Logging analysis data processing method, storage medium and equipment

Abstract

The invention provides a logging analysis data processing method, a storage medium and equipment, and the method comprises the following steps: carrying out logging analysis through employing different logging instruments, and obtaining an analysis result; analyzing a result, and establishing a standardized data conversion model according to different logging instruments; and carrying out outlier data processing by the data conversion model, and then carrying out data filtering smoothing processing to obtain a final result. According to the method, appropriate outlier data and a data filtering processing algorithm are optimized, and finally an analysis data while-drilling rapid processing method taking rock debris as an object is formed, so that the data can truly reflect underground geologic characteristics, a basis is provided for while-drilling decision making, scheme making and the like, and drilling construction is guided.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration data processing, and in particular relates to a logging analysis data processing method and a storage medium and equipment. Background Art

[0002] Rock cuttings are important geological physical data and the main object for determining lithology and conducting experimental analysis and testing of elements, minerals, rock pyrolysis, nuclear magnetic logging, etc. Rock cuttings are obtained by manual scooping, cleaning and drying after the drill bit breaks the formation at the bottom of the well and is carried to the surface by the drilling fluid. The process of producing rock cuttings shows that there is a possibility of mixing and distortion of rock cuttings. Especially during the construction of horizontal wells, the well wall is prone to collapse and mixed with the returning rock cuttings. Although some of the falling blocks can be preliminarily screened out by the size and roundness of the rock cuttings through naked eye observation, it is difficult to completely remove them. Due to the objective situation that the rock cuttings are mixed and the depth positioning is erroneous, the analysis results cannot fully and accurately reflect the actual geological conditions underground, which has an impact on data application, engineering decision-making, construction plan formulation, coring card layer, stratigraphic division, reservoir drilling evaluation, etc. during the drilling process.

[0003] Therefore, the analysis results using rock cuttings as samples have certain unpredictability and cannot fully represent the true situation of the strata. Before applying the data, it is necessary to carry out corresponding data processing to eliminate the impact. Summary of the invention

[0004] In order to solve the above problems in the prior art, the present invention proposes a logging analysis data processing method, storage medium and equipment, which selects suitable outlier data and data filtering processing algorithms, and finally forms a rapid processing method for analysis data while drilling with rock cuttings as the object, ensuring that the data can truly reflect the underground geological characteristics, provide a basis for while drilling decision-making, plan formulation, etc., and guide drilling construction.

[0005] In a first aspect, the present invention provides a method for processing logging analysis data, comprising the following steps:

[0006] S1: Use different logging instruments to perform logging analysis and obtain the analysis results of different logging instruments;

[0007] S2: using the analysis results obtained in step S1, a standardized data conversion model is established according to different logging instruments;

[0008] S3: Perform outlier data processing on the data conversion model obtained in step S2 to obtain processed data;

[0009] S4: Perform data filtering and smoothing on the processed data obtained in step S3 to obtain a final result.

[0010] As a specific implementation of the present invention, in the step S1, the logging instrument further includes stability calibration before logging; preferably, at least 3 different lithology samples are used for stability calibration.

[0011] Specifically, the lithology of the samples includes shale, limestone, dolomite, etc. In actual implementation, the sample type is determined according to the needs of the work area, and each sample is split equally according to the number of instruments involved in the experimental analysis.

[0012] As a specific implementation of the present invention, in step S2, establishing a standardized data conversion model includes the following steps:

[0013] S21: Calculate the average value of the analysis results of different logging instruments.

[0014] S22: Taking the average value obtained in step S21 as the standard, using the least square method, a linear regression formula is established between the analysis results of each instrument and the average value.

[0015] S23: Correlation coefficient R 2 The linear regression formula with a value of ≥0.8 was used as the data standardization conversion model for this instrument.

[0016] As a specific implementation of the present invention, in step S23, the correlation coefficient R 2 <0.8, re-calibrate the stability. Specifically, for instruments with correlation coefficient R2<0.8, re-calibrate, re-analyze, and re-perform regression analysis. If the correlation coefficient still does not meet the requirements, the instrument will be returned to the factory for repair or abandoned and cannot be used for experimental analysis of cuttings.

