Uranium-bearing reservoir spatio-temporal distribution rule analysis method based on multistage sedimentary facies characteristics

Abstract

The invention discloses a uranium-bearing reservoir spatio-temporal distribution rule analysis method based on multistage sedimentary facies characteristics, which comprises the following steps: acquiring logging data of a certain area, judging the lithology of the area according to the logging data, and determining the sedimentary facies of the area according to the lithology; dividing sedimentary microfacies according to rock properties; calculating the physical property of the sedimentary area by using a capillary model; and analyzing the spatio-temporal distribution rule of the uranium-bearing reservoir in combination with physical properties. According to the method, the structure and stratum data of three-dimensional geological modeling are fused with the sedimentary facies-physical property model, a geology-physical property-mineralization three-in-one analysis system is formed, the credibility of the uranium mineralization prediction result is remarkably improved, and the method can be popularized to sandstone type uranium ore exploration of different sedimentary backgrounds, is especially suitable for medium and low permeability reservoirs, and has good application prospects. And a standardized process is provided for uranium resource evaluation under complex geological conditions.

Description

Technical Field

[0001] This invention relates to the field of spatiotemporal distribution patterns of uranium-bearing reservoirs, specifically a method for analyzing the spatiotemporal distribution patterns of uranium-bearing reservoirs based on multi-level sedimentary facies characteristics. Background Technology

[0002] Sandstone-type uranium deposits are an important type of uranium resource in my country. Their mineralization is closely related to the sedimentary environment. Traditional methods for analyzing uranium-bearing reservoirs often rely on single well logging data or macroscopic geological models, which lack systematicity and cannot meet the current demands for high-precision exploration. Specifically, the main limitations of existing technologies are reflected in the following aspects: 1. Insufficient accuracy in lithological identification and sedimentary facies classification (1) The existing technology usually relies on only a few logging parameters such as natural gamma (GR) for lithological identification, and lacks comprehensive analysis of sonic transit time (AC), resistivity (RT) and density (DEN) data. For example, the natural gamma value of 50-80 API is generally simply classified as siltstone, while ignoring the possible local high value anomalies caused by uranium mineralization, resulting in the omission of favorable target layers.

[0003] (2) The traditional method of sedimentary facies analysis is too coarse and often stops at the macroscopic sedimentary facies or subfacies division (such as "fluvial facies" and "deltaic facies"), and fails to go deep into the sedimentary microfacies level (such as "channel microfacies" and "estuary bar microfacies") which are more controlling to uranium mineralization. This coarse-scale analysis cannot accurately reveal the migration and enrichment patterns of uranium elements in microfacies units such as "channel-natural levee-breach fan", which makes the prediction results not very directional.

[0004] (3) The "geology-physical property¹mineralization" chain is broken. A major drawback of the current method is the serious data silo phenomenon. There is a lack of effective integration mechanism between well logging data, core physical property analysis data and three-dimensional geological modeling data. The geological model describes the structure and stratigraphic framework, the physical property model reflects the permeability and storage capacity of the reservoir, and uranium mineralization is the manifestation of the final mineralization effect. There is a lack of systematic bridge between these three, and a coherent and quantifiable analytical system has not been formed.

[0005] 2. Limitations exist in reservoir property characterization and quantitative models. (1) Simplification of physical property characterization methods. For reservoir physical properties, especially the key parameters controlling uranium solution migration—permeability (K) and porosity (K) Obtaining core samples has traditionally relied heavily on limited core experiments, resulting in high costs and limited representativeness. In terms of theoretical calculations, commonly used empirical formulas such as the Kozeny-Carman model are overly simplistic and cannot accurately characterize the complex pore structure composed of pores and throats in low-to-medium permeability reservoirs.

[0006] (2) Lack of physical property-mineralization correlation model: Existing uranium ore prediction models rarely establish a correlation between permeability (K) and porosity (K). Without a quantitative regression model between physical properties such as uranium mineralization degree (U) and uranium mineralization degree, it is difficult to explain "why uranium is enriched in this area" from a mechanistic perspective. Predictions are mostly based on qualitative or semi-quantitative experience, which lacks scientific basis and results in a low success rate of predictions in new or complex areas.

[0007] 3. Low integration between spatiotemporal distribution prediction and 3D geological modeling (1) Model disconnection: Three-dimensional geological modeling technology can better show the structure and spatial morphology of strata, but the assignment of internal attributes (such as permeability field) is often not strongly related to the sedimentary facies analysis results, forming a "good-looking skeleton" but lacking "accurate flesh and blood". The geological model and the physical property model are in a state of "two skins".

