Potential layer selection method and device, computer equipment and storage medium

Abstract

The invention provides a potential layer selection method and device, computer equipment and a storage medium. The method comprises the steps of determining an index mean value and dispersion of a candidate layer based on index values of the candidate layer under a plurality of logging indexes; determining a target sorting index of the candidate layer based on the index mean value and the dispersion; and selecting a potential layer from the candidate layers according to the target sorting indexes of the candidate layers. According to the method, the technical effect of improving the layer selection efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of oil exploration, and in particular to a potential layer selection method, device, computer equipment and storage medium. Background Art

[0002] A potential layer is a stratum with potential oil or natural gas reserves. In the field of oil exploration, it is usually necessary to identify the potential layer among many bottom layers.

[0003] In related technologies, after obtaining the data of logging indicators, logging engineers are usually required to select potential layers based on experience. This process involves data of multiple logging indicators for multiple formations, with a large amount of data and high calculation difficulty, resulting in low efficiency in potential layer selection. Summary of the invention

[0004] The present application provides a potential layer selection method, apparatus, computer equipment and storage medium to achieve the effect of improving the efficiency of potential layer selection.

[0005] In a first aspect, the present application provides a potential layer selection method, comprising: determining the indicator mean and dispersion of the candidate layer based on the indicator values ​​of the candidate layer under multiple logging indicators; determining the target ranking indicator of the candidate layer based on the indicator mean and dispersion; and selecting the potential layer from the candidate layer according to the target ranking indicator of the candidate layer.

[0006] In some embodiments, there are multiple candidate layers; based on the index values ​​of the candidate layers under multiple logging indicators, the index mean and dispersion of the candidate layers are determined, including: for each logging indicator, obtaining the index values ​​of multiple candidate layers under the targeted logging indicator; normalizing the index values ​​of the multiple candidate layers under the targeted logging indicator to obtain the normalized index values ​​of the multiple candidate layers under the targeted logging indicator; for each candidate layer, based on the normalized index values ​​of the candidate layer under multiple logging indicators, determining the index mean of the candidate layer; based on the multiple normalized index values ​​and the index mean of the candidate layer, determining the dispersion of the candidate layer.

[0007] In some embodiments, based on the normalized index values ​​of the candidate layer under multiple well logging indicators, the index mean of the candidate layer is determined, including: obtaining weights of multiple well logging indicators; determining the weighted mean of the candidate layer according to the weights of the multiple well logging indicators and the index values ​​of the candidate layer under multiple well logging indicators; and using the weighted mean of the candidate layer as the index mean of the candidate layer.

[0008] In some embodiments, a target ranking indicator of a candidate layer is determined based on an indicator mean and a dispersion, including: determining the candidate ranking indicator of the candidate layer under multiple adjustment factors based on the indicator mean and the dispersion; determining the production potential indicator of the candidate layer according to multiple indicator values ​​of the candidate layer; selecting a target adjustment factor from multiple adjustment factors based on the production potential indicator of the candidate layer and the candidate ranking indicator of the candidate layer under multiple adjustment factors; and using the candidate ranking indicator of the candidate layer under the target adjustment factor as the target ranking indicator of the candidate layer.

[0009] In some embodiments, based on the indicator mean and dispersion, the candidate ranking indicators of the candidate layer under multiple adjustment factors are determined, including: for each adjustment factor, determining the product between the dispersion and the targeted adjustment factor to obtain the adjusted dispersion; and using the difference between the indicator mean and the adjusted dispersion as the candidate ranking indicator of the candidate layer under the targeted adjustment factor.

[0010] In some embodiments, there are multiple candidate layers; based on the production potential indicators of the candidate layers and the candidate ranking indicators of the candidate layers under multiple adjustment factors, a target adjustment factor is selected from the multiple adjustment factors, including: for each adjustment factor, the candidate ranking indicators of the multiple candidate layers under the targeted adjustment factor are sorted to obtain a candidate ranking indicator sequence corresponding to the targeted adjustment factor; the production potential indicators of the multiple candidate layers are sorted according to the order of the multiple candidate layers in the candidate ranking indicator sequence to obtain a production potential indicator sequence corresponding to the targeted adjustment factor; based on the candidate ranking indicator sequence and the production potential indicator sequence corresponding to the targeted adjustment factor, the residual corresponding to the targeted adjustment factor is determined; based on the residuals corresponding to each of the multiple adjustment factors, a target adjustment factor is selected from the multiple adjustment factors.

[0011] In some embodiments, the residual corresponding to the targeted adjustment factor is determined based on the candidate ranking index sequence and the production potential index sequence corresponding to the targeted adjustment factor, including: determining the mean envelope based on the production potential index sequence corresponding to the targeted adjustment factor; determining the production potential mean index sequence of multiple candidate layers based on the mean envelope; determining the difference values ​​of multiple candidate layers respectively based on the production potential mean index sequence and the candidate ranking index sequence; determining the sum of the squares of the difference values ​​of multiple candidate layers to obtain the residual corresponding to the targeted adjustment factor.

[0012] In a second aspect, the present application provides a potential layer selection device, comprising:

[0013] An index value processing module, used to determine the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indicators;

[0014] A ranking index determination module is used to determine the target ranking index of the candidate layer based on the index mean and dispersion;

[0015] The selection module is used to select potential layers from the candidate layer according to the target ranking index of the candidate layer.

