Drilling point self-adaptive layout method and device, terminal and storage medium

Abstract

The invention provides a drilling point self-adaptive layout method and device, a terminal and a storage medium, and belongs to the technical field of geological exploration, and the method comprises the steps: randomly laying a plurality of initial drilling points in a target exploration space; obtaining a plurality of three-dimensional lithofacies field samples; the lithofacies information entropy of each grid node in the target exploration space is calculated, and the position of a newly-added drilling point is determined and arranged based on the lithofacies information entropy; and iteratively performing stratigraphic structure data acquisition, three-dimensional lithofacies field sample generation, lithofacies information entropy calculation, and determination and layout of newly added positions of the drilling points until the total number of the drilling points reaches a preset threshold value, and completing layout of all the drilling points. According to the method, a plurality of three-dimensional lithofacies field samples are generated by utilizing the stratigraphic structure data acquired each time, the lithofacies information entropy of each grid node in the target exploration space is calculated, and the position of a newly added drilling point is determined by taking the lithofacies information entropy as an index for measuring the uncertainty of the lithofacies space, so that the precision and efficiency of geological exploration are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, in particular to a drilling point adaptive layout method and device, a terminal and a storage medium. BACKGROUND

[0002] In the field of hydrogeological investigation, drilling sampling is a core means to obtain key parameters such as rock-soil physical and mechanical properties and lithofacies distribution characteristics. The scientificity of its layout directly determines the reliability of the investigation results. However, the traditional drilling point layout usually adopts a uniform random layout method. This method forms a one-time layout scheme. This static layout scheme cannot dynamically optimize the drilling point position according to the collected stratum data, and there is a possibility of over-sampling in low-information-value areas and insufficient sampling in high-uncertainty areas, which restricts the efficiency and accuracy of geological investigation.

[0003] Therefore, the prior art has defects and needs to be improved and developed. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a drilling point adaptive layout method, device, terminal and storage medium to solve the problem that the one-time layout scheme in the prior art restricts the efficiency and accuracy of geological investigation.

[0005] The technical solution adopted by the present application to solve the technical problem is as follows: In a first aspect, the present application provides a drilling point adaptive layout method, which comprises: randomly laying out a plurality of initial drilling points in a target survey space; collecting a plurality of stratum structure data along the vertical direction at each of the initial drilling points, processing based on all the stratum structure data to obtain a plurality of three-dimensional lithofacies field samples; calculating the lithofacies information entropy of each grid node in the target survey space, determining the position of the new drilling point based on all the lithofacies information entropy and laying out; iteratively collecting stratum structure data, generating three-dimensional lithofacies field samples, calculating lithofacies information entropy, determining the position of the new drilling point and laying out, until the total number of drilling points reaches a preset threshold, completing the layout of all drilling points; The three-dimensional lithofacies field sample is a data structure in which each grid node in the three-dimensional grid model corresponding to the target survey space is assigned a lithofacies category.

[0006] In one embodiment, the stratum structure data is the permeability coefficient; processing based on all the stratum structure data to obtain a plurality of three-dimensional lithofacies field samples comprises: Obtain the three-dimensional coordinate range of the target survey space, the preset grid resolution, and the coordinates of each permeability coefficient acquisition location; Based on each of the collected permeability coefficients, the corresponding lithofacies category is determined; The three-dimensional coordinate range, the grid resolution, the coordinates of the permeability coefficient acquisition location, and the lithofacies category corresponding to each permeability coefficient are input into the lithofacies simulation software to generate several three-dimensional lithofacies field samples.

[0007] In one implementation, determining the corresponding lithofacies category based on each of the collected permeability coefficients includes: Obtain a preset set of quantile value ratios, wherein the set of quantile value ratios includes multiple quantile value ratios; All the permeability coefficients are sorted in ascending order of their numerical values, and the quantile value corresponding to the proportion of each quantile value is calculated based on the sorting results. Using all percentile values ​​as the dividing points, the sorting result is divided into multiple consecutive and non-overlapping intervals, each interval corresponding to a unique interval number; Based on the preset mapping relationship, all permeability coefficients within the same interval are identified as the same lithofacies category, and permeability coefficients in different intervals are identified as different lithofacies categories. The mapping relationship is a mapping relationship between interval number and lithofacies category.

