A method and device for quantitatively evaluating data quality of dual lateral logging

Abstract

The application discloses a kind of dual lateral well logging data acquisition quality quantitative evaluation method and device, the method includes: according to instrument calibration file, instrument calibration quality score is calculated;Check the overspeed in speed curve, obtain speed quality score;Based on main measurement deep lateral curve and repeated measurement deep lateral curve, calculate and determine repeated measurement error score;Check whether double-track abnormality appears in main measurement deep and shallow lateral curve, determine double-track abnormality score;The similarity of main measurement deep and shallow lateral curve is calculated using sliding window, determine similarity abnormality score;Check the coincidence degree of main measurement deep and shallow lateral curve in non-permeable layer section, determine non-permeable layer abnormality score;According to all abnormality scores described above, dual lateral well logging data acquisition quality score is calculated.The method quickly and accurately evaluates dual lateral well logging data quality by quantitative index and algorithm, improves inspection efficiency and accuracy, reduces human error, and has wide application potential.

Description

Technical Field

[0001] This invention relates to a method and apparatus for quantitatively evaluating the quality of data acquired through dual-lateral logging. Background Technology

[0002] Dual-lateral logging is an important resistivity measurement method widely used in oil and gas exploration. This method collects two sets of apparent resistivity data reflecting strata characteristics at measurement points at different depths within the wellbore, representing the deep and shallow sides respectively. These data are typically displayed as deep and shallow lateral apparent resistivity curves, providing information on lithological variations at different depths within the wellbore. However, during logging operations, factors such as the wellbore environment, instrument performance, and logging techniques can distort the logging data. Therefore, in practice, logging interpreters conduct manual visual inspections to check data quality. Based on the overlap of the two apparent resistivity curves, they determine the distribution of underground oil, gas, and water layers, thus providing strong support for the location, stratification, and production analysis of oil and gas layers. Summary of the Invention

[0003] To obtain more accurate quality scores for dual-lateral logging data, this invention provides a method and apparatus for quantitatively evaluating the quality of dual-lateral logging data.

[0004] In a first aspect, embodiments of the present invention provide a method for quantitatively evaluating the quality of dual-lateral logging acquisition data, which may include:

[0005] Acquire dual lateral logging data; the dual lateral logging data includes a master measurement dataset and a repeat measurement dataset; the master measurement dataset includes instrument calibration files, velocity curves, master measurement deep lateral curves, and master measurement shallow lateral curves; the repeat measurement dataset includes repeat measurement deep lateral curves;

[0006] Based on the instrument calibration file, the relative error of the calibration values ​​before and after logging is calculated, and the instrument calibration mass fraction is obtained.

[0007] Check the overspeed situation in the speed measurement curve to obtain the speed measurement mass fraction;

[0008] Based on the main measurement depth lateral curve and the repeated measurement depth lateral curve, the repeated measurement error value is calculated, and the repeated measurement error fraction is determined;

[0009] Check whether there is a double-track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and determine the double-track anomaly score;

[0010] The similarity between the main measured deep lateral curve and the main measured shallow lateral curve is calculated using a sliding window to determine the similarity anomaly score;

[0011] Check the overlap between the main deep lateral curve and the main shallow lateral curve in the non-permeable layer to determine the anomaly score of the non-permeable layer;

[0012] The quality score of the dual lateral logging data is calculated based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

[0013] In one or more optional embodiments of this application, the step of checking whether a double-track anomaly occurs in the main measurement deep lateral curve and the main measurement shallow lateral curve, and determining the double-track anomaly score, includes:

[0014] Calculate the difference between the main measured depth lateral curve and the main measured shallow lateral curve at each corresponding depth point to obtain a distance array;

[0015] The difference array is obtained by performing a difference calculation based on the distance array.

[0016] All depth points in the difference array whose values ​​are less than a preset difference threshold are marked as dual-track abnormal depth points;

[0017] The dual-track anomaly score is determined based on the connection length of the dual-track anomaly depth points.