[0016] In a third aspect, the present application further provides a computer device, the computer comprising: a processor, and a memory communicatively connected to the processor;

[0017] The memory stores computer-executable instructions;

[0018] The above method is implemented when the processor executes the computer-executable instructions stored in the memory.

[0019] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to implement the above method when executed by a processor.

[0020] In a fifth aspect, the present application also provides a computer program product, comprising computer execution instructions, which implement the above method when executed by a processor.

[0021] The present application provides a potential layer selection method, device, computer equipment and storage medium, which determine the index mean and dispersion of the candidate layer based on the index value of the candidate layer under multiple logging indicators; determine the target ranking index of the candidate layer based on the index mean and dispersion; and select the potential layer from the candidate layer based on the target ranking index of the candidate layer. This process does not rely on manual work, and can automatically process data of complex index values, and then select potential layers from the candidate layer, effectively improving the efficiency of potential layer selection; in addition, the target ranking index is determined by the index mean and dispersion, so that the index values ​​under multiple logging indicators act on the target ranking index in a balanced manner, improving the quality of the target ranking index, and selecting the potential layer based on the target ranking index that acts in a balanced manner on multiple logging indicators, improving the accuracy and reliability of the selected potential layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] Figure 1 A schematic diagram of an application scenario of a potential layer selection method provided in an embodiment;

[0024] Figure 2 The process of the potential layer selection method provided in one embodiment is as follows Figure 1 ;

[0025] Figure 3 This is a schematic diagram of the indicator value when the production potential indicator is large in one embodiment;

[0026] FIG4(a) is a diagram showing the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment. Figure 1 ;

[0027] FIG4(b) is a diagram showing the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment. Figure 2 ;

[0028] FIG4(c) is a diagram showing the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment. Figure 3 ;

[0029] FIG4(d) is a fourth schematic diagram of the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment;

[0030] FIG4(e) is a diagram showing the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment. Figure 5 ;

[0031] FIG4(f) is a diagram showing the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence in one embodiment. Figure 6 ;

[0032] Figure 5 is a schematic diagram showing residuals corresponding to different adjustment factors in one embodiment;

[0033] Figure 6 The process of the potential layer selection method provided in one embodiment is as follows Figure 2 ;

[0034] Figure 7 A comparison chart is shown according to target ranking indicators of multiple candidate layers in one embodiment;

[0035] Figure 8 A schematic diagram of the structure of a potential layer selection device in one embodiment;

[0036] Fig. 9 A hardware structure diagram of a computer device provided in one embodiment.

[0037] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0039] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0040] First, the terms involved in this application are explained:

[0041] Potential layer: refers to the stratum with potential oil or natural gas reserves.

[0042] Candidate layer: a stratum that may have potential oil or natural gas reserves; the embodiment of the present application is used to select a potential layer from each candidate layer.

[0043] Well logging indicators are an important basis for evaluating formation characteristics and reservoir quality; well logging indicators may include but are not limited to thickness, permeability, saturation and porosity; among them, thickness is the physical thickness of the formation; permeability is used to reflect the difficulty of fluid passing through the pores in the formation; saturation is used to reflect the content of oil, gas or water in the formation, and porosity is used to reflect the volume fraction of pore space in the formation.

[0044] In related technologies, after obtaining the data of logging indicators, logging engineers are usually required to select potential layers based on experience. This process involves data of multiple logging indicators for multiple formations, with a large amount of data and high calculation difficulty, resulting in low efficiency in potential layer selection.

[0045] The potential layer selection method provided in the present application determines the mean and dispersion of the candidate layer's indicators based on the candidate layer's indicator values ​​under multiple well logging indicators; determines the target ranking indicator of the candidate layer based on the mean and dispersion of the indicators; and selects the potential layer from the candidate layer based on the target ranking indicator of the candidate layer. This process does not rely on manual work, and can automatically process complex indicator values, and then select potential layers from the candidate layer, effectively improving the efficiency of potential layer selection; in addition, by determining the target ranking indicator through the mean and dispersion of the indicators, the indicator values ​​under multiple well logging indicators act on the target ranking indicator in a balanced manner, improving the quality of the target ranking indicator, and selecting the potential layer based on the target ranking indicator that acts in a balanced manner on multiple well logging indicators, improving the accuracy and reliability of the selected potential layer.

[0046] The potential layer selection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0047] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0048] In some embodiments, Figure 2 As shown, a potential layer selection method is provided, and the method is applied to a computer device as an example for explanation. The computer device can be Figure 1 The terminal or server in the embodiment of the present invention comprises:

[0049] S202, determining the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indexes.

[0050] Among them, the indicator mean is used to reflect the average value of the indicator values ​​under multiple logging indicators; the dispersion is used to reflect the dispersion degree of the distribution of multiple indicator values.

[0051] Specifically, the computer device obtains the index value of the candidate layer under multiple logging indicators, calculates the average of the multiple index values, takes the number of multiple logging indicators as the exponent of the average, determines the exponential power of the average, and obtains the index mean; the computer device respectively determines the difference between the multiple index values ​​and the index mean, and determines the discreteness according to the difference between the multiple index values ​​and the index mean.