[0008] (2) Lack of standardized process for uranium ore prediction under different sedimentary backgrounds. A set of standardized technical processes that can be promoted and reused has not yet been formed, resulting in poor applicability of the methodology in different basins and making it difficult to compare and promote the application of research results.

[0009] Therefore, a method for analyzing the spatiotemporal distribution of uranium-bearing reservoirs based on the characteristics of multi-level sedimentary facies is proposed to address the above problems. Summary of the Invention

[0010] The purpose of this invention is to provide a method for analyzing the spatiotemporal distribution patterns of uranium-bearing reservoirs based on multi-level sedimentary facies characteristics, the steps of which include: Step 1: Collect well logging data for a certain area, determine the lithology of the area based on the well logging data, and determine the sedimentary facies of the area based on the lithology; Step 11: Collect natural gamma, sonic transit time, resistivity, and density data from the well logging data; Step 12: Determine the lithology of the area based on different natural gamma, sonic transit time, resistivity, and density data values; Step 13: The sandstone has a natural gamma < 50 API, a sonic transit time between 180-220 μs / m, a resistivity between 10-1000 Ω·m, and a density of 2.65 g / cm³. 3 The natural gamma of siltstone is between 50 and 100 API, the sonic transit time is between 220 and 260 μs / m, the resistivity is between 1 and 10 Ω·m, and the density is 2.39 to 2.51 g / cm³. 3 The mudstone has a natural gamma ray >100 API, a sonic transit time >260 μs / m, a resistivity between 0.1 and 100, and a density of 2.39–2.51 g / cm³. 3 ; Step 14: Uranium-bearing reservoirs comprise four sedimentary facies: alluvial, fluvial floodplain, deltaic, and lacustrine. Among them, the uranium-bearing reservoir is dominated by sandstone containing carbonate minerals, with massive or graded bedding. The fluvial floodplain facies consists of medium- to coarse sandstone interbedded with mudstone, exhibiting parallel or cross-bedding. The deltaic front subfacies is dominated by interbedded siltstone and mudstone, with sand-like bedding. The lacustrine facies consists of interbedded sandstone and mudstone in shallow lacustrines and fine-grained mudstone in deep lacustrines, containing bioturbation structures. Step 2: Classify sedimentary microfacies based on rock properties; Step 21: If determined to be an alluvial facies, then the alluvial facies is subdivided into three parts: fan root subfacies, fan mid-fan subfacies, and fan edge subfacies. Fan-root subfacies: dominated by conglomerate, massive structure, and extremely poorly sorted; Fan-shaped subfacies: interbedded conglomerate and sandstone with cross-bedding; Fan-edge subfacies: siltstone and mudstone, mainly with horizontal bedding; Step 22: If determined to be a river floodplain facies, it can be divided into three parts: channel microfacies, natural levee microfacies, and breach fan microfacies, among which: Channel microfacies: medium to coarse sandstone, large cross-bedding, bottom scour surface; Natural levee microfacies: thin interbedded layers of siltstone and mudstone, with wavy bedding; Crack spur microfacies: fine sandstone, graded grain, small cross-bedding; Step 23: If it is determined to be a deltaic facies, it can be divided into three parts: deltaic plain subfacies, delta front subfacies, and predeltaic subfacies, among which: Delta plain subfacies: tributary channels are composed of medium to coarse sandstone, and swamps are composed of mudstone; Delta front subfacies: underwater tributary channels are composed of medium to fine sandstone, and the deltaic bar deposits have uniform particle size with little variation in coarseness. Predeltaic subfacies: dominated by mudstone, with horizontal bedding; Step 24: If it is determined to be a lacustrine facies, it can be divided into two parts: a shallow lacustrine subfacies and a deep lacustrine subfacies, among which: Lacustrine subfacies: alternating layers of sandstone and mudstone, with ripple marks; Deep lacustrine subfacies: dominated by mudstone, with horizontal bedding and containing fossils; Step 3: Calculate the physical properties of the sedimentary region using a capillary model; Step 31: Simulate the actual uranium-bearing reservoir using a tandem capillary model. Define the larger diameter portions as pores and the smaller diameter portions as throats. Assume all pores are the same size, and all throats have equal diameter and length. Pores and throats alternate periodically in the model. - Pore diameter, ; - Roar diameter, ; - Pore length, ; -Basic unit length, To establish the theoretical relationship between the microscopic parameters and macroscopic parameters porosity and permeability of the capillary model; Step 32: First, assuming the cross-sectional area of ​​the rock is A and the length of the rock is L, simulate the pore structure of the rock using parallel series capillary bundles of length L. The number of capillaries per unit area is... 1. Serial capillary tubes, each with M segments of length M. Given the basic unit tube, the relationship between the microscopic parameters of the capillary model and the macroscopic permeability K is as follows: ; in The number of tandem capillaries per unit area, in μm -2 ; Microscopic parameters and macroscopic porosity of capillary model The relationship between them is: ; Where M is the number of basic unit tubes, which is dimensionless; A is the cross-sectional area of ​​the rock. L is the length of the rock. .

