Method and device for improving estimation precision of liquid mineral resource quantity, medium and equipment

Abstract

The invention provides a method, a device, a medium and equipment for improving liquid mineral resource quantity estimation precision, and relates to the technical field of mineral resource evaluation and management. According to the method, the spatial form difference of the halogen storage layer is considered in the first block section divided based on the deposition characteristics, and the calculated first resource total amount can more accurately reflect the geometric structure of the ore body; the non-uniform distribution of stratum void attributes is considered in the second block section divided based on the porosity, and the actual exploitable resource quantity can be more accurately estimated through the calculated second resource total quantity; the third block segment based on grade partition division considers the spatial differentiation characteristic of liquid mineral component ion distribution, and the calculated third resource total amount can more accurately reflect the actual distribution condition of mineral resources. And finally, by calculating the average value of the total amounts of the three resources, deviation possibly brought by different estimation methods is further balanced, so that the estimation precision of the liquid mineral resource amount can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of mineral resource evaluation and management, and in particular to a method, device, medium and equipment for improving the estimation accuracy of liquid mineral resource quantity. Background Art

[0002] Liquid minerals refer to mineral resources that exist in the pore space of the stratum and have a certain degree of fluidity, such as natural gas, crude oil, salt lake brine, etc. Compared with solid minerals, liquid minerals are particularly important for accurate estimation of their resources due to their special existence form and occurrence environment. Accurately evaluating the amount of liquid mineral resources not only helps to rationally plan mining plans and formulate scientific resource utilization strategies, but also has important significance for protecting the environment and achieving sustainable development.

[0003] The characteristics of liquid minerals are: 1. They are fluid and can migrate in pore space; 2. They are stored in the pores of the formation. The volume of the pores directly determines the amount of liquid minerals, which is significantly different from solid minerals. Because liquid minerals are fluid, their occurrence and distribution are affected by a variety of geological conditions, making resource estimation more complicated.

[0004] In related technologies, the resource estimation method for liquid minerals mostly adopts the geological block method. However, in the actual calculation process, this method usually only adjusts and optimizes one parameter in the resource estimation formula (thickness, grade, porosity, ore body delineation area). This relatively single estimation method is difficult to fully reflect the actual distribution status of liquid minerals, resulting in large errors in the estimation results and inaccurate estimates. Summary of the invention

[0005] The present application provides a method, device, medium and equipment for improving the estimation accuracy of liquid mineral resources, which can improve the estimation accuracy of liquid mineral resources.

[0006] In a first aspect, the present application provides a method for improving the accuracy of liquid mineral resource estimation, the method comprising: Obtain the sedimentary characteristics, porosity and component ion distribution of the mineral area to be estimated; Dividing the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculating the sum of the first resource amounts corresponding to the first blocks to obtain the total first resource amount corresponding to the mining area to be estimated; Dividing the mining area to be estimated into a plurality of second blocks according to the porosity, and calculating the sum of the second resource amounts corresponding to each of the second blocks to obtain the total second resource amount corresponding to the mining area to be estimated; Dividing the mining area to be estimated into a plurality of third blocks according to the component ion distribution, and calculating the sum of the third resource amounts corresponding to each of the third blocks to obtain the total third resource amount corresponding to the mining area to be estimated; Calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

[0007] By adopting the above technical solution, the first block segment divided based on sedimentary characteristics takes into account the spatial morphological differences of the brine reservoir, and the calculated first total resource amount can more accurately reflect the geometric structure of the ore body; the second block segment divided based on porosity takes into account the uneven distribution of stratum void properties, and the calculated second total resource amount can more accurately estimate the actual exploitable resource amount; the third block segment divided based on grade zoning takes into account the spatial differentiation characteristics of the ion distribution of liquid mineral components, and the calculated third total resource amount can more accurately reflect the actual distribution of mineral resources. Finally, by calculating the average value of these three total resource amounts, the deviations that may be caused by different estimation methods are further balanced, thereby improving the estimation accuracy of liquid mineral resources.

[0008] Optionally, the sedimentary characteristics include sedimentary thickness distribution, and the step of dividing the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculating the sum of the first resource amounts corresponding to the first blocks to obtain the total first resource amount corresponding to the mining area to be estimated includes: Dividing the mining area to be estimated into a plurality of first blocks according to the sediment thickness distribution; Constructing a first three-dimensional ore body model corresponding to each of the first blocks, and calculating a first block volume of each of the first blocks; Calculate the first resource amount corresponding to each of the first blocks according to the volume of each of the first blocks; The first resource quantities corresponding to each of the first blocks are summed to obtain the total first resource quantity corresponding to the mining area to be estimated.

[0009] By adopting the above technical solution, the sedimentary characteristics are concretized as sedimentary thickness distribution, and based on this, the mining area to be estimated is divided into several first blocks, realizing a refined description of the spatial morphology of the mining area. By constructing a corresponding first three-dimensional ore body model for each first block, this method can more accurately capture the complex geometric shape and spatial changes of the ore body. By summing the first resource volume of each first block, the total first resource volume corresponding to the mining area to be estimated is obtained. This bottom-up accumulation method ensures that the overall resource volume estimation result fully considers the spatial heterogeneity within the mining area.

[0010] Optionally, dividing the mining area to be estimated into a plurality of second blocks according to the porosity, and calculating the sum of the second resource amounts corresponding to each of the second blocks to obtain the total second resource amount corresponding to the mining area to be estimated includes: Dividing the mining area to be estimated into a plurality of second blocks according to the porosity distribution; Constructing a second three-dimensional ore body model corresponding to each of the second blocks, and calculating the second block volume of each of the second blocks; Calculate the second resource amount corresponding to each of the second blocks according to the volume of each of the second blocks; The second resource quantities corresponding to each of the second blocks are summed to obtain the total second resource quantity corresponding to the mining area to be estimated.

[0011] By adopting the above technical solution, the mining area to be estimated is divided into several second blocks, which realizes the refined characterization of the internal reservoir space characteristics of the mining area. Constructing a corresponding second three-dimensional ore body model for each second block not only improves the accuracy of the second block volume calculation, but also can more accurately reflect the spatial variation characteristics of the porosity in the mining area. The actual geological conditions of the occurrence of liquid mineral resources are taken into account, and the estimation error caused by the uneven distribution of the stratum void properties is effectively eliminated. By accurately calculating the volume of the second block, combined with the specific porosity characteristics of each block, this method can more accurately calculate the second resource corresponding to each second block, thereby effectively improving the accuracy of resource estimation.

[0012] Optionally, the component ion distribution includes potassium ion distribution, and the mining area to be estimated is divided into a plurality of third blocks according to the component ion distribution, and the sum of the third resource amounts corresponding to each of the third blocks is calculated to obtain the total third resource amount corresponding to the mining area to be estimated, including: Dividing the mining area to be estimated into a plurality of third blocks according to the potassium ion distribution; Constructing a third three-dimensional ore body model corresponding to each of the third blocks, and calculating the third block volume of each of the third blocks; Calculate the third resource amount corresponding to each of the third blocks according to the volume of each of the third blocks; The third resource quantities corresponding to each of the third blocks are summed to obtain the total third resource quantity corresponding to the mining area to be estimated.

