Method, device and medium for judging uranium-rich grade based on logging response

Abstract

This invention discloses a method, apparatus, equipment, and medium for determining uranium enrichment levels based on well logging response. The method includes: constructing a correspondence between multiple evaluation parameters in the well logging response results and uranium content levels based on pre-statistically analyzed well logging response results and target formations of candidate wells; determining multiple target evaluation parameters in the well logging response results of the target formation of the well to be evaluated, and constructing a parameter feature matrix based on the target evaluation parameters; constructing an evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and the correspondence; determining fuzzy similarity parameters between the parameter feature matrix and each evaluation matrix based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, and determining the target uranium content level of the target formation of the well to be evaluated based on the fuzzy similarity parameters, as the uranium enrichment level determination result. This invention achieves objective and accurate acquisition of uranium-enriched areas, improving the efficiency and accuracy of uranium exploration.

Description

Technical Field

[0001] This invention relates to the field of sandstone-type uranium exploration technology, and in particular to a method, apparatus, equipment and medium for determining uranium enrichment level based on well logging response. Background Technology

[0002] With the rapid development of nuclear power, the demand for uranium, the main fuel for nuclear power, continues to rise. Sandstone-type uranium deposits, with their clean, environmentally friendly, and economical characteristics, have become the main targets of uranium exploration. Most sandstone-type uranium deposits are blind deposits buried deep in oil and gas basins. Early exploration mainly focused on areas shallower than 500 meters in the basin, while most areas in deep basins are exploration blind zones, making mineral exploration difficult.

[0003] In recent years, the discovery of sandstone-type uranium deposits has largely been based on radioactive anomalies from well logging in oil and gas fields / coalfields, highlighting the crucial role of geophysical logging in uranium exploration. Identifying uranium reservoirs through well logging and subsequently predicting uranium deposits is a complex and systematic task. Traditional linear models, while capable of accurate description and processing, suffer from significant errors. Machine learning methods, such as neural networks, support vector machines, and Naive Bayes, while possessing excellent nonlinear fitting capabilities, also have their limitations. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for determining uranium enrichment level based on well logging response, in order to solve the problem of inaccurate acquisition of grade in sandstone-type uranium deposits.

[0005] According to one aspect of the present invention, a method for determining uranium richness level based on well logging response is provided, comprising:

[0006] Based on the pre-statistical logging response results and uranium content levels of the target intervals of candidate wells, a correspondence between multiple evaluation parameters in the logging response results and uranium content levels is constructed.

[0007] Multiple target evaluation parameters are determined from the logging response results of the target interval of the well to be evaluated, and a parameter characterization matrix is ​​constructed based on the target evaluation parameters;

[0008] An evaluation matrix corresponding to each uranium content level is constructed based on the target evaluation parameters and the corresponding relationship.

[0009] Based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix are determined, and the target uranium content level of the target layer of the well to be evaluated is determined based on the fuzzy similarity parameters, which serves as the uranium enrichment level judgment result.

[0010] According to another aspect of the present invention, a device for determining uranium enrichment level based on well logging response is provided, comprising:

[0011] The correspondence construction module is used to construct the correspondence between multiple evaluation parameters in the logging response results and uranium content levels based on the pre-statistical logging response results and uranium content levels of the target intervals of candidate wells;

[0012] The parameter characterization matrix construction module is used to determine multiple target evaluation parameters in the logging response results of the target layer of the well to be evaluated, and to construct a parameter characterization matrix based on the target evaluation parameters.

[0013] The evaluation matrix construction module is used to construct an evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and the corresponding relationship.

[0014] The uranium enrichment level determination module is used to determine the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, and to determine the target uranium content level of the target layer of the well to be evaluated based on the fuzzy similarity parameters, as the uranium enrichment level determination result.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the uranium-rich level determination method based on well logging response as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the uranium-rich level determination method based on well logging response as described in any embodiment of the present invention.

