A method and device for grading evaluation of a helium-rich favorable zone

Abstract

The application discloses a helium enrichment favorable area grading evaluation method and device. The method comprises the following steps: obtaining a plurality of geological factors related to helium reserve abundance of a high exploration degree scale area, and determining a scatter diagram of each geological factor and the helium reserve abundance; further determining a relationship equation and a correlation coefficient of each geological factor and the helium reserve abundance; for each geological factor, a geological factor with a correlation coefficient greater than a correlation coefficient threshold value between the geological factor and the helium reserve abundance is regarded as a related geological factor; according to the relationship equation of all the related geological factors and the helium reserve abundance, an initial model is constructed, weight coefficients and regression coefficients of the initial model are determined, and a prediction model is obtained; according to parameters of each related geological factor of a target area, the target area helium resource abundance distribution is predicted based on the prediction model; and according to the target area helium resource abundance distribution, the target area is divided into a plurality of different grade helium resource grading favorable areas.

Description

Technical Field

[0001] This invention relates to a method and apparatus for classifying and evaluating the beneficial properties of helium enrichment. Background Technology

[0002] my country's helium resources account for 1.8% of the global total, while its production accounts for only 0.3%, resulting in an extremely high dependence on imports (current data shows it exceeds 95%). Helium has become a critically scarce strategic resource. Currently, conducting helium resource assessments to understand the potential and distribution of helium resources in key domestic and international sectors, and selecting favorable enrichment areas to ensure national energy security, has become a crucial research objective for those skilled in the art. Although helium and natural gas coexist in the same geosynthetic environment, the formation, accumulation, and distribution characteristics of helium are completely unrelated to those of natural gas resources; helium formation, migration, and accumulation follow their own inherent laws.

[0003] Currently, both domestically and internationally, the helium percentage content method and the genetic method are mainly used to calculate helium reserves and resources. For example, Zhang Fuli et al. (2012) determined that the Weihe Basin has a high degree of helium enrichment based on the composition characteristics of water-soluble gas components. Based on helium isotope analysis confirming that the Yanshanian uranium-rich granite is the main source of helium, they calculated the water-soluble helium resource quantity using both the helium-containing water-soluble gas calculation method and the uranium radioactive decay calculation method. Zhang Zuoxiang et al. discussed methods for evaluating helium resources in natural gas, proposing that domestic methods mainly use volumetric methods, Monte Carlo methods, residual hydrocarbon methods, thermal simulation methods, grey system prediction methods, reservoir scale sequence methods, and dynamic methods to predict natural gas resources. Furthermore, they calculated the helium resource quantity by multiplying the helium content test results of discovered helium fields by the natural gas resource quantity. J. Richard Bowersox (2019) introduced the helium resource evaluation for Central Kentucky, USA, pointing out that the minimum standard for helium content in local helium resource evaluation is 0.2% (considered to have commercial value). Helium and oil and gas resources are considered to share similarities in migration and accumulation, exhibiting nearly identical trap-forming elements. For the "source rock," the uranium and thorium content of the strata should be considered; the current standard for natural gamma-ray logging values ​​is 120–108 API. The source rock lithology is predominantly shale, and gamma-ray radiometric contour maps are drawn accordingly. Based on the calculated volume of the helium-producing source rock, the model established by Brown (2010) is used, with a limit of 500 Ma, to analyze the uranium and thorium content in the source rock, forming a genetic evaluation of helium resources. Further consideration is given, with a reservoir porosity limit of 10%, to obtain the final result. However, there are currently no mature helium resource evaluation methods available domestically or internationally. Summary of the Invention

[0004] To better optimize the selection of favorable helium enrichment regions, this application provides a method and apparatus for classifying and evaluating favorable helium enrichment regions.

[0005] In a first aspect, embodiments of this application provide a method for evaluating the favorable classification of helium enrichment, the method comprising:

[0006] Obtain multiple geological factors related to helium reserve abundance in high exploration degree scale areas, and determine a scatter plot of each geological factor and helium reserve abundance;

[0007] Based on the scatter plot of each geological factor and helium reserve abundance, determine the relationship equation and correlation coefficient between each geological factor and helium reserve abundance;

[0008] For each geological factor, determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold.