[0018] In one or more optional embodiments of this application, the step of calculating the repeat measurement error value and determining the repeat measurement error fraction based on the main measurement depth lateral curve and the repeat measurement depth lateral curve includes:

[0019] Depth correction is performed on the main measured depth lateral curve and the repeated measured depth lateral curve;

[0020] Based on the corrected main measurement depth lateral curve and the repeated measurement depth lateral curve, the repeated measurement error value is calculated using the following formula:

[0021]

[0022] In the formula, A i B is the value of the corrected master depth lateral curve at the i-th depth point. i The value of the corrected repeated deep lateral curve at the i-th depth point is given by n, where n is the total number of depth points in the repeated deep lateral curve.

[0023] The repeatability error fraction is determined based on the corrected repeatability error value.

[0024] In one or more optional embodiments of this application, the master measurement dataset includes natural gamma curves;

[0025] The process of checking the overlap between the main measured deep lateral curve and the main measured shallow lateral curve in the non-permeable layer segment to determine the non-permeable layer anomaly score includes:

[0026] Based on the natural gamma curve, multiple non-permeable segments were obtained;

[0027] For each depth point in each non-permeable layer, the difference between the main measured deep lateral curve and the main measured shallow lateral curve is calculated to obtain the spacing array;

[0028] All depth points in the spacing array whose values ​​are greater than the preset non-permeable layer spacing threshold are marked as non-permeable layer abnormal depth points;

[0029] The non-permeable layer anomaly score is determined based on the connection length of the abnormal depth points of the non-permeable layer.

[0030] In one or more optional embodiments of this application, the step of using a sliding window to calculate the similarity between the main measured depth lateral curve and the main measured shallow lateral curve, and determining the similarity anomaly score, includes:

[0031] Calculate the similarity coefficient between the main measured deep lateral curve and the main measured shallow lateral curve within each preset sliding window to obtain a similarity coefficient array;

[0032] All depth points in the similarity coefficient array whose values ​​are less than a preset similarity threshold are marked as depth points with abnormal similarity.

[0033] The similarity anomaly score is determined based on the connection length of the similarity anomaly depth points.

[0034] In one or more optional embodiments of this application, the instrument calibration file includes standard calibration values, pre-test calibration values, and post-test calibration values ​​corresponding to various calibration methods; the dual-lateral logging data also includes the validity period of the instrument master calibration.

[0035] The step of calculating the relative error of the calibration values ​​before and after logging based on the instrument calibration file, and obtaining the instrument calibration quality fraction, includes:

[0036] For each calibration method in the instrument calibration file, the pre-measurement relative error and the post-measurement relative error are calculated based on the standard calibration value, the pre-measurement calibration value, and the post-measurement calibration value of the calibration method.

[0037] The validity period of the instrument's main scale is used to determine the validity of the instrument's scale.

[0038] Check whether the pre-measurement relative error and post-measurement relative error exceed the corresponding preset scale error threshold, and determine the total error result;

[0039] The instrument scale mass fraction is calculated by combining the validity of the instrument scale and the total error result.

[0040] In one or more optional embodiments of this application, the step of checking the overspeed situation in the speed measurement curve to obtain the speed measurement quality score includes:

[0041] Mark all depth points in the speed measurement curve whose values ​​are greater than the first preset overspeed threshold as speed anomaly points;

[0042] The velocity measurement quality fraction is determined based on the connection length of the velocity anomaly points.

[0043] In one or more optional embodiments of this application, after calculating the repeatability error value and determining the repeatability error fraction based on the main measurement depth lateral curve and the repeatability measurement depth lateral curve, the method further includes:

[0044] Check whether there are any values ​​not greater than 0 in the main measurement deep lateral curve and the main measurement shallow lateral curve;

[0045] If so, an interpolation algorithm is used to smooth the main measurement deep lateral curve or the main measurement shallow lateral curve.