[0008] In one implementation, calculating the lithofacies entropy at each grid node in the target survey space includes: For each grid node, the frequency of each lithofacies category at that grid node is statistically analyzed based on all three-dimensional lithofacies field samples; The frequency is used as the probability of the lithofacies category appearing at that grid node; Obtain the preset number of lithofacies categories; The lithofacies information entropy of the grid node is calculated based on the probability and the number of lithofacies categories.

[0009] In one implementation, calculating the lithofacies information entropy of the grid node based on the probability and the number of lithofacies categories includes: Substituting the probability and the number of lithofacies categories into a preset calculation formula, the lithofacies information entropy of the grid node is obtained; The calculation formula is as follows: ; in, For grid nodes The entropy of lithofacies information at the location For grid nodes Lithofacies appeared at the location The probability, This represents the number of lithofacies categories.

[0010] In an embodiment, the position of the new drilling point is determined based on the total lithofacies information entropy, comprising: obtaining a preset lithofacies information entropy quantile threshold value; screening out a set of grid nodes in the target survey space with lithofacies information entropy higher than the lithofacies information entropy quantile threshold value as a candidate space; applying a clustering algorithm to process the position coordinates of all grid nodes in the candidate space, and outputting the coordinates of a plurality of cluster centers as the position of the new drilling point.

[0011] In an embodiment, the clustering algorithm is a K-means clustering algorithm or a DBSCAN clustering algorithm.

[0012] In a second aspect, the embodiments of the present application further provide a drilling point adaptive layout device, comprising: a data acquisition module for randomly laying out a plurality of initial drilling points in a target survey space; a sample generation module for collecting a plurality of stratigraphic structure data along the vertical direction at each initial drilling point, processing based on all the stratigraphic structure data, and obtaining a plurality of three-dimensional lithofacies field samples, the three-dimensional lithofacies field sample being a data structure formed by assigning a lithofacies category to each grid node in the three-dimensional grid model corresponding to the target survey space; an information entropy calculation module for calculating the lithofacies information entropy of each grid node in the target survey space, determining the position of the new drilling point based on all the lithofacies information entropy, and laying out the new drilling point; an iteration module for iteratively collecting stratigraphic structure data, generating three-dimensional lithofacies field samples, calculating lithofacies information entropy, determining the position of the new drilling point, and laying out the new drilling point until the total number of drilling points reaches a preset threshold value, and completing the layout of all drilling points.

[0013] In a third aspect, the embodiments of the present application further provide a terminal, comprising a memory, a processor, and a drilling point adaptive layout program stored on the memory and executable on the processor, wherein the drilling point adaptive layout program is executed by the processor to implement the steps of the drilling point adaptive layout method as described above.

[0014] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium storing a drilling point adaptive layout program, wherein the drilling point adaptive layout program can be executed to implement the steps of the drilling point adaptive layout method as described above.

[0015] The beneficial effects of the present application are as follows: the present application randomly arranges a plurality of initial drilling points in a target survey space; obtains a plurality of three-dimensional lithofacies field samples; calculates lithofacies information entropy of each grid node in the target survey space, determines the position of a newly added drilling point based on the lithofacies information entropy and arranges the newly added drilling point; iteratively performs stratum structure data collection, three-dimensional lithofacies field sample generation, lithofacies information entropy calculation, determination and arrangement of the position of a newly added drilling point, until the total number of drilling points reaches a preset threshold, and the arrangement of all drilling points is completed. The present application generates a plurality of three-dimensional lithofacies field samples by using the stratum structure data collected each time, calculates the lithofacies information entropy of each grid node in the target survey space, takes the lithofacies information entropy as an index for measuring the uncertainty of the lithofacies space, and then determines the position of a newly added drilling point, thereby effectively improving the accuracy and efficiency of geological exploration. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of a preferred embodiment of the drilling point self-adaptive arrangement method in the present application.

[0017] Figure 2 is a schematic diagram of the random arrangement of initial drilling points in the present application.

[0018] Figure 3 is a curve diagram of the change of the permeability coefficient with depth in the drilling point collection in the present application Figure 1 .

[0019] Figure 4 is a curve diagram of the change of the permeability coefficient with depth in the drilling point collection in the present application Figure 2 .

[0020] Figure 5 is a curve diagram of the change of the permeability coefficient with depth in the drilling point collection in the present application Figure 3 .

