Method, device and program product for reconstructing logging data of in-situ leaching sandstone type uranium ore

Abstract

The invention discloses a method for reconstructing logging data of an in-situ leaching sandstone type uranium deposit, and the method comprises the steps: achieving the dynamic weighting of the old and new logging data through the collected old logging data, the newly collected new logging data, the standard deviation of the logging data in the depth direction, the porosity, and the correlation coefficient with a geologic model; the method comprises the following steps: obtaining dynamically weighted logging data on each depth point, calculating differences between the dynamically weighted logging data on each depth point and old and new logging data, selecting optimal logging data on the depth point according to the differences, and reconstructing new logging data on the basis of the optimal logging data in the depth direction. And the accuracy of the logging data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical technology, in particular to a method, device and program product for reconstructing logging data of in-situ sandstone type uranium mine. BACKGROUND

[0002] In the exploration and development process of in-situ sandstone type uranium mine, logging data is the core data source for evaluating ore-bearing and water-bearing strata and dividing industrial mining sections. However, due to the non-uniformity of logging series, combined with the influence of factors such as natural gamma curve being easily affected by radioactive balance destruction, well fluid mineralization degree change, casing corrosion, etc., the original logging data generally has defects, such as base value drift and dimension non-uniformity caused by different years, different instruments and different construction teams, making it difficult to directly compare the data.

[0003] At present, the commonly used logging curve reconstruction method on the market includes data preparation, correction, standardization and outlier rejection. The logging data reconstructed by the traditional correction method has great defects in accuracy, and cannot meet the resolution requirements of complex geological conditions. The conventional fitting method (such as linear regression) is difficult to handle the nonlinear relationship between logging curves, and the problem of multicollinearity will reduce the model accuracy. Early logging data is easily affected by complex geological conditions such as borehole collapse, mud invasion and fracture zone, resulting in unstable quality of shallow exploration data such as logging curves. How to better utilize early logging data and achieve higher resolution logging data reconstruction is the current difficulty. SUMMARY

[0004] Therefore, the present application aims to provide a method, device and program product for reconstructing logging data of in-situ sandstone type uranium mine to solve the problems in the background art.

[0005] Specifically, the present application provides a method, device and program product for reconstructing logging data of in-situ sandstone type uranium mine. Through the collected old logging data and newly collected new logging data, the standard deviation of the logging data in the depth direction, the porosity and the correlation coefficient with the geological model are used to realize dynamic weighting of the old and new logging data at each depth point, and the dynamic weighted logging data at each depth point is obtained. The difference between the dynamic weighted logging data at each depth point and the old and new logging data is calculated, and the optimal logging data at the depth point is selected according to the difference, and then the new logging data is reconstructed based on the optimal logging data in the depth direction, which improves the accuracy of the logging data and solves the problem of low resolution in the background art.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, a method for reconstructing logging data of in-situ sandstone type uranium mine is provided, comprising the following steps:

[0008] Step 1: Collecting old logging data and newly collected new logging data for the same well;

[0009] Step 2: Preprocessing the old logging data and the new logging data to obtain corrected old logging data O1 and new logging data N1;

[0010] Step 3: Setting depth points at intervals of L meters in the depth direction, ensuring that the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and sequentially processing each depth point;

[0011] Step 4: Obtaining the weight W 11 of the corrected old logging data O1 and the weight coefficient W 12 of the new logging data N1 based on the standard deviation of the corrected old logging data O1 and the new logging data N1;

[0012] Step 5: Obtaining the weight W 21 of the corrected old logging data O1 and the weight W 22 of the new logging data N1 based on the matching degree of the porosity measured by the core and the porosity measured by the corrected old logging data O1 and the new logging data N1;

[0013] Step 6: Obtaining the weight W 31 of the old logging data O1 and the weight W 32 of the new logging data N1 based on the matching degree of the corrected old logging data O1 and the new logging data N1 and the geological model;

[0014] Step 7: Dynamically weighting the old logging data O1 and the new logging data N1 based on the weights W 11 , W 12 , W 21 , W 22 , W 31 , W 32 to obtain the logging data LW at each depth point;

[0015] Step 8: Calculating the difference between the logging data LW at each depth point and the old logging data O1 and the new logging data N1, respectively, and selecting the appropriate logging data as the optimal logging data at each depth point according to the difference size, selecting the smaller one as the logging data, and selecting LW as the logging data when the difference is the same;

[0016] Step 9: Reconstructing new logging data based on the optimal logging data selected at multiple depth points.