[0011] Step 4: Analyze the spatiotemporal distribution patterns of uranium-bearing reservoirs by combining physical properties.

[0012] Step 41: Calculate the porosity and permeability of low-permeability reservoirs in this region over the years according to the formula, and integrate them with the structural and stratigraphic data from the three-dimensional geological model to establish a correlation model between uranium mineralization and sedimentary facies. Step 42: Establish a multiple linear regression model with permeability and porosity as independent variables and uranium mineralization degree as the dependent variable: ; Where U is the uranium mineralization index and K is the permeability. Porosity; Step 43: The final uranium mineralization index is the predicted result of the spatial distribution of uranium mineralization.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, through multi-level sedimentary facies division and combined with lithology-physical property coupling analysis, can accurately identify favorable facies zones of uranium-bearing reservoirs, clarify the spatiotemporal correspondence between uranium mineralization and sedimentary microfacies, provide geological basis for target area selection, and integrate the structural and stratigraphic data of three-dimensional geological modeling with sedimentary facies-physical property models to form a three-in-one analysis system of "geology-physical property-mineralization", which significantly improves the reliability of uranium mineralization prediction results. This method can be extended to the exploration of sandstone-type uranium deposits with different sedimentary backgrounds, and is especially suitable for medium and low permeability reservoirs, providing a standardized process for uranium resource evaluation under complex geological conditions. Attached Figure Description

