Method for estimating permeability coefficient of ore-bearing and water-bearing stratum of sandstone type uranium deposit

Abstract

The invention relates to a sandstone type uranium deposit ore-bearing and water-bearing stratum permeability coefficient estimation method and spatial distribution prediction thereof, in particular to a sandstone type uranium deposit ore-bearing and water-bearing stratum permeability coefficient estimation method. The method comprises the following steps: 1, analyzing permeability coefficient influence factors of the ore-bearing and water-bearing stratum; 2, analyzing a permeability coefficient estimation method of the ore-bearing and water-bearing stratum; 3, carrying out basic analysis on the permeability coefficient estimation data of the ore-bearing and water-bearing stratum; 4, predicting the permeability coefficient of the ore-bearing and water-bearing stratum of the flood-sea ditch uranium ore deposit; and 5, acquiring the permeability coefficient spatial distribution characteristics of the ore-bearing and water-bearing stratum of the flood-sea ditch uranium ore deposit. The method has the remarkable effects that the method for estimating the permeability coefficient of the ore-bearing and water-bearing stratum is discussed, the fitting equation of the apparent resistivity logging data and the water pumping test permeability coefficient is established by analyzing the basic data of the flood sea ditch uranium ore deposit, and the permeability coefficient of the flood sea ditch uranium ore deposit ore-bearing and water-bearing stratum is estimated. And the influence of the spatial distribution characteristics of the permeability coefficients on the underground water seepage direction and the interlayer oxidation zone development direction is predicted.

Description

Technical Field

[0001] The invention relates to a method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer and a spatial distribution prediction thereof, and in particular to a method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer. Background Art

[0002] For sandstone-type uranium deposits, the permeability coefficient of its ore-bearing aquifer is one of the important indicators for evaluating the effectiveness of in situ leaching. The spatial distribution characteristics of the permeability coefficient are not only an important factor affecting the direction of groundwater seepage and solute migration, but also an important parameter for evaluating the heterogeneity of the aquifer.

[0003] The methods for obtaining permeability coefficient can be divided into three categories according to different testing methods: ① Core analysis method, by taking pore permeability samples, the core permeability coefficient is measured in the laboratory, which belongs to the rock physics concept. ② Well logging evaluation method, the relationship between a certain well logging parameter and the permeability coefficient of the sand body is established to estimate the permeability coefficient of the sand body, which belongs to the geophysical concept. ③ Pumping test method, the hydrogeological bore determines the influence radius and the permeability coefficient within the influence radius through pumping test, which belongs to the hydrogeological concept. Therefore, when evaluating the heterogeneity of aquifers, accurately obtaining the spatial distribution characteristics of permeability coefficient is one of the most difficult problems in uranium exploration.

[0004] Although there is a general consensus on the importance of permeability of mineral-bearing aquifers, the information provided by field methods at this stage cannot meet the requirements for predicting the seepage direction of groundwater in mineral-bearing aquifers. There are two main factors that limit the accuracy of traditional methods: 1. Dependence on existing hydrological parameter holes, which are usually sparsely distributed in the ore deposit, so it is difficult to obtain permeability information outside the surrounding areas of the existing hydrological parameter holes; 2. The representativeness of the pore permeability samples is insufficient, and there are inevitable errors in the sample collection and measurement process, which affect the permeability value. Therefore, it is of great exploration significance and economic value to use the exploration borehole logging data throughout the ore deposit to study the spatial distribution of permeability of mineral-bearing aquifers. Summary of the invention

[0005] The purpose of the present invention is to provide a method for estimating the permeability coefficient of a sandstone-type mineral-bearing aquifer, which effectively improves the application of logging data and provides a basis for predicting the permeability coefficient of a sandstone-type uranium mine exploration.

[0006] The characteristics of the present invention are as follows: A method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer comprises the following steps:

[0007] Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers;

[0008] Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers;

[0009] Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers;

[0010] Step 4: Prediction of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit;

[0011] Step 5: Spatial distribution characteristics of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit.

[0012] The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as described above, wherein the first step includes the following contents:

[0013] Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers

[0014] Influencing factors include: permeability coefficient and its spatial distribution; porosity and its geometry; mud content; median particle size.