[0052] In one possible way, different logging indicators may have different dimensions. In order to convert the logging indicators of different dimensions to a unified scale, the indicator value of each logging indicator may be normalized, and then the indicator mean and dispersion may be determined based on the normalized indicator value.

[0053] It should be noted that there are multiple candidate layers, and for each candidate layer, the index mean and dispersion of the candidate layer are determined according to the index values ​​of the candidate layer under multiple logging indicators.

[0054] S204, determining the target ranking index of the candidate layer based on the index mean and dispersion.

[0055] Among them, the target ranking index can reflect the energy storage situation of the candidate layer; when two target ranking indexes are compared, the larger target ranking index may correspond to a better energy storage situation; it should be noted that the target ranking index is calculated based on the index value. In some scenarios, the candidate layer with better energy storage situation can be determined by comparing the target ranking index. The target ranking index is not the actual reserve of the candidate layer.

[0056] Specifically, the computer device can use the difference between the indicator mean and the dispersion as the target ranking indicator of the candidate layer; it can also adjust the dispersion through the adjustment factor to reduce or increase the impact of the dispersion on the target ranking indicator, and then calculate the target ranking indicator of the candidate layer based on the adjusted dispersion and the indicator mean; it can also calculate the candidate ranking indicators under multiple adjustment factors based on the indicator mean and dispersion, and select one of the candidate ranking indicators under multiple adjustment factors as the target ranking indicator.

[0057] In some embodiments, a target ranking indicator of a candidate layer is determined based on an indicator mean and a dispersion, including: determining the candidate ranking indicator of the candidate layer under multiple adjustment factors based on the indicator mean and the dispersion; determining the production potential indicator of the candidate layer according to multiple indicator values ​​of the candidate layer; selecting a target adjustment factor from multiple adjustment factors based on the production potential indicator of the candidate layer and the candidate ranking indicator of the candidate layer under multiple adjustment factors; and using the candidate ranking indicator of the candidate layer under the target adjustment factor as the target ranking indicator of the candidate layer.

[0058] Among them, the adjustment factor is used to adjust the influence of the discreteness on the candidate sorting index. The larger the adjustment factor, the greater the influence of the discreteness on the candidate sorting index. The candidate energy storage index can also reflect the energy storage situation of the candidate layer.

[0059] Multiple adjustment factors are pre-set; multiple adjustment factors can be multiple values ​​selected according to preset intervals in a preset numerical range, for example, the preset numerical range is [0,1], the preset interval is 0.01, then the multiple adjustment factors may include: 0, 0.01, 0.02, ..., 0.99, 1; multiple adjustment factors can also be multiple values ​​set to belong to the preset numerical range; multiple adjustment factors can also be multiple values ​​randomly selected from the preset numerical range; the specific values ​​of multiple adjustment factors can be set according to actual needs, and the embodiment of the present application does not limit the specific values ​​of multiple adjustment factors.

[0060] Among them, the production potential indicator is the actual reserves of the candidate layer.

[0061] Specifically, for each adjustment factor, the computer device determines the product between the adjustment factor and the discreteness, and then determines the candidate sorting index based on the index mean and the product; it should be noted that there are multiple candidate layers, and for each candidate layer, the candidate sorting index of the candidate layer under multiple adjustment factors can be calculated.

[0062] In some embodiments, based on the indicator mean and dispersion, the candidate ranking indicators of the candidate layer under multiple adjustment factors are determined, including: for each adjustment factor, determining the product between the dispersion and the targeted adjustment factor to obtain the adjusted dispersion; and using the difference between the indicator mean and the adjusted dispersion as the candidate ranking indicator of the candidate layer under the targeted adjustment factor.

[0063] Specifically, the computer device may calculate the candidate ranking index according to formula (1).

[0064]

[0065] Among them, is the candidate ranking index of candidate layer c1, is the mean index of candidate layer c1, α is the adjustment factor, σ c1 is the discreteness of the candidate layer c1.

[0066] In the above embodiment, the candidate ranking index is determined by the index mean and dispersion, so that the index values ​​under multiple well logging indexes act on the candidate ranking index in a balanced manner, thereby improving the quality of the candidate ranking index.

[0067] For each candidate layer, the computer device determines the product of multiple indicator values ​​of the candidate layer to obtain the production potential indicator of the candidate layer; as shown in formula (2).

[0068]

[0069] Among them, S n is the production potential index of the nth candidate layer, M is the number of logging indicators, Xn,m It is the index value of the nth candidate layer under the mth logging index.

[0070] There are multiple candidate layers, and the computer device obtains the candidate ranking index of each candidate layer under multiple adjustment factors, that is, obtains the candidate ranking indexes of multiple candidate layers under each adjustment factor; for each adjustment factor, the order of the multiple candidate layers is determined according to the candidate ranking indexes of the multiple candidate layers under the adjustment factor to obtain a candidate ranking index sequence, and the production potential indicators of the multiple candidate layers are sorted according to the order to obtain a production potential indicator sequence, and the energy storage difference under the adjustment factor is determined according to the candidate ranking indicator sequence and the generation potential indicator sequence. In this way, the energy storage difference corresponding to each of the multiple adjustment factors can be obtained, and the adjustment factor corresponding to the smallest energy storage difference can be used as the target adjustment factor, and the candidate ranking indicators of the multiple candidate layers calculated based on the target adjustment factor can be used as the target ranking indicators of the multiple candidate layers.