[0021] Figure 6 is a diagram showing the distribution of lithofacies information entropy calculated according to the initial drilling points in the present application.

[0022] Figure 7 is a diagram showing the result of stratum structure depiction in the present application.

[0023] Figure 8 is a diagram showing the random arrangement of four newly added drilling points in the experiment.

[0024] Figure 9 is a diagram showing the arrangement of four newly added drilling points by using the self-adaptive arrangement method in the experiment.

[0025] Figure 10 is a structural diagram of a preferred embodiment of the drilling point self-adaptive arrangement device in the present application.

[0026] Figure 11 is a terminal principle block diagram of the present application. DETAILED DESCRIPTION

[0027] In order to make the objects, technical solutions and advantages of the present application clearer and more apparent, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0028] In the field of hydrogeological investigation, drilling sampling is a core means to obtain key parameters such as rock-soil physical and mechanical properties and lithofacies distribution characteristics, and the scientificity of the layout directly determines the reliability of the investigation results. However, the traditional drilling point layout usually adopts a uniform random layout method. This method forms a one-time layout scheme, and such a static layout scheme cannot dynamically optimize the drilling point position according to the collected stratum data, and there is a possibility of over-sampling in the low-information-value area and insufficient sampling in the high-uncertainty area, which restricts the efficiency and accuracy of geological investigation.

[0029] In view of the above defects of the prior art, the present application provides a drilling point adaptive layout method, device, terminal and storage medium, the method comprising: randomly laying a plurality of initial drilling points in a target survey space; obtaining a plurality of three-dimensional lithofacies field samples; calculating the lithofacies information entropy of each grid node in the target survey space, and determining and laying the position of the newly added drilling point based on the lithofacies information entropy; iteratively collecting stratum structure data, generating three-dimensional lithofacies field samples, calculating lithofacies information entropy, determining and laying the position of the newly added drilling point, until the total number of drilling points reaches a preset threshold, and the layout of all drilling points is completed. The present application generates a plurality of three-dimensional lithofacies field samples by collecting stratum structure data each time, calculates the lithofacies information entropy of each grid node in the target survey space, takes the lithofacies information entropy as an index to measure the uncertainty of the lithofacies space, and then determines the position of the newly added drilling point, thereby effectively improving the accuracy and efficiency of geological exploration.

[0030] Referring to Figure 1 The drilling point adaptive layout method described in the embodiments of the present application comprises the following steps: Step S100, a plurality of initial drilling points are randomly laid in a target survey space.

[0031] Specifically, the target survey space is a space to be surveyed, which can be a three-dimensional hydrogeological structure. The schematic diagram of the initial drilling point layout is shown in Figure 2 .

[0032] Referring to Figure 1 The drilling point adaptive layout method described in the embodiments of the present application further comprises the following steps: Step S200, a plurality of stratum structure data are collected at each initial drilling point along the vertical direction, and a plurality of three-dimensional lithofacies field samples are obtained by processing all the stratum structure data.

[0033] Specifically, the formation structure data is a permeability coefficient. The present application provides a vertical resolution, and the permeability coefficient is collected along the vertical direction according to the vertical resolution. Based on processing of all formation structure data, a plurality of three-dimensional facies field samples are obtained, including: obtaining a three-dimensional coordinate range of a target survey space, a preset grid resolution, and a coordinate of each permeability coefficient collection position; determining a corresponding facies category according to each collected permeability coefficient; inputting the three-dimensional coordinate range, the grid resolution, the coordinate of the permeability coefficient collection position, and the facies category corresponding to each permeability coefficient into facies simulation software to generate a plurality of three-dimensional facies field samples. The three-dimensional facies field sample is a data structure in which each grid node in the three-dimensional grid model corresponding to the target survey space is assigned a facies category. Specifically, after inputting the three-dimensional coordinate range, the grid resolution, the coordinate of the permeability coefficient collection position, and the facies category corresponding to each permeability coefficient into the facies simulation software, the facies simulation software discretizes the target survey space according to the three-dimensional coordinate range and the grid resolution to generate a corresponding three-dimensional grid model, and then assigns a facies category to each grid node in the three-dimensional grid model based on the input coordinate of the permeability coefficient collection position and the facies category corresponding to each permeability coefficient through a geostatistical simulation algorithm to generate a plurality of facies field samples. The facies simulation software can be TPROGS software. A curve showing the change of the permeability coefficient collected by the drilling point with depth is shown in FIGS. Figure 3 、 Figure 4 and Figure 5 .