[0015] The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as described above, wherein the second step includes the following contents:

[0016] Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers

[0017] The estimation methods include the following:

[0018] 2.1 Estimation of permeability using spontaneous potential logging data

[0019] The amplitude of the natural potential anomaly reflects the strength of the percolation process between the sand body and the mud filtrate in the well. The porosity and mud content of the sand body that affect the amplitude of the natural potential anomaly are also the main factors affecting the permeability coefficient of the mineral-bearing aquifer. However, there is no clear functional relationship between the permeability coefficient and the natural potential. Therefore, the natural potential logging data can only roughly and qualitatively estimate the permeability coefficient of the sand body.

[0020] 2.2 Estimation of permeability using apparent resistivity logging data

[0021] This method needs to meet certain prerequisites, that is, the cement content in the sand body and the formation water mineralization should be stable. At this time, the main factors determining the permeability coefficient of the mineral-bearing aquifer are rock particle size and mud content, and the changes in rock particle size and mud content are also the main factors causing the differences in rock apparent resistivity logging parameters. Therefore, it is believed that the changes in rock apparent resistivity logging parameters can reflect the differences in permeability coefficients of mineral-bearing aquifers.

[0022] 2.3 Estimation of permeability using natural gamma logging data

[0023] Natural gamma logging data can reflect the particle size or median particle size of sandstone rock particles, and assume that there is a monotonic relationship between the permeability coefficient and the particle size or median particle size of rock particles. However, this assumption is restricted by many factors and requires that the particle size and mud content of rock particles are relatively close.

[0024] The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as described above, wherein the third step includes the following contents:

[0025] Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers

[0026] Through the pumping test of hydrogeological parameter wells, various hydrogeological parameters of mineral-bearing aquifers are quantitatively evaluated.

[0027] The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as described above, wherein the fourth step includes the following contents:

[0028] Step 4: Prediction of permeability of uranium deposit-bearing aquifer

[0029] The permeability coefficient equation is:

[0030] K=-1.028+0.86Log(ρ) (1)

[0031] In formula (1), K is the permeability coefficient of the sand body, m·d -1 ; ρ is the average apparent resistivity of the sand body after thickness weighting, Ω·m.

[0032] The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as described above, wherein the fifth step includes the following contents:

[0033] Step 5: Spatial distribution characteristics of permeability coefficient of uranium deposit-bearing aquifer

[0034] The formula in the fourth step is used to calculate the permeability coefficient of the sand body, and the spatial distribution characteristics of the permeability coefficient of the ore-bearing aquifer in the uranium deposit are analyzed based on the changes in this coefficient.

[0035] The remarkable effect of the present invention is that: a method for estimating the permeability coefficient of a ore-bearing aquifer is discussed, and by analyzing the basic data of the Honghaigou uranium deposit, a fitting equation of apparent resistivity logging data and permeability coefficient of a pumping test is established to estimate the permeability coefficient of the ore-bearing aquifer of the Honghaigou uranium deposit, and to predict the influence of the spatial distribution characteristics of the permeability coefficient on the groundwater seepage direction and the development direction of the interlayer oxidation zone. Description of the drawings:

[0036] Figure 1 Regression curve fitting diagram of permeability and apparent resistivity in Honghaigou area

[0037] Figure 2Groundwater seepage (a) and oxidation zone distribution (b) in the upper section of the Xishanyao Formation of the Honghaigou uranium deposit

[0038] Figure 3 Flowchart for estimating permeability coefficient of mineral-bearing aquifers Specific implementation method:

[0039] The present invention is further described in detail below with reference to the accompanying drawings and examples.

[0040] (1) A technical solution for implementing the present invention: A method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer, the method comprising the following steps: Figure 3 ):

[0041] Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers;

[0042] Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers;

[0043] Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers;

[0044] Step 4: Prediction of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit;

[0045] Step 5: Spatial distribution characteristics of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit.

[0046] Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers

[0047] For sandstone-type uranium deposits, the permeability coefficient of its ore-bearing aquifer is one of the important indicators for evaluating the effectiveness of in situ leaching. The spatial distribution characteristics of the permeability coefficient are not only an important factor affecting the direction of groundwater seepage and solute migration, but also an important parameter for evaluating the heterogeneity of the aquifer.

[0048] From the perspective of diagenesis, early diagenesis in sandstone layers, such as compaction, pressure solution, cementation and replacement, can reduce porosity and permeability. However, authigenic kaolinite cementation can retain intergranular micropores, which has little effect on permeability and can even enhance permeability. Under mid- and late-stage diagenesis, organic matter matures until hydrocarbon generation and the acidic water produced dissolves to form secondary pores, which can greatly enhance the permeability of sandstone and is a favorable prerequisite for the formation of sandstone-type uranium deposits.