[0020] This invention provides a method for identifying enriched uranium zones in sandstone-type uranium deposits based on well logging response. It constructs a parameter characterization matrix of the target zone of the well to be evaluated and evaluation matrices for various uranium content levels. Fuzzy similarity parameters are calculated between the evaluation matrices and the parameter characterization matrices, and fuzzy pattern recognition is performed. Based on the magnitude of the fuzzy similarity parameters, the uranium enrichment status of the target zone of the well to be evaluated is obtained. This invention features a simple calculation process that does not rely on human subjective experience, achieving objective and accurate acquisition of uranium-enriched areas. It provides methodological support for preliminary investigations of uranium-enriched areas, improves exploration efficiency and accuracy, and lays the foundation for efficient exploration and development of sandstone-type uranium deposits.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a method for determining uranium enrichment level based on well logging response according to an embodiment of the present invention;

[0024] Figure 2 This is an algorithm diagram of another method for determining uranium richness level based on well logging response provided by an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of a uranium-rich level determination device based on well logging response according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the uranium-rich level determination method based on well logging response according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "candidate," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Figure 1 This invention provides a flowchart of a method for determining uranium enrichment levels based on well logging response. This embodiment is applicable to situations where the grade of sandstone-type uranium deposits is accurately identified using well logging response data. This method can be executed by a well logging response-based uranium enrichment level determination device, which can be implemented in hardware and / or software and configured on a server with computing power. Figure 1 As shown, the method includes:

[0030] S110. Based on the pre-statistical logging response results and uranium content levels of the target intervals of the candidate wells, construct the correspondence between multiple evaluation parameters in the logging response results and uranium content levels.

[0031] In this context, the target intervals of candidate wells refer to multiple intervals within exploration wells whose uranium content levels have been determined through other measurement methods. Multiple candidate wells are used to ensure the accuracy of the correspondence determination. Several evaluation parameters are identified from the logging response results of the target intervals of these candidate wells. These evaluation parameters are related to the uranium enrichment level. For example, multiple candidate parameters from the logging response results of the target intervals of the candidate wells are pre-calculated, and the correlation parameters between each candidate parameter and the uranium enrichment level are determined. Based on these correlation parameters, multiple evaluation parameters with high correlation are selected from the candidate parameters. The number of selected evaluation parameters can be determined based on the actual correlation parameter determination results. In a feasible embodiment, the evaluation parameters include at least natural gamma, radioactivity, density, and resistivity. A uranium enrichment level classification scheme for sandstone-type uranium deposits is established based on the logging response results of the target intervals of multiple candidate wells and the uranium content levels, including multiple evaluation parameters and corresponding multiple uranium content levels. In a feasible embodiment, the correspondence includes the parameter range for each evaluation parameter corresponding to each uranium content level. The classification of uranium content grades can be determined according to the actual scenario. For example, in this embodiment of the invention, it is divided into three grades: good uranium quality (Ⅰ), medium uranium quality (Ⅱ), and poor uranium quality (Ⅲ).

[0032] Table 1 shows the schematic diagram of the correspondence between multiple evaluation parameters in the well logging response results and the uranium content level. The data in the table are determined based on the selection of the target layer of the candidate well and the required accuracy of the scenario.

[0033] Table 1

[0034]

[0035] S120. Determine multiple target evaluation parameters in the logging response results of the target layer of the well to be evaluated, and construct a parameter characterization matrix based on the target evaluation parameters.

[0036] The target stratum of the well to be evaluated refers to the geological stratum in the well to which the uranium enrichment level needs to be determined. The target evaluation parameters refer to multiple or all evaluation parameters in the corresponding relationship.

[0037] Specifically, multiple target evaluation parameters are obtained from the logging response results of the target layer of the well to be evaluated. The target evaluation parameters include natural gamma, radioactivity, density and resistivity. Since the order of magnitude of the target evaluation parameters are not the same, in order to ensure the accuracy of subsequent fuzzy identification, the target evaluation parameters are characterized, and a parameter characteristic matrix is ​​constructed based on the characteristic-processed target evaluation parameters.

[0038] In one feasible embodiment, S120 includes:

[0039] Based on the relationship between target evaluation parameters and uranium enrichment, the target evaluation parameters are classified into positively correlated evaluation parameters and negatively correlated evaluation parameters.

[0040] Based on the classification results and correspondence of the target evaluation parameters, the target evaluation parameters are characterized to obtain the parameter characteristic matrix.

[0041] First, the target evaluation parameters are categorized. Higher values ​​of the characteristic parameters for positively correlated quantitative parameters are more conducive to uranium enrichment, while lower values ​​of the characteristic parameters for negatively correlated quantitative parameters are more conducive to uranium enrichment. In this embodiment, the positively correlated parameters are natural gamma, radioactivity, and resistivity; the negatively correlated parameter is density.

