Quantitative description method and device for thick sand body thin dessert based on seismic forward iterative driving, medium and equipment

Abstract

The invention discloses a thick sand body thin dessert quantitative description method based on seismic forward iteration driving. The method comprises the steps of determining single well lithofacies division, a reservoir type and a dessert reservoir type; constructing a lithofacies and dessert seismic inversion attribute probability body; constructing a three-dimensional lithofacies model by taking the lithofacies probability body as a trend constraint, and constructing a double-constraint dessert body three-dimensional model by taking the model as phase control and the dessert probability body as a constraint; a wave impedance body is constructed according to a reservoir type statistical wave impedance mean value, a near-angle superposition seismic body is selected as a test seismic body, and seismic wavelets are preferably selected; taking the wave impedance body, the test seismic body and the seismic wavelets as input, generating a forward modeling seismic data body through convolution operation, and calculating a three-dimensional correlation coefficient between the forward modeling seismic data body and the original seismic body; and when the three-dimensional correlation coefficient is greater than a set threshold value, outputting a double-constraint dessert body three-dimensional model, otherwise, correcting geologic model parameters, updating the model, carrying out iterative calculation again until conditions are met, and finally depicting thin dessert distribution in the thick sand body according to the conditions.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas reservoir prediction technology, and more specifically, it relates to a method, apparatus, medium and equipment for quantitative characterization of thin sweet spots in thick sand bodies based on seismic forward modeling iteration. Background Technology

[0002] In the field of oil and gas exploration and development, thick sandstone reservoirs (typically referring to those with a thickness greater than 10 meters) are widely distributed in various basins and are important exploration targets. These reservoirs exhibit strong heterogeneity, and their core production capacity is mainly contributed by several thin layers (typically less than 10 meters thick) of "sweet spots" within the reservoir. These sweet spots have significantly higher porosity, permeability, or hydrocarbon saturation than other reservoirs, making them key targets for efficient development. Therefore, accurately identifying and locating these thin sweet spots within thick sandstone bodies is crucial for optimizing horizontal well trajectory design, increasing single-well production, reducing development risks, and improving economic efficiency.

[0003] However, existing technologies face several major bottlenecks in achieving reliable prediction of thin-layer sweet spots within thick sand bodies: ① Insufficient resolution for thin-layer recognition Currently widely used conventional seismic inversion techniques, such as post-stack impedance inversion, are limited by the inherent physical characteristics of seismic waves (dominant frequency, bandwidth) and noise levels, with a theoretical vertical resolution limit typically around 10-15 meters. This resolution scale is much larger than the typical thickness of the target sweet spot layer (less than 10 meters). Therefore, within thick sand bodies, seismic inversion results often exhibit relatively uniform "blocky" response characteristics, failing to effectively distinguish and characterize the physical property differences between these sweet spots and non-sweet spots. Sweet spot layers are essentially "indistinguishable" in seismic data and their inversion results, with their fine structural information severely blurred or completely obscured.

[0004] ② Inter-well prediction blind zone In the early stages of exploration and development, or in areas with sparse well networks, the well point data (logging, core samples) available for constraining reservoir modeling are extremely limited, with well spacing often reaching hundreds or even thousands of meters. Existing mainstream geostatistical modeling methods, such as Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS), face significant challenges in spatially predicting thin sweet spots within thick sand bodies under such sparse well control conditions. The core problem lies in the weakening of variogram constraints and the dominance of stochasticity. Due to the lack of sufficient close-well-spacing data points, it is difficult to accurately obtain characteristics of the spatial distribution patterns of these thin sweet spot layers (such as directionality and continuity), resulting in highly unreliable predictions that are difficult to effectively guide development decisions.

[0005] ③ Lack of model validation mechanism A common drawback in existing technologies is the lack of a rigorous, closed-loop model validation mechanism based on real seismic data. While existing methods may incorporate seismic attributes or geostatistical methods to predict the sweet spot, the resulting 3D geological model of the sweet spot often fails to undergo forward modeling to generate synthetic seismic records and quantitative, channel-by-channel verification with actual acquired seismic data. This "open-loop" prediction model leads to questionable model reliability and prominent issues of multiple solutions. Without this validation step, it is impossible to effectively select the optimal model or assess the range of uncertainty in the prediction results.