[0009] If so, then the geological factor is identified as a relevant geological factor;

[0010] Based on the relationship equations between all relevant geological factors and helium reserves and abundance, an initial model is constructed and the weight coefficients and regression coefficients of the initial model are determined to obtain a prediction model.

[0011] Based on the parameters of the relevant geological factors in the target area, the abundance distribution of helium resources in the target area is predicted using the prediction model.

[0012] Based on the distribution of helium resource abundance in the target area, the target area is divided into several different levels of favorable helium resource grading areas.

[0013] In one or more optional embodiments of this application, the step of obtaining multiple geological factors related to the helium reserve abundance in a high-exploration-level scale area and determining a scatter plot of each geological factor versus helium reserve abundance includes:

[0014] Multiple geological factors related to the abundance of helium reserves in high-exploration-level scale areas were obtained, and the parameters of each geological factor were preprocessed to obtain the parameters of each geological factor after preprocessing.

[0015] Based on the helium reserve abundance of the high exploration degree scale area obtained in advance and the parameters of each geological factor after preprocessing, a scatter plot of each geological factor and helium reserve abundance is determined.

[0016] In one or more optional embodiments of this application, the preprocessing method is normalization;

[0017] For each geological factor, the parameters are preprocessed as follows:

[0018] Based on the maximum and minimum parameter values ​​among the geological factors, the calculation coefficients are determined according to the following formula 1;

[0019]

[0020] In the formula, k is the calculation coefficient; Y max Y is the minimum parameter value among the parameters of geological factors. min This is the minimum parameter value among the parameters of geological factors;

[0021] Based on the calculated coefficients, the parameters of the preprocessed geological factors are determined according to the following formula 2:

[0022]

[0023] In the formula, Y nor Here are the parameters of the preprocessed geological factors; k is the calculated coefficient; Y max Y is the minimum parameter value among the parameters of geological factors. min It is the smallest parameter value among the parameters of geological factors.

[0024] In one or more optional embodiments of this application, determining the relationship equation and correlation coefficient between each geological factor and helium reserve abundance based on the scatter plot of each geological factor and helium reserve abundance includes:

[0025] The scatter plots of each geological factor and helium reserve abundance were subjected to univariate and multivariate nonlinear regression analyses. The relationship equation and correlation coefficient between each geological factor and helium reserve abundance were determined using a fitting function. The relationship equation is shown in Formula 3 below.

[0026] F i =B(x) i ) Formula 3;

[0027] In the formula, B is the correlation coefficient; F i Helium reserves abundance under different geological factors; X i For different geological factors; i = 1, 2, ...

[0028] In one or more optional embodiments of this application, the step of constructing an initial model and determining the weight coefficients and regression coefficients of the initial model based on the relationship equations between all relevant geological factors and helium reserve abundance, to obtain a prediction model, includes:

[0029] Based on the relationship equations between all relevant geological factors and helium reserves and abundance, the optimal combination of multiple relevant geological factors is determined by multiple linear regression.

[0030] Based on the optimal combination of the aforementioned multiple relevant geological factors, an initial model is constructed and the weight coefficients of the initial model are determined;

[0031] Based on the weight coefficients of the initial model, the regression coefficients of the initial model are determined to obtain the prediction model.

[0032] In one or more optional embodiments of this application, determining the regression coefficients of the initial model based on the weight coefficients of the initial model includes:

[0033] Based on the weight coefficients of the initial model, the regression coefficients are determined according to the following formulas 4 and 5:

[0034]

[0035] In the formula, W is the weighting coefficient; a0 is a regression coefficient;

[0036]

[0037] In the formula, W is the weighting coefficient; a i , where i = 1, 2, ...

[0038] In one or more optional embodiments of this application, dividing the target area into multiple different levels of helium resource-favorable zones based on the helium resource abundance distribution of the target area includes:

[0039] Based on the distribution of helium resource abundance in the target area, a contour map of helium reserve abundance evaluation value was obtained.

[0040] Based on the contour map of the helium reserve abundance evaluation value, the target area is divided into several different levels of favorable helium resource classification areas.