[0013] By adopting the above technical solution, the component ion distribution is concretized as potassium ion distribution, and based on this, the mining area to be estimated is divided into several third blocks, realizing a refined description of the spatial distribution of the grade in the mining area. Constructing a corresponding third three-dimensional ore body model for each third block not only improves the accuracy of the volume calculation of the third block, but also can more accurately reflect the spatial variation characteristics of the potassium ion concentration in the mining area. Through the accurate calculation of the volume of the third block, combined with the specific potassium ion concentration characteristics of each block, this method can more accurately calculate the third resource amount corresponding to each third block, thereby effectively improving the accuracy and reliability of resource estimation. Finally, the third resource amount of each third block is summed to obtain the total third resource amount corresponding to the mining area to be estimated. This bottom-up accumulation method ensures that the overall resource estimation result fully considers the spatial heterogeneity of the grade within the mining area.

[0014] Optionally, after constructing the third three-dimensional ore body model corresponding to each of the third blocks and calculating the third block volume of each of the third blocks, the method further includes: Determine whether there is the third block segment with the same grade; If the third blocks with the same grade exist, the third blocks with the same grade are classified into the third block types of the same category.

[0015] By adopting the above technical solution, the spatial distribution of potassium ion concentration in the mining area is taken into account, and third blocks with the same grade but may not be spatially adjacent are also identified. By classifying third blocks with the same grade into the same category of third block types, this method can more comprehensively capture the grade structure and distribution pattern within the mining area.

[0016] Optionally, obtaining the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated includes: Acquire sampling data of a plurality of sample points in the mining area to be estimated, wherein the spatial positions of the sample points are different; The sampling data are analyzed to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated.

[0017] By adopting the above technical solution, multiple sample points with different spatial positions are set in the mining area to be estimated, and the sampling data of these sample points are obtained, so as to realize the comprehensive and systematic collection of geological characteristics of the mining area. It can effectively capture the spatial heterogeneity within the mining area, and provide a rich and reliable original data basis for subsequent data analysis and resource estimation. Through in-depth analysis of these sampling data, this method can simultaneously obtain multiple key geological parameters such as sedimentary characteristics, porosity, and component ion distribution of the mining area to be estimated.

[0018] In a second aspect, the present application provides a system for improving the accuracy of liquid mineral resource estimation, the system comprising: Data acquisition module, used to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated; A first calculation module, used for dividing the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculating the sum of the first resource quantities corresponding to the first blocks to obtain the total first resource quantity corresponding to the mining area to be estimated; A second calculation module is used to divide the mining area to be estimated into a plurality of second blocks according to the porosity, and calculate the sum of the second resource quantities corresponding to each of the second blocks to obtain the total second resource quantity corresponding to the mining area to be estimated; A third calculation module, used for dividing the mining area to be estimated into a plurality of third blocks according to the component ion distribution, and calculating the sum of the third resource amounts corresponding to each of the third blocks to obtain the total third resource amount corresponding to the mining area to be estimated; The mean calculation module is used to calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

[0019] In a third aspect, the present application provides a computer storage medium, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing any one of the above methods.

[0020] In a fourth aspect, the present application provides an electronic device, comprising a processor, a memory and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any one of the methods described above.

[0021] In summary, the beneficial effects brought about by the technical solution of this application include: By adopting the above technical solution, the first block segment divided based on sedimentary characteristics takes into account the spatial morphological differences of the brine reservoir, and the calculated first total resource amount can more accurately reflect the geometric structure of the ore body; the second block segment divided based on porosity takes into account the uneven distribution of stratum void properties, and the calculated second total resource amount can more accurately estimate the actual exploitable resource amount; the third block segment divided based on grade zoning takes into account the spatial differentiation characteristics of the ion distribution of liquid mineral components, and the calculated third total resource amount can more accurately reflect the actual distribution of mineral resources. Finally, by calculating the average value of these three total resource amounts, the deviations that may be caused by different estimation methods are further balanced, thereby improving the estimation accuracy of liquid mineral resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a method for improving the estimation accuracy of liquid mineral resources provided by an embodiment of the present application; Figure 2 It is a sample point distribution map with estimated mining areas provided in an embodiment of the present application; Figure 3 It is a color distribution map of the sediment thickness distribution of a mining area to be estimated provided in an embodiment of the present application; Figure 4 is a schematic diagram of dividing a first block segment provided by an embodiment of the present application; Figure 5 is a schematic diagram of a first three-dimensional ore body model provided in an embodiment of the present application; Figure 6 is a decomposed schematic diagram of a first three-dimensional model corresponding to a first block segment provided in an embodiment of the present application; Figure 7 It is a color distribution map of the porosity distribution of a mining area to be estimated provided in an embodiment of the present application; Figure 8 is a decomposed schematic diagram of a second three-dimensional model corresponding to a second block segment provided in an embodiment of the present application; Fig. 9 It is a color distribution map of potassium ion distribution in a mining area to be estimated provided in an embodiment of the present application; Fig.10 is an exploded schematic diagram of a third three-dimensional model corresponding to a third block segment provided in an embodiment of the present application; Fig.11 It is a classification diagram of a third block segment provided in an embodiment of the present application; Fig.12 This is a schematic diagram of the structure of a system for improving the estimation accuracy of liquid mineral resources according to an embodiment of the present application; Fig.13 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0023] Description of reference numerals: 1300, electronic device; 1301, processor; 1302, communication bus; 1303, user interface; 1304, network interface; 1305, memory. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0025] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a specific way.

[0026] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0027] See also Figure 1 , is a flow chart of a method for improving the estimation accuracy of liquid mineral resources provided by an embodiment of the present application. The method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on a system for improving the estimation accuracy of liquid mineral resources based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application. The specific steps of the method for improving the estimation accuracy of liquid mineral resources are described in detail below.

[0028] S101: Obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated.

[0029] The mining area to be estimated refers to a specific geographical area where accurate resource estimation has not yet been performed, and the area contains liquid mineral resources. In the embodiment of the present application, it can be understood as a three-dimensional space with clear boundaries, which contains geological structures where liquid mineral resources may exist. The mining area to be estimated can be a complete oil and gas field, or a block or a combination of multiple blocks in an oil and gas field.

[0030] Sedimentary characteristics refer to the physical, chemical and biological properties of rocks or sediments formed during geological sedimentation. In the embodiments of the present application, they can be understood as describing the key geological attributes of the stratum where the liquid mineral resources are located, mainly including sediment thickness, lithology changes, sedimentary structure and sedimentary environment.

[0031] Among them, porosity refers to the ratio of the pore volume in a rock or sediment to the total volume of the rock. In the embodiments of the present application, it can be understood as the proportion of space in the liquid mineral resource reservoir rock that can accommodate fluids, which is an important parameter for measuring the storage capacity of the reservoir. Porosity is usually expressed as a percentage, reflecting the size of the space in the rock that can be used for the storage of liquid mineral resources.

[0032] Among them, the component ion distribution refers to the content change and distribution law of various ion components in the ore body in three-dimensional space. In the embodiment of the present application, it can be understood as the spatial differentiation characteristics of various ion concentrations in liquid mineral resources at different locations and depths in the mining area to be estimated. This distribution includes both horizontal changes and vertical differentiation, reflecting the spatial heterogeneity of the ore body grade.

