A method and system for well logging curve standardization based on high frequency range constraints

Abstract

The invention discloses a logging curve standardization method and system based on high-frequency range constraint. Based on the formation lithology and physical property development law, the method of high-frequency range constraint is mainly established, and under the control of sedimentary phase, the condition standard of marker layer selection is reduced, and multiple sets of lithology layers with stable distribution of lithology and physical properties can be selected by using high-frequency statistical characteristics, and the existing stratigraphic units can be directly used as marker layers, and there is no need to manually perform lithology stratification on the marker layers in the entire work area, which is a cumbersome work. In addition, there is no need to select key wells, which greatly reduces the workload. Based on the high-frequency range constraint, the range of a single main peak in the traditional histogram method is expanded, the high-frequency range is tested and analyzed, and the parameters d and k values ​​are optimized, which can effectively improve the stability of standardized lithology screening to improve the accuracy of the final correction. It is not only applicable to logging data of single-peak frequency histograms, but also applicable to bimodal frequency histogram data.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum exploration and development, and in particular relates to a logging curve standardization method and system based on high-frequency range constraints. Background Art

[0002] Well logging data contains a large amount of rock physical information. Through well logging evaluation, geological information is mined and exploration and development are carried out under the guidance of geological theory, which is particularly important for reservoir evaluation. However, due to the influence of factors such as instrument type, instrument scale, wellbore environment, and manual operation method, the well logging data will have certain systematic differences in the horizontal direction. This difference is particularly obvious between the well logging data measured by different instruments at different times, resulting in poor applicability of the reservoir parameter model established in the block, which will cause great interference to the parameter calculation and interpretation of the reservoir. Therefore, it is necessary to standardize the well logging curve to make the geological information reflected by the well logging curve more accurate.

[0003] Common logging curve standardization methods include frequency histogram method, mean correction method, trend surface method, etc. Among them, the histogram method is simple to operate and has high processing efficiency. It is widely used in oil and gas fields, but the error is relatively large; the mean correction method has very high requirements for the marker layer. If the marker layer is unstable, large errors will occur. This method often requires people to manually divide the pure lithology layer, which is relatively troublesome to operate. The histogram method usually selects mudstone or dense limestone with stable deposition in a well or several wells as the marker layer, and uses the main peak of the marker layer histogram as the correction standard to make the main peak of other marker layers consistent with the key wells. The traditional histogram method is based on two assumptions. First, marker layers in the same phase belt, with relatively stable deposition and consistent logging response characteristics can be found in the work area; second, it is required that the logging curve has no vertical systematic errors, only errors between wells. However, in actual work, it is difficult to find marker layers that are completely in the same phase belt in many work areas. In actual standardization operations, due to the influence of factors such as the wellbore environment and different vertical logging curves (splicing together multiple logging curves), the standardization of layers that are too far away from the target layer often has poor results. This greatly limits the scope of marker layer selection using traditional methods, and even many work areas can hardly find marker layers that meet the conditions.

[0004] In particular, the standardization of logging data in carbonate formations is more complicated than that in sandstone and mudstone formations. The heterogeneity of its lithology and physical properties makes the application of the traditional histogram method to carbonate rocks more limited. It is more difficult to find mudstone with stable distribution and stable material supply in carbonate formations, and the selection of marker layers is more difficult. The difficulty in selecting marker layers and the instability of the main characteristic peak have become the main problems facing the standardization of logging curves in carbonate formations. Therefore, the geological significance specifically indicated by the frequency histogram of the logging curve is specially proposed.