[0041] Secondly, embodiments of this application provide a helium enrichment advantageous classification and evaluation device, the device comprising:

[0042] The first determination module is used to acquire multiple geological factors related to the abundance of helium reserves in high exploration degree scale areas and determine a scatter plot of each geological factor and helium reserve abundance.

[0043] The second determining module is used to determine the relationship equation and correlation coefficient between each geological factor and helium reserve abundance based on the scatter plot of each geological factor and helium reserve abundance.

[0044] The judgment module is used to determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold for each geological factor.

[0045] The third determination module is used to determine whether geological factors are relevant geological factors;

[0046] The fourth determination module is used to construct an initial model based on the relationship equation between all relevant geological factors and helium reserves and abundance, and to determine the weight coefficients and regression coefficients of the initial model to obtain a prediction model.

[0047] The prediction module is used to predict the abundance distribution of helium resources in the target area based on the parameters of various relevant geological factors obtained in the target area and the prediction model.

[0048] The division module is used to divide the target area into multiple helium resource grading favorable areas of different levels according to the helium resource abundance distribution of the target area.

[0049] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the helium enrichment advantageous classification evaluation method as described above.

[0050] Fourthly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the helium enrichment advantageous classification evaluation method as described above.

[0051] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on a computer device, cause the computer device to perform the helium enrichment advantageous classification evaluation method as described above.

[0052] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the helium enrichment advantageous classification and evaluation method as described above.

[0053] The beneficial effects of the above-mentioned technical solutions provided in the embodiments of this application include at least the following:

[0054] The helium enrichment favorable zone classification and evaluation method provided in this application constructs a multi-factor helium resource abundance prediction model by combining the helium reserve abundance in high-exploration-level scale areas with various geological factors. This model predicts the helium resource abundance distribution in the target area. Compared to conventional methods, this method, based on the influence of various geological factors on helium resource abundance, does not require attention to the special accumulation characteristics of helium, enabling accurate calculation of the helium resource abundance in the target area. This facilitates the analysis of helium resource potential and distribution, the selection of favorable enrichment areas, and ensures the development and utilization of helium resources. The helium enrichment favorable zone classification results obtained by this method can serve as a reliable basis for helium resource and helium asset assessment, long-term development planning of helium-bearing fields, and the integration of the entire helium industry chain.

[0055] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0056] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 A schematic diagram illustrating the steps of the helium enrichment advantageous classification and evaluation method provided in the embodiments of this application;

[0059] Figure 2 This is a schematic diagram of the structure of the helium enrichment advantageous classification and evaluation device provided in the embodiments of this application. Detailed Implementation

[0060] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0061] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0062] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0063] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0064] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0065] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0066] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0067] To illustrate the technical solution of this application, specific embodiments are described below.

[0068] The inventors discovered that, in existing technologies, helium systems, like natural gas systems, require reservoir-forming elements such as source rocks, primary and secondary migration, reservoirs, traps, and preservation conditions. Although helium and natural gas accumulate in the same trap, a comparison of helium content in natural gas and the distribution trend of helium in some gas reservoirs in the study area suggests that there is no direct correlation between natural gas reserves and helium reserve distribution; the content and distribution of helium reserves have their own inherent patterns. However, as an inorganic gas, helium's reservoir-forming mechanism has three unique characteristics: first, helium formation is slow, source rocks are abundant, and the process takes a long time, exhibiting characteristics of weak-source reservoir formation; the source of helium can be uranium-rich sedimentary layers or fault basements; second, the helium transport system is complex, with faults, formation water, and gases all serving as transport systems; and third, helium cannot form reservoirs independently but can only exist parasitically in carrier gases such as nitrogen, carbon dioxide, and methane. It is evident that, due to the unique accumulation characteristics of helium, conventional methods for predicting favorable distribution areas of oil and gas resources are difficult to apply to the selection of favorable helium-rich areas. Furthermore, the mechanisms by which various geological factors control the accumulation and distribution of helium resources remain unclear. Based on this, the inventors, through further research and development, have created this invention, providing a method and apparatus for the graded evaluation of favorable helium enrichment areas.