[0071] Next, the principle of the method proposed in the embodiment of the present application is explained.

[0072] The larger the values ​​of multiple indicators of the candidate layer, the better the energy storage of the candidate layer; therefore, the larger the production potential indicator, the better the energy storage of the candidate layer; in related technologies, potential layers are selected by production potential indicators, but a large production potential indicator does not mean that multiple indicator values ​​are all large. It may be that some indicator values ​​are large and some indicator values ​​are small, resulting in a large production potential indicator of the candidate layer. In this case, the candidate layer may be difficult to mine.

[0073] For example, the multiple logging indicators may include thickness H, permeability K, saturation S and porosity F, and the production potential indicator of the candidate layer is the product of thickness H, permeability K, saturation S and porosity F. When the production potential indicator is large, there may be Figure 3 The situation shown, that is, the production potential index of the candidate layer is large, does not mean that the thickness H, permeability K, saturation S and porosity F of the candidate layer are all large.

[0074] In order to solve the problem that the selected potential layer is difficult to use in actual scenarios when the production potential index is used to select the potential layer, the embodiment of the present application determines the index mean and dispersion of multiple index values, and determines the candidate sorting index according to the index mean and dispersion.

[0075] The calculation method of the production potential index of the candidate layer can refer to formula (2); according to the Cauchy inequality, formula (3) can be obtained.

[0076]

[0077] in, is the mean value of the indicator, and the equal condition is X n,1=X n,2 =…=X n,m =…X n,m =X n,M-1 =X n,M .

[0078] That is to say, according to the Cauchy inequality, the product of multiple index values ​​can be expressed, and the relationship between the index mean is expressed in each X n,m When the difference between them is 0, formula (3) can be equal.

[0079] Each X n,m The difference between them is 0, indicating that the candidate layers have equal index values ​​under different logging indicators. In practical applications, there may not be X n,m When the difference between them is 0, in order to improve the accuracy of the candidate ranking index, the adjustment factor and the discreteness (characterizing the difference) can be added on the basis of the index mean, so that the candidate ranking index is as close to the production potential index as possible; therefore, the candidate ranking index is determined according to the index mean, the discreteness and the adjustment factor, and the candidate ranking index under multiple adjustment factors is calculated to select the target ranking index close to the production potential index from multiple candidate ranking indicators. In this way, the target ranking index is calculated under the balanced effect of multiple logging indicators, and can reflect the actual energy storage situation of the candidate layer, thereby improving the quality of the target ranking index.

[0080] S206, selecting a potential layer from the candidate layer according to the target ranking index of the candidate layer.

[0081] Specifically, the computer device can take the candidate layer with the largest target ranking index as a potential layer among multiple candidate layers, or can sort the multiple candidate layers according to the target ranking index from small to large (or from large to small) to obtain a candidate layer sequence, and select one or more potential layers from the candidate layer sequence.

[0082] In a possible scenario, after obtaining the candidate layer sequence, a scatter plot can be drawn according to the order of the candidate layer sequence and the multiple candidate layers and their corresponding target ranking indicators. The energy storage conditions of the multiple candidate layers can be intuitively understood through the scatter plot.

[0083] The above potential layer selection method determines the mean and dispersion of the candidate layer's indicators based on the candidate layer's indicator values ​​under multiple well logging indicators; determines the target ranking indicator of the candidate layer based on the mean and dispersion of the indicators; and selects the potential layer from the candidate layer based on the target ranking indicator of the candidate layer. This process does not rely on manual work, and can automatically process complex indicator values, and then select potential layers from the candidate layer, effectively improving the efficiency of potential layer selection; in addition, by determining the target ranking indicator through the mean and dispersion of the indicators, the indicator values ​​under multiple well logging indicators act on the target ranking indicator in a balanced manner, improving the quality of the target ranking indicator, and selecting the potential layer based on the target ranking indicator that multiple well logging indicators act on in a balanced manner improves the accuracy and reliability of the selected potential layer.

[0084] In some embodiments, there are multiple candidate layers; based on the index values ​​of the candidate layers under multiple logging indicators, the index mean and dispersion of the candidate layers are determined, including: for each logging indicator, obtaining the index values ​​of multiple candidate layers under the targeted logging indicator; normalizing the index values ​​of the multiple candidate layers under the targeted logging indicator to obtain the normalized index values ​​of the multiple candidate layers under the targeted logging indicator; for each candidate layer, based on the normalized index values ​​of the candidate layer under multiple logging indicators, determining the index mean of the candidate layer; based on the multiple normalized index values ​​and the index mean of the candidate layer, determining the dispersion of the candidate layer.

[0085] Specifically, since there are multiple candidate layers, each candidate layer has index values ​​under multiple well logging indicators. The index values ​​of multiple candidate layers under multiple well logging indicators can be represented by a matrix. For example, the index values ​​of N candidate layers under M well logging indicators are represented as follows:

[0086]

[0087] Among them, X 11 is the index value of the first candidate layer under the first logging index, X NM The index value of the Nth candidate layer under the Mth logging index.