[0042] Different feature processing methods are applied to target evaluation parameters based on their different types to make the feature processing results more accurate. For example, since target evaluation parameters include positively correlated and negatively correlated evaluation parameters, the feature processing methods for the two types of parameters are opposite to construct an accurate parameter feature matrix.

[0043] In a feasible embodiment, the target evaluation parameters are characterized based on the classification results and correspondences of the target evaluation parameters to obtain a parameter characteristic matrix, including:

[0044] If the target evaluation parameter is a positively correlated evaluation parameter, then the target uranium content level and parameter range type corresponding to the target evaluation parameter are determined according to the parameter range of each uranium content level in the correspondence relationship.

[0045] If the parameter range type is the first type of parameter range, then the target evaluation parameters are characterized based on the first characteristic processing formula;

[0046] If the parameter range type is the second type of parameter range, then the target evaluation parameters are characterized based on the second characteristic processing formula;

[0047] The parameter characterization matrix is ​​determined based on the target evaluation parameters and target uranium content level after characterization processing;

[0048] Wherein, the range of the first type of parameter is (m, n], the range of the second type of parameter is (n, +∞), and the first feature processing formula is: The second feature processing formula is p z =1,x z p is a positive correlation evaluation parameter. z These are the positive correlation evaluation parameters after feature processing.

[0049] Characterization of positively correlated evaluation parameters: Obtain the parameter values ​​of each positively correlated evaluation parameter in the target interval of the well to be evaluated, compare the parameter values ​​of the positively correlated evaluation parameters with the parameter range of each uranium content level corresponding to the target evaluation parameter in the corresponding relationship, and characterize the positively correlated evaluation parameters using the following formula:

[0050]

[0051] Where, p z x is the positive correlation evaluation parameter after feature processing. z represents the parameter value of the positive correlation evaluation parameter; (m,n] and (n,+∞) represent the parameter range of the evaluation result corresponding to the parameter value of the positive correlation evaluation parameter in the corresponding relationship.

[0052] For example, if the natural gamma of the actual logging curve of a borehole is 440 API, according to Table 1, this value belongs to Class II (a2, a1). Since natural gamma is a positively correlated evaluation parameter, substituting it into the formula yields a characteristic value of 0.3. That is, the value of natural gamma in Class II is "0.3", and the value in other uranium content levels is "0", and its matrix expression is [0, 0.3, 0]. The determination process for other positively correlated evaluation parameters follows the same logic.

[0053] In a feasible embodiment, the target evaluation parameters are characterized based on the classification results and correspondences of the target evaluation parameters to obtain a parameter characteristic matrix, including:

[0054] If the target evaluation parameter is a negatively correlated evaluation parameter, then the target uranium content level and parameter range type corresponding to the target evaluation parameter are determined according to the parameter range of each uranium content level in the correspondence relationship.

[0055] If the parameter range type is the first type of parameter range, then the target evaluation parameters are characterized based on the third characteristic processing formula;

[0056] If the parameter range type is the second type of parameter range, then the target evaluation parameters are characterized based on the fourth characteristic processing formula;

[0057] The parameter characterization matrix is ​​determined based on the target evaluation parameters and target uranium content level after characterization processing;

[0058] The first type of parameter ranges from (m, n], the second type of parameter ranges from (n, +∞), and the third characteristic processing formula is... The fourth feature processing formula is p f =0, x f p is a negative correlation evaluation parameter. f These are the negative correlation evaluation parameters after feature processing.

[0059] Characterization of negative correlation evaluation parameters: Obtain the parameter values ​​of each negative correlation evaluation parameter in the target interval of the well to be evaluated, compare the parameter values ​​of the negative correlation evaluation parameters with the parameter range of each uranium content level corresponding to the target evaluation parameter in the corresponding relationship, and characterize the negative correlation evaluation parameters using the following formula:

[0060]

[0061] Where pf is the negative correlation evaluation parameter after feature processing, xf is the parameter value of the negative correlation evaluation parameter; (m,n] and (n,+∞) are the parameter ranges of the evaluation results corresponding to the parameter values ​​of the negative correlation evaluation parameters in the corresponding relationship.

[0062] For example, if the density of the actual logging curve of a borehole is 2.195 g*cm⁻³, according to Table 1, this value belongs to Class I [0, d1). Since density is a negatively correlated evaluation parameter, substituting it into the above formula yields a characteristic value of 0.0023. That is, the density value is "0.0023" under Class I, and "0" under other uranium content levels. Its matrix expression is [0, 0.0023, 0]. The determination process for other negatively correlated evaluation parameters follows the same logic.