[0046] Secondly, embodiments of the present invention provide a device for quantitatively evaluating the quality of dual-lateral logging data, which may include:

[0047] The first acquisition module is used to acquire dual-lateral logging data; the dual-lateral logging data includes a master measurement dataset and a repeat measurement dataset; the master measurement dataset includes instrument calibration files, velocity curves, master measurement deep lateral curves, and master measurement shallow lateral curves; the repeat measurement dataset includes repeat measurement deep lateral curves;

[0048] The first calculation module is used to calculate the relative error of the scale values ​​before and after logging based on the instrument calibration file, and to obtain the instrument calibration mass fraction.

[0049] The second calculation module is used to check for overspeeding in the speed measurement curve and obtain the speed measurement quality score.

[0050] The third calculation module is used to calculate the repeat measurement error value and determine the repeat measurement error fraction based on the main measurement depth lateral curve and the repeat measurement depth lateral curve;

[0051] The fourth calculation module is used to check whether there is a double-track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and to determine the double-track anomaly score.

[0052] The fifth calculation module is used to calculate the similarity between the main measured deep lateral curve and the main measured shallow lateral curve using a sliding window, and to determine the similarity anomaly score;

[0053] The sixth calculation module is used to check the overlap between the main measurement deep lateral curve and the main measurement shallow lateral curve in the non-permeable layer section, and to determine the non-permeable layer anomaly score.

[0054] The total score calculation module is used to calculate the quality score of the dual lateral logging data based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

[0055] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the method for quantitative evaluation of the quality of dual-lateral logging acquisition data as described above.

[0056] Fourthly, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for quantitative evaluation of the quality of dual-lateral logging acquisition data as described above.

[0057] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-described method for quantitative evaluation of the quality of dual-lateral logging acquisition data.

[0058] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0059] This invention provides a method for quantitatively evaluating the quality of dual-lateral logging data. This method acquires master and repeat measurement datasets, calculates instrument calibration quality based on instrument calibration files, checks for overspeed in velocity curves, calculates repeat measurement errors, checks for double-track anomalies in the dual-lateral curves, uses a sliding window to check the similarity of the dual-lateral curves, and analyzes the curve overlap in non-permeable sections. Finally, the quality score of the dual-lateral logging data is calculated by comprehensively considering various quality scores. This method, through the design of a series of quantitative indicators and calculation algorithms, can comprehensively and quickly evaluate the quality of dual-lateral logging data, rapidly check for influencing factors such as logging operations, wellbore environment, and instrument status, automatically identify typical anomalies in the logging data, and conduct a comprehensive analysis of data quality using a standardized evaluation system. Compared with traditional manual inspection methods, this method significantly improves the efficiency and accuracy of data quality inspection, reduces human error, and is easily implemented in computer software, possessing strong potential for widespread application. It can be widely used in oil and gas exploration, wellbore environment monitoring, and other fields to improve the reliability of logging data and provide strong support for subsequent oil and gas layer identification and reservoir assessment.

[0060] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0061] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0062] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0063] Figure 1 A flowchart illustrating the method for quantitatively evaluating the quality of dual-lateral logging data provided in an embodiment of the present invention;

[0064] Figure 2 Example diagram of speed measurement curve provided in the embodiment of the present invention;

[0065] Figure 3 Example diagrams of the main measured depth lateral curve and the corrected repeated measured depth lateral curve provided for embodiments of the present invention;

[0066] Figure 4 Example diagrams of the main measurement deep lateral curve and the main measurement shallow lateral curve provided in the embodiments of the present invention;

[0067] Figure 5Example diagrams of low similarity scenarios provided in embodiments of the present invention;

[0068] Figure 6 Example diagrams of the main measurement deep lateral curve and the main measurement shallow lateral curve in the non-permeable layer provided for embodiments of the present invention;

[0069] Figure 7 A schematic diagram of the structure of the dual-lateral logging data quality quantification evaluation device provided in the embodiments of this application. Detailed Implementation

[0070] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0071] The inventors discovered that in existing technologies, data quality checks primarily rely on manual visual inspection by well logging supervisors. However, this method lacks quantitative indicators and evaluation results, depends entirely on the technical experience of the inspectors, and is time-consuming and inefficient. Based on this, the inventors conducted further research and development, resulting in this invention, which provides a method and apparatus for quantitatively evaluating the quality of dual-lateral well logging data.