[0088] For each well logging index, the computer device obtains the index values ​​of multiple candidate layers under the targeted well logging index, that is, obtains a column vector in the matrix X. All index values ​​in the column vector correspond to the same well logging index. Therefore, all index values ​​in the column vector (index values ​​under the targeted well logging index) have the same dimension. The computer device obtains the maximum index value and the minimum index value from the index values ​​under the targeted well logging index. According to the maximum index value and the minimum index value, each index value under the targeted well logging index is normalized to obtain the normalized index value under the targeted well logging index; as shown in formula (4).

[0089]

[0090] Among them, X n,m is the index value of the nth candidate layer under the mth logging index, Yes X n,m The corresponding normalized index value, max(X ·,m ) is the maximum value of the index values ​​of multiple candidate layers under the mth logging index, min(X ·,m ) is the minimum value of the index values ​​of multiple candidate layers under the mth logging index.

[0091] For the normalized index value of each candidate layer under multiple logging indicators, the mean value between the multiple normalized index values ​​is determined, and the number of multiple logging indicators is used as an index to determine the exponential power of the mean value to obtain the index mean value of the candidate layer, as shown in formula (5).

[0092]

[0093] in, is the mean index of the nth candidate layer, is the normalized index of the nth candidate layer under the mth logging index, and M is the number of logging indicators.

[0094] For each candidate layer, the computer device can calculate the variance according to the index mean value and multiple normalized index values ​​of the candidate layer, and use the variance as the discreteness; as shown in formula (6).

[0095]

[0096] Among them, is the discreteness of the nth candidate layer, is the mean of the indicators of the nth candidate layer, and is the number of logging indicators.

[0097] In the above embodiment, the obstacles of different dimensions between different logging indicators can be eliminated through normalization processing, so as to determine the indicator mean and dispersion on a unified scale, thereby improving the accuracy of the candidate ranking indicators determined based on the indicator mean and dispersion.

[0098] In some embodiments, based on the normalized index values ​​of the candidate layer under multiple logging indicators, the index mean of the candidate layer is determined, including: obtaining weights of multiple logging indicators; determining the weighted mean of the candidate layer according to the weights of the multiple logging indicators and the normalized index values ​​of the candidate layer under multiple logging indicators; and using the weighted mean of the candidate layer as the index mean of the candidate layer.

[0099] The weight is used to reflect the importance of different well logging indicators; the weight is pre-set and can be pre-set by a domain expert; and the sum of the weights of multiple well logging indicators can be 1.

[0100] Specifically, the mean value of the index for the candidate layer can be determined by formula (7).

[0101]

[0102] st w1+w2+…+w M =1 (7)

[0103] Among them, w m is the weight of the mth logging indicator, is the mean index of the nth candidate layer, is the normalized index value of the nth candidate layer under the mth logging index, and M is the number of logging indicators.

[0104] It should be noted that, when the mean index value of the candidate layer is determined by formula (7), the process of determining the discreteness can be, for each candidate layer under each logging index, calculating the product of the normalized index value and the weight of the corresponding logging index to obtain the index value to be processed, and calculating the discreteness of the candidate layer according to the index value to be processed and the mean index value of the candidate layer under multiple logging indexes, as shown in formula (8).

[0105]

[0106] st w1+w2+…+w M =1 (8)

[0107] in, is the pending index value of the nth candidate layer under the first logging index, is the indicator mean, σ n,w is the discreteness of the nth candidate layer.

[0108] Accordingly, the candidate ranking index for the candidate layer can be determined according to formula (9).

[0109]

[0110] In the above embodiment, the weights of multiple logging indicators are considered when calculating the indicator mean to adjust the contribution of the indicator values ​​of multiple logging indicators to the candidate energy storage index, so that the candidate ranking indicators are closer to practical applications, the quality of the candidate ranking indicators is improved, and the accuracy and reliability of potential layer selection can be improved.

[0111] In some embodiments, there are multiple candidate layers; based on the production potential indicators of the candidate layers and the candidate ranking indicators of the candidate layers under multiple adjustment factors, a target adjustment factor is selected from the multiple adjustment factors, including: for each adjustment factor, the candidate ranking indicators of the multiple candidate layers under the targeted adjustment factor are sorted to obtain a candidate ranking indicator sequence corresponding to the targeted adjustment factor; the production potential indicators of the multiple candidate layers are sorted according to the order of the multiple candidate layers in the candidate ranking indicator sequence to obtain a production potential indicator sequence corresponding to the targeted adjustment factor; based on the candidate ranking indicator sequence and the production potential indicator sequence corresponding to the targeted adjustment factor, the residual corresponding to the targeted adjustment factor is determined; based on the residuals corresponding to each of the multiple adjustment factors, a target adjustment factor is selected from the multiple adjustment factors.

[0112] Specifically, for each adjustment factor, the candidate ranking indicators of multiple candidate layers are sorted in order from large to small to obtain a candidate ranking indicator sequence corresponding to the adjustment factor, the candidate layer order in the candidate ranking indicator sequence is obtained, and the production potential indicators of multiple candidate layers are sorted according to the candidate layer order to obtain a production potential indicator sequence corresponding to the adjustment factor; based on the candidate ranking indicator sequence and the production potential indicator sequence corresponding to the adjustment factor, the residual corresponding to the adjustment factor is determined, and the adjustment factor corresponding to the smallest residual is used as the target adjustment factor.

