Thin reservoir prediction method based on pre-stack frequency extension processing

Abstract

The invention discloses a thin reservoir prediction method based on pre-stack frequency extension processing, and the technical scheme is that on the basis of improving the resolution to process seismic data, reservoir rock physical analysis is carried out through actually measured transverse wave data, a rock physical model is constructed, thin reservoir identification sensitive parameters are determined, and a bridge between logging and earthquake is built; for seismic data, pre-stack seismic certainty inversion of sand group thickness resolution capability and pre-stack statistical high-resolution inversion of single sand body thickness are respectively carried out, a sensitive parameter data body is obtained through calculation, and according to a rock physical quantity plate, the spatial description precision of the thin sand body is effectively improved. The method is high in operability and easy to apply, and has good application prospects for similar ultra-deep and ultra-thin layer sand bodies such as cretaceous systems and triassic systems of tahe oil fields in the Tarim Basin; and meanwhile, the method is also suitable for sweet spot identification and reservoir space fine description of ultra-deep and thin reservoirs of basins in the middle and western parts, and is of great significance for improving the drilling success rate, productivity construction and the like.

Description

Technical Field

[0001] The invention relates to the field of thin reservoir prediction, and more specifically, to a thin reservoir prediction method based on pre-stack spectrum processing. Background Art

[0002] Thin layers are generally defined as strata smaller than the seismic resolution limit. With the continuous deepening of exploration and development, thin layers are receiving more and more attention. As reservoirs, they contain large-scale oil and gas resources. As mudstone caprocks, they control the migration and enrichment of oil and gas under certain geological conditions. In view of the characteristics of thin thickness, large variations, poor lateral continuity and strong heterogeneity of thin layers, how to improve the effectiveness of thin layer identification, tracking and evaluation is a current research hotspot.

[0003] Many scholars at home and abroad have conducted many studies on thin reservoir prediction methods from both qualitative and quantitative aspects. Qualitatively, it is mainly based on geological knowledge, and comprehensively uses seismic multi-attributes, seismic phase classification and logging and drilling data to describe the reservoir distribution law and distribution characteristics, and combines actual drilling information to statistically calculate the distribution range of favorable reservoirs. This type of method is currently relatively mature and has obvious application effects (Yang Zhanlong et al. (2019). For example: the Songliao Basin uses "two widths and one height" seismic data and uses the root mean square amplitude attribute to predict thin sand bodies, and the compliance rate is significantly improved (Nietal, 2018); Karamay on the west slope of Mahu in the Junggar Basin The group used the minimum interference frequency data to better predict the boundary of a single thin layer (Liu Huaqing et al., 2018). However, these methods have limited accuracy in reservoir characterization, making it difficult to predict the thickness of a single sand body in a thin interbedded sand body, and cannot meet the needs of high-precision rolling development and production (Widess, 2020; Yuan Cheng et al., 2021). Quantitatively, it mainly uses pre-stack and post-stack inversion methods, with the help of rock physical analysis, and uses geological statistical methods to quantitatively describe reservoir thickness, pores, and spatial distribution (Huang Xude, 1994).

[0004] The Carboniferous reservoir types in Tahe are mainly CK1 stratigraphic traps and CK5 lithologic traps. Multi-well logging, testing, and production conditions show that the Carboniferous has good evaluation and development potential. The following are the main problems in the prediction of thin reservoirs in this study area: First, the resolution of seismic data is low, which makes it difficult to meet the reservoir prediction accuracy; second, there are up-dip pinch-out stratigraphic traps in the area, and the reservoirs are difficult to characterize due to the strong reflection of T50; third, the reservoirs are buried deep, thin, and change rapidly laterally. The longitudinal impedance of sandstone and mudstone is superimposed, making it difficult to quantitatively predict the reservoirs, and the distribution characteristics of favorable reservoirs are unclear. Due to the constraints of seismic vertical resolution and the strong interference of adjacent layers, the seismic attribute characteristics in the thin interbedded sandstone and mudstone formations cannot accurately reflect the true change law of the reservoir, and it is difficult to achieve the purpose of fine description of the reservoir.