[0033] In this embodiment, in order to fully understand the geological characteristics, reservoir properties and ore grade distribution of the mining area, and lay the foundation for subsequent three-dimensional modeling and resource calculation, obtaining the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated is a key step in resource estimation.

[0034] In specific implementation, the sedimentary characteristics are mainly obtained through the analysis of existing drilling cores and logging data in the mining area, combined with the interpretation of seismic data. This includes detailed studies on factors such as sediment thickness, lithology changes, sedimentary structure and sedimentary environment. The acquisition of porosity data mainly depends on logging data and core analysis. Porosity information of reservoir rocks can be obtained through methods such as sonic logging, density logging and neutron logging. At the same time, laboratory analysis of the obtained core samples can obtain more accurate porosity data. The acquisition of component ion distribution data requires systematic collection and chemical analysis of liquid samples in the mining area. The ion content of samples at different locations and depths is measured.

[0035] Specifically, with respect to the acquisition of the sedimentary characteristics, porosity and component ion distribution of the ore area to be estimated, in order to further improve the estimation accuracy, step S101 also includes steps S1011 - S1012 .

[0036] S1011: Acquire sampling data of multiple sample points in the mining area to be estimated, where the spatial positions of the sample points are different.

[0037] See also Figure 2 , which is a sample point distribution map with an estimated mining area provided in an embodiment of the present application.

[0038] In actual operation, obtaining the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated usually involves setting up multiple sample points or drilling points in the mining area. Figure 2 As shown, there are multiple points numbered from 1 to 10 distributed in the mining area, which represent drilling locations or sampling locations.

[0039] This multi-point sampling strategy can provide more comprehensive and representative data for the mining area. For sedimentary characteristics, the sediment thickness, lithology and structural information at different locations can be obtained through the analysis of core samples at these points. Porosity data can be obtained by logging these wells or laboratory analysis of core samples. The distribution of component ions can be determined by collecting liquid samples at these points and performing chemical analysis. Through this gridded sampling method, data points at different locations in the mining area can be obtained, which provides a basis for subsequent spatial interpolation and three-dimensional modeling.

[0040] S1012: Analyze the sampling data to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated.

[0041] In the specific implementation, the collected core samples and logging data are first comprehensively analyzed to obtain the sedimentary characteristics of the mining area. This includes quantitative analysis of parameters such as sediment thickness, lithology changes and sedimentary structure. By statistically analyzing and spatially interpolating the data of multiple sample points, a three-dimensional sedimentary model of the entire mining area can be constructed to reveal the spatial morphological characteristics of the brine reservoir. The purpose of this step is to provide a basis for the first step of dividing blocks according to sedimentary characteristics, which helps to eliminate the volume calculation errors caused by differences in the spatial morphology of the brine reservoir.

[0042] Secondly, the porosity distribution information of the mining area is obtained by analyzing the logging data and core test results. This includes in-situ porosity data obtained by acoustic logging, density logging and other methods, as well as accurate porosity values ​​obtained by laboratory analysis of core samples. Through geostatistical methods, such as Kriging interpolation, a continuous porosity distribution model for the entire mining area can be constructed based on these discrete data points. This step provides the necessary data support for the second step of dividing blocks according to porosity distribution, which helps to eliminate the impact of uneven distribution of formation void properties.

[0043] Finally, the collected liquid samples are chemically analyzed to obtain the distribution information of component ions. Special attention is paid to the content distribution of key components such as potassium ions. Through the systematic analysis of samples at different locations and depths, combined with geochemical simulation methods, a three-dimensional ion distribution model of the mining area can be constructed. This step provides a basis for the third step of dividing blocks by grade, which helps to eliminate the spatial differentiation characteristics of the distribution of ions of liquid mineral components.

[0044] S102: Divide the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculate the sum of the first resource quantities corresponding to the first blocks to obtain the total first resource quantity corresponding to the mining area to be estimated.

[0045] Among them, the first block refers to the mining area unit divided according to the sedimentary characteristics (mainly thickness) of the ore body. In the embodiment of the present application, it can be understood as an ore body area with similar sedimentary thickness and spatial morphological characteristics. The first block is used to eliminate the volume calculation error caused by the difference in the spatial morphology of the brine reservoir. The division of the first block is based on a detailed analysis of the sedimentary characteristics of the mining area. Through the comprehensive interpretation of drilling cores, logging data and seismic data, the changes in the sedimentary thickness of the ore body at different locations can be determined. Areas with similar sedimentary thickness and similar spatial morphological characteristics are divided into the same first block.

[0046] Among them, the first resource refers to the estimated result of the liquid mineral resource calculated based on the first block division method. In the embodiment of the present application, it can be understood as the block divided according to the sedimentary characteristics of the ore body, and the resource calculated in combination with the average porosity and grade data of the block. Specifically, the calculation process of the first resource first divides the entire mining area into several first blocks according to the sedimentary characteristics, and then accurately calculates the volume of each first block. This step takes into account the actual geometry and thickness changes of the ore body. In each first block, the average porosity and average grade data of the block are used to multiply the volume of each first block with its corresponding average porosity and average grade to obtain the resource of the block. Finally, the first resource of all first blocks is added to obtain the total first resource of the entire mining area.

[0047] In this embodiment, the sedimentary characteristics of the estimated mining area are first analyzed in detail based on drilling data, geophysical logging data and seismic interpretation results. Focus on factors such as thickness changes, lithology distribution and sedimentary structure of the brine reservoir. A three-dimensional sedimentary model of the mining area is constructed using geostatistical methods, such as Kriging interpolation or conditional simulation. Based on this model, areas with similar sedimentary characteristics are divided into the same first block segment. The principle of division is to make the sedimentary characteristics within each first block segment relatively uniform, while there are obvious differences between different blocks. This division method can effectively capture the spatial heterogeneity of the ore body and provide a more accurate geometric basis for subsequent resource calculations.

[0048] For each first block segment, calculate its volume accurately, taking into account the actual geometry and boundary conditions of the block segment. Then, calculate the average porosity and average grade based on the drilling and logging data within the block segment. Multiply the block segment volume, average porosity and average grade to obtain the first resource corresponding to the first block segment. Repeat this process to calculate the first resource of all first blocks, and finally add them together to obtain the total first resource corresponding to the entire mining area to be estimated.

[0049] Based on the above embodiment, as an optional implementation, step S102 also includes steps S1021-S1024.

[0050] S1021: Divide the mining area to be estimated into several first blocks according to the distribution of sediment thickness.

[0051] Among them, the sediment thickness distribution refers to the thickness variation of the liquid mineral reservoir in the entire mining area to be estimated. In the embodiment of the present application, it can be understood as a three-dimensional thickness variation model of the brine reservoir in space, reflecting the vertical thickness of the reservoir at different locations and its spatial variation trend. The sediment thickness distribution is used to accurately characterize the spatial geometry of the ore body, providing a key basis for the division of the first block segment and the calculation of the first resource amount.

[0052] For details, please see Figure 3 , which is a color distribution map of the sediment thickness distribution of the mining area to be estimated provided in an embodiment of the present application.

[0053] The thickness of the deposit is distributed in Figure 3 The figure is presented intuitively through a gradient color contour map, showing the spatial variation characteristics of the brine reservoir thickness in the mining area to be estimated. The figure uses a continuous color spectrum from blue to red to represent the thickness variation, where blue represents thinner areas with a thickness close to 0 meters, and red represents the thickest areas with a thickness of up to 15 meters or more. Overall, the sedimentary thickness shows significant heterogeneity, with obvious thickness gradients and local variations within the mining area.