[0049] From the analysis of lithological characteristics, the permeability coefficient of sand bodies is closely related to porosity, mud content and median particle size. Porosity indicates the percentage of pore volume in the sand body, and when analyzing the permeability coefficient of sand bodies, only effective pores contribute to the permeability coefficient. Generally speaking, sand bodies with large porosity also have correspondingly large permeability coefficients, but there are also many exceptions. For example, siltstone formations have large porosity, but small permeability coefficients. Porosity mainly depends on the pore volume of the sand body, and the permeability coefficient is not only related to the pore volume of the sand body, but also controlled by the pore geometry. The permeability coefficient is a function of effective porosity and pore geometry. The reason why siltstone has a low permeability coefficient is that its pores are too narrow and curved, which hinders the smooth flow of groundwater.

[0050] The mud content is the percentage of fine silt, clay and the water contained in the rock to the volume of the rock. As the mud content increases, the permeability decreases. Generally speaking, for sand bodies with relatively stable formation water resistivity, the same mud composition and no radioactive minerals, it is best to use natural gamma or natural potential to obtain the mud content.

[0051] The median grain size is a comprehensive indicator that measures the sizes of various components that make up the sand body skeleton, so the median grain size has a stronger ability to reflect the complexity of the sand body pore structure. In a broad sense, mud is also a kind of grain size concept, so there is a certain connection between the median grain size and mud. The median grain size has a good correlation with the natural gamma logging data. The size of the rock grain size reflects the changes in the sedimentary environment and sedimentation rate, and also reflects the size of the rock particle adsorption capacity during the sedimentation process. As the median grain size changes from large to small (the grain size changes from coarse to fine), the mud content gradually increases and the permeability coefficient gradually decreases.

[0052] Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers

[0053] The main factors that cause the difference in permeability coefficient of mineral-bearing aquifers are rock porosity, mud content and median particle size. In the well logging data of sandstone-type uranium mine exploration, the natural potential, apparent resistivity and natural gamma logging data are closely related to rock porosity, mud content and median particle size. The use of different logging data to estimate the permeability coefficient of mineral-bearing aquifers has its own characteristics, which are briefly analyzed as follows.

[0054] Estimation of Permeability Coefficient Using Natural Potential Logging Data

[0055] The amplitude of the natural potential anomaly reflects the strength of the percolation process between the sand body and the mud filtrate in the well. The porosity and mud content of the sand body that affect the amplitude of the natural potential anomaly are also the main factors affecting the permeability coefficient of the mineral-bearing aquifer. However, there is no clear functional relationship between the permeability coefficient and the natural potential. Therefore, the natural potential logging data can only roughly and qualitatively estimate the permeability coefficient of the sand body.

[0056] Estimation of Permeability Coefficient Using Apparent Resistivity Logging Data

[0057] This method needs to meet certain prerequisites, that is, the cement content in the sand body and the formation water mineralization should be stable. At this time, the main factors determining the permeability coefficient of the mineral-bearing aquifer are rock particle size and mud content, and the changes in rock particle size and mud content are also the main factors causing the differences in rock apparent resistivity logging parameters. Therefore, it is believed that the changes in rock apparent resistivity logging parameters can reflect the differences in permeability coefficients of mineral-bearing aquifers.

[0058] Estimation of Permeability Using Natural Gamma Logging Data

[0059] Natural gamma logging data can reflect the particle size or median particle size of sandstone rock particles, and assume that there is a monotonic relationship between the permeability coefficient and the particle size or median particle size of rock particles. However, this assumption is restricted by many factors and requires that the particle size and mud content of rock particles are relatively close.