[0014] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a lithology comparison diagram under different well logging data of the present invention; Figure 3 These are the logging data of wells 1-10 in Embodiment 1 of the present invention; Figure 4 These are the logging data from wells 11-20 in Embodiment 1 of this invention; Figure 5 These are the logging data from wells 21-30 in Embodiment 1 of this invention; Figure 6 These are the logging data from wells 31-40 in Embodiment 1 of this invention; Figure 7 These are the logging data for wells 41-50 in Embodiment 1 of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] As attached Figure 1 As shown, the present invention provides a method for analyzing the spatiotemporal distribution patterns of uranium-bearing reservoirs based on multi-level sedimentary facies characteristics, comprising the following steps: Step 1: Collect well logging data for a certain area, determine the lithology of the area based on the well logging data, and determine the sedimentary facies of the area based on the lithology; Step 11: Collect natural gamma, sonic transit time, resistivity, and density data from the well logging data; Step 12: Determine the lithology of the area based on different natural gamma, sonic transit time, resistivity, and density data values; Step 13: As attached Figure 2As shown, the natural gamma of the sandstone is <50 API, the sonic transit time is between 180-220 μs / m, the resistivity is between 10-1000 Ω·m, and the density is 2.65 g / cm³. 3 The natural gamma of siltstone is between 50 and 100 API, the sonic transit time is between 220 and 260 μs / m, the resistivity is between 1 and 10 Ω·m, and the density is 2.39 to 2.51 g / cm³. 3 The mudstone has a natural gamma >100 API, a sonic transit time >260 μs / m, a resistivity between 0.1 and 100, and a density of 2.39–2.51 g / cm³. 3 ; Step 14: Uranium-bearing reservoirs comprise four sedimentary facies: alluvial, fluvial floodplain, deltaic, and lacustrine. Among them, the uranium-bearing reservoir is dominated by sandstone containing carbonate minerals, with massive or graded bedding. The fluvial floodplain facies consists of medium- to coarse sandstone interbedded with mudstone, exhibiting parallel or cross-bedding. The deltaic front subfacies is dominated by interbedded siltstone and mudstone, with sand-like bedding. The lacustrine facies consists of interbedded sandstone and mudstone in shallow lacustrines and fine-grained mudstone in deep lacustrines, containing bioturbation structures. Step 2: Classify sedimentary microfacies based on rock properties; Step 21: If determined to be an alluvial facies, then the alluvial facies is subdivided into three parts: fan root subfacies, fan mid-fan subfacies, and fan edge subfacies. Fan-root subfacies: dominated by conglomerate, massive structure, and extremely poorly sorted; Fan-shaped subfacies: interbedded conglomerate and sandstone with cross-bedding; Fan-edge subfacies: siltstone and mudstone, mainly with horizontal bedding; Step 22: If determined to be a river floodplain facies, it can be divided into three parts: channel microfacies, natural levee microfacies, and breach fan microfacies, among which: Channel microfacies: medium to coarse sandstone, large cross-bedding, bottom scour surface; Natural levee microfacies: thin interbedded layers of siltstone and mudstone, with wavy bedding; Crack spur microfacies: fine sandstone, graded grain, small cross-bedding; Step 23: If it is determined to be a deltaic facies, it can be divided into three parts: deltaic plain subfacies, delta front subfacies, and predeltaic subfacies, among which: Delta plain subfacies: tributary channels are composed of medium to coarse sandstone, and swamps are composed of mudstone; Delta front subfacies: underwater tributary channels are composed of medium to fine sandstone, and the deltaic bar deposits have uniform particle size with little variation in coarseness. Predeltaic subfacies: dominated by mudstone, with horizontal bedding; Step 24: If it is determined to be a lacustrine facies, it can be divided into two parts: a shallow lacustrine subfacies and a deep lacustrine subfacies, among which: Lacustrine subfacies: alternating layers of sandstone and mudstone, with ripple marks; Deep lacustrine subfacies: dominated by mudstone, with horizontal bedding and containing fossils; Step 3: Calculate the physical properties of the sedimentary region using a capillary model; Step 31: Simulate the actual uranium-bearing reservoir using a tandem capillary model. Define the larger diameter portions as pores and the smaller diameter portions as throats. Assume all pores are the same size, and all throats have equal diameter and length. Pores and throats alternate periodically in the model. - Pore diameter, ; - Roar diameter, ; - Pore length, ; -Basic unit length, To establish the theoretical relationship between the microscopic parameters and macroscopic parameters porosity and permeability of the capillary model; Step 32: First, assuming the cross-sectional area of ​​the rock is A and the length of the rock is L, simulate the pore structure of the rock using parallel series capillary bundles of length L. The number of capillaries per unit area is... 1. Serial capillary tubes, each with M segments of length M. Given the basic unit tube, the relationship between the microscopic parameters of the capillary model and the macroscopic permeability K is as follows: ; in The number of capillaries connected in series per unit area. ; Microscopic parameters and macroscopic porosity of capillary model The relationship between them is: ; Where M is the number of basic unit tubes, which is dimensionless; A is the cross-sectional area of ​​the rock. L is the length of the rock. .

[0017] Step 4: Analyze the spatiotemporal distribution patterns of uranium-bearing reservoirs by combining physical properties.

[0018] Step 41: Calculate the porosity and permeability of low-permeability reservoirs in this region over the years according to the formula, and integrate them with the structural and stratigraphic data from the three-dimensional geological model to establish a correlation model between uranium mineralization and sedimentary facies. Step 42: Establish a multiple linear regression model with permeability and porosity as independent variables and uranium mineralization degree as the dependent variable: ; Where U is the uranium mineralization index and K is the permeability. Porosity; Step 43: The final uranium mineralization index is the predicted result of the spatial distribution of uranium mineralization.

[0019] Example 1: Taking the prediction of sandstone-type uranium deposits in the northeastern Ordos Basin as an example: 1. Data Acquisition and Lithology Identification: As attached Figure 3-7 As shown, logging data (natural gamma, sonic transit time, resistivity, density) were collected from 50 wells in groups of 10. Identify lithological distribution: Sandstone layer: natural gamma value 35-48 API, sonic transit time 185-215 μs / m Silty sandstone layer: natural gamma value 65-95 API, sonic transit time 235-255 μs / m Mudstone layer: natural gamma value 110-180 API, sonic transit time 265-320 μs / m 2. Sedimentary facies classification results Three main sedimentary facies were identified: River floodplains account for 45%; Deltaic facies, accounting for 35%; Lake-like features account for 20%; 3. Results of sedimentary microfacies division: River floodplain phase: Channel microfacies (60%): medium-coarse sandstone, natural gamma value 35-45 API, sonic transit time 190-210 μs / m, resistivity 50-800 Ω·m, density 2.62-2.68 g / cm³ 3 ; Natural dike microfacies (25%): thin interbedded siltstone and mudstone, natural gamma value 70-90 API, sonic transit time 230-250 μs / m, resistivity 5-20 Ω·m, density 2.45-2.55 g / cm³ 3 ; Crack spur microfacies (15%): fine sandstone, natural gamma value 55-75 API, sonic transit time 240-260 μs / m, resistivity 10-50 Ω·m, density 2.50-2.60 g / cm³ 3 .