[0017] Further, the step 1 includes collecting old logging data previously collected for the same wellbore and newly collecting new logging data based on the same collection parameters to ensure consistency of the old and new logging data.

[0018] Further, the step 2 comprises: pre-processing including wellbore correction, invasion correction and temperature correction.

[0019] Further, the step 4 comprises:

[0020]

[0021] W 12 =1-W 11 ;

[0022] wherein, is the standard deviation of O1 and N1 in the sliding window, the length of the sliding window can be set according to the depth range of the actual data.

[0023] Further, the step 5 comprises:

[0024]

[0025] W 22 =1-W 21

[0026] wherein, φ core is the measured porosity of the core, is the porosity calculated based on O1, is the porosity calculated based on N1.

[0027] Further, the step 6 comprises:

[0028]

[0029] W 32 =1-W 31 ;

[0030] wherein, is the correlation coefficient of O1 and the geological model, is the correlation coefficient of N1 and the geological model.

[0031] Further, the step 7 comprises:

[0032] W O1 =(W 11 +W 21 +W 31 ) / 3;

[0033] W N1 =(W 12 +W 22 +W 32 ) / 3;

[0034] LW=W O1 ×O1+WN1 XN1;

[0035] wherein, W O1 is the comprehensive weight of O1 for constructing LW, W N1 is the comprehensive weight of N1 for constructing LW.

[0036] Further, the step 8 comprises: judging by depth point according to the data interval L in the depth dimension, and the judging formula is as follows:

[0037]

[0038] wherein, is the difference between LW and O1, is the difference between LW and N1,

[0039] The judging rule is: comparing and selecting the party with the smaller difference value as the optimal logging data of this depth point; when the difference and is equal, selecting LW as the logging data.

[0040] In a second aspect, a device for reconstructing logging data of a sandstone type uranium deposit is provided, comprising:

[0041] A first unit: collecting old logging data and newly collected new logging data for the same well;

[0042] A second unit: preprocessing the old logging data and the new logging data to obtain corrected old logging data O1 and new logging data N1;

[0043] A third unit: setting depth points at an interval of L meters in the depth direction, ensuring that the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and sequentially processing each depth point;

[0044] A fourth unit: obtaining the weight W11 of the corrected old logging data O1 and the weight coefficient W12 of the new logging data N1 based on the standard deviation of the corrected old logging data O1 and the new logging data N1;

[0045] A fifth unit: obtaining the weight W21 of the corrected old logging data O1 and the weight W22 of the new logging data N1 based on the matching degree of the porosity measured by the core and the porosity measured by the corrected old logging data O1 and the new logging data N1; and a sixth unit: obtaining the weight W31 of the old logging data O1 and the weight W32 of the new logging data N1 based on the matching degree of the corrected old logging data O1 and the new logging data N1 and the geological model.

[0046] Seventh unit: based on the above weights W11, W12, W21, W22, W31, W32, dynamically weighting the old logging data O1 and the new logging data N1 to obtain the logging data LW at each depth point;

[0047] Eighth unit, respectively calculating the difference between the logging data LW at each depth point and the old logging data O1 and the new logging data N1, and selecting appropriate logging data as the optimal logging data at each depth point according to the difference size;

[0048] Ninth unit: reconstructing new logging data based on the selected optimal logging data at multiple depth points.

[0049] In a third aspect, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method for reconstructing logging data of in-situ leaching sandstone type uranium mine.

[0050] In a fourth aspect, an electronic device is provided, comprising a memory for storing a computer program, and a processor for implementing the steps of the method for reconstructing logging data of in-situ leaching sandstone type uranium mine when executing the computer program.