[0006] In summary, the current field of predicting thin sweet spots in thick sandstone bodies is mired in a triple technical dilemma: "unclear visibility" (insufficient seismic resolution), "inaccurate calculation" (high randomness in modeling under sparse well control), and "unverifiable" (lack of seismically driven closed-loop verification). These bottlenecks severely restrict the accurate prediction of sweet spots and cannot meet the demand for reliable and high-precision prediction of thin sweet spots in the efficient development of oil and gas fields. Therefore, there is an urgent need to develop an innovative technical method that can overcome the indirect limitations of existing seismic resolution, effectively reduce prediction uncertainty under sparse well control conditions, and introduce a seismically driven closed-loop verification mechanism. Ultimately, this will enable reliable and high-precision prediction of thin sweet spots within thick sandstone bodies, providing solid support for the efficient development of oil and gas fields. Summary of the Invention

[0007] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention provides a quantitative characterization method for thin sweet spots in thick sand bodies based on seismic forward modeling iteration, aiming to solve the problems of insufficient accuracy of geophysical techniques and difficulty in controlling inter-well properties with large well spacing.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for quantitative characterization of thin sweet spots in thick sand bodies based on seismic forward modeling iteration, comprising the following steps: Based on the analysis of reservoir properties and grain size of single wells in the study area, the lithofacies classification, reservoir type and sweet spot reservoir type of single wells were determined; By analyzing the correlation between seismic inversion attributes of well-side passages and reservoir lithology and physical properties in the study area, a probabilistic volume of lithofacies seismic inversion attributes with high correlation and a probabilistic volume of sweet spot seismic inversion attributes were constructed. A three-dimensional lithofacies model is constructed using the probabilistic volume of lithofacies seismic inversion attributes as a trend constraint; A dual-constraint sweet spot volume 3D model is constructed using a 3D lithofacies model as facies control and a sweet spot seismic inversion attribute probability volume as constraints. Based on the determined reservoir type, the average wave impedance of each type of reservoir is statistically analyzed to construct a wave impedance volume; Based on the actual conditions of the study area, near-angle stacked seismic bodies were selected as test seismic bodies, and seismic wavelets containing seismic attributes were selected by analyzing the seismic composite records of well-side tunnels in the study area. The wave impedance volume, the test seismic volume, and the seismic wavelet are used as inputs, and a forward modeling simulation seismic data volume is generated through convolution operation. Calculate the three-dimensional correlation coefficient between the forward-modeled seismic body and the original seismic body; When the three-dimensional correlation coefficient is greater than the set threshold, the three-dimensional model of the double-constrained sweet spot body is output. Otherwise, the parameters are corrected and adjusted based on the geological model to update the three-dimensional model of the double-constrained sweet spot body. The three-dimensional correlation coefficient is calculated again in an iterative loop until it is greater than the set threshold. The distribution of the thin sweet spot in the actual thick sand body is characterized according to the distribution of the thin sweet spot in the final iteration.

[0009] Preferably, the sweet spot reservoir type is classified based on the physical properties of core data and sandstone grain size analysis. Thick sandstone bodies are classified into four types of reservoirs according to porosity, permeability, and sandstone grain size: Type I has a grain size greater than 60% and a porosity greater than 10% and a permeability greater than 10 mD; Type II has a grain size greater than 60% and a porosity greater than 10% and a permeability greater than 5 mD; Type III has a grain size less than 60% and a porosity greater than 10% and a permeability less than 5 mD; Type IV has a grain size less than 60% and a porosity less than 10% and a permeability less than 5 mD. Sweet spot reservoirs are classified into Type I and Type II reservoirs.

[0010] Preferably, the sandstone grain size is the mass percentage of coarse sand grains in the core test sample. Preferably, the three-dimensional lithofacies model and the dual-constraint sweet spot three-dimensional model are constructed using sequential instruction simulation with the help of Petrel software.

[0011] Preferably, the three-dimensional correlation coefficient is calculated using the following formula:

[0012] In the formula, R c The three-dimensional correlation coefficient; f i The amplitude of the original seismic body; g i This represents the amplitude of the forward-modeled seismic body.

[0013] Preferably: the three-dimensional correlation coefficient R c The set threshold is 0.7.