[0069] Example 1

[0070] This application provides a method for evaluating the favorable classification of helium enrichment, referring to... Figure 1 As shown, the method includes:

[0071] S101: Obtain multiple geological factors related to helium reserve abundance in high exploration degree scale areas, and determine a scatter plot of each geological factor and helium reserve abundance.

[0072] In this embodiment of the application, by dissecting the high exploration scale area, it is possible to obtain the helium reserve abundance and various geological factors and their parameters within the high exploration scale area. Since the helium reserve abundance in the target area is related to multiple geological factors, but different geological factors have different effects on the helium resource abundance, it is necessary to select several geological factors that have a high correlation with the helium reserve abundance.

[0073] To determine the correlation between geological factors and helium reserve abundance, correlation analysis can be performed on each geological factor and helium reserve abundance. The method of correlation analysis is chosen based on the specific circumstances; for example, the Pearson correlation analysis function in SPSS data analysis software can be used. Correlation analysis identifies the geological factors associated with helium reserve abundance.

[0074] In one specific embodiment, a correlation analysis was conducted between geological factors and helium reserve abundance to determine the geological factors related to helium reserve abundance. These geological factors include: effective helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume, reservoir volume, trap area, sedimentary rock area, effective caprock area, fault activity period, helium source rock type, gas reservoir type, and gas reservoir burial depth, totaling 12 geological factors related to helium reserve abundance in high exploration degree scale areas.

[0075] In step S101 above, obtaining multiple geological factors related to the helium reserve abundance in the high-exploration-level scale area and determining a scatter plot of each geological factor versus helium reserve abundance specifically includes:

[0076] Multiple geological factors related to the abundance of helium reserves in high-exploration-level scale areas were obtained, and the parameters of each geological factor were preprocessed to obtain the parameters of each geological factor after preprocessing.

[0077] Based on the helium reserve abundance of the high exploration degree scale area obtained in advance and the parameters of each geological factor after preprocessing, a scatter plot of each geological factor and helium reserve abundance is determined.

[0078] In this embodiment of the application, the parameters of various geological factors are preprocessed by normalization.

[0079] For each geological factor, the parameters are preprocessed as follows:

[0080] Based on the maximum and minimum parameter values ​​among the geological factors, the calculation coefficients are determined according to the following formula 1;

[0081]

[0082] In the formula, k is the calculation coefficient; Ymax Y is the minimum parameter value among the parameters of geological factors. min This is the minimum parameter value among the parameters of geological factors;

[0083] Based on the calculated coefficients, the parameters of the preprocessed geological factors are determined according to the following formula 2:

[0084]

[0085] In the formula, Y nor Here are the parameters of the preprocessed geological factors; k is the calculated coefficient; Y max Y is the minimum parameter value among the parameters of geological factors. min It is the smallest parameter value among the parameters of geological factors.

[0086] In this embodiment, after obtaining various geological factors related to the helium reserve abundance in the high exploration degree scale area, the parameters of the geological factors need to be preprocessed. The preprocessing method is normalization. The specific preprocessing method refers to Formulas 1 and 2 above, obtaining the preprocessed parameters of each geological factor. For each geological factor, with the geological factor as the independent variable and the helium reserve abundance as the dependent variable, the dissected high exploration degree scale area is used as the experimental sample to create a scatter plot of the geological factors versus helium reserve abundance, resulting in a scatter plot of each geological factor versus helium reserve abundance.

[0087] S102: Based on the scatter plot of each geological factor and helium reserve abundance, determine the relationship equation and correlation coefficient between each geological factor and helium reserve abundance.

[0088] In step S102 above, determining the relationship equation and correlation coefficient between each geological factor and helium reserve abundance based on the scatter plot of each geological factor and helium reserve abundance includes:

[0089] The scatter plots of each geological factor and helium reserve abundance were subjected to univariate and multivariate nonlinear regression analyses. The relationship equation and correlation coefficient between each geological factor and helium reserve abundance were determined using a fitting function. The relationship equation is shown in Formula 3 below.

[0090] F i =B(x) i ) Formula 3;

[0091] In the formula, B is the correlation coefficient; F i Helium reserves abundance under different geological factors; X i For different geological factors; i = 1, 2, ...