[0072] Example 1

[0073] Embodiment 1 of this invention provides a method for quantitatively evaluating the quality of dual-lateral logging acquisition data, referring to... Figure 1 As shown, the method may include the following steps S101-S108:

[0074] S101: Acquire dual lateral logging data. Dual lateral logging data includes a master measurement dataset and a repeat measurement dataset. The master measurement dataset includes instrument calibration files, velocity profiles, master measurement deep lateral profiles, and master measurement shallow lateral profiles. The repeat measurement dataset includes repeat measurement deep lateral profiles.

[0075] S102: Based on the instrument calibration file, calculate the relative error of the calibration values ​​before and after logging, and obtain the instrument calibration quality fraction.

[0076] S103: Check for overspeeding in the speed measurement curve and obtain the speed measurement quality score.

[0077] S104: Based on the main measurement deep lateral curve and the repeated measurement deep lateral curve, calculate the repeated measurement error value and determine the repeated measurement error fraction.

[0078] S105: Check whether there is a double-track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and determine the double-track anomaly score.

[0079] S106: Use a sliding window to calculate the similarity between the main measured deep lateral curve and the main measured shallow lateral curve, and determine the similarity anomaly score.

[0080] S107: Check the overlap between the main measurement deep lateral curve and the main measurement shallow lateral curve in the non-permeable layer to determine the non-permeable layer anomaly score.

[0081] S108: The quality score of the dual-lateral logging data is calculated based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

[0082] This invention provides a method for quantitatively evaluating the quality of dual-lateral logging data. This method acquires master and repeat measurement datasets, calculates instrument calibration quality based on instrument calibration files, checks for overspeed in velocity curves, calculates repeat measurement errors, checks for double-track anomalies in the dual-lateral curves, uses a sliding window to check the similarity of the dual-lateral curves, and analyzes the curve overlap in non-permeable sections. Finally, the quality score of the dual-lateral logging data is calculated by comprehensively considering various quality scores. This method, through the design of a series of quantitative indicators and calculation algorithms, can comprehensively and quickly evaluate the quality of dual-lateral logging data, rapidly check for influencing factors such as logging operations, wellbore environment, and instrument status, automatically identify typical anomalies in the logging data, and conduct a comprehensive analysis of data quality using a standardized evaluation system. Compared with traditional manual inspection methods, this method significantly improves the efficiency and accuracy of data quality inspection, reduces human error, and is easily implemented in computer software, possessing strong potential for widespread application. It can be widely used in oil and gas exploration, wellbore environment monitoring, and other fields to improve the reliability of logging data and provide strong support for subsequent oil and gas layer identification and reservoir assessment.

[0083] In step S101 above, dual-lateral logging data is acquired. Dual-lateral logging data includes the master measurement dataset and the repeat measurement dataset. Specifically, it includes the following steps S1011-S1013:

[0084] S1011: Conduct logging operations at the target measurement depth to obtain the main measurement depth lateral curve and the main measurement shallow lateral curve measured by the dual-directional logging instrument within the target well section. At the same time, use a natural gamma measurement instrument to measure the natural gamma curve and record the velocity curve reflecting the movement of the instrument in the wellbore.

[0085] S1012: Select a well section at the target measurement depth that is not shorter than the preset repeated measurement threshold length for repeated measurements to obtain a repeated measurement dataset. The measurement method and the obtained data are consistent with those used in the logging operation in step S101 above, i.e., the repeated measurement dataset includes repeated measurement deep lateral curves and repeated measurement shallow lateral curves. The preset repeated measurement threshold can be set to 50 meters for example.

[0086] S1013: Obtain the calibration information of the dual-lateral logging instrument, including the standard calibration values ​​corresponding to various calibration methods, the pre-test calibration values, the post-test calibration values, and the validity period of the instrument's main calibration.

[0087] In this embodiment of the application, by obtaining dual lateral logging data through the above step S101, a comprehensive measurement basis can be provided for subsequent data analysis, including the collection of the main measurement dataset and the repeat measurement dataset, ensuring the accuracy of data under different measurement conditions, better reflecting the geological characteristics inside the wellbore, and improving the reliability and accuracy of subsequent analysis.