[0113] In some embodiments, the residual corresponding to the targeted adjustment factor is determined based on the candidate ranking index sequence and the production potential index sequence corresponding to the targeted adjustment factor, including: determining the mean envelope based on the production potential index sequence corresponding to the targeted adjustment factor; determining the production potential mean index sequence of multiple candidate layers based on the mean envelope; determining the difference values ​​of multiple candidate layers respectively based on the production potential mean index sequence and the candidate ranking index sequence; determining the sum of the squares of the difference values ​​of multiple candidate layers to obtain the residual corresponding to the targeted adjustment factor.

[0114] Among them, the mean envelope is used to reflect the average value of the production potential indicators of multiple candidate layers.

[0115] Specifically, for the production potential indicator sequence corresponding to the targeted adjustment factor, the computer device obtains the maximum value in the production potential indicator sequence, performs cubic spline interpolation on each maximum value, and determines the upper envelope according to the interpolation result; obtains the minimum value in the production potential indicator sequence, performs cubic spline interpolation on each minimum value, and determines the lower envelope according to the interpolation result; and calculates the mean envelope according to the upper envelope and the lower envelope.

[0116] The computer device obtains the mean production potential indicators of multiple candidate layers in the mean envelope, and then determines the mean production potential indicator sequence corresponding to the targeted adjustment factor according to the mean envelope; determines the difference values ​​between the mean production potential indicators and the candidate ranking indicators of the multiple candidate layers according to the mean production potential indicator sequence and the candidate ranking indicator sequence corresponding to the targeted adjustment factor, calculates the squares of the difference values ​​of the multiple candidate layers, and then determines the sum of the squares of the difference values ​​of the multiple candidate layers to obtain the residual corresponding to the targeted adjustment factor.

[0117] In a possible implementation, after determining the difference between the mean production potential index and the candidate ranking index of multiple candidate layers, the average of the difference values ​​of the multiple candidate layers can be used as the residual corresponding to the adjustment factor.

[0118] For example, Figure 4(a) to Figure 4(f) The horizontal axis is the sorting index of multiple candidate layers under different adjustment factors. The sorting index is used to indicate the order of multiple candidate layers in the candidate sorting index sequence. Figure 4(a) to Figure 4(f) The vertical axes are all production potential indicators.

[0119] It should be noted that, since the candidate ranking index sequences under different adjustment factors may be different, the ranking indexes of multiple candidate layers under different adjustment factors are different. Figure 4(a) to Figure 4(f) The horizontal axis of is labeled the same, but multiple candidate layers are Figure 4(a) to Figure 4(f) The sorting index in may be different, i.e. Figure 4(a) to Figure 4(f) The same sorting index in may correspond to different candidate layers.

[0120] Figure 4(a) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0. Figure 4(a) is only sorted according to the mean of the index, without considering the discreteness between the index values; Figure 4(b) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0.004; Figure 4(c) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0.008; Figure Figure 4(d) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0.0012; Figure 4(e) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0.0020; Figure 4(f) shows the relationship between the scatter curve corresponding to the candidate ranking index sequence, the upper envelope, the lower envelope and the mean envelope corresponding to the production potential index sequence when the adjustment factor is equal to 0.0100; in Figure 4(f), the degree of dispersion has the greatest influence on the candidate ranking index.

[0121] pass Figure 4(a) to Figure 4(f) It can be seen that when the adjustment factor is equal to 0, the adjustment factor is equal to 0.0004 and the adjustment factor is equal to 0.0100, the differences between the scatter curve corresponding to the candidate ranking index sequence and the upper envelope, lower envelope and mean envelope corresponding to the production potential index sequence are large; when the adjustment factor is equal to 0.0008, the adjustment factor is equal to 0.0012 and the adjustment factor is equal to 0.0020, the differences between the scatter curve corresponding to the candidate ranking index sequence and the upper envelope, lower envelope and mean envelope corresponding to the production potential index sequence are small, so the target adjustment factor can be selected among 0.0008, 0.0012 and 0.0020.

[0122] For example, Figure 5 As shown, Figure 5 The horizontal axis is the value of the adjustment factor, and the vertical axis is the residual. It can be seen that when the adjustment factor is 0.0008, the residual is the smallest, so 0.0008 can be used as the target adjustment factor.

[0123] In the above embodiment, after calculating the candidate ranking indicators of the candidate layer under multiple adjustment factors, according to the residual between the production potential indicator and the candidate ranking indicator, the target adjustment factor is selected from the multiple adjustment factors according to the residual, and the candidate ranking indicator corresponding to the target adjustment factor is used as the target ranking indicator, so that the difference between the target ranking indicator and the production potential indicator is small, that is, the target ranking indicator can reflect the actual energy storage situation of the candidate layer, and the target ranking indicator is calculated under the balanced effect of multiple logging indicators, so that the potential layer selected according to the target ranking indicator is available in real application scenarios, thereby improving the practicability of potential layer selection.

[0124] In some embodiments, the potential layer selection method can be used in oil exploration scenarios; Figure 6 As shown, multiple logging indicators may include thickness H, permeability K, saturation S and porosity F; the computer device obtains the indicator values ​​of each candidate layer in multiple logging indicators (thickness H, permeability K, saturation S and porosity F), and these indicator values ​​may be preprocessed. The preprocessing may include noise reduction and interpolation operations, and then the preprocessed indicator values ​​are normalized to obtain normalized indicator values, and the weights of multiple logging indicators are obtained. According to the normalized indicator values ​​and weights, the indicator mean and dispersion are determined.