[0014] Secondly, the present invention provides a quantitative characterization device for thin sweet spots in thick sand bodies based on seismic forward modeling iteration, comprising: The first processing unit is used to determine the lithofacies classification, reservoir type, and sweet spot reservoir type of a single well based on the analysis of reservoir properties and grain size of a single well in the study area. The second processing unit is used to construct a probabilistic volume of lithofacies seismic inversion attributes and a probabilistic volume of sweet spot seismic inversion attributes by analyzing the correlation between the seismic inversion attributes of the well-side passages in the study area and the reservoir lithology and physical properties. The third processing unit is used to construct a three-dimensional lithofacies model with the lithofacies seismic inversion attribute probability volume as a trend constraint; The fourth processing unit is used to construct a dual-constraint sweet spot volume 3D model, with the 3D lithofacies model as the facies control and the sweet spot seismic inversion attribute probability volume as the constraint. The fifth processing unit is used to construct a wave impedance volume by statistically analyzing the average wave impedance of each type of reservoir according to the determined reservoir type. The sixth processing unit is used to select near-angle superimposed seismic bodies as test seismic bodies based on the actual conditions of the study area, and to select seismic wavelets containing seismic attributes by analyzing the seismic synthetic records of well-side tunnels in the study area. The seventh processing unit is used to take the wave impedance volume, the test seismic volume, and the seismic wavelet as inputs, and generate forward modeling simulation seismic data volume through convolution operation; The eighth processing unit is used to calculate the three-dimensional correlation coefficient between the forward modeled seismic body and the original seismic body; The ninth processing unit is used to output the 3D model of the double-constrained sweet spot body when the 3D correlation coefficient is greater than the set threshold. Otherwise, it corrects and adjusts the parameters based on the geological model to update the 3D model of the double-constrained sweet spot body, and iterates again to calculate the 3D correlation coefficient until it is greater than the set threshold. The distribution of thin sweet spots in the actual thick sand body is characterized according to the final iteration of the thin sweet spot distribution.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which is executed by a processor to control the device where the processor is located to implement the steps of the method for quantitative characterization of thick sand body thin dessert as described in the first aspect of the present invention.

[0016] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for quantitative characterization of thick sand body thin sweet spot as described in another aspect of the present invention.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: Based on the spatial prediction of sweet spots constrained by seismic inversion, this invention corrects the spatial distribution of thin sweet spots in thick sand bodies through seismic forward modeling iteration, further improving the reliability and accuracy of sweet spot prediction and providing a basis for exploration and development deployment. It is particularly suitable for solving the problems of identifying thin sweet spots (thickness less than 10m) in low-permeability thick sand bodies and predicting spatial sweet spots in areas with large well spacing (well spacing greater than 800m). Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a flowchart of the method for quantitative characterization of thin sweet spots in a thick sand body according to an embodiment of the present invention; Figure 2 This is a reservoir partitioning cross-sectional view in an embodiment of the present invention; Figure 3 This is a volumetric profile of the probabilistic attributes of lithofacies seismic inversion in an embodiment of the present invention; Figure 4 This is a cross-sectional view of the probability volume of the seismic inversion attributes of dessert in an embodiment of the present invention; Figure 5 This is a box plot of various reservoir wave impedance distributions based on well logging calculations in an embodiment of the present invention; Figure 6 This is a wave impedance volume diagram based on reservoir partitioning in an embodiment of the present invention; Figure 7 These are the original earthquakes, forward modeling results, and comparative cross-sectional views of the correlation between the original earthquakes and the forward modeled earthquakes in this embodiment of the invention. Figure 8 This is a cross-sectional view comparing the thin dessert distribution after forward iterative optimization with the initial dessert distribution in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] The present invention provides a method for quantitative characterization of thin sweet spots in thick sandstone bodies, comprising: analyzing the reservoir properties and grain size of a single well to determine the lithofacies classification, reservoir type, and sweet spot reservoir type; constructing a probabilistic volume of lithofacies and sweet spot seismic inversion attributes; constructing a three-dimensional lithofacies model with the lithofacies probabilistic volume as a trend constraint, and constructing a dual-constraint sweet spot volume three-dimensional model with the lithofacies probabilistic volume as a constraint; constructing a wave impedance volume based on the statistical mean wave impedance value of the reservoir type, selecting a near-angle superimposed seismic volume as the test seismic volume, and selecting a seismic wavelet; using the wave impedance volume, the test seismic volume, and the seismic wavelet as inputs, generating a forward modeling simulated seismic data volume through convolution operation, and calculating its three-dimensional correlation coefficient with the original seismic volume; outputting the dual-constraint sweet spot volume three-dimensional model when the three-dimensional correlation coefficient is greater than a set threshold, otherwise correcting the geological model parameters, updating the model, and iterating again until the conditions are met, and finally characterizing the distribution of thin sweet spots in thick sandstone bodies accordingly.