[0051] In a fifth aspect, a computer readable storage medium is provided, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for reconstructing logging data of in-situ leaching sandstone type uranium mine.

[0052] Compared with the prior art, the specific embodiments of the present application have the following beneficial effects:

[0053] The present application provides a method, device and program product for reconstructing logging data of in-situ leaching sandstone type uranium mine, which dynamically weights the old logging data and the newly collected new logging data in the depth direction by using the standard deviation of the logging data, the porosity and the correlation coefficient with the geological model, obtains the dynamically weighted logging data at each depth point, calculates the difference between the dynamically weighted logging data at each depth point and the old and new logging data, selects the optimal logging data at the depth point according to the difference, and further reconstructs the new logging data in the depth direction based on the optimal logging data. The present application avoids single data deviation by multi-dimensional (comprehensive data quality (standard deviation), core data (porosity correlation coefficient) and geological data (geological model correlation coefficient)) and multi-data (old and new logging data) fusion. Through point-by-point difference analysis, the data selection is optimized in the vertical resolution, the high-precision logging curve can be efficiently reconstructed, the reliability of reservoir evaluation and geological modeling is significantly improved, the unstable quality of early logging data is solved, and the accuracy of logging data is improved. BRIEF DESCRIPTION OF DRAWINGS

[0054] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0055] Figure 1 An implementation flowchart of the method for reconstructing logging data according to the embodiment of the application is shown in the figure, and details are as follows:

[0056] Figure 2 An implementation flowchart of the method for outputting optimal logging data according to the difference principle according to the embodiment of the application is shown in the figure, and details are as follows:

[0057] Figure 3 A structure diagram of the device for reconstructing logging data according to the embodiment of the application is shown in the figure, and details are as follows: DETAILED DESCRIPTION

[0058] In order to make the technical personnel of the art better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0059] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0060] Figure 1 An implementation flowchart of the method for reconstructing logging data according to the embodiment of the application is shown in the figure, and details are as follows:

[0061] A method for reconstructing logging data of in-situ leaching sandstone type uranium mine, comprising the following steps:

[0062] Step 1: For the same well, collect old logging data and newly collected new logging data;

[0063] Further, the step 1 comprises: collecting old logging data previously acquired and newly acquired logging data based on the same acquisition parameters for the same wellbore, and the logging data obtained by the same acquisition parameters are more convenient to align on the subsequent depth of the new and old logging data, thereby ensuring the consistency of the old and new logging data.

[0064] Step 2: pre-processing the old logging data and the new logging data to obtain corrected old logging data O1 and new logging data N1; the pre-processing comprises wellbore correction, invasion correction and temperature correction.

[0065] Specifically, the wellbore correction is to eliminate the influence of wellbore expansion or mud cake on the logging data, and the formula is as follows:

[0066]

[0067] The CALI standard is the standard caliper, i.e. the caliper in the ideal state, and the drill bit size can be selected as the standard caliper data, and the CALI measured is the measured caliper data;

[0068] The invasion correction is to correct the influence of drilling fluid invasion on the resistivity, and the formula is as follows:

[0069] O1 = O1 + a · (R LLD -R LLS )

[0070] Like the wellbore correction, the invasion correction of N1 is the same as that of O1;

[0071] Wherein, R LLD is the deep lateral resistivity, and R LLS is the shallow lateral resistivity; a is a regional empirical coefficient, which can be determined by core calibration;

[0072] The temperature correction is to eliminate the influence of formation temperature change on the resistivity, and the formula is as follows:

[0073] O1 = O1 × [1 + b · (T 实际 -T 基准 )]

[0074] The temperature correction of N1 is the same as that of O1;

[0075] Wherein, T 实际 is the measured formation temperature, and T 基准 is the reference temperature, which can be selected as 25 degrees Celsius, and b is the temperature coefficient, which can be calibrated by laboratory;

[0076] Through the above preprocessing, the impact of wellbore diameter differences caused by wellbore collapse, the impact of mud invasion on logging data, and the impact of temperature differences on logging data are reduced, further improving the accuracy of old and new logging data, and reducing errors in the subsequent weight calculation and selection of optimal logging data.