[0017] As a specific implementation of the present invention, in step S3, the outlier data processing includes: using the interquartile range to determine whether there is outlier data, and replacing the outlier data with the median.

[0018] According to the present invention, preliminary analysis is carried out in combination with drilling and geological data during the drilling process. If there are special conditions such as faults, well leakage, overflow, thin layers (drilling thickness less than 2 meters), special lithology, etc., the well sections with such special conditions are not involved in the outlier data processing. For well sections without special conditions, the interquartile range is used to determine whether there are outlier data, and the median is used to replace the outlier data.

[0019] As a specific implementation of the present invention, in step S3, the use of the interquartile range to determine whether there is outlier data includes the following steps:

[0020] S31: extracting target logging instrument analysis data, preferably extracting 20 to 100 data;

[0021] S32: Arrange the extracted data from small to large and divide them into four equal parts. The values ​​at the three division points are the quartiles, which are Q1, Q2 and Q3 respectively;

[0022] S33: Take the quartiles (Q3, Q1) as the upper and lower sides of the data interval, define IQR = Q3-Q1, and the values ​​exceeding the interval [Q1-1.5×IQR, Q3+1.5×IQR] are outliers, and the median Q2 is used to replace the outliers.

[0023] As a specific implementation of the present invention, in step S4, the filtering and smoothing process includes using a Savitzky-Golay filtering method for processing.

[0024] As a specific implementation of the present invention, in step S4, the Savitzky-Golay filtering method includes using commercial software or self-programming to achieve it.

[0025] According to the present invention, the self-programming takes the open source Python language programming as an example, and calls the singal.savgol_filter function in the scipy library.

[0026] X_smooth=scipy.signal.savgol_filter(X,window_length,k)

[0027] Parameter meaning:

[0028] X: data to be processed;

[0029] window_length: window length, must be a positive odd integer;

[0030] k: Fit the k-order polynomial to the data points. This value needs to be less than window_length;

[0031] X_smooth: The result after filtering and smoothing.

[0032] The smaller the window_length value, the closer the curve is to the real curve, and the larger the window_length value, the stronger the smoothing effect. The larger the k value, the closer the curve is to the real curve, and the smaller the k value, the stronger the curve smoothing. Taking XRF element analysis data as an example, it is recommended that the window_length value is 21 and the k value is 4. Complete the rapid processing of data.

[0033] In a second aspect, the present invention provides a computer-readable storage medium, wherein a computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method described in the first aspect.

[0034] In a third aspect, the present invention provides an electronic device comprising a memory and one or more processors, wherein the memory stores a computer program, and the memory and the one or more processors are communicatively connected to each other, and when the computer program is executed by the one or more processors, the method described in the first aspect is performed.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The logging analysis data processing method of the present invention eliminates the analysis errors of different instruments. By comparing logging instruments, a standardized data conversion model of different instruments is established, eliminating the analysis errors caused by different instruments.

[0037] 2. The data processing method for logging analysis of the present invention improves the credibility of data with rock cuttings as the analysis object. By removing outliers, the sample influence caused by well wall collapse, rock cuttings distortion, etc. is eliminated. Through smoothing filtering, it is ensured that the data is not distorted, and the burrs caused by data drawing are eliminated, interference is eliminated, and the credibility of the data is improved.

[0038] 3. The logging analysis data processing method of the present invention has strong timeliness. The method is carried out while drilling, so the analysis can be carried out in real time during the drilling process, without waiting for logging after drilling to verify. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1a The data after the standardized conversion of the instrument a in the embodiment of the present invention;

[0040] Figure 1b The data after the standardization conversion of the instrument b in the embodiment of the present invention;

[0041] Figure 1c The data after the standardization conversion of the instrument c in the embodiment of the present invention;

[0042] Figure 2 It is a well logging GR curve diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with specific embodiments, but they do not constitute any limitation to the present invention.

[0044] The logging analysis data processing method of the present invention has the following specific process:

[0045] S1: Use different logging instruments to perform logging analysis and obtain the analysis results of different logging instruments;

[0046] Specifically, take at least three samples of different lithology, including shale, limestone, dolomite, etc., and determine the sample type according to the needs of the work area. Split each sample equally according to the number of instruments involved in the experimental analysis. After each instrument is calibrated according to the corresponding operating specifications, carry out the experimental analysis and obtain the analysis results.