[0021] The following is a detailed description of the method and apparatus for quantitative characterization of thin sweet spots in thick sand bodies based on seismic forward modeling and iterative driving, provided by embodiments of the present invention, with reference to the accompanying drawings.

[0022] Example 1: Please see Figure 1 This embodiment provides a quantitative characterization method for thin sweet spots in thick sand bodies based on seismic forward modeling iteration, which includes the following steps: S100. Based on the analysis of reservoir properties and grain size of single wells in the study area, the lithofacies classification, reservoir type and sweet spot reservoir type of single wells are determined.

[0023] In this embodiment, the classification of sweet spot reservoirs mainly relies on the physical properties (porosity and permeability) analyzed from core data and sandstone grain size analysis. Based on porosity, permeability, and sandstone grain size, thick sand bodies are classified into four types of reservoirs: Type I: grain size greater than 60%, porosity greater than 10%, permeability greater than 10 mD; Type II: grain size greater than 60%, porosity greater than 10%, permeability greater than 5 mD; Type III: grain size less than 60%, porosity greater than 10%, permeability less than 5 mD; Type IV: grain size less than 60%, porosity less than 10%, permeability less than 5 mD. Sweet spot reservoirs belong to Types I and II (see [link to relevant documentation]). Figure 2 ). Among them, sandstone grain size is the mass percentage of coarse sand grains in the core test sample points.

[0024] S200. By analyzing the correlation between seismic inversion attributes from well access points in the study area and reservoir lithology and physical properties, a probabilistic volume of highly correlated lithofacies seismic inversion attributes was constructed (see [link]). Figure 3 ) and the probability volume of the dessert earthquake inversion properties (see Figure 4 ).

[0025] S300. Using the probabilistic volume of lithofacies seismic inversion attributes as a trend constraint, a three-dimensional lithofacies model is established using sequential indicator simulation with the help of Petrel software.

[0026] S400. Using a three-dimensional lithofacies model as the facies control and a sweet spot seismic inversion attribute probability volume as the constraint, a dual-constraint sweet spot volume three-dimensional model is established using Petrel software with sequential indicator simulation.

[0027] S500. Based on the reservoir types in step S100, construct a wave impedance volume by calculating the average wave impedance for each type of reservoir. Please refer to [link to relevant documentation]. Figure 5 , Figure 6 The acoustic impedance of the reservoir was quantitatively assigned according to the reservoir type, and the parameter values ​​were taken as the average value of the statistical results of a single well. In this work area, the acoustic impedance of mudstone was set to 8023 (g / cm). 3 • (m / s), the wave impedance of the four types of reservoirs is 9316 (g / cm) 3 The impedance of the three types of reservoirs is 12061 g / cm³ (m / s). 3 The impedance of the Class II reservoir is 12587 g / cm² (m / s). 3 • (m / s), the wave impedance of a Class I reservoir is 12795 (g / cm²). 3 )•(m / s)

[0028] S600. Based on the actual conditions of the study area, near-angle superimposed seismic bodies were selected as test seismic bodies, and seismic wavelets containing seismic attributes were selected through analysis of seismic composite records from well-side tunnels in the study area.

[0029] S700. Using the wave impedance volume, the test seismic volume, and the seismic wavelet as input, it generates a forward-modeled seismic data volume through convolution operations (see [link to relevant documentation]). Figure 7 ); S800. Calculate the three-dimensional correlation coefficient between the forward-modeled seismic body and the original seismic body using the following formula. R c :

[0030] In the formula, f i The amplitude of the original seismic body; g i This represents the amplitude of the forward-modeled seismic body.

[0031] S900. When R c If the value is ≥0.7, output the 3D model of the double-constrained sweet spot volume; otherwise, adjust the parameters based on the geological model (see Table 1), return to step S400 to adjust the sequential indicator simulation parameters (including the primary and secondary ranges of the lithofacies variation function and the provenance direction), update the 3D model of the double-constrained sweet spot volume, and repeat steps S400-S800 until...R c The iteration cycle ends at ≥0.7. The distribution of thin sweet spots in the actual thick sand body is characterized according to the distribution of thin sweet spots in the final iteration (see [link]). Figure 8 ).