[0077] Step 3: Set depth points at intervals of L meters in the depth direction to ensure that the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and perform subsequent processing on each depth point in turn; the depth point interval L can be set according to the length of the actual data or the accuracy requirements of the exploration.

[0078] Step 4: Obtain the weight W of the corrected old logging data O1 based on the standard deviation of the corrected old logging data O1 and the new logging data N1 11 and the new logging data N1 weight coefficient W 12 ;

[0079] The weight formula calculated based on the standard deviation of new and old data is:

[0080]

[0081] W 12 =1-W 11 ;

[0082] in, The standard deviation of O1 and N1 within the sliding window. The length of the sliding window can be set according to the depth range of the actual data. The window length can be selected as 2m, 3m, 5m, 10m or other;

[0083] The method for calculating the standard deviation based on well logging data can adopt the existing calculation method in the field; taking the well logging data as the resistivity value as an example, the standard deviation calculation process is as follows: calculate the average value ave of y data points O1 or N1 in the sliding window, and then calculate the square of the difference between each data point and the average value (O1-ave) 2 , and then find multiple data points (O1-ave) 2 Average (O1-ave) 2 / y, then (O1-ave) 2 / y square root; the standard deviation of N1 is calculated in the same way as O1; the weight is based on the standard deviation. A smaller standard deviation means higher data stability, and the corresponding weight is higher.

[0084] Step 5: Based on the porosity measured by the core and the porosity calculated by the corrected old logging data O1 and the porosity calculated by the new logging data N1, the weight W of the corrected old logging data O1 is obtained. 21 and the new logging data N1 weight W22 ;

[0085] The weight formula based on the matching degree of the new and old data porosity calculation is:

[0086]

[0087] W 22 =1-W 21

[0088] wherein φ core is the measured porosity of the core, is the porosity calculated based on O1, is the porosity calculated based on N1.

[0089] wherein, is the porosity calculated based on O1, and is the porosity calculated based on N1, that is, the porosity calculated based on the old and new logging data. The calculation method can adopt the existing calculation method in the art; taking the old and new resistivity logging data as an example, the resistivity logging data can be calculated by using Archie's Equation to obtain the porosity;

[0090] The formula for calculating the porosity from the resistivity logging data is:

[0091] wherein R t : formation resistivity, unit Ω·m; R w : formation water resistivity, unit Ω·m; S w : water saturation; a: proportionality coefficient, a<1; m: cementation exponent; n: saturation exponent, usually taken as 2; wherein the acquisition and setting of each parameter are recorded in the prior art;

[0092] The closer the porosity calculated from the logging is to the core porosity, the closer the logging data is to the real core, and therefore, the higher the weight given.

[0093] Step 6, obtaining the old logging data O1 weight W 31 and the new logging data N1 weight W 32 based on the matching degree of the corrected old logging data O1 and the new logging data N1 and the geological model;

[0094] The weight formula based on the matching degree of the new and old data geological model calculation is:

[0095]

[0096] W 32 =1-W 31 ;

[0097] wherein, is a correlation coefficient of O1 and the geological model, is a correlation coefficient of N1 and the geological model;

[0098] The specific implementation is as follows: a geological model is constructed based on geological data, seismic data, drilling data, lithology and the like; subsequently, based on logging data, correlation analysis and pattern recognition are performed to analyze the correlation between the logging data and the lithology, the lithology categories are divided through cluster analysis, and the correlation coefficients of natural gamma (GR), acoustic time difference (AC), resistivity (RT) and the like with the lithology categories are calculated; taking the resistivity logging data as an example, the correlation coefficient (i.e. the matching degree) of the resistivity logging data and the geological model can adopt the Pearson coefficient, and the specific formula is as follows: r is the correlation coefficient of the logging data and the geological model, wherein X i and Y i represent the logging data (such as resistivity and the like) and the geological model attribute value (such as porosity and the like) respectively, and are the logging data mean value and the geological model attribute data mean value respectively; in addition, the correlation analysis between the logging data and the physical property parameters of the geological model can be performed, the capillary pressure experimental data and the resistivity logging data are combined to establish a model, the correlation coefficient is fitted, and the model can be a Li-Williams model; there are various correlation coefficients of the geological model in the prior art, in the present application, the correlation coefficient of the logging data and the geological model obtained according to the actual data needs can be selected by the Pearson coefficient or the Li-Williams model or other methods, and according to actual needs, a plurality of correlation coefficients can be weighted and summed to obtain a correlation coefficient that takes into account the lithology parameters, the physical property parameters of the stratum and the fusion of other geophysical data results. The more consistent the logging data and the predicted value of the geological model, the higher the corresponding weight.