[0034] In an implementation manner, the corresponding facies category is determined according to each collected permeability coefficient, including: obtaining a preset set of quantile ratios, the set of quantile ratios including a plurality of quantile ratios; sorting all the permeability coefficients in ascending order of values, and calculating a quantile value corresponding to each quantile ratio based on the sorting result; dividing the sorting result into a plurality of continuous and non-overlapping intervals with all quantile values as successive dividing points; based on a preset mapping relationship, determining all the permeability coefficients in the same interval as the same facies category, and determining the permeability coefficients in different intervals as different facies categories; wherein the mapping relationship is a mapping relationship between the intervals and the facies categories.

[0035] Specifically, the quantile value proportion set includes 15%, 25%, 50%, and 75%. Based on these four quantile values, the ranking results can be divided into five intervals: the first interval is for permeability coefficients less than or equal to 15% in the ranking results; the second interval is for permeability coefficients greater than 15% and less than or equal to 25% in the ranking results; the third interval is for permeability coefficients greater than 25% and less than or equal to 50% in the ranking results; the fourth interval is for permeability coefficients greater than 50% and less than or equal to 75% in the ranking results; and the fifth interval is for permeability coefficients greater than 75% in the ranking results. The mapping relationship is as follows: the first interval corresponds to clay, the second interval corresponds to silty clay, the third interval corresponds to silt, the fourth interval corresponds to medium-coarse sand, and the fifth interval corresponds to gravel. The number of lithofacies categories in this invention is five, namely clay, silty clay, silt, coarse sand, and gravel.

[0036] Please see Figure 1 The adaptive drilling point layout method described in this embodiment of the invention further includes the following steps: Step S300: Calculate the lithofacies information entropy of each grid node in the target exploration space, and determine and deploy the location of the new drilling point based on all the lithofacies information entropies.

[0037] Specifically, the calculation of the lithofacies information entropy at each grid node in the target survey space includes: for each grid node, statistically analyzing the frequency of each lithofacies category at that grid node based on all three-dimensional lithofacies field samples; and using the frequency as the probability of that lithofacies category appearing at that grid node. Obtain the preset number of lithofacies categories; calculate the lithofacies information entropy of the grid node based on the probability and the number of lithofacies categories. The probability calculation formula can be expressed as: .in, The preset number of three-dimensional lithofacies field samples. lithofacies Number of times it appears.

[0038] In one implementation, the lithofacies information entropy of the grid node is calculated based on the probability and the number of lithofacies categories, including: Substituting the probability and the number of lithofacies categories into a preset calculation formula, the lithofacies information entropy of the grid node is obtained; The calculation formula is as follows: ; in, For grid nodes The entropy of lithofacies information at the location For grid nodes Lithofacies appeared at the location The probability, This represents the number of lithofacies categories.

[0039] Specifically, the position with high lithofacies information entropy indicates that the lithofacies of the position has multiple possibilities in the simulation, that is, the lithofacies uncertainty is high. The position with low lithofacies information entropy indicates that the lithofacies of the position has less possibility in the simulation, that is, the lithofacies certainty is high. The present application takes the lithofacies information entropy as an index for measuring the lithofacies spatial uncertainty, which can effectively determine the position of the newly added drilling point, and further ensure the effectiveness of the exploration. After the initial drilling point is laid out, the lithofacies information entropy distribution calculated is as shown in Figure 6

[0040] In an implementation manner, the position of the newly added drilling point is determined based on all the lithofacies information entropies, comprising: obtaining a preset lithofacies information entropy quantile threshold; screening a set of grid nodes with the lithofacies information entropy higher than the lithofacies information entropy quantile threshold in the target exploration space as a candidate space; applying a clustering algorithm to process the position coordinates of all the grid nodes in the candidate space, and outputting the coordinates of a plurality of clustering centers as the positions of the newly added drilling points.

[0041] Specifically, the candidate space means that the lithofacies of the space has multiple possibilities in a plurality of three-dimensional lithofacies field samples, and the lithofacies uncertainty is high, which is a candidate space of the potential newly added drilling point. Laying out the drilling point in this kind of candidate space can effectively reduce the uncertainty of the lithofacies by collecting the measured data, and improve the accuracy of the stratum structure description. The lithofacies information entropy quantile threshold can be 70%. The clustering algorithm can adopt a K-means clustering algorithm or a DBSCAN clustering algorithm.