[0020] Deltaic phase: Delta front subfacies (80%): Interbedded underwater distributary channels (medium-fine sandstone) and mouth bars (siltstone), natural gamma value 60-80 API, sonic transit time 220-240 μs / m, resistivity 15-30 Ω·m, density 2.50-2.55 g / cm³ 3 ; Delta plain subfacies (15%): alternating tributary channels (medium-coarse sandstone) and swamps (mudstone), with natural gamma values ​​of 45-65 API (sandstone) and 110-150 API (mudstone), and sonic transit times of 200-220 μs / m (sandstone) and 270-300 μs / m (mudstone). Predeltaic subfacies (5%): dominated by mudstone, natural gamma value >120 API, sonic transit time >280 μs / m.

[0021] Lake Xiang: Lacustrine subfacies (70%): interbedded sand and mudstone, natural gamma values ​​of 50-70 API (sandstone) and 90-110 API (mudstone), and sonic transit times of 210-230 μs / m (sandstone) and 250-270 μs / m (mudstone). Deep lacustrine subfacies (30%): fine-grained mudstone, natural gamma value 130-180 API, sonic transit time 290-320 μs / m.

[0022] 4. Examples of material property parameter calculation Taking a certain borehole data as an example: pore diameter Assuming all pores are the same size; larynx diameter The smaller diameter portion represents the larynx. Pore ​​length

[0023] Length of the larynx

[0024] Basic unit length

[0025] Number of capillaries in series per unit area

[0026] Number of basic unit tubes M: 8 (dimensionless) Rock cross-sectional area A: 80

[0027] Rock length L: 800

[0028] ; ; 5. Establish the regression equation: Based on the given borehole data, the permeability K is calculated to be 12.5%, and the porosity is... =15%, substitute it into the regression equation (assuming the regression constant has been determined through data analysis and other methods). Regression coefficient and And the random error term ε is negligible or has been adjusted for in the current context, for ease of calculation, the assumptions are... In practice, it needs to be determined based on a large amount of statistical analysis.

[0029] U = 0 + 1 × 12.5% ​​+ 1 × 15% = 27.5% The figure of 27.5% indicates that, considering the combined effects of physical properties such as permeability and porosity, this area exhibits a certain degree of uranium mineralization. If this figure is higher than that of other areas, it suggests that the degree of uranium mineralization in this area is more significant and may have higher uranium mining value.

[0030] Any technical solution that achieves the above-mentioned technical effects by utilizing the technical solutions described in this invention, or by designing similar technical solutions by those skilled in the art under the inspiration of the technical solutions described in this invention, falls within the protection scope of this invention.