[0099] Figure 2 is an implementation step of outputting the optimal logging data according to the difference principle, and the specific implementation is as follows: step 7: based on the weights W 11 , W 12 , W 21 , W 22 , W 31 , W 32 , the old logging data O1 and the new logging data N1 are dynamically weighted to obtain the logging data LW at each depth point;

[0100] The step 7 calculates the comprehensive weight based on the correlation coefficients of the old and new logging data in the standard deviation, porosity and geological model calculated in steps 4-6, and the specific formula is as follows:

[0101] W O1 =(W 11 +W 21 +W31 ) / 3;

[0102] W N1 =(W 12 +W 22 +W 32 ) / 3;

[0103] LW=W O1 ×O1+W N1 ×N1;

[0104] Wherein, W O1 is the comprehensive weight of O1 for constructing LW, W N1 is the comprehensive weight of N1 for constructing LW; the application constructs a multi-dimensional fusion logging data LW which comprehensively integrates data quality, core data and geological data through multiple dimensions (standard deviation, porosity, geological model), and the multi-dimensional fusion logging data LW obtained by dynamically weighting the above data improves the accuracy in subsequent optimal logging data screening;

[0105] Step 8, respectively calculate the difference between the logging data LW and the old logging data O1 and the new logging data N1 at each depth point, select appropriate logging data as the optimal logging data at each depth point according to the difference size, select the smaller one as the logging data when the difference is the same, and select LW as the logging data when the difference is the same;

[0106] According to the data interval L in the depth dimension, the judgment is made point by point, and the formula is as follows:

[0107]

[0108] Wherein, is the difference between LW and O1, is the difference between LW and N1,

[0109] The rule for determination is that the difference value is compared and The smaller one is selected as the optimal logging data at this depth point; when the difference is equal to , LW is selected as the logging data.

[0110] The smaller the difference is, the closer the logging data is to the multi-dimensional fusion logging data LW, and the higher the reliability of the data is, so the data is identified as the data with higher accuracy at this depth point, and the data interval in the depth dimension is used to judge point by point, and the rule for determination is as follows:

[0111] If is less than , O1 is selected;

[0112] If greater than then select N1;

[0113] if equal to then select LW;

[0114] wherein equal to is not the absolute equality of the two, when subtracted The absolute value b is less than or equal to a threshold a, a can be set as a constant value, or a = LW * c, c is the weight coefficient of setting threshold a, it can be determined that equal to

[0115] After extracting the data of each data point in the depth direction, the depth points of and / or X is a value much larger than a, which can be set to 10 times or other multiples of a, when there are and / or The depth point may have a large data anomaly, and after review, if there is an anomaly, the logging data of the depth point can be determined based on the logging data of the front and rear multiple depth points;

[0116] Step 9: Reconstruct new logging data based on the optimal logging data selected at multiple depth points.

[0117] Reconstruct the data according to the following rules:

[0118]

[0119] Obtain the optimal logging data at each depth point, that is, obtain all the optimal logging data of the depth points in the entire depth direction, and reconstruct the final logging curve based on the optimal logging data at each depth point (i.e. all the optimal logging data). The application avoids single data deviation through multi-dimensional (comprehensive data quality (standard deviation), core data (porosity correlation coefficient) and geological data (geological model correlation coefficient)) and multi-data (new and old logging data) fusion; through point-by-point difference analysis, the data selection is optimized in the longitudinal resolution, the high-precision logging curve can be efficiently reconstructed, the reliability of reservoir evaluation and geological modeling is significantly improved, and the early logging data quality is improved. The accuracy of the logging data is improved.