[0125] The computer device determines the candidate ranking index of each candidate layer under multiple adjustment factors according to the index mean and dispersion; calculates the product of the normalized index value of each candidate layer to obtain the production potential index of each candidate layer.

[0126] According to the production potential index, a target ranking index is selected from the candidate ranking indexes of the candidate layer under multiple adjustment factors, and the potential layer is selected according to the target ranking indexes of multiple candidate layers. The candidate layer with the largest target ranking index among the multiple candidate layers can be selected as the potential layer, or several candidate layers with larger target ranking indexes (such as the candidate layers with the target ranking index ranked in the top 3) can be selected as the potential layer; and a comparison chart can be displayed according to the target ranking indexes of multiple candidate layers, and the comparison chart is as follows Figure 7 As shown, Figure 7 In the comparison chart shown, the horizontal axis is the serial number of the candidate layer, and the vertical axis is the target ranking index of the candidate layer; the comparison chart provides a visualization method to intuitively understand the differences in energy storage conditions of multiple candidate layers.

[0127] The above potential layer selection method determines the mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indicators; determines the candidate ranking index of the candidate layer under multiple adjustment factors based on the mean and dispersion of the index; determines the production potential index of the candidate layer based on the multiple index values ​​of the candidate layer; selects the target ranking index of the candidate layer from the candidate ranking indexes of the candidate layer under multiple adjustment factors based on the production potential index of the candidate layer; selects the potential layer from the candidate layer based on the target ranking index of the candidate layer. This process does not rely on manual work, and can automatically process complex index values, and then select potential layers from the candidate layer, which effectively improves the efficiency of potential layer selection; in addition, by determining the candidate ranking index through the mean and dispersion of the index, the index values ​​under multiple well logging indicators act on the candidate ranking index in a balanced manner, which improves the quality of the candidate ranking index, and selects the target ranking index from the candidate ranking index based on the production potential index, so that the target ranking index is as close to the production potential index as possible, that is, the potential layer is selected based on the target ranking index that plays a role in a balanced manner and is close to the production potential index, which improves the accuracy and reliability of the selected potential layer.

[0128] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0129] Based on the same inventive concept, an embodiment of the present application also provides a potential layer selection device. The implementation solution for solving the problem provided by the device is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more potential layer selection device embodiments provided below can refer to the limitations on the potential layer selection method above, and will not be repeated here.

[0130] In some embodiments, Figure 8 As shown, a potential layer selection device is provided, comprising:

[0131] The index value processing module 801 is used to determine the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indicators;

[0132] A ranking index determination module 802 is used to determine the candidate ranking index of the candidate layer under multiple adjustment factors based on the index mean and dispersion;

[0133] The selection module 803 is used to select a potential layer from the candidate layer according to the target ranking index of the candidate layer.

[0134] In some embodiments, there are multiple candidate layers; the index value processing module 801 is also used to obtain, for each well logging index, index values ​​of multiple candidate layers under the targeted well logging index; normalize the index values ​​of multiple candidate layers under the targeted well logging index to obtain normalized index values ​​of multiple candidate layers under the targeted well logging index; for each candidate layer, determine the index mean of the candidate layer based on the normalized index values ​​of the candidate layer under multiple well logging indexes; determine the discreteness of the candidate layer based on the multiple normalized index values ​​and the index mean of the candidate layer.

[0135] In some embodiments, the indicator value processing module 801 is also used to obtain weights of multiple logging indicators; determine the weighted mean of the candidate layer based on the weights of the multiple logging indicators and the indicator values ​​of the candidate layer under the multiple logging indicators; and use the weighted mean of the candidate layer as the indicator mean of the candidate layer.

[0136] In some embodiments, the ranking index determination module 802 is also used to determine the candidate ranking index of the candidate layer under multiple adjustment factors based on the index mean and dispersion; determine the production potential index of the candidate layer according to the multiple index values ​​of the candidate layer; select a target adjustment factor from the multiple adjustment factors based on the production potential index of the candidate layer and the candidate ranking index of the candidate layer under multiple adjustment factors; and use the candidate ranking index of the candidate layer under the target adjustment factor as the target ranking index of the candidate layer.

[0137] In some embodiments, the candidate ranking index determination module 802 is also used to determine, for each adjustment factor, the product between the discreteness and the targeted adjustment factor to obtain the adjusted discreteness; and use the difference between the index mean and the adjusted discreteness as the candidate ranking index of the candidate layer under the targeted adjustment factor.

[0138] In some embodiments, there are multiple candidate layers; the sorting index determination module 802 is also used to sort the candidate sorting indexes of multiple candidate layers under the targeted adjustment factor for each adjustment factor, and obtain a candidate sorting index sequence corresponding to the targeted adjustment factor; sort the production potential indexes of multiple candidate layers according to the order of the multiple candidate layers in the candidate sorting index sequence, and obtain a production potential index sequence corresponding to the targeted adjustment factor; determine the residual corresponding to the targeted adjustment factor based on the candidate sorting index sequence and the production potential index sequence corresponding to the targeted adjustment factor; select a target adjustment factor from the multiple adjustment factors based on the residuals corresponding to each of the multiple adjustment factors.