[0005] Standardized logging curves generally include gamma, acoustic time difference, density, and neutron curves (hereinafter referred to as GR, AC, DEN, and CNL curves), and resistivity curves are generally not standardized. The main peak of the GR curve histogram represents the lithology with the highest depositional frequency. In fact, even if the marker layer is in the same phase belt, the logging response characteristics of the lithology will show certain differences due to the influence of factors such as the source supply, paleogeology, and hydrodynamic conditions during the deposition period. At this time, the lithology with the highest depositional frequency will vary in the same phase belt, and the main peak of the histogram will also vary within a range; while the responses of AC, DEN, and CNL curves all reflect physical property characteristics. The shape of the bar graph of physical property logging curves (AC, CNL, and DEN) is mainly controlled by the lithology distribution of a single well and reservoir development. The degree of homogeneity of the pure limestone section (low GR) where the reservoir is not developed is relatively good, while the reservoir section has a relatively fast change in logging response due to heterogeneity, and its curve jumps significantly, and it is generally difficult to be stably distributed in a certain range. Therefore, in the frequency statistical histogram, the logging response values ​​of the non-reservoir section are relatively concentrated, and its high-frequency characteristics are very obvious, while the frequency characteristics reflected by the logging values ​​of the reservoir section are large range and low frequency, which has little effect on the scale of standardized physical properties. Affected by lithology, pore development degree and fluid, the high-frequency non-reservoir main peak value still varies within a certain range. In addition, the stability of the marker layer determines the range of variation of the main peak values ​​of the lithology and physical properties of the marker layer in the same phase belt, so it is obviously inappropriate to standardize the single main peak value of the logging data histogram as a characteristic value. It is necessary to propose a logging curve standardization method and system based on high-frequency range constraints. Summary of the invention

[0006] In order to solve the technical problems existing in the background technology, the present invention aims to provide a logging curve standardization method and system based on high-frequency range constraints. On the basis of the traditional histogram method, the main peak constraint is improved to a high-frequency range constraint, and the average level of the high-frequency range interval of the lithology and physical property curves is used as the characteristic value, and each well is standardized according to the sedimentary phase zone.

[0007] In order to solve the technical problem, the technical solution of the present invention is:

[0008] A method and system for normalizing a well logging curve based on high frequency range constraints, the method comprising:

[0009] Obtain regional survey data, conventional logging data and geological stratification data to determine the optimal marker layer; based on the sedimentary facies data of the optimal marker layer, obtain the facies belt classification of each well in the work area;

[0010] Obtain various logging curve data of each well in each phase belt from conventional logging data, perform data analysis and high-frequency range feature extraction, and obtain characteristic values ​​of various logging curve data of each well in each phase belt;

[0011] According to the classification of the phase belt to which each well in the work area belongs, the logging curve data of each type of all wells in each phase belt in the work area are merged and processed, and data cleaning, screening, analysis and high-frequency range feature extraction are performed to obtain the standard values ​​of the scales of various logging curves in each phase belt;

[0012] The characteristic values ​​of various logging curve data of each well in each phase belt are subtracted from the standard values ​​of the scales of various logging curves in each phase belt to obtain the standardized correction values ​​of various logging curves of each well in each phase belt. The various logging curves of each well in each phase belt are corrected by using the correction values ​​to obtain the standardized logging curves.

[0013] Furthermore, the various types of logging curve data include: gamma data, acoustic wave time difference data, density data and neutron curve data.

[0014] Furthermore, the determination of the optimal marker layer is specifically as follows: taking the target layer in the geological stratification data as the center, referring to the logging curve quality, lithology and physical property factors in the regional survey data and conventional logging data, and determining the optimal marker layer in the geological stratification data.

[0015] Furthermore, the sedimentary phase data of the deposition period of the optimal marker layer is obtained, and all the wells in the work area in the zoning data are zoned using the sedimentary phase data to obtain the phase belt classification of each well in the work area.

[0016] Furthermore, the data analysis and high-frequency range feature extraction specifically include: obtaining various logging curve data of each well in each phase belt in the optimal marker layer from conventional logging data;

[0017] For each type of logging curve data of each well in each phase belt, equally spaced bins are counted with a bin spacing d to obtain binned data of a histogram of each type of logging curve of each well in each phase belt;

[0018] The binned data of various logging curves of each well in each phase belt are sorted by frequency, and the top k binned data in the sorting results are merged to obtain high-frequency binned data;

[0019] The point data within the merged high-frequency binned data are averaged to obtain the characteristic values ​​of various logging curve data for each well in each phase belt.

[0020] Furthermore, the high-frequency binned data are binned data of various types of well logging curves merged for each well in each phase belt.

[0021] Furthermore, the standardized logging curve is tested for its effect. If the effect does not meet the standard, the d and k values ​​are adjusted and the standardization correction is performed again.