[0042] Referring to Figure 1 The drilling point self-adaptive laying method provided by the embodiment of the present application further comprises the following steps: Step S400, iteratively performing stratum structure data collection, three-dimensional lithofacies field sample generation, lithofacies information entropy calculation, determination and laying of the position of the newly added drilling point, until the total number of the drilling points reaches a preset threshold, and the laying of all the drilling points is completed.

[0043] Specifically, for the area with complex geological conditions and uneven lithofacies distribution, it is difficult for the fixed drilling scheme to comprehensively capture the stratum characteristics. However, the iterative method can dynamically adjust the laying point strategy according to the real-time collected data, flexibly cope with the complex conditions such as stratum mutation, and ensure that the final drilling point network can effectively cover the key area of the stratum change. After the laying of all the drilling points is completed, the three-dimensional lithofacies field sample generated in the last iteration is taken as the final stratum structure description result. The stratum structure description result can be as shown in Figure 7 In addition, the positions of all the iterative drilling points are also supported to be taken as the final drilling point laying scheme and output.

[0044] ​To verify the technical effect of the present application, an experiment was conducted in region A, and the region with information entropy quantile higher than 70% was defined as a high entropy region. The percentage reduction in the number of high entropy regions (referred to as entropy reduction) was used as an effectiveness evaluation index of the present application. After laying out 6 initial drilling points, 4 new drilling points were randomly laid out, and the scheme of adaptively laying out 4 new drilling points was compared. The schematic diagram of random layout is shown in Figure 8 The schematic diagram of adaptive layout is shown in Figure 9 The comparison results are shown in Table 1.

[0045] Table 1

[0046] As can be seen from Table 1, compared with the traditional random layout scheme, the adaptive layout scheme of the present application improves the entropy reduction by 11.95%, which proves that it can effectively reduce the uncertainty of facies field, thereby improving the accuracy and efficiency of representing the heterogeneous structure of aquifer. The present experiment verifies the effectiveness of the method of the present application in complex heterogeneous aquifer, and the same logic can be extended to pollution site remediation, exploration in arid areas, etc.

[0047] The present application is suitable for hydrogeological investigation, groundwater pollution monitoring, groundwater resource assessment and other fields, especially for the high-precision characterization demand of heterogeneous stratum structure. Through the dynamic optimization mechanism driven by information entropy, the information collection efficiency of drilling point layout can be significantly improved, and reliable data basis is provided for groundwater flow simulation, pollutant migration prediction, etc.

[0048] In one embodiment, as shown in Figure 10 Based on the above-mentioned adaptive drilling point layout method, the present application also correspondingly provides an adaptive drilling point layout device, which comprises: A data acquisition module 100 is used for randomly laying out a plurality of initial drilling points in a target survey space; A sample generation module 200 is used for collecting a plurality of stratum structure data along the vertical direction at each of the initial drilling points, processing based on all the stratum structure data, and obtaining a plurality of three-dimensional facies field samples. The three-dimensional facies field sample is a three-dimensional data volume formed by assigning a specified facies category to each grid node in a three-dimensional grid model after discretization of the target survey space; An information entropy calculation module 300 is used for calculating the facies information entropy at each grid node in the target survey space, and determining and laying out the position of the new drilling point based on all the facies information entropy; An iteration module 400 is used for iteratively collecting stratum structure data, generating three-dimensional facies field samples, calculating facies information entropy, determining and laying out the position of new drilling points, until the total number of drilling points reaches a preset threshold, and completing the layout of all drilling points.

[0049] In an embodiment, the formation structure data is a permeability coefficient; and the device further comprises: a parameter acquisition unit configured to acquire a three-dimensional coordinate range of a target survey space, a preset grid resolution, and a coordinate of each permeability coefficient acquisition position; a type determination unit configured to determine a corresponding lithofacies category according to each acquired permeability coefficient; a sample generation unit configured to input the three-dimensional coordinate range, the grid resolution, the coordinate of the permeability coefficient acquisition position, and the lithofacies category corresponding to each permeability coefficient into a lithofacies simulation software to generate a plurality of three-dimensional lithofacies field samples.