[0063] In one feasible embodiment, the parameter characterization matrix is ​​determined based on the characteristicized target evaluation parameters and the target uranium content level, including:

[0064] Set the matrix elements corresponding to the target uranium content level of the target evaluation parameters as the characteristic-processed target evaluation parameters;

[0065] Set the matrix elements corresponding to other uranium content levels for the target evaluation parameters to 0;

[0066] The matrix corresponding to each target evaluation parameter is converted into a column vector and then concatenated to obtain the parameter feature matrix.

[0067] After characterizing the target evaluation parameters of the target interval of the well to be evaluated, a parameter characterization matrix is ​​constructed using the characteristic values ​​of each target evaluation parameter. The position of each parameter characteristic value in the matrix corresponds to the position of the target uranium content level in the corresponding relationship, and the position value of other uranium content levels is 0. For example, The result after column vector transformation and concatenation is (n is the number of target evaluation parameters, and in this embodiment of the invention, n is 4).

[0068] S130. Construct an evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and their corresponding relationships.

[0069] The uranium content grading system is divided into three levels: Class I, Class II, and Class III. Therefore, in order to facilitate fuzzy pattern recognition of the three levels in the evaluation system, we construct Qq (q = I, II, III) as the evaluation matrix corresponding to each uranium content level.

[0070] In one feasible embodiment, S130 includes:

[0071] Set the matrix elements of one uranium content level corresponding to each target evaluation parameter to 1, and set the matrix elements of other uranium content levels to 0, to construct a single evaluation matrix corresponding to each target evaluation parameter of that uranium content level.

[0072] The individual evaluation matrix corresponding to each target evaluation parameter of the uranium content level is converted into a column vector and then concatenated to obtain the evaluation matrix corresponding to the uranium content level.

[0073] The evaluation matrix corresponding to each uranium content level is as follows:

[0074] Q I =[x i1 =1x i2 =0x i3 =0];

[0075] Q II =[x i1 =0x i2 =1x i3 =0];

[0076] Q III =[x i1 =0x i2 =0x i3 =1];

[0077] In the formula, i = 1, 2, ..., n (n is the number of target evaluation parameters; in this embodiment, n is taken as 4); x ij For the elements in the evaluation matrix, "0" and "1" represent the standard values ​​of the evaluation parameters at that uranium content level.

[0078] The result after column vector transformation and concatenation is

[0079] S140. Based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, determine the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix, and determine the target uranium content level of the target layer of the well to be evaluated based on the fuzzy similarity parameters, as the uranium enrichment level judgment result.

[0080] Fuzzy pattern recognition is performed on the parameter feature matrix of the target segment of the well to be evaluated and each evaluation matrix. The closeness between the parameter feature matrix of the target segment of the well to be evaluated and each evaluation matrix is ​​calculated. For example, the similarity between the parameter feature matrix of the target segment of the well to be evaluated and each evaluation matrix is ​​determined by the similarity calculation method, and used as the fuzzy similarity parameter.

[0081] The target uranium content level of the target layer of the well to be evaluated is determined based on the comparison results of the fuzzy similarity parameters corresponding to the similarity of each evaluation matrix, and the target uranium content level is used as the uranium-rich level judgment result.

[0082] In one feasible embodiment, S140 includes:

[0083] The cosine similarity between the parameter feature matrix and each evaluation matrix is ​​determined separately, and used as the fuzzy similarity parameter between the parameter feature matrix and each evaluation matrix.

[0084] Determine the maximum fuzzy similarity parameter among the fuzzy similarity parameters corresponding to each evaluation matrix and take the uranium content level of the evaluation matrix corresponding to the maximum fuzzy similarity parameter as the target uranium content level;

[0085] The uranium enrichment level is determined based on the target uranium content level.

[0086] The parameter characterization matrix A of the target interval of the well to be evaluated and each evaluation matrix B q Cosine similarity α(A, B) q The calculation formula for ) is as follows:

[0087]

[0088] In the formula, i = 1, 2, ..., n (n is the number of target evaluation parameters, and n is 4 in this embodiment); j = 1, 2, 3; q = I, II, III.

[0089] Table 2 shows the fuzzy similarity parameters for each uranium content level at different sampling depths in the target section of the well to be evaluated.