[0022] Further, according to the phase belt classification of each well in the work area, each type of logging curve data of all wells in each phase belt in the work area is merged and processed to obtain each type of logging curve data merged in each phase belt; each type of logging curve data merged in each phase belt is cleaned to obtain each type of logging curve data in each phase belt after outlier screening;

[0023] Given the statistical interval of each type of logging curve data in each phase belt after outlier screening, the effective statistics of each type of logging curve data in each phase belt are screened out according to the statistical interval; based on the effective statistics, the above-mentioned binning, sorting, merging and averaging processes are repeated to obtain the standard values ​​of the scales of each type of logging curve in each phase belt.

[0024] Furthermore, the calculation method of the bin spacing d is:

[0025]

[0026] Among them, x is the value of each logging curve, n is the average number of data points of the logging curve, and [x] is the rounding operation; the calculation method of the number of merged boxes k is:

[0027]

[0028] Among them, t is the stable fluctuation range of the corresponding curve, and d is the bin spacing.

[0029] A logging curve normalization system based on high frequency range constraints, the system comprising:

[0030] one or more processors;

[0031] A memory for storing one or more programs;

[0032] When the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned method and system for normalizing well logging curves based on high-frequency range constraints.

[0033] Compared with the prior art, the advantages of the present invention are:

[0034] (1) This method is based on the development laws of lithology and physical properties of the formation, focusing on establishing a method of high-frequency range constraints, and under the control of sedimentary facies, lowering the condition standards for marker layer selection.

[0035] (2) High-frequency statistical characteristics can be used to select multiple sets of lithology layers with stable distribution of lithology and physical properties (non-reservoir). Existing stratigraphic units can be directly used as marker layers, without the need to manually stratify the marker layers in the entire work area, which is a tedious task. In addition, there is no need to select key wells, which greatly reduces the workload.

[0036] (3) Based on the high-frequency range constraint, this method expands the range of the single main peak in the traditional histogram method, tests and analyzes the high-frequency range, and optimizes the parameters d and k values, which can effectively improve the stability of standardized lithology screening and thus improve the accuracy of the final correction.

[0037] (4) This method has good compatibility and is not only applicable to well logging data with a single-peak frequency histogram, but also to data with a double-peak frequency histogram. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 is a regional sedimentary facies diagram of an embodiment of the present invention;

[0040] Figure 3 is a multi-well frequency line graph of each curve before normalization in an embodiment of the present invention;

[0041] Figure 4 It is a multi-well cumulative frequency histogram of each curve of the intra-platform subfacies in an embodiment of the present invention;

[0042] Figure 5 is a multi-well frequency line graph of each curve after normalization by the method of the present invention in an embodiment of the present invention;

[0043] Figure 6 It is the standardized effect test of the adjacent wells in the embodiment of the present invention. DETAILED DESCRIPTION

[0044] The specific implementation mode of the present invention is described below in conjunction with embodiments:

[0045] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0046] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0047] Example 1

[0048] A method and system for normalizing a well logging curve based on high frequency range constraints, the method comprising:

[0049] Obtain regional survey data, conventional logging data and geological stratification data to determine the optimal marker layer; based on the sedimentary facies data of the optimal marker layer, obtain the facies belt classification of each well in the work area;

[0050] Obtain various logging curve data of each well in each phase belt from conventional logging data, perform data analysis and high-frequency range feature extraction, and obtain characteristic values ​​of various logging curve data of each well in each phase belt;

[0051] According to the classification of the phase belt to which each well in the work area belongs, the logging curve data of each type of all wells in each phase belt in the work area are merged and processed, and data cleaning, screening, analysis and high-frequency range feature extraction are performed to obtain the standard values ​​of the scales of various logging curves in each phase belt;

[0052] The characteristic values ​​of various logging curve data of each well in each phase belt are subtracted from the standard values ​​of the scales of various logging curves in each phase belt to obtain the standardized correction values ​​of various logging curves of each well in each phase belt. The various logging curves of each well in each phase belt are corrected by using the correction values ​​to obtain the standardized logging curves.

[0053] The various types of logging curve data include: gamma data, acoustic wave time difference data, density data and neutron curve data.

[0054] The determination of the optimal marker layer specifically includes: taking the target layer in the geological stratification data as the center, referring to the logging curve quality, lithology and physical property factors in the regional survey data and conventional logging data, and determining the optimal marker layer in the geological stratification data.