[0050] In an embodiment, the device further comprises: a quantile proportion set acquisition unit configured to acquire a preset quantile proportion set, the quantile proportion set comprising a plurality of quantile proportions; a quantile value calculation unit configured to sort all the permeability coefficients in ascending order of values, and calculate a quantile value corresponding to each quantile proportion based on a sorting result; an interval division unit configured to divide the sorting result into a plurality of continuous and non-overlapping intervals by taking all the quantile values as successive division points; a lithofacies category determination unit configured to determine all the permeability coefficients in a same interval as a same lithofacies category and determine the permeability coefficients in different intervals as different lithofacies categories based on a preset mapping relationship, the mapping relationship being a mapping relationship between intervals and lithofacies categories.

[0051] In an embodiment, the device further comprises: a frequency calculation unit configured to, for each grid node, statistically calculate a frequency of occurrence of each lithofacies category at the grid node based on all the three-dimensional lithofacies field samples; a probability determination unit configured to take the frequency as a probability of occurrence of the lithofacies category at the grid node; a number acquisition unit configured to acquire a preset number of lithofacies categories; an information entropy calculation unit configured to calculate a lithofacies information entropy of the grid node according to the probability and the number of lithofacies categories.

[0052] In an embodiment, the device further comprises: a formula calculation unit configured to substitute the probability and the number of lithofacies categories into a preset calculation formula to obtain the lithofacies information entropy of the grid node; the calculation formula being: ; wherein, is the lithofacies information entropy of the grid node ; for the grid nodes occurrence of lithofacies probability, for the number of lithofacies categories.

[0053] In an embodiment, the apparatus further comprises: a quantile threshold obtaining unit configured to obtain a preset lithofacies information entropy quantile threshold; a screening unit configured to screen out a set of grid nodes in the target survey space whose lithofacies information entropy is higher than the lithofacies information entropy quantile threshold as candidate spaces; a position determining unit configured to apply a clustering algorithm to process position coordinates of all grid nodes in the candidate spaces and output coordinates of a plurality of clustering centers as positions of new drilling points.

[0054] Based on the above-mentioned embodiments, the present application further provides a terminal, a structure diagram of which can be shown as Figure 11 The terminal comprises a processor, a memory, a network interface and a display screen connected through an apparatus bus. The processor of the terminal is configured to provide computing and control capabilities. The memory of the terminal comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating device and a drilling point adaptive layout program. The internal memory provides an environment for the operation of the operating device and the drilling point adaptive layout program in the non-volatile storage medium. The network interface of the terminal is configured to communicate with external terminals through network connections. The drilling point adaptive layout program is executed by the processor to implement the steps of any of the drilling point adaptive layout methods described above. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen.

[0055] Those skilled in the art can understand that Figure 11 the structure diagram shown in the above-mentioned embodiments is only a schematic diagram of part of the structure related to the present application scheme, and does not constitute a limitation on the terminal to which the present application scheme is applied. Specifically, the terminal can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0056] In an embodiment, a terminal is provided, which comprises a memory, a processor and a drilling point adaptive layout program stored in the memory and executable on the processor. When the drilling point adaptive layout program is executed by the processor, the steps of any of the drilling point adaptive layout methods provided by the present application embodiment are implemented.

[0057] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a drilling point adaptive layout program.

[0058] It should be understood that the sequence of the steps in the above embodiment does not mean the order of execution, and the execution order of the processes should be determined according to the function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is taken as an example, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the above device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of the functional units and modules are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the device can be referred to the corresponding process in the foregoing method embodiment, which will not be described here.

[0060] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0061] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0062] In the embodiments provided by the present application, it should be understood that the disclosed device / terminal equipment and method can be implemented by other ways. For example, the device / terminal equipment embodiments described above are only schematic, and the division of the above modules or units is only a logical function division, and there can be another division way in actual implementation, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0063] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those ordinarily skilled in the art should understand; the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of adaptive placement of drilling points, characterized in that, The method comprises: randomly arranging a plurality of initial drilling points in a target survey space; collecting formation structure data in a vertical direction at each of the initial drilling points, processing based on all the formation structure data to obtain a plurality of three-dimensional facies field samples; calculating facies information entropy at each grid node in the target survey space, and determining the position of a new drilling point based on all the facies information entropy and arranging the new drilling point; iteratively collecting formation structure data, generating three-dimensional facies field samples, calculating facies information entropy, determining the position of a new drilling point, and arranging the new drilling point until the total number of drilling points reaches a preset threshold, and completing the arrangement of all drilling points; wherein the three-dimensional facies field sample is a data structure in which each grid node in a three-dimensional grid model corresponding to the target survey space is assigned a facies category.