[0088] In step S102 above, the relative error of the calibration values ​​before and after logging is calculated based on the instrument calibration file, resulting in the instrument calibration quality fraction. The instrument calibration file includes standard calibration values, pre-logging calibration values, and post-logging calibration values ​​corresponding to various calibration methods. For dual-lateral logging data, it also includes the validity period of the instrument's main calibration. Step S102 specifically includes the following steps S1021-S1024:

[0089] S1021: For each calibration method in the instrument calibration file, calculate the pre-measurement relative error and post-measurement relative error based on the standard calibration value, pre-measurement calibration value, and post-measurement calibration value of the calibration method.

[0090] S1022: The validity of the instrument scale is determined based on the validity period of the instrument's main scale.

[0091] S1023: Check whether the pre-measurement relative error and post-measurement relative error exceed the corresponding preset scale error threshold, and determine the total error result.

[0092] Specifically, this can be achieved by comparing the pre- and post-measurement relative errors with a preset dual-lateral logging tool calibration value and error checklist to check for any calibration values ​​exceeding the limits. The number of calibration values ​​exceeding the limits is then used as the total error result. An example table of dual-lateral logging tool calibration values ​​and error checks is shown in Table 1 below:

[0093] Table 1. Examples of Dual-Side Logging Tool Scale Values ​​and Error Checks

[0094]

[0095] Among them, the EB method (Eccentric Borehole Calibration Method), the EC method (Electrical Calibration Method), and the Groningen method (Groningen Calibration Method) are three different calibration methods. LLD (Deep Laterolog Resistivity) represents deep lateral resistivity, and LLS (Shallow Laterolog Resistivity) represents shallow lateral resistivity.

[0096] S1024: Calculate the instrument scale mass fraction by combining the validity of the instrument scale and the total error results.

[0097] Specifically, one could assign a validity score to the instrument scale based on its validity, and then sum this validity score with the total error result using weighted averages to obtain the instrument scale quality score. For example, if the instrument scale is invalid and the total error result is 1, the validity score is -40, and the weighted average score of the total error result is -20, resulting in a calculated instrument scale quality score of -60.

[0098] In this embodiment of the application, step S102 above can detect instrument deviations in a timely manner by calculating the relative error of the instrument scale value, providing quality assurance for subsequent measurement data, effectively reducing data deviations caused by instrument errors, and improving the reliability of the data.

[0099] In step S103 above, the overspeed situation in the speed measurement curve is checked to obtain the speed measurement mass fraction. Specifically, this includes the following steps S1031-S1032:

[0100] S1031: Mark all depth points in the speed measurement curve whose values ​​are greater than the first preset overspeed threshold as speed anomaly points.

[0101] Specifically, the velocity profile itself can be viewed as an array, where each element represents a depth point and the velocity of the dual-lateral logging instrument as it passes that depth. In the depth domain, it is an equally spaced sequence, recording the start and end depths and the depth interval between adjacent depth positions. An example of a velocity profile is shown below. Figure 2 As shown in the figure, the blue curve is the speed measurement curve, and the corresponding data is shown in Table 2 below:

[0102] Table 2 Example of speed measurement curves

[0103]

[0104] The value of each depth point in the speed measurement curve is compared with the first preset overspeed threshold. For depth points that are greater than the threshold, they are marked as speed anomaly points, and their corresponding depth and speed values ​​are recorded.

[0105] S1032: Determine the speed measurement quality fraction based on the connection length of the speed anomaly points.

[0106] Specifically, it can be that, based on the speed anomaly points marked in step S1031, it is calculated whether these anomaly points are connected. If the total length of the connected speed anomaly points does not exceed a preset specific depth segment (e.g., 50m), it is considered that no speeding behavior has occurred and the speed measurement quality score is determined to be 0. Otherwise, it is considered as speeding behavior.