[0055] Furthermore, the optimal marker layer needs to consider three factors: first, the quality of the logging curve needs to be good; on the other hand, the lithology does not require mudstone, only a large set of relatively pure sandstone or limestone is required (the cumulative proportion of pure lithology segments needs to be greater than 50%). In the marine facies, the high frequency of mudstone reflects the degree of material supply, which is more unstable and has poor practical application effect; in terms of physical properties, in order to maintain the stability of the high-frequency statistical characteristics of non-reservoir layers, according to the results obtained from actual tests, the condition that the degree of non-reservoir development is greater than 40% (the critical value obtained by the test) needs to be met; finally, strata close to the target formation should be selected as much as possible to avoid excessive vertical errors.

[0056] The sedimentary phase data of the deposition period of the optimal marker layer are obtained, and all the wells in the work area in the regional survey data are divided into zones using the sedimentary phase data to obtain the phase belt classification of each well in the work area.

[0057] Furthermore, when sedimentary facies data are insufficient, subfacies or microfacies can be divided based on the basic regional geological background using data such as seismic time (depth) thickness, logging facies, and core cuttings.

[0058] The data analysis and high-frequency range feature extraction specifically include: obtaining various logging curve data of each well in each phase belt in the optimal marker layer from conventional logging data;

[0059] For each type of logging curve data of each well in each phase belt, equally spaced bins are counted with a bin spacing d to obtain binned data of a histogram of each type of logging curve of each well in each phase belt;

[0060] Furthermore, the bin spacing d should be appropriate. Too large a spacing will distort the frequency data, and too small a spacing will reduce the robustness of the algorithm. By testing various logging curves, the calculation method of d is given here:

[0061]

[0062] Among them, x is the value of various logging curves, n is the average number of data points of the logging curve, and [x] is the rounding operation. Generally, the GR curve box spacing is 1-3API, the AC curve box spacing is 0.5-2us / ft, the CNL curve box spacing is 0.2-0.5%, and the DEN curve box spacing is 0.01-0.03g / cm 2 It is recommended to try it within the given range first.

[0063] The binned data of various logging curves of each well in each phase belt are sorted by frequency, and the top k binned data in the sorting results are merged to obtain high-frequency binned data;

[0064] Furthermore, sorting and filtering is actually to arrange the columns in the histogram from high to low. The purpose of merging bins is to expand the range to find the relatively stable lithology and physical property fluctuation range within the phase belt. The approximate calculation method for the number of merged bins k is:

[0065]

[0066] Among them, t is the stable fluctuation range of the corresponding curve, and d is the binning interval calculated above. This calculation method only provides an approximate range reference for k. The specific adjustment attempts of adjacent intervals need to be made according to the standardized stratum lithology.

[0067] The point data within the merged high-frequency binned data are averaged to obtain the characteristic values ​​of various logging curve data for each well in each phase belt.

[0068] The high-frequency binned data are binned data of various types of well logging curves merged for each well in each phase belt.

[0069] According to the phase belt classification of each well in the work area, each type of logging curve data of all wells in each phase belt in the work area is merged and processed to obtain each type of logging curve data after merging in each phase belt; each type of logging curve data after merging in each phase belt is cleaned to obtain each type of logging curve data of each phase belt after outlier screening;

[0070] Given the statistical interval of each type of logging curve data in each phase belt after outlier screening, the effective statistics of each type of logging curve data in each phase belt are screened out according to the statistical interval; based on the effective statistics, the above-mentioned binning, sorting, merging and averaging processes are repeated to obtain the standard values ​​of the scales of each type of logging curve in each phase belt.

[0071] Furthermore, due to the certain differences in the main lithologies of different phase belts, the characteristic logging response values ​​of different phase belts will also show differences. This is retained by the data itself, that is, it distinguishes the differences between the phase belts while also providing a reference value for standardization correction.

[0072] The standardized logging curve is tested for effectiveness. If the effect does not meet the standard, the d and k values ​​are adjusted and the standardization correction is performed again.

[0073] Furthermore, the effect test provides a complete reference process. Initially, check the overlap of the frequency line graphs of all wells. If the overlap is good, further check the adjacent wells and select adjacent wells with similar geological profiles to check whether the frequency distribution characteristics of various curves can correspond well.

[0074] Embodiment 2:

[0075] like Figure 1 As shown, a method and system for normalizing well logging curves based on high frequency range constraints include the following steps:

[0076] S1. Collection and organization of logging data, including: regional survey data, conventional logging data, and stratification data.