[0047] S2: using the analysis results obtained in step S1, a standardized data conversion model is established according to different logging instruments;

[0048] Specifically, according to the analysis results of different instruments, the average value of each analysis result was obtained, and the linear regression analysis of the data was carried out based on the average value. The least squares method was used to establish the linear regression formula between the analysis results of each instrument and the average value, and the correlation coefficient R 2 ≥0.8, the instrument analysis results are considered to be relatively reliable, and the subsequent analysis results of the instrument are reliable, and the data can be applied. 2 For instruments with a correlation coefficient <0.8, recalibrate, reanalyze, and perform regression analysis again. If the correlation coefficient still does not meet the requirements, return the instrument to the factory for repair or abandon it and cannot be used for experimental analysis of cuttings.

[0049] S3: Perform outlier data processing on the data conversion model obtained in step S2 to obtain processed data;

[0050] Specifically, a preliminary analysis is carried out in combination with the drilling and geological data during the drilling process. If there are special conditions such as faults, well leakage, overflow, thin layers (drilling thickness less than 2 meters) and special lithology, the well sections with such special conditions will not be involved in the outlier data processing. For well sections without special conditions, the interquartile range is used to determine whether there are outliers. First, the logging instrument analysis data is obtained in sections. The data sample points should not be too many or too few. It is recommended that the data sample points should be no less than 20 and no more than 100. The quartiles of the data are obtained (all values ​​are arranged from small to large and divided into four equal parts. The values ​​at the three division points are the quartiles, which are Q1, Q2 and Q3 respectively). The quartiles (Q3, Q1) are the upper and lower sides of the data interval, and IQR = Q3-Q1 is defined. The values ​​exceeding the interval [Q1-1.5×IQR, Q3+1.5×IQR] are outliers, and the median Q2 is used to replace the outliers.

[0051] S4: Perform data filtering and smoothing on the data processed in step S3 to obtain a final result;

[0052] Specifically, filtering and smoothing are performed on the basis of processing outlier data. The Savitzky-Golay filtering method (hereinafter referred to as SG filtering) is adopted. The SG filtering method is a filtering method based on local polynomial least squares fitting, which can ensure that the shape and width of the signal remain unchanged while filtering out noise. SG filtering is difficult to calculate manually and needs to be implemented by commercial software or programming. Taking the open source Python language programming as an example, the singal.savgol_filter function in the scipy library is called.

[0053] X_smooth=scipy.signal.savgol_filter(X,window_length,k)

[0054] Parameter meaning:

[0055] X: data to be processed;

[0056] window_length: window length, must be a positive odd integer;

[0057] k: Fit the k-order polynomial to the data points. This value needs to be less than window_length;

[0058] X_smooth: The result after filtering and smoothing.

[0059] The smaller the window_length value, the closer the curve is to the real curve, and the larger the window_length value, the stronger the smoothing effect. The larger the k value, the closer the curve is to the real curve, and the smaller the k value, the stronger the curve smoothing. Taking XRF element analysis data as an example, it is recommended that the window_length value is 21 and the k value is 4. Complete the rapid processing of data.

[0060] Example

[0061] This embodiment provides a method for processing logging analysis data, which is used to process the logging data of an element logging project. The specific details are as follows:

[0062] S1: Use different logging instruments to perform logging analysis and obtain the analysis results of different logging instruments;

[0063] Take three samples of different lithology, including shale, limestone and dolomite, and split each sample equally according to the number of instruments involved in the experimental analysis. Each instrument is calibrated according to the corresponding operating specifications and then the experimental analysis is carried out to obtain the analysis results.

[0064] Instrument a is the Chengdu Xinxinda CIT-3000SY element logging instrument, instrument b is the Jiangsu Tianrui EDX element logging instrument, and instrument c is the Chongqing Aoneng Ruike element logging instrument.