[0092] In one specific embodiment, 12 geological factors related to helium reserve abundance were identified. After obtaining scatter plots of the 12 geological factors and helium reserve abundance, univariate and multivariate nonlinear regression analyses were performed on each scatter plot. By fitting functions, the relationship equation and correlation coefficient between each geological factor and helium reserve abundance were determined. The relationship equation is shown in Formula 3 above. In Formula 3, i is the index of the different geological factors, and the 12 geological factors are i = 1, 2, ..., 12. Based on the relationship equations, the correlation coefficients between each geological factor and helium reserve abundance can be determined. The magnitude of the correlation coefficient can be used to determine the closeness of the relationship between different geological factors and helium reserve abundance.

[0093] S103: For each geological factor, determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold. If yes, proceed to step S104; otherwise, exclude the geological factor.

[0094] In this embodiment, the degree of correlation between different geological factors and helium reserve abundance is determined based on the correlation coefficients in the relationship equations corresponding to various geological factors, and geological factors with low correlation are excluded. A larger correlation coefficient indicates a closer relationship between helium reserve abundance and the geological factor; a smaller correlation coefficient indicates a more distant relationship. For each geological factor, it is determined whether the correlation coefficient between the geological factor and helium reserve abundance is greater than a correlation coefficient threshold. If so, step S104 is executed to determine that the geological factor is a relevant geological factor; otherwise, the geological factor is excluded. The correlation coefficient threshold is set according to the actual situation.

[0095] S104: The geological factors are identified as relevant geological factors.

[0096] In one specific embodiment, 12 geological factors related to helium reserve abundance were identified. After determining whether the correlation coefficient between the geological factors and helium reserve abundance was greater than a correlation coefficient threshold, 9 geological factors were identified as having correlation coefficients greater than the threshold. These 9 geological factors were then determined to be relevant geological factors. For example, the correlation coefficient threshold could be set to 0.9. When the correlation coefficient R of the prediction model... 2 A correlation coefficient greater than 0.9 indicates a high degree of fit in the prediction model, meaning this model can be used to predict helium resource abundance. 2 Parameters greater than 0.9 include effective helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume, reservoir volume, trap area, sedimentary rock area, effective caprock area, and fault activity period. Correlation coefficient R0 2 Parameters less than 0.9 can be excluded, such as helium source rock type, gas reservoir type, and gas reservoir burial depth.

[0097] S105: Based on the relationship equations between all relevant geological factors and helium reserves and abundance, an initial model is constructed and the weight coefficients and regression coefficients of the initial model are determined to obtain a prediction model.

[0098] In step S105 above, the step of constructing an initial model based on the relationship equation between all relevant geological factors and helium reserve abundance, and determining the weight coefficients and regression coefficients of the initial model to obtain a prediction model, specifically includes:

[0099] Based on the relationship equations between all relevant geological factors and helium reserves and abundance, the optimal combination of multiple relevant geological factors is determined by multiple linear regression.

[0100] Based on the optimal combination of the aforementioned multiple relevant geological factors, an initial model is constructed and the weight coefficients of the initial model are determined;

[0101] Based on the weight coefficients of the initial model, the regression coefficients of the initial model are determined to obtain the prediction model.

[0102] In this embodiment of the application, after obtaining the relationship equations between all relevant geological factors and helium reserves and abundance, the optimal combination of multiple relevant geological factors is determined by multiple linear regression method to establish an initial model.

[0103] In one specific embodiment, nine geological factors related to helium reserve abundance were identified, including: effective helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume, reservoir volume, trap area, sedimentary rock area, effective caprock area, and fault activity period. Using multiple linear regression, the nine geological factors related to helium reserve abundance were recombined to determine the optimal combination of seven relevant geological factors and the overall regression coefficient (i.e., weighting coefficient) of the optimal combination of the seven relevant geological factors. The optimal combination of the seven relevant geological factors includes: effective helium source rock area / helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume / reservoir volume, trap area / sedimentary rock area, effective caprock area / sedimentary rock area, and fault activity period. The initial model constructed based on the optimal combination of the seven relevant geological factors is shown in Formula 6 below.