[0077] S2. Taking the target layer as the center, determine the optimal standard layer from the adjacent layers. The target layer to be evaluated is located in the C2 layer, which is a marine carbonate rock formation. By comparing the adjacent layers such as C1 and C3, the C2 layer has a relatively pure lithology. The wells in the work area all meet the conditions of more than 50% vertical limestone section and more than 40% non-reservoir development section, which can ensure the stability of the method.

[0078] S3. Collect sedimentary facies data during the deposition of the C2 layer. The work area is located in the open platform facies under the carbonate platform deposition model, such as Figure 2As shown in the figure, the main subfacies developed in the area are intra-platform beach, inter-beach, and platform-sag. Since the platform-sag subfacies in the area is still located in a relatively shallow position and has a similar sedimentary environment to the inter-beach subfacies, it is merged into the inter-beach subfacies. That is, in this embodiment, the phase belt is divided into the intra-platform beach subfacies and the inter-beach subfacies. To avoid being redundant, the well logging curve standardization in the intra-platform beach subfacies is taken as an example. First, the frequency line graph (histogram trend) is used to show the quality of the well logging curves of multiple wells in the intra-platform beach subfacies before the curve standardization, as shown in the figure. Figure 3 As shown in the figure, each broken line represents the data distribution of the corresponding curve of a well. Before standardization, the curve values ​​of each well are scattered, which obviously does not conform to the geological law and requires standardization of the logging curve.

[0079] S4. Substitute the formula to calculate the binning interval d of GR logging data as 1.5, the binning interval d of AC, CNL and DEN logging data as 0.75, 0.3 and 0.01 respectively. Count the bins of each logging curve data according to the above binning intervals. The binned data class of each interval is equivalent to each rectangular column in the histogram, and has the attributes of interval and frequency.

[0080] S4. The logging curve data is divided into bins with equal intervals and counted at bin intervals d to form bin data (including intervals and frequencies) of a histogram.

[0081] S5. Sort the binned data by frequency to form a reverse sequence from large to small, which is essentially equivalent to sorting the rectangular columns of the histogram from large to small based on the column height of the histogram.

[0082] S6. The first n sorted bins of data are merged, and the curve value data samples with the highest frequency are integrated together, which represents the lithology and physical properties with relatively high frequency development in the same phase belt. Compared with a single peak, the average level of the data in the merged bins is more stable in the phase belt, and the heterogeneity in the phase belt is more reasonably considered, so the value of the points inside the box is averaged as the characteristic value. After optimizing the optimal number of merged bins n for the GR curve is 6, the optimal number of merged bins n for the AC curve is 3, the optimal number of merged bins n for the CNL curve is 3, and the optimal number of merged bins n for the DEN curve is 4. This step is the core of the method of the present invention, which embodies the superiority of the method. Compared with the traditional method, the standard layer of this method can select the target stratum system itself, and there is no need to manually divide the pure lithology segment. In terms of lithology, the most distributed mud crystal bioclastic limestone and spar bioclastic limestone in the C2 layer segment are automatically screened out through high-frequency range constraints. In terms of physical properties, high-frequency stable non-reservoir layers are screened out.

[0083] S7, locate the intra-platform shoal subfacies of the C2 layer, and merge the GR, AC, CNL, and DEN logging data values ​​of all wells in the facies belt (such as Figure 4), clean the data, remove invalid values, and set the effective statistical interval of each curve. In this way, the interference of individual abnormal curves on the determination of standard values ​​is reduced, making the calculated standard values ​​more reasonable. The specific GR effective range is 15-40API, AC effective range is 40-90us / ft, CNL effective range is -15-25P.U, and DEN effective range is 2.4-3.0g / cm 3 , the effective statistics of the above four curves were counted respectively and the three steps of S4~S6 binning, sorting and merging were carried out. The data in the merged boxes were further averaged to obtain the standard values ​​of GR, AC, CNL and DEN curves, which were 25.3, 49.86, 1.06 and 2.69 respectively. The standard values ​​calculated in this way are different among the phase belts, among which the standard value difference of GR curve is more significant, and the difference can reach 2. The high frequency band of physical property curve is mainly affected by stable non-reservoir, and the difference between phase belts is not large.

[0084] S8. Subtract the characteristic value of the well curve from the standard value of each sedimentary facies curve to obtain the standardized correction value of each well curve, and correct the curve to form a set of standardized logging curves.