[0054] On this basis, see Figure 4 , which is a schematic diagram of dividing the first block segment provided in an embodiment of the present application.

[0055] Figure 4 The entire mining area to be estimated is divided into four different first blocks, marked as 1, 2, 3 and 4. Figure 3 In contrast, the first block, labeled 1, covers the southwest of the mining area, corresponding to the red to yellow area in the sediment thickness distribution map, representing the area with the largest sediment thickness. This block has the largest area, reflecting the relative uniformity of the sediment thickness distribution in this area. The first block, labeled 2, is located in the middle of the mining area, which may correspond to the area where the sediment thickness transitions from thicker to thinner, and is shown as a transition zone from yellow-green to light blue in the thickness distribution map. The first block, labeled 3, and the first block, labeled 4, are located in the northeast of the mining area, corresponding to the blue area in the sediment thickness distribution map, representing relatively thin sedimentary areas. This division method fully considers the spatial variation characteristics of sediment thickness. The sediment thickness within each block is relatively uniform, while there are obvious differences between different blocks. Such a division helps to improve the accuracy of subsequent resource estimation because it allows more accurate parameters to be used for each block in the calculation.

[0056] In this embodiment, according to the characteristics of sediment thickness distribution, areas with similar thickness characteristics are divided into the same first block segment. The principle of division is to ensure that the sediment thickness within each block segment is relatively uniform, while there are obvious differences between different blocks. This process requires comprehensive consideration of the distribution of thickness contours, local thickness variation characteristics, and the overall shape and boundary conditions of the mining area.

[0057] S1022: construct a first three-dimensional ore body model corresponding to each first block segment, and calculate the first block segment volume of each first block segment.

[0058] The first three-dimensional ore body model refers to a three-dimensional spatial representation divided according to the ore body deposition characteristics (mainly thickness). In the embodiment of the present application, it can be understood that this is the first level of the ore body space model divided based on the ore body thickness variation characteristics.

[0059] See also Figure 5 , which is a schematic diagram of a first three-dimensional ore body model provided in an embodiment of the present application. In the figure, the first three-dimensional ore body model is presented in a three-dimensional form. The distribution characteristics of the first block segment in the three-dimensional space are clearly shown. The first three-dimensional ore body model not only reflects the plane range of the ore body (corresponding to the X and Y axes), but also accurately depicts the thickness change of the ore body (corresponding to the Z axis).

[0060] Specifically, for each first block, these data are used to construct a detailed three-dimensional geological grid model. This process usually uses professional geological modeling software and geostatistical methods such as Kriging interpolation or sequential Gaussian simulation to convert discrete exploration data point information into a continuous three-dimensional spatial distribution model. Subsequently, various geological parameters such as lithology, porosity, permeability, and grade are assigned to the three-dimensional grid model. This step usually uses advanced methods such as conditional simulation or multi-point geostatistics to ensure that the model not only reflects the information of known data points, but also can reasonably infer the geological characteristics of unsampled areas. For liquid minerals, special attention should be paid to the spatial distribution patterns of porosity and grade, because these parameters directly affect the calculation results of resource quantities.

[0061] After the first three-dimensional ore body model is constructed, in order to calculate the volume of each first block segment, the first three-dimensional ore body model needs to be decomposed.

[0062] See also Figure 6 , which is a decomposed schematic diagram of a first three-dimensional model corresponding to a first block segment provided in an embodiment of the present application.

[0063] From top to bottom, the figure shows the first blocks corresponding to four different numbers, each of which is represented by a three-dimensional model, specifically the first blocks divided according to the thickness of the ore body deposition.

[0064] In the specific implementation, the first three-dimensional ore body model that has been constructed is processed using professional three-dimensional modeling and calculation software, such as Petrel, Surpac or GOCAD. These software have advanced volume calculation algorithms and can handle complex geological body morphology. The calculation process is mainly based on the three-dimensional grid model. The software divides each first block into a large number of small voxel units. By performing precise mathematical integration or numerical accumulation of these voxel units, the accurate volume of each first block can be obtained.

[0065] S1023: Calculate the first resource quantity corresponding to each first block segment according to the volume of each first block segment.

[0066] In this embodiment, calculating the first resource volume corresponding to each first block segment according to the volume of each first block segment is a key step to achieve accurate assessment of liquid mineral resources. The core of this step is to make full use of the first blocks divided according to the sedimentary characteristics (mainly thickness) of the ore body, thereby effectively eliminating the volume calculation error caused by the difference in the spatial morphology of the brine reservoir.

[0067] In specific implementation, since the brine reservoir of liquid minerals (such as salt lake brine) usually presents a complex spatial morphology, the thickness may vary greatly. The volume of the first block has been obtained through precise three-dimensional modeling in the previous step. This volume calculation fully considers the thickness variation characteristics of the ore body within the block. Specifically, the geological block method can be used. For each first block, its horizontal projection area, average thickness, average grade of useful components and average porosity need to be determined. The horizontal projection area and average thickness are used to calculate the block volume, while the average grade and average porosity reflect the quality and occurrence characteristics of the mineral resources. After the parameters are determined, the calculation principle of the geological block method can be applied to calculate the first resource of each first block. This process needs to consider factors such as the volume of the block, the grade of useful components and the porosity. The result of the calculation is the first resource of the first block.

[0068] S1024: Sum the first resource quantities corresponding to the first blocks to obtain the total first resource quantity corresponding to the mining area to be estimated.

[0069] In the previous step, the first resource corresponding to each first block has been calculated separately. The resource of each first block calculated separately before is integrated to obtain the overall resource of the entire mining area to be estimated, so as to comprehensively evaluate the resource reserves of the mining area.

[0070] S103: Divide the mining area to be estimated into a plurality of second blocks according to the porosity, and calculate the sum of the second resource quantities corresponding to the second blocks to obtain the total second resource quantity corresponding to the mining area to be estimated.

[0071] Among them, the second block refers to a mining area unit divided based on the porosity distribution of the mining area to be estimated. In the embodiment of the present application, it can be understood as a three-dimensional space unit divided according to the porosity distribution characteristics in the mining area, wherein the porosity in each second block is relatively uniform or has a similar change trend. This division takes porosity as the main basis, taking into account factors such as the absolute value of porosity, change gradient or distribution pattern.

[0072] Among them, the second resource corresponding to the second block refers to the estimated liquid mineral resource in each second block divided based on the porosity distribution. In the embodiment of the present application, it can be understood that by calculating the volume of each second block, and combining the average porosity, mineralization and other parameters of the block, the liquid mineral resource that may exist in the block is estimated. This concept reflects the resource distribution of different porosity areas in the mining area, and takes into account the impact of porosity on the occurrence of liquid minerals.

[0073] Among them, the second total resource refers to the sum of the second resource amounts corresponding to all second blocks divided based on the porosity distribution. In the embodiment of the present application, it can be understood that after the entire mining area to be estimated is divided into a number of second blocks according to the porosity distribution characteristics, the resource amount of each second block is calculated separately, and then the sum of these resource amounts is added together. This total amount reflects the total amount of liquid mineral resources in the entire mining area estimated from the porosity perspective. The calculation of the second total resource amount is used to provide an overall resource amount estimation result based on the porosity characteristics.