[0090] Table 2

[0091]

[0092] By comparing the target formation of the well under evaluation with the fuzzy similarity parameters of the three uranium content grades, the larger the fuzzy similarity parameter, the higher the uranium grade of the formation. The fuzzy similarity parameters are ranked, and based on the ranking results, in this embodiment, the uranium content grade with the largest fuzzy similarity parameter value is selected as the final target uranium content grade. For example, in Table 2, the proximity of the three uranium content grades at 462.1m clearly shows that the fuzzy similarity parameter value of grade I is much larger than the other two. Therefore, the target uranium content grade for this formation is grade I.

[0093] This invention provides a method for identifying enriched uranium zones in sandstone-type uranium deposits based on well logging response. It constructs a parameter characterization matrix of the target zone of the well to be evaluated and evaluation matrices for various uranium content levels. Fuzzy similarity parameters are calculated between the evaluation matrices and the parameter characterization matrices, and fuzzy pattern recognition is performed. Based on the magnitude of the fuzzy similarity parameters, the uranium enrichment status of the target zone of the well to be evaluated is obtained. This invention features a simple calculation process that does not rely on human subjective experience, achieving objective and accurate acquisition of uranium-enriched areas. It provides methodological support for preliminary investigations of uranium-enriched areas, improves exploration efficiency and accuracy, and lays the foundation for efficient exploration and development of sandstone-type uranium deposits.

[0094] Figure 2 This is an algorithm diagram for another method for determining uranium richness level based on well logging response, provided by an embodiment of the present invention. This embodiment further refines the above embodiment. Figure 2 As shown, the method includes:

[0095] Step 1: Determine multiple target evaluation parameters from the logging response results of the target interval of the well to be evaluated, and classify these parameters into qualitative and quantitative parameters. Quantitative parameters are those with accurate values, while qualitative parameters are those without accurate values. Based on the relationship between the target evaluation parameters and uranium enrichment, the quantitative parameters are further classified into positively correlated and negatively correlated evaluation parameters. For the classification results of the target evaluation parameters, different characteristic processing formulas are used to characterize the different categories of target evaluation parameters, resulting in characteristic-processed target evaluation parameters.

[0096] Step 2: Based on the logging response results and uranium content levels of the target intervals of the candidate wells as pre-statistically analyzed, construct the correspondence between multiple evaluation parameters in the logging response results and uranium content levels. Determine the interval evaluation system based on this correspondence, and construct the evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and the correspondence.

[0097] Step 3: Convert the evaluation matrix corresponding to each uranium content level into a column vector, and convert the evaluation parameter matrix composed of the characteristic-processed target evaluation parameters into a column vector.

[0098] Step 4: Perform fuzzy recognition on the evaluation matrix and evaluation parameter matrix converted into column vectors, and determine the fuzzy similarity parameter, i.e., fuzzy proximity, between the parameter feature matrix and each evaluation matrix.

[0099] Step 5: Determine the maximum fuzzy similarity parameter among the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix, and take the uranium content level of the evaluation matrix corresponding to the maximum fuzzy similarity parameter as the target uranium content level; determine the uranium enrichment level judgment result based on the target uranium content level.

[0100] Classifying and identifying reservoirs using their comprehensive response characteristics is characterized by fuzziness and uncertainty. Fuzzy pattern recognition, as a novel pattern recognition method, has played a significant role in many fields, solving problems that traditional pattern recognition methods cannot or making classification decisions more accurate. Identifying uranium-rich zones based on well logging data can be considered a typical fuzzy pattern recognition process, classifying uranium-rich levels within sandstone. Therefore, applying fuzzy pattern recognition methods to establish well logging models for uranium-rich zones and to identify uranium ore levels shows promising development prospects. In the process of exploring sandstone-type uranium deposits using oil and gas field well logging, fuzzy pattern recognition can more accurately and effectively identify ore-bearing layers of different levels, providing technical support for uranium ore classification decisions.

[0101] This invention provides a method for identifying enriched uranium zones in sandstone-type uranium deposits based on well logging response. It constructs a parameter characterization matrix of the target zone of the well to be evaluated and evaluation matrices for various uranium content levels. Fuzzy similarity parameters are calculated between the evaluation matrices and the parameter characterization matrices, and fuzzy pattern recognition is performed. Based on the magnitude of the fuzzy similarity parameters, the uranium enrichment status of the target zone of the well to be evaluated is obtained. This invention features a simple calculation process that does not rely on human subjective experience, achieving objective and accurate acquisition of uranium-enriched areas. It provides methodological support for preliminary investigations of uranium-enriched areas, improves exploration efficiency and accuracy, and lays the foundation for efficient exploration and development of sandstone-type uranium deposits.