[0065] S2: using the analysis results obtained in step S1, a standardized data conversion model is established according to different logging instruments;

[0066] According to the analysis results of instrument a, instrument b and instrument c, the average value of each analysis result is obtained, as shown in Table 1. Taking the average value as the standard, linear regression analysis of the data is carried out. The least squares method is used to establish the linear regression formula between the analysis results of each instrument and the average value, and the correlation coefficient is obtained, as shown in Figure 1a-Figure 1c As shown, the correlation coefficient R of instrument a is 2 ≥0.8, such as Figure 1a As shown, instrument a is qualified; the correlation coefficient R of instrument b is 2 ≥0.8, such as Figure 1b As shown, instrument b is qualified and can be used for data application. Figure 1c The correlation coefficient R of instrument c 2 <0.8, recalibrate, reanalyze, and perform regression analysis again. If the correlation coefficient still does not meet the requirements, the instrument will be returned to the factory for repair or abandoned and will not be used for experimental analysis of cuttings in this project.

[0067] Table 1 Average value of analysis results of different instruments

[0068] project Instrumenta Instrument b Instrument c average value Parameter 1 2 1.8 3 2.3 Parameter 2 3 3.2 3 3.1 Parameter 3 4 4.2 3.3 3.8

[0069] The formula established based on linear regression is used as the standardized conversion calculation formula for the instrument analysis data. Figure 1a Instrument a is used in the experiment, and the measurement result of the instrument is taken as x. The data after standardization conversion is 1.2764x-0.9001. Figure 1b The data after normalization transformation of instrument b in the experiment is 1.5335x-1.619.

[0070] The instrument a used in this example has a standardized processing formula of 1.2764x-0.9001.

[0071] S3: Perform outlier data processing on the data conversion model obtained in step S2 to obtain processed data;

[0072] Combined with the drilling and geological data during the drilling process, a preliminary analysis is carried out. If there are special conditions such as faults, well leakage, overflow, thin layers (drilling thickness less than 2 meters) and special lithology, the well sections with such special conditions will not be involved in the outlier data processing. For well sections without special conditions, the interquartile range is used to determine whether there are outliers. First, the logging instrument analysis data is obtained in sections, and the quartiles of the data are obtained (all values ​​are arranged from small to large and divided into four equal parts. The values ​​at the three division points are the quartiles, which are Q1, Q2, and Q3 respectively). The quartiles (Q3, Q1) are the upper and lower sides of the data interval, and IQR = Q3-Q1 is defined. The values ​​exceeding the interval [Q1-1.5×IQR, Q3+1.5×IQR] are outliers, and the median Q2 is used to replace the outliers.

[0073] For the Si element used in this example, 26 data sample points are selected, with a minimum value of 7.26, Q1=8.83, Q2=9.46, Q3=11.98, a maximum value of 56.63, IQR=3.15, [Q1-1.5×IQR, Q3+1.5×IQR] is [4.11, 16.70], and all data not in this range are replaced by Q2.

[0074] S4: Perform data filtering and smoothing on the data processed in step S3 to obtain a final result;

[0075] Filtering and smoothing are performed on the basis of processing outlier data. The Savitzky-Golay filtering method (hereinafter referred to as SG filtering) is used. The SG filtering method is a filtering method based on local polynomial least squares fitting, which can filter out noise while ensuring that the shape and width of the signal remain unchanged. SG filtering is difficult to calculate manually and needs to be implemented using commercial software or programming. Taking open source Python language programming as an example, the singal.savgol_filter function in the scipy library is called.

[0076] X_smooth=scipy.signal.savgol_filter(X,window_length,k)

[0077] Parameter meaning:

[0078] X: data to be processed;

[0079] window_length: window length, must be a positive odd integer;

[0080] k: Fit the k-order polynomial to the data points. This value needs to be less than window_length;

[0081] X_smooth: The result after filtering and smoothing.

[0082] The smaller the window_length value, the closer the curve is to the real curve, and the larger the window_length value, the stronger the smoothing effect. The larger the k value, the closer the curve is to the real curve, and the smaller the k value, the stronger the curve smoothing effect.