[0074] In this embodiment, first, according to the porosity distribution data of the mining area to be estimated, the entire mining area is divided into a plurality of second blocks with similar porosity characteristics. This division method can more carefully reflect the spatial variation of porosity in the mining area, which helps to improve the accuracy of subsequent resource estimation. After the division is completed, a corresponding second three-dimensional ore body model is constructed for each second block, and the volume of each second block is calculated. On this basis, the second resource corresponding to each second block is calculated respectively in combination with the average porosity, mineralization and other parameters of each second block. This resource calculation method based on porosity characteristics can more accurately reflect the distribution of liquid minerals in different porosity areas. Finally, the second resource of all second blocks is added to obtain the total second resource corresponding to the entire mining area to be estimated.

[0075] Based on the above embodiment, as an optional implementation, step S103 also includes steps S1031-S1034.

[0076] S1031: Divide the mining area to be estimated into a number of second blocks according to the porosity distribution.

[0077] Among them, porosity distribution refers to the spatial variation of the volume ratio of pores in rocks or sediments in the mining area to be estimated. In the embodiment of the present application, it can be understood as a three-dimensional spatial distribution model obtained by analyzing and interpolating the porosity data of multiple sample points in the mining area. The model reflects the porosity variation trend and characteristics at different locations and depths in the mining area. Porosity distribution is used to describe the density of the internal structure of the mining area, which directly affects the occurrence space and enrichment of liquid minerals.

[0078] For details, please see Figure 7 , which is a color distribution map of the porosity distribution of a mining area to be estimated provided in an embodiment of the present application. The X-axis and Y-axis in the figure represent the plane coordinates of the mining area, and the color legend is on the left, showing the range of porosity values. The northern part (above) is mainly blue, indicating low porosity. The central and southern parts (below) are mainly green and red, indicating high porosity. There is an obvious high porosity area in the south of the center, which appears in red.

[0079] In the specific implementation, firstly, based on the porosity distribution color map shown in the figure, the changing trend and characteristics of porosity in the mining area are analyzed. It can be clearly seen from the figure that the porosity in the northern part of the mining area is low, showing blue; the porosity in the central and southern parts is high, mainly green and red; there is an obvious high porosity area in the central and southern parts, showing red. According to these characteristics, the mining area can be divided into several main second blocks. For example, the blue area with a porosity value between 0-0.1 can be divided into a low porosity block, the green area between 0.1-0.2 can be divided into a medium porosity block, and the yellow and red areas above 0.2 can be divided into high porosity blocks. In the division process, the continuity and change gradient of porosity need to be considered, and the division can be carried out along the black curve (which may represent some porosity contour lines). In addition, the actual measurement data of the 10 sample points marked in the figure should also be considered to ensure that the divided blocks can represent the characteristics of these sample points. This classification method based on porosity distribution can more accurately reflect the density of the internal structure of the mining area, which will help to more accurately evaluate the storage capacity and potential distribution of liquid mineral resources in different areas in the future.

[0080] S1032: construct a second three-dimensional ore body model corresponding to each second block segment, and calculate the second block segment volume of each second block segment.

[0081] Wherein, the second three-dimensional ore body model corresponding to the second block refers to the geometric representation in three-dimensional space of each second block divided based on the porosity distribution characteristics. In the embodiment of the present application, it can be understood as a digital model in which the spatial range, shape and volume of each second block are presented in a three-dimensional form by computer modeling technology. This model not only includes the plane boundary information of the second block, but also includes its extension and change in the vertical direction. The second three-dimensional ore body model is used to accurately calculate the volume of each second block, providing basic data for subsequent resource estimation. By constructing this detailed three-dimensional model, the spatial distribution and geometric characteristics of different porosity areas in the mining area can be more accurately reflected, thereby improving the accuracy of liquid mineral resource estimation.

[0082] The second block volume of the second block refers to the volume occupied by each second block in three-dimensional space divided based on the porosity distribution characteristics. In the embodiment of the present application, it can be understood as the actual spatial volume of each second block obtained by calculating the second three-dimensional ore body model.

[0083] For details, please see Figure 8 , which is a decomposed schematic diagram of a second three-dimensional model corresponding to a second block segment provided in an embodiment of the present application.

[0084] Figure 8 The figure shows the second three-dimensional ore body model corresponding to each second block after the estimated mining area is divided into three different second blocks according to the porosity distribution. Each yellow part in the figure represents a three-dimensional model of an independent second block.

[0085] In this embodiment, in order to accurately quantify the geometric characteristics and spatial distribution of regions with different porosities, a second three-dimensional ore body model corresponding to each second block is constructed and the second block volume of each second block is calculated.

[0086] In the specific implementation, firstly, based on the second block segment divided by the aforementioned porosity distribution, professional 3D modeling software is used to convert the boundary and internal structure of each second block segment into an accurate 3D digital model. These models use a triangular mesh structure, which can accurately reflect the complex shape and spatial characteristics of each second block segment. As shown in the figure, the mining area to be estimated is divided into three different second blocks, and a corresponding second 3D ore body model is constructed for each block segment. The clearly visible grid line texture in the model indicates the fineness of the modeling and ensures an accurate description of the ore body morphology. After the construction is completed, the volume of each second block segment is accurately calculated through the built-in volume calculation function of the software. As can be seen from the attribute window in the figure, the volume of the first second block segment is 3669.01 cubic meters, the second is 2432.33 cubic meters, and the third is 1515.96 cubic meters. In this way, the scale of different porosity areas can be quantified more accurately, laying a solid foundation for the subsequent resource estimation combined with specific porosity characteristics.

[0087] S1033: Calculate the second resource amount corresponding to each second block segment according to the volume of each second block segment.

[0088] In this embodiment, calculating the second resource amount corresponding to each second block segment according to the volume of each second block segment is a key step to achieve accurate estimation of the liquid mineral resource amount. The purpose of this step is to combine the volume of the second block segment accurately calculated above with the porosity characteristics of each block segment, so as to obtain a more accurate resource estimation result. In specific implementation, the accurate volume data of each second block segment obtained in the previous step is first used. Then, for each second block segment, according to its specific porosity characteristics, the effective porosity value is determined. This effective porosity value can be obtained by sampling and analysis, which reflects the actual space ratio available for liquid mineral occurrence. Next, the volume of each second block segment is multiplied by its corresponding effective porosity to obtain the volume that can theoretically accommodate liquid minerals. Combined with parameters such as the density and mineralization of the liquid mineral, the mass of the liquid mineral in each second block segment, i.e., the second resource amount, is calculated. In this way, the second resource amount corresponding to each second block segment can be calculated separately. The advantage of this calculation method is that it fully considers the difference in porosity in different regions and avoids the error that may be caused by using the average porosity.

[0089] S1034: Sum the second resource quantities corresponding to the second blocks to obtain the total second resource quantity corresponding to the mining area to be estimated.