[0102] Figure 3 This is a schematic diagram of a uranium-rich level determination device based on well logging response, provided as an embodiment of the present invention. Figure 3 As shown, the device includes:

[0103] The correspondence construction module 310 is used to construct the correspondence between multiple evaluation parameters in the logging response results and uranium content levels based on the logging response results and uranium content levels of the target layer of the candidate wells in the pre-statistical data.

[0104] The parameter characterization matrix construction module 320 is used to determine multiple target evaluation parameters in the logging response results of the target layer of the well to be evaluated, and to construct a parameter characterization matrix based on the target evaluation parameters.

[0105] The evaluation matrix construction module 330 is used to construct the evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and their corresponding relationships.

[0106] The uranium enrichment level judgment module 340 is used to determine the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, and to determine the target uranium content level of the target layer of the well to be evaluated based on the fuzzy similarity parameters, as the uranium enrichment level judgment result.

[0107] This invention provides a method for identifying enriched uranium zones in sandstone-type uranium deposits based on well logging response. It constructs a parameter characterization matrix of the target zone of the well to be evaluated and evaluation matrices for various uranium content levels. Fuzzy similarity parameters are calculated between the evaluation matrices and the parameter characterization matrices, and fuzzy pattern recognition is performed. Based on the magnitude of the fuzzy similarity parameters, the uranium enrichment status of the target zone of the well to be evaluated is obtained. This invention features a simple calculation process that does not rely on human subjective experience, achieving objective and accurate acquisition of uranium-enriched areas. It provides methodological support for preliminary investigations of uranium-enriched areas, improves exploration efficiency and accuracy, and lays the foundation for efficient exploration and development of sandstone-type uranium deposits.

[0108] Optional, the parameterized feature matrix construction module includes:

[0109] The evaluation parameter classification unit is used to classify the target evaluation parameters according to the relationship between the target evaluation parameters and uranium enrichment, and to obtain positively correlated evaluation parameters and negatively correlated evaluation parameters.

[0110] The evaluation parameter feature processing unit is used to perform feature processing on the target evaluation parameters based on the classification results and correspondence of the target evaluation parameters, and obtain the parameter feature matrix.

[0111] Optionally, the correspondence may include the parameter range for each evaluation parameter corresponding to each uranium content level.

[0112] Optionally, the evaluation parameter characterization processing unit includes:

[0113] The first parameter determination sub-unit is used to determine the target uranium content level and parameter range type corresponding to the target evaluation parameter based on the parameter range of each uranium content level in the correspondence relationship if the target evaluation parameter is a positively correlated evaluation parameter.

[0114] The first feature processing subunit is used to perform feature processing on the target evaluation parameters based on the first feature processing formula if the parameter range type is the first type of parameter range.

[0115] The second feature processing subunit is used to perform feature processing on the target evaluation parameters based on the second feature processing formula if the parameter range type is the second type of parameter range.

[0116] The parameter characterization matrix determination sub-unit is used to determine the parameter characterization matrix based on the target evaluation parameters and target uranium content level after characterization processing;

[0117] Wherein, the range of the first type of parameter is (m, n], the range of the second type of parameter is (n, +∞), and the first feature processing formula is: The second feature processing formula is p z =1,x z p is a positive correlation evaluation parameter. z These are the positive correlation evaluation parameters after feature processing.

[0118] Optionally, the evaluation parameter characterization processing unit includes:

[0119] The second parameter determination subunit is used to determine the target uranium content level and parameter range type corresponding to the target evaluation parameter based on the parameter range of each uranium content level in the correspondence relationship if the target evaluation parameter is a negatively correlated evaluation parameter.

[0120] The third feature processing subunit is used to perform feature processing on the target evaluation parameters based on the third feature processing formula if the parameter range type is the first type of parameter range.

[0121] The fourth feature processing subunit is used to perform feature processing on the target evaluation parameters based on the fourth feature processing formula if the parameter range type is the second type parameter range.