[0083] In this example, window_length takes the value of 21 and k takes the value of 4. The data is quickly processed and the processed data is shown in Table 2:

[0084] Table 2 Data after data filtering and smoothing

[0085]

[0086]

[0087] According to the processing results, there is no misjudgment of lithology due to the increase of Si content in local well depth in the original data. For example, the well depth is 3808 meters, the original data is 45.08, and the final data after processing is 8.05. Combined with the rock logging, it is believed that the lithology has no obvious change and all are named shale. The increase of local Si content should be caused by the failure to strictly select real rock cuttings during sample analysis. The data obtained in the embodiment was later combined with the well logging GR curve for comprehensive analysis, such as Figure 2 As shown, the GR curve is a high value, and there is no obvious downward trend. Comprehensive analysis shows that the on-site lithology naming is reliable, which supports the on-site lithology naming and other work.

[0088] In summary, the logging analysis data processing method of the present invention eliminates the analysis errors of different instruments. By comparing logging instruments, a standardized data conversion model for different instruments is established, eliminating the analysis errors caused by different instruments. This method improves the credibility of data with rock cuttings as the analysis object. By removing outliers, the sample influence caused by well wall collapse, rock cuttings distortion, etc. is eliminated. Through smoothing filtering, it is ensured that the data is not distorted, and the burrs caused by data drawing are eliminated, interference is eliminated, and the credibility of the data is improved. This method has strong timeliness. This method is carried out while drilling, so it can be analyzed in real time during the drilling process, and there is no need to wait for logging after drilling to verify.

[0089] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by one or more processors to implement the method described in any of the above embodiments.

[0090] In one embodiment, an electronic device is provided, including a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected to each other. When the computer program is executed by the one or more processors, the method described in any of the above embodiments is performed.

[0091] In addition, it should be understood that the method or system disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a computer program segment or a part of a computer program, and the module, computer program segment or a part of a computer program contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings, and can actually be executed in parallel. They can also be executed in the opposite order sometimes, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer programs.

[0092] In the present application, the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or device including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features. In addition, in the description of the present application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two.

[0093] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0094] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined in the attached claims.

Claims

1. A method for processing logging analysis data, characterized in that: The following steps are involved: S1: Use different logging instruments to perform logging analysis and obtain the analysis results of different logging instruments; S2: using the analysis results obtained in step S1, a standardized data conversion model is established according to different logging instruments; S3: Perform outlier data processing on the data conversion model obtained in step S2 to obtain processed data; S4: Perform data filtering and smoothing on the processed data obtained in step S3 to obtain a final result.

2. The method according to claim 1, characterized in that In the step S1, the logging instrument further includes stability calibration before logging; preferably, at least 3 different lithology samples are used for stability calibration.

3. The method according to claim 1 or 2, characterized in that: In step S2, establishing a standardized data conversion model includes the following steps: S21: Calculate the average value of the analysis results of different logging instruments. S22: Taking the average value obtained in step S21 as the standard, using the least square method, a linear regression formula is established between the analysis results of each instrument and the average value. S23: Correlation coefficient R 2 The linear regression formula with a value of ≥0.8 was used as the data standardization conversion model for this instrument.

4. The method according to any one of claims 1 to 3, characterized in that: In step S23, the correlation coefficient R 2 For instruments with a stability value < 0.8, re-calibrate the stability.

5. The method according to any one of claims 1 to 4, characterized in that: In step S3, the outlier data processing includes: using the interquartile range to determine whether outlier data exists, and replacing the outlier data with the median.

6. The method according to any one of claims 1 to 5, characterized in that: In step S3, the use of the interquartile range to determine whether there is outlier data includes the following steps: S31: extracting target logging instrument analysis data, preferably extracting 20 to 100 data; S32: Arrange the extracted data from small to large and divide them into four equal parts. The values ​​at the three division points are the quartiles, which are Q1, Q2 and Q3 respectively; S33: Take the quartiles (Q3, Q1) as the upper and lower sides of the data interval, define IQR = Q3-Q1, and the values ​​exceeding the interval [Q1-1.5×IQR, Q3+1.5×IQR] are outliers, and the median Q2 is used to replace the outliers.

7. The method according to any one of claims 1 to 6, characterized in that: In the step S4, the filtering and smoothing process includes using a Savitzky-Golay filtering method for processing.

8. The method according to any one of claims 1 to 7, characterized in that: In step S4, the Savitzky-Golay filtering method includes using commercial software or self-programming to achieve it.

9. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the one or more processors are communicatively connected with each other, and when the computer program is executed by the one or more processors, the method as described in any one of claims 1 to 8 is executed.

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