[0139] In some embodiments, the ranking index determination module 802 is also used to determine the mean envelope based on the production potential index sequence corresponding to the targeted adjustment factor; determine the production potential mean index sequence of multiple candidate layers based on the mean envelope; determine the difference values ​​of multiple candidate layers respectively based on the production potential mean index sequence and the candidate ranking index sequence; determine the sum of the squares of the difference values ​​of multiple candidate layers to obtain the residual corresponding to the targeted adjustment factor.

[0140] Each module in the above potential layer selection device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0141] Fig. 9 This is a schematic diagram of the structure of the computer device provided in this application. Fig. 9 As shown, the computer device 90 provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the device 90 also includes a communication component 903. The processor 901, the memory 902 and the communication component 903 are connected via a bus 904.

[0142] In a specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, so that at least one processor 901 executes the above method.

[0143] The specific implementation process of the processor 901 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0144] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0145] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0146] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0147] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, each step of the method in the above embodiment is implemented.

[0148] The embodiment of the present application also provides a computer program product, including computer-executable instructions, which implement the various steps of the method in the above embodiment when executed by a processor.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0150] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0152] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0156] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0157] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A potential layer selection method, characterized in that: include: Determine the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indicators; Determining a target ranking index of the candidate layer based on the index mean and the dispersion; A potential layer is selected from the candidate layer according to a target ranking index of the candidate layer.

2. The method according to claim 1, characterized in that: The number of the candidate layers is multiple; The step of determining the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indexes includes: For each well logging indicator, obtaining indicator values ​​of multiple candidate layers under the targeted well logging indicator; Normalizing the index values ​​of the multiple candidate layers under the targeted well logging index to obtain normalized index values ​​of the multiple candidate layers under the targeted well logging index; For each candidate layer, based on the normalized index values ​​of the candidate layer under multiple well logging indicators, determine the index mean value of the candidate layer; Based on a plurality of normalized index values ​​for the candidate layer and the index mean, a discreteness for the candidate layer is determined.

3. The method according to claim 2, characterized in that The step of determining the index mean value of the candidate layer based on the normalized index values ​​of the candidate layer under multiple well logging indexes includes: Get the weights of multiple logging indicators; Determine a weighted mean value of the candidate layer according to the weights of the plurality of well logging indicators and the normalization of the candidate layer under the plurality of well logging indicators; The weighted mean of the candidate layer is taken as the index mean of the candidate layer.

4. The method according to any one of claims 1 to 3, characterized in that The determining the target ranking index of the candidate layer based on the index mean and the dispersion includes: Determining candidate ranking indicators of the candidate layer under multiple adjustment factors based on the indicator mean and the dispersion; Determining a production potential index of the candidate layer according to a plurality of index values ​​of the candidate layer; Based on the production potential index of the candidate layer and the candidate ranking index of the candidate layer under multiple adjustment factors, selecting a target adjustment factor from the multiple adjustment factors; The candidate ranking index of the candidate layer under the target adjustment factor is used as the target ranking index of the candidate layer.

5. The method according to claim 4, characterized in that The determining, based on the indicator mean and the dispersion, the candidate ranking indicator of the candidate layer under multiple adjustment factors includes: For each adjustment factor, determine the product between the discreteness and the adjustment factor to obtain the adjusted discreteness; The difference between the indicator mean and the adjusted dispersion is used as the candidate ranking indicator of the candidate layer under the targeted adjustment factor.

6. The method according to claim 4, characterized in that The number of the candidate layers is multiple; The selecting a target adjustment factor from the multiple adjustment factors based on the production potential index of the candidate layer and the candidate ranking index of the candidate layer under multiple adjustment factors includes: For each adjustment factor, the candidate ranking indicators of multiple candidate layers under the adjustment factor are sorted to obtain a candidate ranking indicator sequence corresponding to the adjustment factor; Sorting the production potential indicators of the multiple candidate layers according to the order of the multiple candidate layers in the candidate sorting indicator sequence to obtain a production potential indicator sequence corresponding to the adjustment factor; Determine the residual corresponding to the targeted adjustment factor according to the candidate ranking index sequence and the production potential index sequence corresponding to the targeted adjustment factor; A target adjustment factor is selected from the multiple adjustment factors according to the residuals corresponding to the multiple adjustment factors.

7. The method according to claim 6, characterized in that The determining of the residual corresponding to the targeted adjustment factor based on the candidate ranking index sequence and the production potential index sequence corresponding to the targeted adjustment factor comprises: Determine the mean envelope curve based on the production potential indicator sequence corresponding to the targeted adjustment factor; Determining the production potential mean index sequence of the plurality of candidate layers according to the mean envelope; Determining the difference values ​​of the plurality of candidate layers respectively according to the production potential mean index sequence and the candidate ranking index sequence; The sum of squares of the difference values ​​of the multiple candidate layers is determined to obtain a residual corresponding to the adjustment factor.

8. A potential layer selection device, characterized in that: The device comprises: An index value processing module, used to determine the index mean and dispersion of the candidate layer based on the index values ​​of the candidate layer under multiple well logging indicators; A ranking index determination module, used to determine a target ranking index of the candidate layer based on the index mean and the dispersion; A selection module is used to select a potential layer from the candidate layer according to a target ranking index of the candidate layer.

9. A computer device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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