[0060] Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers

[0061] A difficulty encountered in estimating the permeability coefficient of mineral-bearing aquifers by using the well logging evaluation method is whether to use the results of porosity and permeability sample analysis or the results of hydrogeological parameter well tests as the basic data for estimating the permeability coefficient of mineral-bearing aquifers from well logging data. Obviously, different basic data will lead to different results. Many studies have been conducted on estimating the permeability coefficient using well logging data, and most of them use the results of porosity and permeability sample analysis as the basic data. However, due to the influence of the distribution of porosity and permeability samples and the number of samples, the estimated permeability coefficient has certain limitations, and there are inevitable errors in the collection and measurement of samples, which affect the permeability coefficient value. Through pumping tests, hydrogeological parameter wells can quantitatively evaluate various hydrogeological parameters of mineral-bearing aquifers, such as permeability coefficient and hydraulic conductivity. The permeability coefficient of mineral-bearing aquifers calculated by pumping tests is a comprehensive reflection within the pumping radius range, which can more realistically and objectively reflect the comprehensive permeability coefficient within the pumping radius range of the hydrogeological parameter wells, and has certain representativeness. Therefore, it is reliable to select the pumping test results of hydrogeological parameter wells as the basic data for estimating the permeability coefficient of mineral-bearing aquifers.

[0062] The characteristics of estimating sand body permeability coefficient from different logging data are that the natural potential logging data is not highly correlated with the permeability coefficient of the mineralized aquifer, and the natural gamma logging data, affected by the mineralized layer, also fails to respond to the sand body permeability coefficient. When the calcium content of the mineralized aquifer is low and does not change much, the main factors that determine the rock permeability coefficient are particle size and mud content. Mud and silt components are filled in the pores between the particles of permeable rocks in a uniformly dispersed state. Changes in their content will cause changes in the permeability coefficient of the rock ore, and also cause changes in the physical properties of the rock ore. The apparent resistivity of the ore also changes accordingly. This is the physical basis for estimating its permeability coefficient using apparent resistivity logging data.

[0063] Step 4: Prediction of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit

[0064] The mineralization of the ore-bearing aquifer in the Honghaigou uranium ore layer is very small (less than 1g / L), and the main reason for the difference in rock apparent resistivity logging parameters is the change in rock grain size and the high and low mud content. Therefore, the change in rock apparent resistivity logging parameters can reflect the difference in permeability of the ore-bearing aquifer. The relationship between sand body apparent resistivity and permeability can be constructed through the curve fitting equation.

[0065] Establishing a curve fitting equation is an important process for studying the relationship between variables. There are many common fitting equations, and different equations have different characteristics. If you randomly select a fitting equation, generally speaking, it is not possible to fit the data well. Through the analysis of the logging data and hydrogeological data of the Honghaigou uranium deposit, it is believed that the correlation between the analysis results of the pore permeability samples and the logging data is not very high. The main reason is that the number of samples in the target layer is not sufficient due to the influence of the core sampling rate. The hydrological parameter hole can obtain a more objective permeability coefficient within the pumping radius. The 10 groups of hydrological parameter hole data that have been constructed in the Honghaigou area are sorted out, and the hydrological parameter hole is taken as the center to calculate the average apparent resistivity of all drilled sand bodies within the influence radius of the hydrological parameter hole. The lithology in the sand body includes fine sandstone, medium sandstone, coarse sandstone, conglomerate and other permeable rocks. Due to the difference in sand body thickness, the sand body thickness is used as the weight in the statistical process to eliminate the difference in the calculation results caused by the different sand body thickness. In addition, data cleaning is required to eliminate the influence of special phenomena such as thin layers of mudstone and cement inside the sand body. Through the preliminary analysis of permeability coefficient and apparent resistivity, and using scatter plots to observe the changes in variables, it is found that the logarithmic changes between the logarithmic values ​​of permeability coefficient and apparent resistivity are observed ( Figure 1 ), the permeability coefficient equation obtained after regression fitting curve is:

[0066] K=-1.028+0.86Log(ρ) (1)

[0067] In formula (1), K is the permeability coefficient of the sand body, m·d -1; ρ is the average apparent resistivity of the sand body after thickness weighting, Ω·m.

[0068] Step 5: Spatial distribution characteristics of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit

[0069] The mineralized aquifers in the upper Xishanyao Formation in the Honghaigou area are mainly composed of coarse sandstone, gravel-bearing coarse sandstone, and conglomerate, with good sorting, mud cementation, and loose consolidation. By statistically analyzing the apparent resistivity data of 336 exploration boreholes in the upper Xishanyao Formation of the Honghaigou uranium deposit, the permeability coefficient of this layer is estimated using the fitting equation (1). Figure 2 a), Overall, the permeability coefficient of the sand body in the upper section of the Xishanyao Formation is distributed continuously and stably on the plane, and is distributed in a northwest-southeast strip shape. Its value does not change much, and the sand body has good permeability. There are high and low permeability coefficient areas in some parts. The permeability coefficient value of the section of exploration lines 78 to 158 is the highest, showing a trend of extending to the northwest; the permeability coefficient value of the section of exploration lines 22 to 30 is low, but the corresponding sand body thickness is large. It is inferred that the Honghaigou fault may have changed the seepage capacity of the sand body.