[0032] Table 1. Geological Model Correction and Adjustment Parameters

[0033] Example 2: The above-described embodiment 1 provides a method for quantitative characterization of thin sweet spots in thick sand bodies based on seismic forward modeling iteration. Correspondingly, this embodiment provides a device for quantitative characterization of thin sweet spots in thick sand bodies based on seismic forward modeling iteration. The device for quantitative characterization of thin sweet spots in thick sand bodies provided in this embodiment can implement the method for quantitative characterization of thin sweet spots in thick sand bodies in embodiment 1. This device can be implemented through software, hardware, or a combination of both. For example, the device can include integrated or separate functional modules or units to perform the corresponding steps in the methods of embodiment 1. Since the formation thickness fine characterization device in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. For relevant details, please refer to the description of embodiment 1. The device for quantitative characterization of thin sweet spots in thick sand bodies in this embodiment is merely illustrative.

[0034] The thick sand body thin dessert quantitative characterization device provided in this embodiment includes: The first processing unit is used to determine the lithofacies classification, reservoir type, and sweet spot reservoir type of a single well based on the analysis of reservoir properties and grain size of a single well in the study area. The second processing unit is used to construct a probabilistic volume of lithofacies seismic inversion attributes and a probabilistic volume of sweet spot seismic inversion attributes by analyzing the correlation between the seismic inversion attributes of the well-side passages in the study area and the reservoir lithology and physical properties. The third processing unit is used to construct a three-dimensional lithofacies model with the lithofacies seismic inversion attribute probability volume as a trend constraint; The fourth processing unit is used to construct a dual-constraint sweet spot volume 3D model, with the 3D lithofacies model as the facies control and the sweet spot seismic inversion attribute probability volume as the constraint. The fifth processing unit is used to construct a wave impedance volume by statistically analyzing the average wave impedance of each type of reservoir according to the determined reservoir type. The sixth processing unit is used to select near-angle superimposed seismic bodies as test seismic bodies based on the actual conditions of the study area, and to select seismic wavelets containing seismic attributes by analyzing the seismic synthetic records of well-side tunnels in the study area. The seventh processing unit is used to take the wave impedance volume, the test seismic volume, and the seismic wavelet as inputs, and generate forward modeling simulation seismic data volume through convolution operation; The eighth processing unit is used to calculate the three-dimensional correlation coefficient between the forward modeled seismic body and the original seismic body; The ninth processing unit is used to output the 3D model of the double-constrained sweet spot body when the 3D correlation coefficient is greater than the set threshold. Otherwise, it corrects and adjusts the parameters based on the geological model to update the 3D model of the double-constrained sweet spot body, and iterates again to calculate the 3D correlation coefficient until it is greater than the set threshold. The distribution of thin sweet spots in the actual thick sand body is characterized according to the final iteration of the thin sweet spot distribution.

[0035] Example 3: This embodiment provides a processing device for implementing the quantitative characterization method for thin sweet spots in thick sand bodies based on seismic forward modeling driven by embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of embodiment 1.

[0036] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the quantitative characterization method for thick sand bodies and thin sweet spots provided in Embodiment 1.

[0037] Preferably, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0038] Preferably, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation herein.

[0039] Example 4: The quantitative characterization method for thin sweet spots in thick sand bodies based on seismic forward modeling iteration in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the method described in Embodiment 1 are loaded.

[0040] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for quantitatively characterizing thick sand bodies and thin sweet spots based on iterative driving of seismic forward modeling, characterized in that, The method comprises the following steps: Based on the single well reservoir property and particle size analysis of the study area, the single well lithofacies division, reservoir type and sweet spot reservoir type are determined; Through the correlation analysis of the seismic inversion attribute of the well trace in the study area and the reservoir lithology and property, the high-correlation lithofacies seismic inversion attribute probability body and sweet spot seismic inversion attribute probability body are constructed; The three-dimensional lithofacies model is constructed by taking the lithofacies seismic inversion attribute probability body as the trend constraint; The double-constrained sweet spot body three-dimensional model is constructed by taking the three-dimensional lithofacies model as the phase control and the sweet spot seismic inversion attribute probability body as the constraint; The wave impedance body is constructed by calculating the average wave impedance of each type of reservoir according to the determined reservoir type; According to the actual situation of the study area, the near-angle stack seismic body is selected as the test seismic body, and the seismic wavelet containing the seismic attribute is optimized through the analysis of the seismic synthetic record of the well trace in the study area; The wave impedance body, the test seismic body and the seismic wavelet are taken as inputs to generate the forward simulation seismic data body through convolution operation; The three-dimensional correlation coefficient of the forward seismic body and the original seismic body is calculated; When the three-dimensional correlation coefficient is greater than the set threshold, the double-constrained sweet spot body three-dimensional model is output, otherwise the parameters are corrected and adjusted based on the geological model to update the double-constrained sweet spot body three-dimensional model, and the three-dimensional correlation coefficient is calculated again until it is greater than the set threshold, and the final iteration thin sweet spot distribution is used to depict the distribution of the thin sweet spot in the actual thick sand body.