[0120] As Figure 3 , the embodiment of the application also provides a device for reconstructing logging data of in-situ leaching sandstone type uranium mine, comprising:

[0121] The first unit: for the same well, old logging data collected and new logging data acquired;

[0122] The second unit: the old logging data and the new logging data are preprocessed to obtain corrected old logging data O1 and new logging data N1;

[0123] The third unit: depth points are set at intervals of L meters in the depth direction, the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and subsequent processing is sequentially performed on each depth point;

[0124] The fourth unit: the weight W of the corrected old logging data O1 and the weight coefficient W of the new logging data N1 are obtained based on the standard deviation of the corrected old logging data O1 and the new logging data N1; 11 12 ;

[0125] The fifth unit: the weight W of the corrected old logging data O1 and the weight W of the new logging data N1 are obtained based on the matching degree of the porosity measured by the core and the porosity measured by the corrected old logging data O1 and the porosity measured by the new logging data N1; 21 22 ;

[0126] The sixth unit: the weight W of the old logging data O1 and the weight W of the new logging data N1 are obtained based on the matching degree of the corrected old logging data O1 and the new logging data N1 and the geological model; 31 32 ;

[0127] The seventh unit: the old logging data O1 and the new logging data N1 are dynamically weighted based on the weights W, 11 W, 12 W, 21 W, 22 W, 31 W, 32 to obtain weighted logging data LW at each depth point;

[0128] The eighth unit: the difference between the logging data LW at each depth point and the old logging data O1 and the new logging data N1 is calculated respectively, and the appropriate logging data is selected as the optimal logging data at each depth point according to the difference size;

[0129] The ninth unit: the new logging data is reconstructed based on the optimal logging data selected at multiple depth points.

[0130] Embodiments of the present application also provide a computer program product comprising a computer program, characterized in that the computer program is executed by a processor to implement the method for reconstructing logging data of sandstone type uranium deposit.

[0131] ​​​The embodiment of the present application provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing steps of a method for reconstructing logging data of in-situ leaching sandstone type uranium mine when executing the computer program.

[0132] The embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program implements steps of a method for reconstructing logging data of in-situ leaching sandstone type uranium mine when executed by a processor.

[0133] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes. The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in one or more flows and / or blocks.

[0134] These computer program instructions can also be stored in a computer readable storage medium capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in one or more flows and / or blocks.

[0135] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the functions specified in one or more flows and / or blocks.

[0136] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely examples of the present application and are not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for reconstructing logging data for in-situ leaching sandstone-type uranium deposits, characterized in that: The following steps are involved: Step 1: For the same well, collect old logging data and newly acquire new logging data; Step 2: Preprocess the old logging data and the new logging data to obtain the corrected old logging data O1 and the new logging data N1; Step 3: Set depth points at intervals of L meters in the depth direction to ensure that the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and perform subsequent processing on each depth point in turn; Step 4: Obtain the weight W of the corrected old logging data O1 based on the standard deviation of the corrected old logging data O1 and the new logging data N1 11 and the new logging data N1 weight coefficient W 12 ; Step 5: Based on the porosity measured by the core and the porosity calculated by the corrected old logging data O1 and the porosity calculated by the new logging data N1, the weight W of the corrected old logging data O1 is obtained. 21 and the new logging data N1 weight W 22 ; Step 6: Based on the matching degree of the corrected old logging data O1 and the new logging data N1 with the geological model, the weight W of the old logging data O1 is obtained. 31 and the new logging data N1 weight W 32 ; Step 7: Based on the above weight W 11 、W 12 、W 21 、W 22 、W 31 、W 32 , dynamically weight the old logging data O1 and the new logging data N1, and obtain the logging data LW at each depth point; Step 8: Calculate the difference between the logging data LW at each depth point and the old logging data O1 and the new logging data N1, and select appropriate logging data as the optimal logging data at each depth point according to the difference. Step 9: Reconstruct new logging data based on the optimal logging data selected at multiple depth points.