[0104]

[0105] In the formula, F represents the abundance of helium resources in the target area (10). 4 m 3 / km 2 );V s Effective helium source rock area / helium source rock area; H i Thickness of the helium source rock, in meters; φ i The content of radioactive minerals; V r Effective storage volume / storage volume; Sn Area of ​​trap / area of ​​sedimentary rock; G m 1 represents the effective caprock area / sedimentary rock area; n represents the fault activity period; a0, a1, a2, a3, a4, a5, a6, and a7 are regression coefficients; b is the correlation coefficient, which is dimensionless.

[0106] In the initial model, the regression coefficients corresponding to different geological factors reflect the degree of correlation between different geological factors and between geological factors and helium resource abundance. To determine the values ​​of the regression coefficients a0, a1, a2, a3, a4, a5, a6, and a7 in the initial model, the values ​​of a0, a1, a2, a3, a4, a5, a6, and a7 can be selected using the least squares method to minimize the weight coefficients. Therefore, the partial derivatives of a0, a1, a2, a3, a4, a5, a6, and a7 are calculated and set to 0. The weight coefficients are obtained according to the following formula 7:

[0107]

[0108] In the formula, W is the weighting coefficient; F k denoted as helium abundance; n represents the number of geological factors; k = 1, 2, ...

[0109] In this embodiment of the application, determining the regression coefficients of the initial model based on the weight coefficients of the initial model includes:

[0110] Based on the weight coefficients of the initial model, the regression coefficients are determined according to the following formulas 4 and 5:

[0111]

[0112] In the formula, W is the weighting coefficient; a0 is a regression coefficient;

[0113]

[0114] In the formula, W is the weighting coefficient; a i , where i = 1, 2, ...

[0115] By solving the system of equations consisting of Equations 4 and 5, the regression coefficients can be obtained. After determining the regression coefficients, the final prediction model, namely the multi-factor helium reserve abundance prediction model, is obtained.

[0116] S106: Based on the parameters of the relevant geological factors in the target area, the abundance distribution of helium resources in the target area is predicted using the prediction model.

[0117] In this embodiment, parameters of relevant geological factors for any target area are obtained, and these parameters are substituted into the prediction model obtained in step S105 above to obtain the prediction result of helium resource abundance in the target area, thus determining the distribution of helium resource abundance in the target area. The target area is a low-exploration-level scale area.

[0118] S107: Based on the distribution of helium resource abundance in the target area, the target area is divided into multiple helium resource grading zones of different levels.

[0119] In step S107 above, dividing the target area into multiple helium resource-rich zones of different levels based on the helium resource abundance distribution specifically includes:

[0120] Based on the distribution of helium resource abundance in the target area, a contour map of helium reserve abundance evaluation value was obtained.

[0121] Based on the contour map of the helium reserve abundance evaluation value, the target area is divided into several different levels of favorable helium resource classification areas.

[0122] In this embodiment of the application, a contour map of helium reserve abundance is drawn based on the predicted helium reserve abundance distribution. According to the constructed helium resource favorable area classification standard, the target area is divided into multiple different levels of helium resource favorable areas, such as dividing helium reserve favorable areas into categories I, II, and III.

[0123] Furthermore, based on dividing the target area into multiple helium resource-rich zones of different levels, and according to the predicted helium resource abundance, the total helium resource quantity of the target area can be predicted using the following formula 8:

[0124] Q = S1F1 + S2F2 + ... + S i F i Formula 8;

[0125] In the formula, Q represents the total helium resource in the target area, 10⁸ m³. 3 S i The area of ​​the evaluation unit is expressed in km². 2 ;F i To predict resource abundance, 10 4 m 3 / km 2 ; i is the number of evaluation units, i≥1.