[0005] Based on the above reasons, the present invention hopes to provide a thin reservoir prediction method based on pre-stack topology processing. Based on compressed sensing topology processing, rock physics analysis modeling, geostatistical inversion, and pre-stack simultaneous inversion technology, subsequent reservoir prediction and analysis are carried out on the basis of improving the original seismic data, which solves the problem of low resolution of original seismic data and difficulty in accurately predicting thin layers, and improves the prediction precision and accuracy of ultra-thin reservoirs. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a thin reservoir prediction method based on pre-stack topology processing, which is based on improving the resolution of seismic data, conducting reservoir rock physics analysis through measured shear wave data, constructing a rock physics model, clarifying sensitive parameters for thin reservoir identification, and building a bridge between well logging and seismic; for seismic data, pre-stack seismic deterministic inversion of sand group thickness resolution and pre-stack statistical high-resolution inversion of single sand body thickness are carried out respectively, and sensitive parameter data bodies are calculated, which effectively improves the spatial characterization accuracy of thin sand bodies based on rock physics quantities.

[0007] To achieve the above object, the present invention provides the following technical solution: a thin reservoir prediction method based on pre-stack spectrum processing, which comprises the following steps:

[0008] S1. Process seismic physical exploration data based on compressed sensing algorithm to improve the resolution of geophysical exploration data.

[0009] S2. Rock physics modeling: Based on the data processed by S1, a rock physics model is established and rock physics parameters are assigned; reservoir physics and rock physics analysis of the entire well section is performed based on the rock physics model; based on the analysis results, multiple elastic parameters that are sensitive to lithology and fluid type are determined; the elastic parameters include longitudinal wave velocity Vp, shear wave velocity Vs, longitudinal wave impedance Pimp, shear wave impedance Simp, acoustic wave resistance AI, shear stiffness Si, Poisson's ratio Pr, longitudinal and shear wave velocity ratio Vp / Vs, bulk elastic modulus λρ, shear elastic modulus uρ, bulk elastic coefficient K, and shear modulus G.

[0010] S3, Prestack deterministic inversion: Based on the rock physics modeling and the data processed by S1, prestack simultaneous inversion is performed to solve various elastic parameters; based on the inversion results, the quantitative cutoff values ​​of various elastic parameters are interpreted according to the rock physics interpretation volume to directly identify the reservoir structure and possible fluid interfaces.

[0011] S4. For reservoirs with a thickness greater than or equal to 10 m, a quantitative description is performed based on the interpretation of S3, and for reservoirs with a thickness less than 10 m, S5 is performed.

[0012] S5, pre-stack high-resolution statistical inversion: Use the data processed by S1 to perform Markov chain Monte Carlo algorithm inversion on various elastic parameters; according to the constrained sparse pulse inversion workflow, integrate and monitor the quality of the data processed by S1, and use the constrained sparse pulse inversion as the prior distribution of the Markov chain Monte Carlo algorithm inversion. Based on the Markov chain Monte Carlo algorithm inversion results, combined with S3 interpretation, quantitatively describe the reservoir with a thickness of less than 10m.

[0013] The present invention is further configured as follows: before S5 performs inversion, it is necessary to perform discrete attribute lithofacies simulation test on the lithofacies data in the seismic data through SIS sequence indication simulation, and perform continuous attribute simulation test on the elastic parameter data in the seismic data through SGS sequence Gaussian simulation. The simulation test results are spatially distributed and quality controlled according to geological information to improve the spatial definition accuracy of statistical inversion parameters.

[0014] The present invention is further configured as follows: the seismic physical exploration data in S1 includes calibrated logging data, synthetic shear wave data, stratigraphic data of the target layer segment and seismic data, wherein the seismic data is full stack seismic data and common angle stack seismic data or full stack seismic data and common offset stack seismic data.

[0015] The present invention is further configured as follows: before the compression sensing algorithm is used for frequency extension, the seismic data, the well logging data and the stratigraphic data need to be integrated and quality-monitored according to the constrained sparse pulse inversion workflow.

[0016] The present invention is further configured as follows: pre-stack simultaneous inversion requires inverting common-angle stacked seismic data or common-offset stacked seismic data based on the Zoppritz equation and its approximate formula within a multi-angle range to generate a P- and S-wave impedance data volume; and obtaining Vp, Vs, Pimp, Simp, AI, Si, Pr, Vp / Vs, λρ, uρ, K, and G based on the P- and S-wave impedance data volumes.

[0017] The present invention is further configured such that before establishing the rock physics model, the well logging data needs to be quality controlled by single well curve quality control, environmental correction and multi-well consistency processing.