2. The method for reconstructing logging data of in-situ leaching sandstone-type uranium deposits according to claim 1, characterized in that: The step 1 further includes: collecting old logging data previously collected for the same wellbore, and collecting new logging data based on the same collection parameters to ensure consistency between the old and new logging data.

3. The method for reconstructing logging data of in-situ leaching sandstone-type uranium deposits according to claim 2, characterized in that: The step 2 further includes: pre-processing including wellbore correction, invasion correction and temperature correction.

4. The method for reconstructing logging data for in-situ leaching sandstone-type uranium deposits according to claim 1, characterized in that: The step 4 further comprises: IN 12 =1-W 11 ; in, is the standard deviation of O1 and N1 within the sliding window. The length of the sliding window can be set according to the depth range of the actual data.

5. The method for reconstructing logging data of in-situ leaching sandstone-type uranium deposits according to claim 4, characterized in that: The step 5 further comprises: IN 22 =1-W 21 Among them, φ core is the porosity measured from the core, is the porosity calculated based on O1, is the porosity calculated based on N1.

6. The method for reconstructing logging data of in-situ leaching sandstone-type uranium deposits according to claim 5, characterized in that: The step 6 further comprises: IN 32 =1-W 31 ; in, is the correlation coefficient between O1 and the geological model, is the correlation coefficient between N1 and the geological model.

7. The method for reconstructing logging data for in-situ leaching sandstone-type uranium deposits according to claim 6, characterized in that: The step 7 further comprises: IN O1 =(W 11 +W 21 +W 31 ) / 3; IN N1 =(W 12 +W 22 +W 32 ) / 3; LW=W O1 ×O1+W N1 ×N1; Among them, W O1 To construct the comprehensive weight of O1 of LW, W N1 is the comprehensive weight of N1 for constructing LW.

8. The method for reconstructing logging data for in-situ leaching sandstone-type uranium deposits according to claim 7, characterized in that: The step 8 further includes: judging depth by depth point according to the data interval L in the depth dimension, and the judgment formula is as follows: in, is the difference between LW and O1, The difference between LW and N1, The judgment rule is: compare and The one with the smaller difference value is selected as the optimal logging data for this depth point; and When they are equal, LW is selected as the logging data.

9. A device for reconstructing logging data of in-situ leaching sandstone-type uranium deposits, characterized in that: include: Unit 1: For the same well, old logging data collected and new logging data newly acquired; Unit 2: Preprocess the old logging data and the new logging data to obtain the corrected old logging data O1 and the new logging data N1; Unit 3: Set depth points at intervals of L meters in the depth direction, ensure that the corrected old logging data O1 and the new logging data N1 are strictly aligned in depth, and perform subsequent processing on each depth point in turn; Unit 4: Obtain the weight W of the corrected old logging data O1 based on the standard deviation of the corrected old logging data O1 and the new logging data N1 11 and the new logging data N1 weight coefficient W 12 ; Unit 5: Based on the porosity measured by the core and the porosity calculated by the corrected old logging data O1 and the porosity calculated by the new logging data N1, the weight W of the corrected old logging data O1 is obtained. 21 and the new logging data N1 weight W 22 ; Unit 6: Obtain the weight W of the old logging data O1 based on the matching degree of the corrected old logging data O1 and the new logging data N1 with the geological model 31 and the new logging data N1 weight W 32 ; Unit 7: Based on the above weights W 11 、W 12 、W 21 、W 22 、W 31 、W 32 , dynamically weight the old logging data O1 and the new logging data N1, and obtain the logging data LW at each depth point; Unit 8: Calculate the difference between the well logging data LW at each depth point and the old well logging data O1 and the new well logging data N1, and select the appropriate well logging data as the optimal well logging data at each depth point according to the size of the difference; Unit 9: Reconstruct new logging data based on the optimal logging data selected at multiple depth points.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for reconstructing logging data of in-situ leaching sandstone-type uranium deposits according to any one of claims 1 to 8 is implemented.

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