[0090] In this embodiment, in order to eliminate the uneven distribution characteristics of the stratum void properties. By integrating the resource quantities of the second blocks with similar porosity characteristics, the influence of the uneven distribution characteristics of the stratum void properties on the resource estimation is effectively eliminated. In the specific implementation, the second resource quantity data corresponding to each second block calculated in the previous step are first collected. These data have fully considered the porosity differences in various regions, and the porosity distribution inside each second block is relatively uniform. Then, the second resource quantities of all second blocks are added together by mathematical addition to obtain the total amount of second resources corresponding to the mining area to be estimated. The advantage of this method is that by dividing the second blocks according to the porosity distribution in the early stage, the uneven porosity distribution inside the mining area has been divided into several relatively uniform internal areas. In the summation process, this division method effectively eliminates the influence of the uneven porosity distribution of the entire mining area on the resource estimation.

[0091] S104: Divide the mining area to be estimated into a plurality of third blocks according to the distribution of component ions, and calculate the sum of the third resource quantities corresponding to the third blocks to obtain the total third resource quantity corresponding to the mining area to be estimated.

[0092] Among them, grade zoning refers to the process of dividing a ore deposit or ore body into different grade areas according to the spatial distribution characteristics of the valuable elements or mineral content in the ore body. In the embodiment of the present application, it can be understood that based on the distribution characteristics of the target ion concentration or total dissolved solid content in the liquid mineral resources, the mining area to be estimated is divided into several areas with similar grade characteristics.

[0093] The third block refers to an ore body area with similar grade characteristics divided according to the grade zoning result. In the embodiment of the present application, it can be understood that the mining area to be estimated is divided into several independent blocks with similar grades based on the distribution characteristics of the target ion concentration or total dissolved solid content in the liquid mineral resources.

[0094] The third resource volume corresponding to the third block refers to the estimated value of the quantity of liquid mineral resources contained in each third block divided based on the grade zoning. In the embodiment of the present application, it can be understood as the mass or volume of the liquid mineral resources in the block calculated by considering the volume, grade characteristics (such as target ion concentration or total dissolved solid content) and other relevant parameters (such as porosity, density, etc.) of each third block.

[0095] In this embodiment, in order to eliminate the influence of the spatial differentiation characteristics of the ion distribution of liquid mineral components, the influence of the spatial uneven distribution of ion concentration in liquid mineral resources on the resource estimation is overcome by grade zoning. In the specific implementation, firstly, based on the grade zoning results obtained in the early stage, the mining area to be estimated is divided into several third blocks. Each third block represents an area with similar ion concentration or total dissolved solid content. This division method effectively divides the spatial differentiation characteristics of ion distribution inside the mining area into several relatively homogeneous areas. Next, for each third block, combined with its volume, specific grade characteristics and other relevant parameters, the third resource corresponding to the block is calculated. This calculation method takes into account the specific ion concentration characteristics of each area, and can more accurately reflect the resource reserves of different grade areas without being affected by the uneven distribution of ions in the entire mining area. Then, the third resource of all third blocks is summed to obtain the total third resource corresponding to the entire mining area to be estimated. By dividing the mining area into multiple third blocks with similar grades, the ion concentration distribution inside each block is relatively uniform, thereby avoiding the error that may be caused by using the average grade.

[0096] Based on the above embodiment, as an optional implementation, step S104 also includes steps S1041-S1044.

[0097] S1041: Divide the mining area to be estimated into several third blocks according to the distribution of potassium ions.

[0098] Among them, potassium ion distribution refers to the concentration change and distribution of potassium ions in liquid mineral resources in space. In the embodiment of the present application, it can be understood as the distribution characteristics and change rules of potassium ion concentration in the three-dimensional space in the mining area to be estimated, including the high and low value areas, gradient changes and spatial distribution patterns of potassium ion concentration.

[0099] For details, please see Fig. 9 , which is a color distribution map of potassium ion distribution in a mining area to be estimated provided in an embodiment of the present application.

[0100] Fig. 9 Different colors are used to represent the changes in potassium ion concentration, from blue to red, representing low concentration to high concentration respectively. The color bar on the left shows the specific range of concentration values, from below 50 to above 500. The X-axis and Y-axis represent the spatial coordinates of the mining area. It can be observed from the figure that the distribution of potassium ions in the mining area shows obvious spatial differentiation characteristics. The upper right area shows a large area of ​​red and orange, indicating that this is a high potassium ion concentration area; while the large blue area in the lower middle and lower right represents a low potassium ion concentration area. In addition, some transition areas can be seen, such as the green and yellow areas in the middle, indicating a medium concentration of potassium ions.

[0101] In the specific implementation, firstly, based on the potassium ion distribution data obtained in the early stage, the entire mining area to be estimated is analyzed in detail, as shown in the color distribution map in the figure. By observing the spatial variation trend of potassium ion concentration, areas with different concentration levels are identified. High-concentration areas (such as the red and orange areas in the upper right of the figure), medium-concentration areas (such as the green and yellow areas in the middle), and low-concentration areas (such as the blue areas in the lower middle and lower right) are identified respectively. Then, according to the spatial continuity and concentration gradient changes of these areas, adjacent areas with similar potassium ion concentration characteristics are divided into the same third block segment. In the division process, special attention is paid to the treatment of concentration transition zones. It may be necessary to set up independent transition third blocks to better reflect the gradient characteristics of concentration. For some special areas, such as the isolated high-concentration area in the lower left corner of the figure, it can be divided into a special third block segment separately. This division method based on potassium ion distribution characteristics can effectively eliminate the influence of spatial differentiation of ion distribution of liquid mineral components on resource estimation. By dividing the mining area into multiple third blocks with similar potassium ion concentrations, the ion concentration distribution within each block segment is relatively uniform, thereby avoiding the error that may be caused by using average concentration.

[0102] S1042: construct a third three-dimensional ore body model corresponding to each third block segment, and calculate the third block segment volume of each third block segment.

[0103] Wherein, the third three-dimensional ore body model corresponding to the third block segment refers to the digital three-dimensional space representation constructed by each third block segment divided based on the potassium ion distribution characteristics. In the embodiment of the present application, it can be understood that the geometric shape, spatial position and internal potassium ion concentration distribution characteristics of each third block segment are converted into a visualized three-dimensional digital model using professional three-dimensional modeling software. The third three-dimensional ore body model is used to accurately describe and quantify the spatial geometric characteristics, volume and internal potassium ion distribution of each third block segment.

[0104] For details, please see Fig.10 , which is a decomposed schematic diagram of a third three-dimensional model corresponding to a third block segment provided in an embodiment of the present application.

[0105] Fig.10 It contains three different 3D models, each representing a third segment divided based on the distribution characteristics of potassium ions. Each third segment shows its 3D structure through grid lines. The shapes of the models are different, reflecting the distribution characteristics of different third segments in space. The coordinate axis shows the position and size of the model in 3D space.

[0106] In the specific implementation, firstly, professional 3D modeling software is used to input the boundary data of each third block segment, internal potassium ion concentration distribution data and other information into the system as shown in the figure. Through the modeling function of the software, these two-dimensional data are converted into a three-dimensional spatial structure, forming a Fig.10 The three-dimensional model shown. During the modeling process, special attention should be paid to the refinement of the model to ensure that it can accurately reflect the spatial geometric characteristics and internal structure of the third block segment. After the model is built, the software will automatically calculate and display the key parameters of each third three-dimensional ore body model, including the number of triangles, coordinate range, center point coordinates, etc., and most importantly, accurate volume data. These accurate volume data are the third block volume of each third block segment.