[0126] The helium enrichment favorable zone classification and evaluation method provided in this application constructs a multi-factor helium resource abundance prediction model by combining the helium reserve abundance in high-exploration-level scale areas with various geological factors. This model predicts the helium reserve abundance distribution in the target area. Compared with conventional methods, this method is based on the mechanism by which various geological factors control the accumulation and distribution of helium resources, without needing to consider the special accumulation characteristics of helium. It can accurately calculate the helium resource abundance in the target area, which helps in analyzing the helium resource potential and distribution, selecting favorable enrichment areas, and ensuring the development and use of helium resources. The helium enrichment favorable zone classification results obtained by this method can serve as a reliable basis for helium resource and helium asset assessment, long-term development planning of helium-bearing fields, and the integration of the entire helium industry chain.

[0127] Example 2

[0128] Based on the same inventive concept, this application also provides a helium enrichment advantageous classification evaluation device, referring to... Figure 2 As shown, the device includes:

[0129] The first determining module 101 is used to acquire multiple geological factors related to the abundance of helium reserves in the high exploration degree scale area and to determine a scatter plot of each geological factor and the abundance of helium reserves.

[0130] The second determining module 102 is used to determine the relationship equation and correlation coefficient between each geological factor and helium reserve abundance based on the scatter plot of each geological factor and helium reserve abundance.

[0131] The judgment module 103 is used to determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold for each geological factor.

[0132] The third determination module 104 is used to determine the geological factors as relevant geological factors;

[0133] The fourth determination module 105 is used to construct an initial model based on the relationship equation between all relevant geological factors and helium reserves and abundance, and to determine the weight coefficients and regression coefficients of the initial model to obtain a prediction model.

[0134] The prediction module 106 is used to predict the abundance distribution of helium resources in the target area based on the parameters of the relevant geological factors of the target area and the prediction model.

[0135] The division module 107 is used to divide the target area into multiple helium resource grading favorable areas of different levels according to the helium resource abundance distribution of the target area.

[0136] Example 3

[0137] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the helium enrichment advantageous classification evaluation method as described in Embodiment 1 above.

[0138] Example 4

[0139] Based on the same inventive concept, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the helium enrichment advantageous classification evaluation method as described in Embodiment 1 above.

[0140] Example 5

[0141] Based on the same inventive concept, this application also provides a computer program product containing instructions that, when run on a computer device, cause the computer device to execute the helium enrichment advantageous classification evaluation method described in Embodiment 1 above.

[0142] Example 6

[0143] Based on the same inventive concept, this application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the helium enrichment advantageous classification and evaluation method described in Embodiment 1 above.

[0144] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0148] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for classifying and evaluating the advantages of helium enrichment, characterized in that, include: To obtain multiple geological factors related to the abundance of helium reserves in high-exploration-level scale areas, including effective helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume, reservoir volume, trap area, sedimentary rock area, effective caprock area, fault activity period, helium source rock type, gas reservoir type, and gas reservoir burial depth. The parameters of each geological factor are preprocessed to obtain the parameters of each geological factor after preprocessing. The preprocessing method is normalization; Based on the helium reserve abundance of the high exploration degree scale area obtained in advance and the parameters of the pre-processed geological factors, a scatter plot of each geological factor and helium reserve abundance is determined with each geological factor as the independent variable and helium reserve abundance as the dependent variable. The scatter plots of each geological factor and helium reserve abundance were subjected to univariate and multivariate nonlinear regression analyses. The relationship equation and correlation coefficient between each geological factor and helium reserve abundance were determined using a fitting function. The relationship equation is shown in Formula 3 below. Official 3; In the formula, B is the correlation coefficient; F i Helium reserves abundance under different geological factors; X i For different geological factors; i=1,2,…; For each geological factor, determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold. If so, then the geological factor is identified as a relevant geological factor; Based on the relationship equations between all relevant geological factors and helium reserves and abundance, the optimal combination of multiple relevant geological factors is determined by multiple linear regression. Based on the optimal combination of the aforementioned multiple relevant geological factors, an initial model is constructed and the weight coefficients of the initial model are determined; Based on the weight coefficients of the initial model, the regression coefficients of the initial model are determined to obtain the prediction model; Based on the parameters of the relevant geological factors in the target area, the abundance distribution of helium resources in the target area is predicted using the prediction model. Based on the distribution of helium resource abundance in the target area, the target area is divided into several different levels of favorable helium resource grading areas.