[0085] S9. The lithology and physical properties (non-reservoir) of the formations in the same phase belt should be similar in general. Preliminarily, a multi-well frequency line graph of each curve after standardization is made, such as Figure 5 For standardized results, Figure 3 , the high-frequency range overlap of the frequency curves of each well is greatly improved; further, two wells, MX-52 and MX-111, with similar sedimentary environments and close distances, are selected. Figure 6 The comparison shows the specific details of the superposition of the GR, AC, CNL, and DEN frequency curves of the two wells before and after standardization. It can be seen that after the standardization process, the characteristic peaks of various logging curves of the adjacent wells correspond very well, which further illustrates that the method of the present invention has a good application effect.

[0086] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0087] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for normalizing well logging curves based on high frequency range constraints, characterized in that: The method comprises: Obtain regional survey data, conventional logging data and geological stratification data to determine the optimal marker layer; based on the sedimentary facies data of the optimal marker layer, obtain the facies belt classification of each well in the work area; Obtain various logging curve data of each well in each phase belt from conventional logging data, perform data analysis and high-frequency range feature extraction, and obtain characteristic values ​​of various logging curve data of each well in each phase belt; The data analysis and high-frequency range feature extraction specifically include: obtaining various logging curve data of each well in each phase belt in the optimal marker layer from conventional logging data; counting various logging curve data of each well in each phase belt by equally spaced bins with a bin spacing d to obtain binned data of histograms of various logging curves of each well in each phase belt; sorting the binned data of various logging curves of each well in each phase belt by frequency, and merging the top k binned data in the sorting results to obtain high-frequency binned data; averaging the point data inside the merged high-frequency binned data to obtain characteristic values ​​of various logging curve data of each well in each phase belt; The calculation method of bin spacing d is: Among them, x is the value of various logging curves, n is the average number of data points of the logging curve, and [x] is the rounding operation; The calculation method of the number of merged boxes k is: Among them, t is the stable fluctuation range of the corresponding curve, and d is the bin spacing; According to the classification of the phase belt to which each well in the work area belongs, the logging curve data of each type of all wells in each phase belt in the work area are merged and processed, and data cleaning, screening, analysis and high-frequency range feature extraction are performed to obtain the standard values ​​of the scales of various logging curves in each phase belt; The characteristic values ​​of various logging curve data of each well in each phase belt are subtracted from the standard values ​​of the scales of various logging curves in each phase belt to obtain the standardized correction values ​​of various logging curves of each well in each phase belt. The various logging curves of each well in each phase belt are corrected by using the correction values ​​to obtain the standardized logging curves.

2. A method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: The various types of logging curve data include: gamma data, acoustic wave time difference data, density data and neutron curve data.

3. A method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: The determination of the optimal marker layer specifically includes: taking the target layer in the geological stratification data as the center, referring to the logging curve quality, lithology and physical property factors in the regional survey data and conventional logging data, and determining the optimal marker layer in the geological stratification data.

4. A method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: The sedimentary phase data of the deposition period of the optimal marker layer are obtained, and all the wells in the work area in the regional survey data are divided into zones using the sedimentary phase data to obtain the phase belt classification of each well in the work area.

5. The method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: The high-frequency binned data are binned data of various types of well logging curves merged for each well in each phase belt.

6. A method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: The standardized logging curve is tested for effectiveness. If the effect does not meet the standard, the d and k values ​​are adjusted and the standardization correction is performed again.

7. A method for normalizing well logging curves based on high frequency range constraints according to claim 1, characterized in that: According to the classification of the phase belt to which each well in the work area belongs, each type of logging curve data of all wells in each phase belt in the work area is merged and processed to obtain each type of merged logging curve data of each phase belt; Perform data cleaning on the merged well logging curve data of each phase belt to obtain the well logging curve data of each phase belt after outlier screening; Given the statistical interval of each type of logging curve data in each phase belt after outlier screening, the effective statistics of each type of logging curve data in each phase belt are screened out according to the statistical interval; based on the effective statistics, the above-mentioned binning, sorting, merging and averaging processes are repeated to obtain the standard values ​​of the scales of each type of logging curve in each phase belt.

8. A logging curve normalization system based on high frequency range constraints, characterized in that: The system comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors execute a well logging curve normalization method based on high frequency range constraints as described in any one of claims 1-7.

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