2. The method of adaptive placement of drilling points according to claim 1, characterized in that, The formation structure data is a permeability coefficient; processing based on all the formation structure data to obtain a plurality of three-dimensional facies field samples comprises: obtaining a three-dimensional coordinate range of the target survey space, a preset grid resolution, and coordinates of each permeability coefficient collection position; determining a corresponding facies category based on each collected permeability coefficient; inputting the three-dimensional coordinate range, the grid resolution, the coordinates of the permeability coefficient collection position, and the facies category corresponding to each permeability coefficient into facies simulation software to generate a plurality of three-dimensional facies field samples.

3. The method of adaptive placement of drilling points of claim 2, wherein, Determining a corresponding facies category based on each collected permeability coefficient comprises: obtaining a preset set of quantile values, the set of quantile values including a plurality of quantile values; sorting all the permeability coefficients in ascending order of value, and calculating a quantile value corresponding to each quantile value based on the sorting result; dividing the sorting result into a plurality of continuous and non-overlapping intervals with all quantile values as successive dividing points; based on a preset mapping relationship, determining all permeability coefficients in the same interval as the same facies category, and determining permeability coefficients in different intervals as different facies categories; wherein the mapping relationship is a mapping relationship between intervals and facies categories.

4. The method of adaptive placement of drilling sites of claim 1, wherein, Calculating facies information entropy at each grid node in the target survey space comprises: for each grid node, based on all three-dimensional facies field samples, counting the frequency of each facies category appearing at the grid node; using the frequency as the probability of the facies category appearing at the grid node; obtaining a preset number of facies categories; based on the probability and the number of facies categories, calculating the facies information entropy of the grid node.

5. The method of adaptive placement of drilling points of claim 4, wherein, Based on the probability and the number of facies categories, calculating the facies information entropy of the grid node comprises: substituting the probability and the number of facies categories into a preset calculation formula to obtain the facies information entropy of the grid node. The calculation formula is: ; wherein, is the lithofacies information entropy at the grid node , is the probability of the lithofacies appearing at the grid node , is the number of lithofacies categories.

6. The method of adaptive placement of drilling sites of claim 1, wherein, Based on all the facies information entropy, determining the position of a new drilling point comprises: obtaining a preset facies information entropy quantile threshold; filtering out a set of grid nodes in the target survey space whose facies information entropy is higher than the facies information entropy quantile threshold as a candidate space; applying a clustering algorithm to process the position coordinates of all grid nodes in the candidate space, and outputting the coordinates of a plurality of cluster centers as the position of a new drilling point.

7. The method of adaptive placement of drilling points according to claim 6, wherein, The clustering algorithm is a K-means clustering algorithm or a DBSCAN clustering algorithm.

8. A drilling spot adaptive placement device, characterized by, It comprises: a data acquisition module for randomly arranging a plurality of initial drilling points in a target survey space; a sample generation module for collecting a plurality of stratigraphic structure data in a vertical direction at each initial drilling point, processing based on all the stratigraphic structure data to obtain a plurality of three-dimensional facies field samples, and the three-dimensional facies field sample being a data structure in which each grid node in a three-dimensional grid model corresponding to the target survey space is assigned a facies category; an information entropy calculation module for calculating the facies information entropy of each grid node in the target survey space, determining the position of a new drilling point based on all the facies information entropy, and arranging the new drilling point; an iteration module for iteratively collecting stratigraphic structure data, generating three-dimensional facies field samples, calculating facies information entropy, determining the position of a new drilling point, and arranging the new drilling point until the total number of drilling points reaches a preset threshold, and completing the arrangement of all drilling points.

9. A terminal, characterized by comprising: The terminal comprises a memory, a processor, and a drilling point adaptive arrangement program stored on the memory and executable on the processor, and the drilling point adaptive arrangement program, when executed by the processor, implements the steps of the drilling point adaptive arrangement method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a drilling point adaptive arrangement program, and the drilling point adaptive arrangement program, when executed by the processor, implements the steps of the drilling point adaptive arrangement method according to any one of claims 1-7.

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