2. The thick sand body thin cookie quantitative characterization method of claim 1, wherein, The division of the sweet spot reservoir type is based on the physical property parameters and sandstone particle size analysis of the core data, and the thick sand body is divided into four types of reservoirs according to porosity, permeability and sandstone particle size: the first type is greater than 60% in particle size, greater than 10% in porosity and greater than 10 mD in permeability; the second type is greater than 60% in particle size, greater than 10% in porosity and greater than 5 mD in permeability; the third type is less than 60% in particle size, greater than 10% in porosity and less than 5 mD in permeability; the fourth type is less than 60% in particle size, less than 10% in porosity and less than 5 mD in permeability; the sweet spot reservoir is the first and second type of reservoir.

3. The thick sand body thin cookie quantification method of claim 2, wherein, The sandstone particle size is the mass proportion of medium-coarse sand particle size in the core test sample point.

4. The thick sand body thin cookie quantitative characterization method of claim 1, wherein, The three-dimensional lithofacies model and the double-constrained sweet spot body three-dimensional model are constructed by means of Petrel software using sequential indicator simulation.

5. The thick sand body thin cookie quantitative characterization method of claim 1, wherein, The three-dimensional correlation coefficient is calculated by the following formula: wherein R c is a three-dimensional correlation coefficient; f i is an original seismic volume amplitude; g i is a forward seismic volume amplitude.

6. The thick sand body thin cookie quantification method of claim 5, wherein, The three-dimensional correlation coefficient R c The set threshold value is 0.

7.

7. A device for quantitatively characterizing thick-sand thin-sweet-spot based on iterative driving of seismic forward, characterized in that, It comprises: The first processing unit is used to determine the single well lithofacies division, reservoir type and sweet spot reservoir type based on the single well reservoir property and particle size analysis of the study area; The second processing unit is used to construct the high-correlation lithofacies seismic inversion attribute probability body and sweet spot seismic inversion attribute probability body through the correlation analysis of the seismic inversion attribute of the well trace in the study area and the reservoir lithology and property; The third processing unit is used to construct the three-dimensional lithofacies model by taking the lithofacies seismic inversion attribute probability body as the trend constraint; The fourth processing unit is used to construct the double-constrained sweet spot body three-dimensional model by taking the three-dimensional lithofacies model as the phase control and the sweet spot seismic inversion attribute probability body as the constraint; The fifth processing unit is used to construct the wave impedance body by calculating the average wave impedance of each type of reservoir according to the determined reservoir type; The sixth processing unit is configured to select a near-angle stacked seismic volume as a test seismic volume according to the actual situation of the research area, and to select a seismic wavelet containing seismic attributes by analyzing a seismic synthetic record of a well trace in the research area; The seventh processing unit is configured to generate forward simulation seismic data volume by convolution operation with the wave impedance volume, the test seismic volume and the seismic wavelet as inputs; The eighth processing unit is configured to calculate a three-dimensional correlation coefficient of the forward seismic volume and the original seismic volume; The ninth processing unit is configured to output a double-constrained sweet spot three-dimensional model when the three-dimensional correlation coefficient is greater than a set threshold, otherwise to correct and adjust parameters based on the geological model to update the double-constrained sweet spot three-dimensional model, and to iteratively calculate the three-dimensional correlation coefficient until it is greater than the set threshold, and to depict the distribution of thin sweet spots in the thick sand body according to the final iteration thin sweet spot distribution.

8. A computer-readable storage medium, characterized in that, A computer program is stored, and the computer program is executed by a processor to control a device where the processor is located to implement the steps of the thick sand body thin sweet spot quantitative depiction method according to any one of claims 1 to 6.

9. A computer device, comprising: A device includes a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the thick sand body thin sweet spot quantitative depiction method according to any one of claims 1 to 6 when executing the computer program.

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