[0018] The present invention is further configured as follows: after the quality control of the logging data is completed, it is necessary to evaluate the reservoir parameters through the reservoir parameter interpretation model, calculate the content of each mineral, bring it into the optimization interpretation model, perform optimization solution, and provide high-precision lithology characteristics and fluid characteristics for the rock physics model.

[0019] The present invention is further configured as follows: for Carboniferous reservoirs, before S1 frequency spreading processing of seismic physical exploration data, the Xu and White model is used to predict shear waves and fit multi-well shear waves.

[0020] The present invention is further configured as follows: the elastic parameter in S2 that is sensitive to sandstone and mudstone properties is Vp / Vs, and the elastic parameter that is sensitive to fluid type is Pimp.

[0021] The present invention further provides that: Vp / Vs of sandstone is less than 1.75; for reservoirs with a porosity greater than 8%, Pimp is less than 1.05*10 7 ; The Pimp of the oil and gas layer is 8.8*10 6 <Pimp<1.1*10 7 , Vp / Vs of oil and gas layer is 1.5 <Vp / Vs<1.75。

[0022] In summary, the present invention has the following beneficial effects compared with the prior art: the present invention is based on improving the resolution of seismic data, carries out reservoir rock physics analysis through measured shear wave data, constructs a rock physics model, clarifies sensitive parameters for thin reservoir identification, and builds a bridge between logging and seismic; for seismic data, respectively, prestack seismic deterministic inversion of sand group thickness resolution and prestack statistical high-resolution inversion of single sand body thickness are carried out, and sensitive parameter data bodies are calculated, and the spatial characterization accuracy of thin sand bodies is effectively improved based on the rock physics quantity version. The present invention is highly operable and easy to apply, and has good application prospects for ultra-deep and ultra-thin sand bodies similar to the Cretaceous and Triassic systems of the Tahe Oilfield in the Tarim Basin; it is also suitable for the identification of ultra-deep and thin reservoir sweet spots and fine characterization of reservoir space in the central and western basins, which is of great significance to improving the success rate of drilling and capacity construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the process of an embodiment;

[0024] Figure 2 It is a schematic diagram of the process of compressed sensing spectrum spreading processing;

[0025] Figure 3 Calibrate the original PSTM (prestack time migration imaging);

[0026] Figure 4 PSTM calibration after compressed sensing spectrum expansion;

[0027] Figure 5 Schematic diagram of the rock physics modeling process;

[0028] Figure 6 This is a schematic diagram of the prestack deterministic inversion process;

[0029] Figure 7 It is the intersection diagram of Pimp and Vp / Vs;

[0030] Figure 8 This is a schematic diagram of the pre-stack high-resolution statistical inversion process;

[0031] Fig. 9 is the probability density function of wave impedance, Vp / Vs and density of different lithologies of CK1;

[0032] Fig.10 It is the target layer fault variation function analysis diagram;

[0033] Fig.11 It is the statistical inversion profile (middle) + TKC1-4H well logging comprehensive evaluation diagram;

[0034] Fig.12 This is the daily production curve of the TKC1-4H oil well. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the invention.

[0036] Example

[0037] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of the present invention, a thin reservoir prediction method based on pre-stack spectrum processing, which comprises the following steps:

[0038] S1. Process seismic physical exploration data based on compressed sensing algorithm to improve the resolution of geophysical exploration data.

[0039] The seismic physical exploration data includes calibrated logging data, synthetic shear wave data, target layer layer data and seismic data, wherein the seismic data is full stack seismic data and common angle stack seismic data or full stack seismic data and common offset stack seismic data. Before frequency extension, the seismic data, logging data and layer data need to be integrated and quality controlled according to the constrained sparse pulse inversion workflow.

[0040] like Figure 2-4 As shown in the figure, when the compressed sensing algorithm performs spectrum expansion, it is first necessary to extract the wavelet from the original seismic record, and perform Fourier transform on the extracted wavelet and the original seismic record to obtain the corresponding frequency domain data; then the reflection coefficient in the frequency domain is calculated through the seismic convolution model, the reflection coefficient is decomposed into odd and even components and different weights are assigned to the odd and even components, and then the sparse constraint of the reflection coefficient is added to solve the objective function of the reflection coefficient, and then the odd and even components of the time domain reflection coefficient are obtained; finally, the reflection coefficient in the time domain is obtained by reconstructing the odd and even components; the reflection coefficient is convolved with the wavelet to obtain a high-resolution seismic record.

