fitting unit is configured to fit a logging P-wave velocity curve and other elastic parameter curves according to logging data of drilled wells in the work area and the measured elastic parameter curves.

[0075] A low-frequency conversion unit is configured to convert seismic velocity into a low-frequency elastic parameter volume according to the fitting relationship, wherein the seismic velocity is a P-wave velocity obtained by processing seismic data.

[0076] A relative curve calculation unit is configured to extract a drilled well elastic parameter pseudo-well curve from the low-frequency elastic parameter volume, and calculate a relative elastic parameter curve with depth removed according to the drilled well elastic parameter pseudo-well curve and the measured elastic parameter curve.

[0077] A relative model generation unit is configured to generate a relative elastic parameter model based on faults, horizon structure framework and the relative elastic parameter curve.

[0078] A multi-information fusion phase-controlled low-frequency model generation unit is configured to backfill the low-frequency elastic parameter volume into the relative elastic parameter model to obtain a multi-information fusion phase-controlled low-frequency model.

[0079] In the embodiment, the faults and horizons obtained by structure interpretation and the logging information of drilled wells are added into the low-frequency modeling process on the basis of obtaining information reflecting geological background by fully utilizing high-precision seismic velocity field, and finally a phase-controlled low-frequency model that is maximally matched with geological changes and drilled wells is obtained, which can effectively improve the precision of low-frequency modeling and further improve the accuracy and reliability of subsequent seismic inversion reservoir prediction results.

[0080] In another preferred embodiment, a computer readable storage medium of the embodiment of the present application stores a computer program, and the computer program is adapted to be loaded by a processor to execute the steps of the multi-information fusion phase-controlled low-frequency modeling method of the above-mentioned embodiment. In the embodiment, the faults and horizons obtained by structure interpretation and the logging information of drilled wells are added into the low-frequency modeling process on the basis of obtaining information reflecting geological background by fully utilizing high-precision seismic velocity field, and finally a phase-controlled low-frequency model that is maximally matched with geological changes and drilled wells is obtained, which can effectively improve the precision of low-frequency modeling and further improve the accuracy and reliability of subsequent seismic inversion reservoir prediction results.

[0081] The computer readable storage medium of the present application can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various computer readable storage media that can store program codes.

[0082] The processor of the present application 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 gates 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] The skilled person can further realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can 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 application.

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

[0085] It can be understood that the above embodiments only express the preferred embodiments of the present application, which are described in detail and in detail, but should not be construed as limiting the scope of the patent of the present application. It should be noted that for those skilled in the art, the above technical features can be freely combined without departing from the concept of the present application, and a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, any equivalent transformation and modification within the scope of the claims of the present application shall be covered by the claims of the present application.

Claims

1. A multi-information fusion phased-array low-frequency modeling method, characterized in that, Includes the following steps: S1. Based on the well logging data of the drilled wells in the work area and the measured elastic parameter curves, fit the fitting relationship between the logging P-wave velocity curve and other elastic parameter curves; the other elastic parameter curves include at least one of the measured P-wave impedance curve, S-wave impedance curve or density curve. S2. Based on the fitting relationship, the seismic velocity is converted into a low-frequency elastic parameter body; the seismic velocity is the P-wave velocity obtained by processing the seismic data; S3. Extract the drilled elastic parameter pseudo-well curve from the low-frequency elastic parameter body, and calculate the relative elastic parameter curve of the burial depth trend based on the drilled elastic parameter pseudo-well curve and the measured elastic parameter curve. S4. Based on the fault, the stratigraphic framework, and the relative elastic parameter curve, a relative elastic parameter model is obtained; S5. The low-frequency elastic parameter volume phase control is backfilled into the relative elastic parameter model to obtain the multi-information fusion phase control low-frequency model.

2. The multi-information fusion phased-array low-frequency modeling method according to claim 1, characterized in that, Each of the other elastic parameters corresponds to a multi-information fusion phased-controlled low-frequency model.

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

4. The multi-information fusion phased array 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 In the formula, Merg_Pimp_trend is the multi-information fusion phased-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 phased-array low-frequency modeling method according to claim 1, characterized in that, The other elastic parameter is the transverse wave impedance, and correspondingly, the multi-information fusion phased-controlled low-frequency model is a multi-information fusion phased-controlled transverse wave impedance low-frequency model.

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

7. The multi-information fusion phased-array low-frequency modeling method according to claim 1, characterized in that, Step S3, which calculates the relative elastic parameter curve of the burial depth reduction trend based on the drilled well elastic parameter pseudo-well curve and the measured elastic parameter curve, includes: The difference between the measured elastic parameter curve and the drilled well elastic parameter pseudo-well curve is calculated to obtain the relative elastic parameter curve of the burial depth reduction trend.

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

9. A multi-information fusion phased-array low-frequency modeling device, characterized in that, include: The curve fitting unit is used to fit the relationship between the logging P-wave velocity curve and other elastic parameter curves based on the well logging data of the work area and the measured elastic parameter curves; the other elastic parameter curves include at least one of the measured P-wave impedance curve, S-wave impedance curve or density curve. The 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 P-wave velocity obtained by processing the seismic data. 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 burial depth trend based on the drilled elastic parameter pseudo-well curve and the measured elastic parameter curve. The relative model generation unit is used to construct a framework based on faults and strata, as well as the relative elastic parameter curves, to obtain a relative elastic parameter model. A multi-information fusion phased-controlled low-frequency model generation unit is used to backfill the low-frequency elastic parameter volume phased into the relative elastic parameter model to obtain a multi-information fusion phased-controlled low-frequency model.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted for loading by a processor to perform the steps of the multi-information fusion phased-array low-frequency modeling method as described in any one of claims 1 to 8.

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