Claims

1. A method for analyzing the spatiotemporal distribution of uranium-bearing reservoirs based on the characteristics of multiple-stage sedimentary facies, characterized by the following steps: The application relates to a method for predicting the spatial distribution of uranium mineralization. The method comprises the following steps: Step 1: collecting well logging data of a certain area, judging the lithology of the area according to the well logging data, and determining the sedimentary facies of the area according to the lithology; Step 2: dividing the sedimentary microfacies according to the rock properties; Step 3: calculating the physical properties of the sedimentary area by using a capillary model; 2. The method according to claim 1, characterized in that: Step 4: combining the physical properties to analyze the spatial and temporal distribution law of the uranium-bearing reservoir. The step 1 comprises the following steps: Step 11: collecting the natural gamma, acoustic time difference, resistivity and density data values in the well logging data; Step 13: where the sandstone has natural gamma < 50 API, acoustic interval between 180-220 μs / m, resistivity between 10-1000 Ω.m, density of 2.65 g / cm 3 ; siltstone has natural gamma between 50-100 API, acoustic interval between 220-260 μs / m, resistivity between 1-10 Ω.m, density of 2.39-2.51 g / cm 3 ; mudstone has natural gamma > 100 API, acoustic interval > 260 μs / m, resistivity between 0.1-100, density of 2.39-2.51 g / cm 3 ; Step 12: judging the lithology of the area according to the different natural gamma, acoustic time difference, resistivity and density data values; 3. The method according to claim 1, characterized in that: Step 14: the uranium-bearing reservoir contains four sedimentary facies, i.e. alluvial facies, flood plain facies, delta facies and lake facies, wherein the uranium-bearing reservoir is mainly sandstone, contains carbonate minerals, and has massive or graded bedding; the flood plain facies is medium-coarse sandstone with mudstone, and develops parallel or cross-bedding; the front subfacies of the delta facies is mainly siltstone and mudstone interbedding, and has sand ripple bedding; the shore-shallow lake of the lake facies is sand and mud interbedding, the deep lake is fine-grained mudstone, and contains bioturbation structure. The step 2 comprises the following steps: Step 21: if the alluvial facies is judged, the alluvial facies is divided into the fan root subfacies, the fan middle subfacies and the fan edge subfacies, wherein: The fan root subfacies is mainly conglomerate, has massive structure and extremely poor sorting; The fan middle subfacies is sand and gravel interbedding, and develops cross-bedding; The fan edge subfacies is mainly siltstone and mudstone, and has horizontal bedding; Step 22: if the flood plain facies is judged, the flood plain facies can be divided into the river channel microfacies, the natural levee microfacies and the crevasse splay microfacies, wherein: The river channel microfacies is medium-coarse sandstone, has large cross-bedding and a bottom scouring surface; The natural levee microfacies is siltstone and mudstone thin interbedding, and has wave bedding; The crevasse splay microfacies is fine sandstone, has normal grain sequence and small cross-bedding; Step 23: if the delta facies is judged, the delta facies can be divided into the delta plain subfacies, the delta front subfacies and the prodelta subfacies, wherein: The delta plain subfacies is medium-coarse sandstone of branch river channel and mudstone of marsh; The delta front subfacies is medium-fine sandstone of underwater branch river channel, and has uniform particle size of river mouth bar deposits with small difference between coarse and fine; The prodelta subfacies is mainly mudstone, and has horizontal bedding; Step 24: if the lake facies is judged, the lake facies can be divided into the shore-shallow lake subfacies and the deep lake subfacies, wherein: The shore-shallow lake subfacies is sandstone and mudstone interbedding, and has wave mark structure; 4. The method according to claim 1, characterized in that: The deep lake subfacies is mainly mudstone, has horizontal bedding and contains biological fossils. Step 31: Simulate the actual uranium-bearing reservoir by using the series capillary model, set the larger diameter part to represent the pore, the smaller diameter part to represent the throat, and assume that all the pore sizes are the same, all the throat diameters and lengths are equal, the pores and the throats periodically alternate, set the model in which - the pore diameter, ; - the throat diameter, ; - the pore length, ; - the base unit length, , establish the theoretical relationship between the micro parameters of the capillary model and the macro parameters of the porosity and the permeability; Step 32: First, assuming that the cross-sectional area of the rock is A and the length of the rock is L, the pore structure of the rock is simulated by parallelly arranging serial capillary bundles with a length of L, and there are serial capillary bundles per unit area, and each serial capillary bundle has M basic unit tubes with a length of The relationship between the micro parameters of the capillary model and the macroscopic permeability K is as follows: ； wherein is the number of capillaries per unit area in series, ​ Microscopic parameters and macroscopic porosity of capillary model The relationship between them is: ； wherein M is the number of elementary unit tubes, dimensionless; A is the cross-sectional area of the rock, L is the length of the rock, .

5. The method according to claim 1, wherein the method is characterized by: Step 3 comprises the following steps: The specific steps of the step 4 comprise the following steps: Step 41: calculating the porosity and permeability of the low-permeability reservoir in the area according to the formula, combining the structure and stratum data of the three-dimensional geological modeling, and establishing a correlation model of the uranium mineralization and the sedimentary facies; ； wherein, is a uranium mineralization indicator, is permeability, is porosity; Step 42: taking the permeability and porosity as independent variables and the uranium mineralization degree as dependent variable, and establishing a multiple linear regression model; Step 43: finally obtaining the uranium mineralization index which is the prediction result of the spatial distribution of the uranium mineralization.

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