[0041] S2. Rock physics modeling:

[0042] like Figure 5 As shown in the figure, based on the S1 processing, the logging data is quality controlled through single well curve quality control, environmental correction and multi-well consistency processing to provide highly consistent and relatively complete logging data. Then the reservoir parameters are evaluated through the reservoir parameter interpretation model, the content of each mineral is calculated, and it is brought into the optimization interpretation model for optimization and solution, providing high-precision lithology and fluid characteristics for the rock physics model. Finally, the rock physics model is established and the rock physics parameters are assigned.

[0043] On the basis of the rock physics model, reservoir physics and rock physics analysis of the entire well section are carried out; based on the analysis results, multiple elastic parameters sensitive to lithology and fluid type are determined; the elastic parameters include longitudinal wave velocity Vp, transverse wave velocity Vs, longitudinal wave impedance Pimp, transverse wave impedance Simp, acoustic wave resistance AI, shear stiffness Si, Poisson's ratio Pr, longitudinal and transverse wave velocity ratio Vp / Vs, bulk elastic modulus λρ, shear elastic modulus uρ, bulk elastic coefficient K, and shear modulus G. Rock physics analysis is a "bridge" between well logging and seismic. Based on rock physics analysis, rock physics elastic parameters sensitive to rock skeletons and fluids in rocks can be determined, and the relationship between well point oil and gas reservoir attributes and seismic attributes (well-seismic relationship) can be established. Through the corresponding oil and gas reservoir parameter disturbance, the change law of elastic parameters is analyzed, and the prediction of oil and gas reservoir elastic parameter characteristics from point to surface is realized.

[0044] The intersection analysis of different elastic parameters shows that the combination of prestack inversion Vp / Vs and Pimp can distinguish the lithology, physical properties and oil-bearing reservoirs of the target layer in the study area. The smaller the overlap between elastic parameters, the higher the prediction reliability, the more concentrated the data, and the easier it is to determine the lithology or physical property boundaries.

[0045] S3, Prestack Deterministic Inversion:

[0046] like Figure 6 As shown in the figure, based on the rock physics modeling and the data processed by S1, pre-stack simultaneous inversion is performed. Pre-stack simultaneous inversion requires inversion of common angle stacked seismic data or common offset stacked seismic data based on the Zoppritz equation and its approximation within a multi-angle range to generate P- and S-wave impedance data bodies; based on the P- and S-wave impedance data bodies, Vp, Vs, Pimp, Simp, AI, Si, Pr, Vp / Vs, λρ, uρ, K, and G are obtained to quantitatively describe the distribution of oil and gas sand bodies in the superimposed geological bodies, and lay the foundation for the inversion in S5. When the simultaneous inversion is completed, all elastic parameter data bodies can be verified by actual logging data.

[0047] Specifically, when performing density inversion, it is necessary to apply co-angle stacking data volumes of at least five angles, that is, only when the far incident angle of the reservoir section is at least 40 degrees can a more reliable density data volume be generated.

[0048] Based on the above inversion results, the quantitative cutoff values ​​of various elastic parameters are interpreted according to the rock physics interpretation plate to directly identify the reservoir structure and possible fluid interfaces; Figure 7 As shown in the figure, the quantitative cutoff values ​​of some elastic parameters in this example are explained as follows: Vp / Vs<1.75 for sandstone; Pimp<1.05*10 7 ; The Pimp of the oil and gas layer is,

[0049] 8.8*10 6 <Pimp<1.1*10 7 , Vp / Vs of oil and gas layer is 1.5 <Vp / Vs<1.75。

[0050] S4. Based on the interpretation of S3, a quantitative description is performed for reservoirs with a thickness greater than or equal to 10 m, and S5 is performed for reservoirs with a thickness less than 10 m.

[0051] S5. Prestack high-resolution statistical inversion:

[0052] like Figure 8-10 As shown, this embodiment analyzes the probability density function and variation function of the lithology (discrete attributes), Pimp, Vp / Vs of the target layer segments CK1-CK5 in the geological model.

[0053] On the basis of S1 processing data, the SIS sequence random indication simulation is first used to determine the lithology proportion of each sublayer of the target layer and the vertical and horizontal variograms of the lithology. Secondly, the SGS sequence Gaussian simulation is used to obtain the probability density function of the elastic parameters of the different lithofacies and its spatial variogram setting; it provides reliable probability density functions and variograms of lithology and elastic parameters for subsequent inversion. This embodiment uses a method combined with the Markov chain Monte Carlo algorithm inversion for inversion. By establishing a probability model simulation result output of the reservoir attributes, multiple realizations are generated to achieve the output of the thin sand body model. Then, the S3 interpretation is combined to optimize the clear sand body model to solve the problem of quantitative prediction of thin reservoirs <10m.