[0070] The permeability distribution of mineral-bearing aquifers is an important factor affecting the migration and transformation of solutes in groundwater systems. The permeability distribution of the sand bodies in the upper section of the Xishanyao Formation of the Honghaigou uranium deposit shows that ( Figure 2 a) The permeability coefficient of the sand body in the southern area of ​​Exploration Line 30 to 94 is relatively high, and the Middle and Lower Jurassic and Middle and Upper Triassic in this area are in direct contact with the Quaternary aquifer in the southern part of the deposit, which is a hydrodynamic window with a good degree of opening. The Honghaigou River, a perennial north-south river, develops near Exploration Line 30. Its upper reaches cut deeply into the Quaternary System. Between Exploration Lines 30 and 38, after the river water infiltrates into the Quaternary System, it can directly recharge the Jurassic aquifers on the east and west sides, forming an important source of groundwater recharge on the east side of the deposit.

[0071] After receiving the infiltration recharge of Quaternary groundwater and Honghaigou River water, the groundwater in the ore-bearing strata of the Honghaigou uranium deposit seeped to the north or northwest along the stratum dip. Affected by the spatial distribution of the ore-bearing sand bodies and the permeability coefficient of the sand bodies, the groundwater seepage direction showed differences in the plane. The main body of the sand body in the upper section of the Xishanyao Formation extends in a northwest-oriented belt, which is basically consistent with the stratum dip (305°). The permeability coefficient of the ore-bearing aquifer in the upper section of the Xishanyao Formation is estimated to be between 0.20 and 0.60 m / d using logging data. Overall, the hydrodynamic conditions of the ore-bearing aquifer in the upper section of the Xishanyao Formation are good. The sand bodies in the 150 to 54 exploration lines are thick and stable. The sand bodies with large permeability coefficients determine the direction of groundwater seepage, resulting in the groundwater seepage direction to the northwest, which is the center of river channel deposition. Although the sand body permeability coefficient is relatively large in the 94 exploration line section, the thickness of the sand body on its north side becomes thinner ( Figure 2b), resulting in a local change in the seepage direction of groundwater along the south-west side where the sand body is thicker. To the west of the 150 exploration line, the permeability coefficient of the sand body does not change much, the sand body is small in size, and the groundwater recharge is also small, which has no impact on the groundwater seepage direction, so it continues to extend to the northwest.

[0072] The groundwater seepage direction in the upper section of the Xishanyao Formation of the Honghaigou uranium deposit is greatly affected by the permeability coefficient and thickness. The area with a large permeability coefficient and a large thickness is the main direction of the groundwater seepage direction; the area with a large permeability coefficient and a small thickness or a small permeability coefficient and a large thickness should be evaluated for heterogeneity, and the various factors affecting the groundwater seepage direction should be comprehensively considered to find out the main factors for discussion. In areas with a small permeability coefficient and a small thickness, the groundwater seepage is not smooth.

[0073] The permeability coefficient of the mineralized aquifer affects the direction of groundwater seepage, and thus affects the development of the interlayer oxidation zone. The upper section of the Xishanyao Formation is mainly a river channel type interlayer oxidation zone. Affected by the spatial distribution characteristics and stability of the sand body, the direction of groundwater seepage and the content of reducing agents, the interlayer oxidation zone of the upper section of the Xishanyao Formation is well developed, generally in a tongue-shaped northwest direction, with a stable development direction of the front line and good uranium mineralization ( Figure 2 ). The 150 to 54 exploration line section is affected by the sand body permeability coefficient, resulting in the groundwater seepage direction to the northwest, and the front line of the interlayer oxidation zone also develops along the northwest. The sand body permeability coefficient and thickness of the 94 exploration line section have changed, resulting in the groundwater seepage direction to change locally along the southwest side where the sand body permeability coefficient and thickness are larger, and the front line of the interlayer oxidation zone also turns to the southwest. Along the K01 exploration line, the sand body is thick (greater than 30m) and stable, and the permeability coefficient is also large, which is the development direction of the river center; from the K01 exploration line to the southwest and northeast sides, the sand body thickness gradually decreases, and its permeability coefficient gradually decreases, and the mud content and organic matter content increase, forming a lateral interlayer oxidation zone developed on both sides of the river sand body. The interlayer oxidation zone controls uranium mineralization. It not only directly affects the migration and enrichment of uranium elements, but also is the migration channel of uranium elements and the storage space of mineralization enrichment. Therefore, uranium mineralization develops on the inner side of the front line of the interlayer oxidation zone on both sides of the river sand body.