[0107] Through this method, the accurate spatial shape and volume information of each third block segment can be obtained. This volume calculation method based on the three-dimensional model is more accurate than the traditional two-dimensional estimation method and can effectively consider the complex shape and internal structure changes of the ore body.

[0108] Based on the above embodiment, as an optional implementation, it is determined whether there is a third block segment with the same grade; if there is a third block segment with the same grade, the third block segment with the same grade is classified into the same category of third block segment types.

[0109] For details, please see Fig.11 , which is a classification diagram of a third block segment provided in an embodiment of the present application.

[0110] like Fig.11 As shown, Fig.11 The upper part shows five different third blocks, labeled 1, 2, 3, 4, and 5. These blocks are spatially separated and located in different locations in the mining area. Fig.11 The bottom half shows the results after reclassification based on the same grade. The key point is that although some blocks are not directly connected in space, they may have the same grade. For example, blocks 2 and 4 in the above figure are separated in space, but after grade analysis, they are found to have the same grade. Therefore, in Fig.11 In the lower half, the two blocks are relabeled 2-1 and 2-2 to indicate that they belong to the same grade category.

[0111] In this embodiment, by judging whether there are third blocks with the same grade, and classifying the third blocks with the same grade into the same category of third blocks, third blocks with similar grade characteristics are identified and classified, even if these blocks may not be adjacent or directly connected in space.

[0112] By comparing the grade data of different third blocks, it is possible to identify blocks that may not be adjacent in space but have highly similar grade characteristics. This classification method based on grade level can effectively reveal the grade distribution pattern that may exist within the mining area, even if these areas of the same grade are scattered in space. S1043: Calculate the third resource quantity corresponding to each third block segment according to the volume of each third block segment.

[0113] In the specific implementation, the third three-dimensional ore body model constructed above is first used to obtain the accurate volume data of each third block. Then, combined with the grade information of each third block, such as potassium ion concentration or other key mineral content indicators, the appropriate resource calculation formula is applied to calculate the third resource corresponding to each third block. This calculation process takes into account key parameters such as block volume, grade, porosity, etc., ensuring the accuracy and reliability of the calculation results.

[0114] S1044: Sum the third resource quantities corresponding to the third blocks to obtain the total third resource quantity corresponding to the mining area to be estimated.

[0115] In this embodiment, the resource data of each third block obtained based on the detailed division and calculation in the early stage are integrated to obtain the overall resource of the entire mining area to be estimated. In the specific implementation, it is first necessary to ensure that the third resource calculation of each third block has been completed, and these calculation results take into account the volume, grade characteristics and other relevant parameters of each block. Then, a simple and effective mathematical summation method is used to accumulate the third resource of all third blocks to obtain the total third resource of the entire mining area to be estimated. The advantage of this summation method is that it retains all the information of the previous detailed division and calculation, and at the same time provides a comprehensive mining area resource estimation result.

[0116] S105: Calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

[0117] Among them, the first total resource is the result of dividing the blocks according to the sedimentary characteristics (thickness) of the ore body. This method eliminates the large volume calculation error caused by the spatial morphological differences of the brine reservoir. By considering the thickness changes of the ore body, the geometric structure of the ore body can be more accurately reflected, thereby improving the accuracy of volume calculation. The second total resource is the result of using the pore distribution as the basis for block division. This method eliminates the uneven distribution characteristics of the pore properties of the formation. By considering the spatial variation of porosity, the actual amount of exploitable resources can be more accurately estimated, avoiding the resource estimation deviation caused by porosity changes. The third total resource is the result of using the grade zoning as the basis for block division. This method eliminates the spatial differentiation of the ion distribution of liquid mineral components. By considering the spatial variation of grade, the actual distribution of mineral resources can be more accurately reflected, avoiding the resource estimation error caused by grade changes. The results of different estimation methods are comprehensively considered, and the errors that may be caused by a single method are reduced by averaging, thereby obtaining a more reliable and robust resource estimation result.

[0118] Based on the above embodiment, as an optional implementation manner, the sediment thickness distribution or porosity distribution or potassium ion distribution of the mining area to be estimated is a plane distribution diagram in the horizontal direction, and the plane distribution diagram also includes a depth value in the vertical direction.

[0119] In the present embodiment, the sediment thickness distribution, porosity distribution and potassium ion distribution of the mining area to be estimated are presented in the form of a plane distribution map in the horizontal direction, and these plane distribution maps also include depth values ​​in the vertical direction. The purpose of this representation method is to comprehensively and accurately describe the three-dimensional spatial distribution characteristics of liquid mineral resources, so as to provide a more accurate and reliable data basis for subsequent resource estimation. During specific implementation, it is first necessary to obtain the sediment thickness, porosity and potassium ion concentration data of the mining area at different depths through geological exploration and measurement. Then, using a geographic information system (GIS) or professional geological modeling software, these data are converted into a plane distribution map in the horizontal direction. Each plane distribution map represents the parameter distribution at a specific depth, and intuitively displays the spatial variation of the parameters by color gradient or contour lines. At the same time, the corresponding depth value is marked in each plane distribution map, so that a series of plane distribution atlases representing different depths are formed. The advantage of this representation method is that it not only retains the intuitiveness and readability of the traditional two-dimensional plane distribution map, but also expands to three-dimensional space by adding depth information, making the three-dimensional structure and parameter distribution characteristics of the mining area clearer. Through this method, the geological characteristics of the mining area can be more comprehensively understood and analyzed: the sediment thickness distribution map reflects the spatial morphological changes of the ore body, which helps to accurately calculate the volume of the ore body; the porosity distribution map reveals the void characteristics of the brine reservoir, which is convenient for estimating the effective pore volume; the potassium ion distribution map directly reflects the grade changes of mineral resources and can provide a basis for resource calculation.

[0120] The following is an embodiment of the system of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the system of the present application, please refer to the embodiment of the method of the application.

[0121] See also Fig.12 , which shows a schematic diagram of the structure of a system for improving the estimation accuracy of liquid mineral resources provided by an exemplary embodiment of the present application. The system can be implemented as all or part of the system through software, hardware, or a combination of both. The system for improving the estimation accuracy of liquid mineral resources includes: Data acquisition module, used to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated; A first calculation module is used to divide the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculate the sum of the first resource quantities corresponding to the first blocks to obtain the total first resource quantity corresponding to the mining area to be estimated; A second calculation module is used to divide the mining area to be estimated into a plurality of second blocks according to the porosity, and calculate the sum of the second resource amounts corresponding to each second block to obtain the total amount of second resources corresponding to the mining area to be estimated; A third calculation module is used to divide the mining area to be estimated into a plurality of third blocks according to the distribution of component ions, and calculate the sum of the third resource amounts corresponding to each third block to obtain the total third resource amount corresponding to the mining area to be estimated; The mean calculation module is used to calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

[0122] Based on the above embodiment, as an optional embodiment, the data acquisition module is also used to obtain sampling data of multiple sample points in the mining area to be estimated, and the spatial positions of each sample point are different; the sampling data are analyzed to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated.

[0123] On the basis of the above embodiment, as an optional embodiment, the first calculation module is also used to divide the mining area to be estimated into several first blocks according to the sedimentation thickness distribution; construct a first three-dimensional ore body model corresponding to each first block, and calculate the first block volume of each first block; calculate the first resource amount corresponding to each first block according to the volume of each first block; sum the first resource amounts corresponding to each first block to obtain the total first resource amount corresponding to the mining area to be estimated.