[0054] Specifically, in this embodiment, the data processed by S1 are integrated and quality monitored according to the constrained sparse pulse inversion workflow, and the constrained sparse pulse inversion is used as the prior distribution of the Markov chain Monte Carlo algorithm inversion. With the help of constrained sparse pulse inversion, the lateral high resolution and predictability of the inversion results are effectively controlled in the seismic data.

[0055] Specifically, for the Carboniferous reservoir, since there is only shear wave data from well TK157, the Xu and White model needs to be used for shear wave prediction before S1 frequency expansion to fit multi-well shear waves for subsequent inversion constraints.

[0056] The Xu and White model is based on The sand-mudstone mixed model is proposed based on the theory, Gassmann equation and effective differential medium (DEM). This model attributes the change of P-wave velocity with clay content to the difference in pore geometry and pore flattening between mudstone and sandstone, and can better reflect the change of P-wave velocity with increasing clay content. Based on this model, the shear wave calculation first estimates the pore volume of sandstone and mudstone, and calculates the P- and S-wave time difference of the rock matrix of the mixed model through the Wyllie time-averaged equation, and then calculates the bulk modulus and shear modulus of the rock matrix; The equation can be used to calculate the bulk modulus and shear modulus of dry rock skeletons; the Gassmann equation is used to calculate the bulk modulus and shear modulus of fluid-saturated rocks, and these modulus parameters can be used to calculate the longitudinal and shear wave velocities.

[0057] Take the TK157 well area as an example. The well area is located in the middle of Tahe District 1. The target layer is the CK1-3# single sand body of the Karashayi Formation in the Carboniferous. The oil layer thickness is 6m and the burial depth is >5000m. It is a structural-lithological composite edge-water oil reservoir. Affected by the reservoir prediction accuracy, the reservoir drilling rate of the TK151X development well deployed in the early stage was only 30%.

[0058] like Figure 11-12 As shown in the figure, in 2021, this method achieved good results in the prediction of Carboniferous reservoirs and horizontal well deployment in Tahe. The 5-10m sand body was well identified, and the sand body matching rate was increased by 10%. Through stereoscopic evaluation, the reservoir inversion sweet spot area was selected, and five long horizontal wells, TKC1-4H~TKC1-8H and TKC1-10H, were deployed and implemented. The horizontal section of the TKC1-4 well that has been put into production has an actual drilling length of 539m, 378m of sand layers, and 309m of oil and gas layers. The sand drilling rate of the long horizontal well under the guidance of reservoir prediction reached 70%, the perforation was 8m, and the average daily production was maintained at 25 tons of oil + 47,000 cubic meters of gas.

[0059] In summary, this embodiment is based on improving the resolution of seismic data, conducting reservoir rock physics analysis through measured shear wave data, constructing a rock physics model, clarifying the sensitive parameters for thin reservoir identification, and building a bridge between logging and seismic; for seismic data, prestack seismic deterministic inversion of sand group thickness resolution and prestack statistical high-resolution inversion of single sand body thickness are carried out respectively, and sensitive parameter data bodies are calculated. According to the rock physics quantity version, the spatial characterization accuracy of thin sand bodies is effectively improved. This method is highly operational and easy to apply. It has good application prospects for ultra-deep and ultra-thin sand bodies similar to the Cretaceous and Triassic systems of the Tahe Oilfield in the Tarim Basin; it is also suitable for the identification of ultra-deep and thin reservoir sweet spots and fine characterization of reservoir space in the central and western basins, which is of great significance to improving the success rate of drilling and capacity construction.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A thin reservoir prediction method based on pre-stack topography processing, characterized by: The steps include: S1. Process seismic physical exploration data based on compressed sensing algorithm to improve the resolution of geophysical exploration data; S2, rock physics modeling: based on the data processed by S1, a rock physics model is established and rock physics parameters are assigned; reservoir physics and rock physics analysis of the entire well section is performed based on the rock physics model; based on the analysis results, multiple elastic parameters sensitive to lithology and fluid type are determined; the elastic parameters include longitudinal wave velocity Vp, shear wave velocity Vs, longitudinal wave impedance Pimp, shear wave impedance Simp, acoustic wave resistance AI, shear stiffness Si, Poisson's ratio Pr, longitudinal and shear wave velocity ratio Vp / Vs, bulk elastic modulus λρ, shear elastic modulus uρ, bulk elastic coefficient K, and shear modulus G; S3, Prestack Deterministic Inversion: Based on the rock physics modeling and the data processed by S1, prestack simultaneous inversion is performed to solve various elastic parameters; based on the inversion results, the quantitative cutoff values ​​of various elastic parameters are interpreted according to the rock physics interpretation volume to directly identify the reservoir structure and possible fluid interfaces; S4, quantitative description of reservoirs with thickness greater than or equal to 10 m according to S3 interpretation, and S5 for reservoirs with thickness less than 10 m; S5, pre-stack high-resolution statistical inversion: Use the data processed by S1 to perform Markov chain Monte Carlo algorithm inversion on various elastic parameters; according to the constrained sparse pulse inversion workflow, integrate and monitor the quality of the data processed by S1, and use the constrained sparse pulse inversion as the prior distribution of the Markov chain Monte Carlo algorithm inversion. Based on the Markov chain Monte Carlo algorithm inversion results, combined with S3 interpretation, quantitatively describe the reservoir with a thickness of less than 10m.