[0122] The parameter characterization matrix determination sub-unit is used to determine the parameter characterization matrix based on the target evaluation parameters and target uranium content level after characterization processing;

[0123] The first type of parameter ranges from (m, n], the second type of parameter ranges from (n, +∞), and the third characteristic processing formula is... The fourth feature processing formula is p f =0, x f p is a negative correlation evaluation parameter. f These are the negative correlation evaluation parameters after feature processing.

[0124] Optionally, the parameterized feature matrix determines the sub-unit, specifically used for:

[0125] Set the matrix elements corresponding to the target uranium content level of the target evaluation parameters as the characteristic-processed target evaluation parameters;

[0126] Set the matrix elements corresponding to other uranium content levels for the target evaluation parameters to 0;

[0127] The matrix corresponding to each target evaluation parameter is converted into a column vector and then concatenated to obtain the parameter feature matrix.

[0128] Optional, the evaluation matrix construction module, specifically used for:

[0129] Set the matrix elements of one uranium content level corresponding to each target evaluation parameter to 1, and set the matrix elements of other uranium content levels to 0, to construct a single evaluation matrix corresponding to each target evaluation parameter of that uranium content level.

[0130] The individual evaluation matrix corresponding to each target evaluation parameter of the uranium content level is converted into a column vector and then concatenated to obtain the evaluation matrix corresponding to the uranium content level.

[0131] Optional, the uranium enrichment level determination module includes:

[0132] The cosine similarity between the parameter feature matrix and each evaluation matrix is ​​determined separately, and used as the fuzzy similarity parameter between the parameter feature matrix and each evaluation matrix.

[0133] Determine the maximum fuzzy similarity parameter among the fuzzy similarity parameters corresponding to each evaluation matrix and take the uranium content level of the evaluation matrix corresponding to the maximum fuzzy similarity parameter as the target uranium content level;

[0134] The uranium enrichment level is determined based on the target uranium content level.

[0135] Optionally, the evaluation parameters may include at least natural gamma, radioactivity, density, and resistivity.

[0136] The uranium-rich level determination device based on well logging response provided in this embodiment of the invention can execute the uranium-rich level determination method based on well logging response provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0137] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals.

[0138] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0139] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0140] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0141] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0142] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the uranium enrichment level determination method based on well logging response.

[0143] In some embodiments, the uranium enrichment level determination method based on well logging response can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the uranium enrichment level determination method based on well logging response described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the uranium enrichment level determination method based on well logging response by any other suitable means (e.g., by means of firmware).

[0144] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific reference products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0148] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data servers), or computing systems that include switching components (e.g., application servers), or computing systems that include front-end components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such back-end, switching, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0149] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0150] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0151] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining uranium enrichment level based on well logging response, characterized in that, The method includes: Based on the pre-statistical logging response results and uranium content levels of the target intervals of candidate wells, a correspondence between multiple evaluation parameters in the logging response results and uranium content levels is constructed. Multiple target evaluation parameters are determined from the logging response results of the target interval of the well to be evaluated, and a parameter characterization matrix is ​​constructed based on the target evaluation parameters; An evaluation matrix corresponding to each uranium content level is constructed based on the target evaluation parameters and the corresponding relationship. Based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix are determined, and the target uranium content level of the target layer of the well to be evaluated is determined based on the fuzzy similarity parameters, which serves as the uranium enrichment level judgment result.

2. The method of claim 1, wherein, Multiple target evaluation parameters are determined from the logging response results of the target interval of the well to be evaluated, and a parameter characterization matrix is ​​constructed based on the target evaluation parameters, including: Based on the relationship between the target evaluation parameters and uranium enrichment, the target evaluation parameters are classified to obtain positively correlated evaluation parameters and negatively correlated evaluation parameters. Based on the classification results and the corresponding relationship of the target evaluation parameters, the target evaluation parameters are characterized to obtain the parameter characteristic matrix.

3. The method of claim 2, wherein, The correspondence includes the parameter range for each evaluation parameter corresponding to each uranium content level.