[0124] On the basis of the above embodiment, as an optional embodiment, the second calculation module is also used to divide the mining area to be estimated into a number of second blocks according to the porosity distribution; construct a second three-dimensional ore body model corresponding to each second block, and calculate the second block volume of each second block; calculate the second resource corresponding to each second block according to the volume of each second block; sum the second resource corresponding to each second block to obtain the total second resource corresponding to the mining area to be estimated.

[0125] On the basis of the above embodiment, as an optional embodiment, the third calculation module is also used to divide the mining area to be estimated into a number of third blocks according to the distribution of potassium ions; construct a third three-dimensional ore body model corresponding to each third block, and calculate the third block volume of each third block; calculate the third resource corresponding to each third block according to the volume of each third block; sum the third resource corresponding to each third block to obtain the total third resource corresponding to the mining area to be estimated.

[0126] Based on the above embodiment, as an optional embodiment, the third calculation module is also used to determine whether there is a third block segment with the same grade; if there is a third block segment with the same grade, the third block segment with the same grade is classified into the same category of third block segment types.

[0127] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1-Figure 12 The method for improving the estimation accuracy of liquid mineral resources in the embodiment shown in the figure can be specifically implemented by referring to Figure 1-Figure 12 The specific description of the illustrated embodiment will not be repeated here.

[0128] See also Fig.13 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.13 As shown, the electronic device 1300 may include: at least one processor 1301 , at least one network interface 1304 , a user interface 1303 , a memory 1305 , and at least one communication bus 1302 .

[0129] The communication bus 1302 is used to realize the connection and communication between these components.

[0130] The user interface 1303 may include a standard wired interface or a wireless interface.

[0131] The network interface 1304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0132] Among them, the processor 1301 may include one or more processing cores. The processor 1301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1305, and calling data stored in the memory 1305. Optionally, the processor 1301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1301, and it can be implemented separately through a chip.

[0133] Among them, the memory 1305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1305 includes a non-transitory computer-readable storage medium. The memory 1305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1305 may optionally be at least one storage device located away from the aforementioned processor 1301. As Fig.13 As shown, the memory 1305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for improving the accuracy of liquid mineral resource estimation.

[0134] exist Fig.13In the electronic device 1300 shown, the user interface 1303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1301 can be used to call an application program stored in the memory 1305 for a method for improving the accuracy of liquid mineral resource estimation. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0135] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more methods in the above embodiments.

[0136] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0137] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0142] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure.

Claims

1. A method for improving the estimation accuracy of liquid mineral resources, characterized in that: The method comprises: Obtain the sedimentary characteristics, porosity and component ion distribution of the mineral area to be estimated; Dividing the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculating the sum of the first resource amounts corresponding to the first blocks to obtain the total first resource amount corresponding to the mining area to be estimated; Dividing the mining area to be estimated into a plurality of second blocks according to the porosity, and calculating the sum of the second resource amounts corresponding to each of the second blocks to obtain the total second resource amount corresponding to the mining area to be estimated; Dividing the mining area to be estimated into a plurality of third blocks according to the component ion distribution, and calculating the sum of the third resource amounts corresponding to each of the third blocks to obtain the total third resource amount corresponding to the mining area to be estimated; Calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

2. The method according to claim 1, characterized in that The sedimentary characteristics include sedimentary thickness distribution, and the mining area to be estimated is divided into a plurality of first blocks according to the sedimentary characteristics, and the sum of the first resource amounts corresponding to each of the first blocks is calculated to obtain the total first resource amount corresponding to the mining area to be estimated, including: Dividing the mining area to be estimated into a plurality of first blocks according to the sediment thickness distribution; Constructing a first three-dimensional ore body model corresponding to each of the first blocks, and calculating a first block volume of each of the first blocks; Calculate the first resource amount corresponding to each of the first blocks according to the volume of each of the first blocks; The first resource quantities corresponding to each of the first blocks are summed to obtain the total first resource quantity corresponding to the mining area to be estimated.

3. The method according to claim 1, characterized in that The method of dividing the mining area to be estimated into a plurality of second blocks according to the porosity, and calculating the sum of the second resource amounts corresponding to each of the second blocks to obtain the total second resource amount corresponding to the mining area to be estimated includes: Dividing the mining area to be estimated into a plurality of second blocks according to the porosity distribution; Constructing a second three-dimensional ore body model corresponding to each of the second blocks, and calculating the second block volume of each of the second blocks; Calculate the second resource amount corresponding to each of the second blocks according to the volume of each of the second blocks; The second resource quantities corresponding to each of the second blocks are summed to obtain the total second resource quantity corresponding to the mining area to be estimated.

4. The method according to claim 1, characterized in that: The component ion distribution includes potassium ion distribution, and the mining area to be estimated is divided into a plurality of third blocks according to the component ion distribution, and the sum of the third resource amounts corresponding to each of the third blocks is calculated to obtain the total third resource amount corresponding to the mining area to be estimated, including: Dividing the mining area to be estimated into a plurality of third blocks according to the potassium ion distribution; Constructing a third three-dimensional ore body model corresponding to each of the third blocks, and calculating the third block volume of each of the third blocks; Calculate the third resource amount corresponding to each of the third blocks according to the volume of each of the third blocks; The third resource quantities corresponding to each of the third blocks are summed to obtain the total third resource quantity corresponding to the mining area to be estimated.

5. The method according to claim 4, characterized in that After constructing the third three-dimensional ore body model corresponding to each of the third blocks and calculating the third block volume of each of the third blocks, the method further includes: Determine whether there is the third block segment with the same grade; If the third blocks with the same grade exist, the third blocks with the same grade are classified into the third block types of the same category.

6. The method according to claim 1, characterized in that The obtaining of sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated includes: Acquire sampling data of a plurality of sample points in the mining area to be estimated, wherein the spatial positions of the sample points are different; The sampling data are analyzed to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated.

7. The method according to any one of claims 2 to 4, characterized in that: The sediment thickness distribution or the porosity distribution or the potassium ion distribution of the mining area to be estimated is a planar distribution diagram in the horizontal direction, and the planar distribution diagram also includes a depth value in the vertical direction.

8. A system for improving the estimation accuracy of liquid mineral resources, characterized in that: The system comprises: Data acquisition module, used to obtain the sedimentary characteristics, porosity and component ion distribution of the mining area to be estimated; A first calculation module, used for dividing the mining area to be estimated into a plurality of first blocks according to the sedimentary characteristics, and calculating the sum of the first resource quantities corresponding to the first blocks to obtain the total first resource quantity corresponding to the mining area to be estimated; A second calculation module is used to divide the mining area to be estimated into a plurality of second blocks according to the porosity, and calculate the sum of the second resource quantities corresponding to each of the second blocks to obtain the total second resource quantity corresponding to the mining area to be estimated; A third calculation module, used for dividing the mining area to be estimated into a plurality of third blocks according to the component ion distribution, and calculating the sum of the third resource amounts corresponding to each of the third blocks to obtain the total third resource amount corresponding to the mining area to be estimated; The mean calculation module is used to calculate the average value of the first total resource amount, the second total resource amount and the third total resource amount to obtain the mineral resource amount corresponding to the mining area to be estimated.

9. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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