2. The thin reservoir prediction method based on pre-stack spectrum processing according to claim 1 is characterized in that: Before S5 inversion, it is necessary to perform discrete attribute lithofacies simulation tests on the lithofacies data in the seismic data through SIS sequence indicator simulation, and perform continuous attribute simulation tests on the elastic parameter data in the seismic data through SGS sequence Gaussian simulation. The simulation test results are spatially distributed and quality controlled based on geological information to improve the spatial definition accuracy of statistical inversion parameters.

3. The thin reservoir prediction method based on pre-stack spectrum processing according to claim 1 is characterized in that: The seismic physical exploration data in S1 include calibrated logging data, synthetic shear wave data, stratigraphic data of the target layer and seismic data, wherein the seismic data are full stack seismic data and common angle stack seismic data or full stack seismic data and common offset stack seismic data.

4. The thin reservoir prediction method based on pre-stack spectrum processing according to claim 3 is characterized by: Before the compressed sensing algorithm is used for spectrum expansion, the seismic data, well logging data and stratigraphic data need to be integrated and quality-monitored according to the constrained sparse pulse inversion workflow.

5. The thin reservoir prediction method based on pre-stack spectrum processing according to claim 3 is characterized by: Pre-stack simultaneous inversion requires inverting the common-angle stacked seismic data or the common-offset stacked seismic data based on the Zoppritz equation and its approximate formula within a multi-angle range to generate P- and S-wave impedance data volumes; based on the P- and S-wave impedance data volumes, Vp, Vs, Pimp, Simp, AI, Si, Pr, Vp / Vs, λρ, uρ, K, and G are obtained.

6. The thin reservoir prediction method based on pre-stack frequency spreading processing according to claim 3 is characterized by: Before establishing the rock physics model, the logging data needs to be quality controlled through single well curve quality control, environmental correction and multi-well consistency processing.

7. A thin reservoir prediction method based on pre-stack spectrum processing according to claim 6, characterized in that: After the quality control of logging data is completed, it is necessary to evaluate the reservoir parameters through the reservoir parameter interpretation model, calculate the content of each mineral, bring it into the optimization interpretation model, perform optimization solution, and provide high-precision lithology and fluid characteristics for the rock physics model.

8. The thin reservoir prediction method based on pre-stack spectrum processing according to claim 1 is characterized by: For Carboniferous reservoirs, before S1 frequency extension processing of seismic physical exploration data, the Xu and White model should be used to predict shear waves and fit multi-well shear waves.

9. A thin reservoir prediction method based on pre-stack spectrum processing according to claim 8, characterized in that: In S2, the elastic parameter sensitive to sand and shale properties is Vp / Vs, and the elastic parameter sensitive to fluid type is Pimp.

10. A thin reservoir prediction method based on pre-stack spectrum processing according to claim 9, characterized in that: The Vp / Vs of sandstone is less than 1.75; for reservoirs with a porosity greater than 8%, the Pimp is less than 1.05*10 7 ; The Pimp of the oil and gas layer is 8.8*10 6 <Pimp<1.1*10 7 , Vp / Vs of oil and gas layer is 1.5 <Vp / Vs<1.75。

Images