4. The method of claim 3, wherein, Based on the classification results and the corresponding relationship of the target evaluation parameters, the target evaluation parameters are characterized to obtain a parameter characteristic matrix, including: If the target evaluation parameter is a positively correlated evaluation parameter, then the target uranium content level and parameter range type corresponding to the target evaluation parameter are determined according to the parameter range of each uranium content level corresponding to the target evaluation parameter in the correspondence relationship; If the parameter range type is the first type of parameter range, then the target evaluation parameter is characterized based on the first characteristic processing formula; If the parameter range type is the second type of parameter range, then the target evaluation parameter is characterized based on the second feature processing formula; The parameter characterization matrix is ​​determined based on the target evaluation parameters after characterization and the target uranium content level; Wherein, the first type parameter range is (m, n], the second type parameter range is (n, +∞), the first characteristic processing formula is The second characteristic processing formula is p z =1, x z is a positive correlation evaluation parameter, p z is a positive correlation evaluation parameter after characteristic processing.

5. The method of claim 3, wherein, Based on the classification results and the corresponding relationship of the target evaluation parameters, the target evaluation parameters are characterized to obtain a parameter characteristic matrix, including: If the target evaluation parameter is a negatively correlated evaluation parameter, then the target uranium content level and parameter range type corresponding to the target evaluation parameter are determined according to the parameter range of each uranium content level corresponding to the target evaluation parameter in the correspondence relationship; If the parameter range type is the first type of parameter range, then the target evaluation parameter is characterized based on the third characteristic processing formula; If the parameter range type is the second type of parameter range, then the target evaluation parameter is characterized based on the fourth characteristic processing formula; The parameter characterization matrix is ​​determined based on the target evaluation parameters after characterization and the target uranium content level; Wherein, the first type parameter range is (m, n], the second type parameter range is (n, +∞), and the third characteristic processing formula is The fourth characteristic processing formula is p f = 0, x f is a negative correlation evaluation parameter, p f is a negative correlation evaluation parameter after characteristic processing.

6. The method according to claim 4 or 5, characterized in that, The parameter characterization matrix is ​​determined based on the characteristic-processed target evaluation parameters and the target uranium content level, including: Set the matrix elements of the target uranium content level corresponding to the target evaluation parameters as the characteristic-processed target evaluation parameters; Set the matrix elements corresponding to other uranium content levels for the target evaluation parameters to 0; The matrix corresponding to each target evaluation parameter is converted into a column vector and then concatenated to obtain the parameter feature matrix.

7. The method of claim 1, wherein, An evaluation matrix is ​​constructed for each uranium content level based on the target evaluation parameters and the corresponding relationship, including: Set the matrix elements of one uranium content level corresponding to each target evaluation parameter to 1, and set the matrix elements of other uranium content levels to 0, to construct a single evaluation matrix corresponding to each target evaluation parameter of that uranium content level. The individual evaluation matrix corresponding to each target evaluation parameter of the uranium content level is converted into a column vector and then concatenated to obtain the evaluation matrix corresponding to the uranium content level.

8. The method of claim 1, wherein, Based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix are determined, and the target uranium content level of the target layer of the well to be evaluated is determined based on the fuzzy similarity parameters as the uranium enrichment level judgment result, including: The cosine similarity between the parameter feature matrix and each evaluation matrix is ​​determined respectively, and used as the fuzzy similarity parameter between the parameter feature matrix and each evaluation matrix; The maximum fuzzy similarity parameter among the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix is ​​determined, and the uranium content level of the evaluation matrix corresponding to the maximum fuzzy similarity parameter is taken as the target uranium content level. The uranium enrichment level is determined based on the target uranium content level.

9. The method of claim 1, wherein, in, The evaluation parameters include at least natural gamma, radioactivity, density, and resistivity.

10. A device for determining uranium enrichment level based on well logging response, characterized in that, The device includes: The correspondence construction module is used to construct the correspondence between multiple evaluation parameters in the logging response results and uranium content levels based on the pre-statistical logging response results and uranium content levels of the target intervals of candidate wells; The parameter characterization matrix construction module is used to determine multiple target evaluation parameters in the logging response results of the target layer of the well to be evaluated, and to construct a parameter characterization matrix based on the target evaluation parameters. The evaluation matrix construction module is used to construct an evaluation matrix corresponding to each uranium content level based on the target evaluation parameters and the corresponding relationship. The uranium enrichment level determination module is used to determine the fuzzy similarity parameters corresponding to the parameter feature matrix and each evaluation matrix based on the fuzzy recognition results of the parameter feature matrix and each evaluation matrix, and to determine the target uranium content level of the target layer of the well to be evaluated based on the fuzzy similarity parameters, as the uranium enrichment level determination result.

11. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the uranium-rich level determination method based on well logging response as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the uranium-rich level determination method based on well logging response as described in any one of claims 1-9.

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