2. The method as described in claim 1, characterized in that, For each geological factor, the parameters are preprocessed as follows: Based on the maximum and minimum parameter values ​​among the geological factors, the calculation coefficients are determined according to the following formula 1; ； In the formula, k is the calculation coefficient; Y max Y represents the maximum parameter value among the parameters of geological factors. min This is the minimum parameter value among the parameters of geological factors; Based on the calculated coefficients, the parameters of the preprocessed geological factors are determined according to the following formula 2: Official 2; In the formula, Y nor represents the parameters of the preprocessed geological factors; k is the calculated coefficient; Y represents the parameters of the geological factors; Y max Y represents the maximum parameter value among the parameters of geological factors. min It is the smallest parameter value among the parameters of geological factors.

3. The method as described in claim 1, characterized in that, The step of determining the regression coefficients of the initial model based on the weight coefficients of the initial model includes: Based on the weight coefficients of the initial model, the regression coefficients are determined according to the following formulas 4 and 5: Official 4; In the formula, W is the weighting coefficient; a0 is a regression coefficient; Official 5; In the formula, W is the weighting coefficient; a i , where i is the regression coefficient; i = 1, 2, ...

4. The method as described in claim 1, characterized in that, The step of dividing the target area into multiple levels of favorable helium resource zones based on the helium resource abundance distribution in the target area includes: Based on the distribution of helium resource abundance in the target area, a contour map of helium reserve abundance evaluation value was obtained. Based on the contour map of the helium reserve abundance evaluation value, the target area is divided into several different levels of favorable helium resource classification areas.

5. A helium enrichment-advantageous classification and evaluation device, characterized in that, include: The first determining module is used to obtain various geological factors related to the abundance of helium reserves in high exploration degree scale areas. These various geological factors include effective helium source rock area, helium source rock thickness, radioactive mineral content, effective reservoir volume, reservoir volume, trap area, sedimentary rock area, effective caprock area, fault activity period, helium source rock type, gas reservoir type, and gas reservoir burial depth. The parameters of each geological factor are preprocessed to obtain the parameters of each geological factor after preprocessing. The preprocessing method is normalization; Based on the helium reserve abundance of the high exploration degree scale area obtained in advance and the parameters of the pre-processed geological factors, a scatter plot of each geological factor and helium reserve abundance is determined with each geological factor as the independent variable and helium reserve abundance as the dependent variable. The second determining module is used to perform univariate and multivariate nonlinear regression analysis on the scatter plots of each geological factor and helium reserve abundance, and to determine the relationship equation and correlation coefficient between each geological factor and helium reserve abundance through a fitting function; the relationship equation is as follows: Formula 3: Official 3; In the formula, B is the correlation coefficient; F i Helium reserves abundance under different geological factors; X i For different geological factors; i=1,2,…; The judgment module is used to determine whether the correlation coefficient between the geological factor and the abundance of helium reserves is greater than the correlation coefficient threshold for each geological factor. The third determination module is used to determine whether geological factors are relevant geological factors; The fourth determination module is used to determine the optimal combination of multiple relevant geological factors based on the relationship equation between all relevant geological factors and helium reserve abundance, using the multiple linear regression method. Based on the optimal combination of the aforementioned multiple relevant geological factors, an initial model is constructed and the weight coefficients of the initial model are determined; Based on the weight coefficients of the initial model, the regression coefficients of the initial model are determined to obtain the prediction model; The prediction module is used to predict the abundance distribution of helium resources in the target area based on the parameters of various relevant geological factors obtained in the target area and the prediction model. The division module is used to divide the target area into multiple helium resource grading favorable areas of different levels according to the helium resource abundance distribution of the target area.

6. A computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the helium enrichment advantageous classification evaluation method as described in any one of claims 1-4.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the helium enrichment advantageous classification evaluation method as described in any one of claims 1-4.

8. A computer program product containing instructions that, when run on a computer device, causes the computer device to perform the helium enrichment advantageous classification evaluation method as described in any one of claims 1-4.

9. A chip comprising a processor and a communication interface coupled to the processor, the processor being configured to run a computer program or instructions to implement the helium enrichment advantageous classification and evaluation method as described in any one of claims 1-4.

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