[0074] The results of permeability prediction of mineralized aquifers show that the spatial distribution of permeability controls the development position of the front line of the interlayer oxidation zone, which has a certain indicative effect on uranium mineralization prediction. Through this study of the Honghaigou area, it can provide a reference for the estimation of permeability of mineralized aquifers in the surrounding and other areas.

[0075] (2) Verification: Figure 1 , Figure 2 Yes Figure 3 The step is to obtain a curve fitting diagram of the mineral-bearing aquifer and a spatial distribution characteristic diagram estimated by the method of the present invention.

Claims

1. A method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer, characterized in that: The steps include: Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers; Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers; Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers; Step 4: Prediction of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit; Step 5: Spatial distribution characteristics of permeability coefficient of ore-bearing aquifer in Honghaigou uranium deposit.

2. The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer according to claim 1, characterized in that: The first step includes the following: Step 1: Analysis of factors affecting permeability of mineral-bearing aquifers Influencing factors include: permeability coefficient and its spatial distribution; porosity and its geometry; mud content; median particle size.

3. The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer according to claim 2, characterized in that: The second step includes the following: Step 2: Analysis of the method for estimating the permeability coefficient of mineral-bearing aquifers The estimation methods include the following: 2.1 Estimation of permeability using spontaneous potential logging data The amplitude of the natural potential anomaly reflects the strength of the percolation process between the sand body and the mud filtrate in the well. The porosity and mud content of the sand body that affect the amplitude of the natural potential anomaly are also the main factors affecting the permeability coefficient of the mineral-bearing aquifer. However, there is no clear functional relationship between the permeability coefficient and the natural potential. Therefore, the natural potential logging data can only roughly and qualitatively estimate the permeability coefficient of the sand body. 2.2 Estimation of permeability using apparent resistivity logging data This method needs to meet certain prerequisites, that is, the cement content in the sand body and the formation water mineralization should be stable. At this time, the main factors determining the permeability coefficient of the mineral-bearing aquifer are rock particle size and mud content, and the changes in rock particle size and mud content are also the main factors causing the differences in rock apparent resistivity logging parameters. Therefore, it is believed that the changes in rock apparent resistivity logging parameters can reflect the differences in permeability coefficients of mineral-bearing aquifers. 2.3 Estimation of permeability using natural gamma logging data Natural gamma logging data can reflect the particle size or median particle size of sandstone rock particles, and assume that there is a monotonic relationship between the permeability coefficient and the particle size or median particle size of rock particles. However, this assumption is restricted by many factors and requires that the particle size and mud content of rock particles are relatively close.

4. A method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as claimed in claim 3, characterized in that: The third step includes the following: Step 3: Basic analysis of permeability estimation data of mineral-bearing aquifers Through the pumping test of hydrogeological parameter wells, various hydrogeological parameters of mineral-bearing aquifers are quantitatively evaluated.

5. The method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer according to claim 4, characterized in that: The fourth step includes the following: Step 4: Prediction of permeability of uranium deposit-bearing aquifer The permeability coefficient equation is: K=-1.028+0.86Log(ρ) (1) In formula (1), K is the permeability coefficient of the sand body, m·d -1 ; ρ is the average apparent resistivity of the sand body after thickness weighting, Ω·m.

6. A method for estimating the permeability coefficient of a sandstone-type uranium ore-bearing aquifer as claimed in claim 5, characterized in that: The fifth step includes the following: Step 5: Spatial distribution characteristics of permeability coefficient of uranium deposit-bearing aquifer The formula in the fourth step is used to calculate the permeability coefficient of the sand body, and the spatial distribution characteristics of the permeability coefficient of the ore-bearing aquifer in the uranium deposit are analyzed based on the changes in this coefficient.

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