[0107] If speeding occurs, the speed measurement quality score is further calculated based on the speeding value (i.e., the ratio of the speed value to the first preset speeding threshold). For example, if the maximum speed value does not exceed 10% of the first preset speeding threshold, the speed measurement quality score is determined to be -20; otherwise, the speed measurement quality score is -40.

[0108] In this embodiment, step S103, by checking for overspeed in the velocity measurement curve, ensures the stability and accuracy of the instrument as it moves within the wellbore. This method, by promptly detecting overspeed issues, helps optimize the measurement process, reduces data anomalies caused by uneven instrument movement or inaccurate measurements, thereby improving data quality.

[0109] In step S104 above, based on the main measured depth lateral curve and the repeated measured depth lateral curve, the repeated measurement error value is calculated, and the repeated measurement error fraction is determined. Specifically, this includes the following steps S1041-S1043:

[0110] S1041: Perform depth correction on the main measured depth lateral curve and the repeated measured depth lateral curve.

[0111] Specifically, the main measurement dataset and the repeat measurement dataset are obtained by two downhole measurements using a dual-lateral logging instrument. The two cable deployments and downhole chucks may cause depth discrepancies between the two datasets, so depth correction is necessary.

[0112] Using the natural gamma curve obtained in step S101 as a reference curve, the depth lateral curves in the main measurement dataset and the repeated measurement dataset are compared. By performing local scaling correction on the natural gamma curve of the repeated measurements, the depth deviation between the repeated measurement data and the main measurement data is calculated. This depth deviation is then applied to the repeated measurement depth lateral curves to align them with the typical features of the main measurement depth lateral curves. This step helps eliminate depth deviations caused by equipment and environmental factors, ensuring the accuracy and consistency of the two datasets, thereby improving the precision of subsequent data analysis.

[0113] S1042: Based on the corrected master measurement depth lateral curve and the repeated measurement depth lateral curve, calculate the repeated measurement error value using the following formula:

[0114]

[0115] In the formula, A i B is the value of the corrected master depth lateral curve at the i-th depth point. i is the value of the corrected repeatable depth lateral curve at the i-th depth point, and n is the total number of depth points in the corrected repeatable depth lateral curve.

[0116] S1043: Determine the repeatability error fraction based on the corrected repeatability error value.

[0117] Specifically, it can be determined whether the corrected repeatability error value is greater than the preset repeatability error threshold (e.g., 5%). If so, the corrected repeatability error score is set to -20; otherwise, the repeatability error score is set to 0.

[0118] To facilitate understanding of this solution by those skilled in the art, the following provides a clearer and more complete description of the main measured depth lateral curve and the corrected repeated measured depth lateral curve in step S104 of this application embodiment, referring to... Figure 3 As shown in the figure, examples of the primary measurement depth lateral curve and the corrected repeat measurement depth lateral curve are displayed. The blue curve is the primary measurement depth lateral curve, and the red curve is the corrected repeat measurement depth lateral curve. DDLL (Dual Laterolog Logging) is a dual lateral logging technique, and ohm (ohm·meter) is the unit of resistivity. Figure 3 The corresponding data is shown in Table 3 below:

[0119] Table 3. Examples of lateral curves for main measurement depth and corrected repeat measurement depth.

[0120] depth Main measurement of deep lateral curves Repeated measurements of deep lateral curves 3405.00 808.25 4178.735 3405.10 960.429 4495.163 3405.20 1260.337 5051.744 3405.30 1903.371 6088.771 3405.40 3156.757 7864.487 3405.50 5092.409 10390.841 3405.60 7317.724 13575.952 3405.70 9045.365 17171.924 3405.80 9580.739 20443.623 3405.90 8862.873 22847.672 3406.00 7530.567 25014.426 3406.10 6424.21 27306.316 3406.20 6064.658 29549.514 3406.30 6518.881 31583.344 3406.40 7504.175 32982.92

[0121] In this embodiment, step S104 calculates the error value based on repeated measurement data, which can assess the consistency and reliability of the measurement. The calculation of the repeated measurement error value provides a quantitative basis for judging whether the measurement is stable, helps confirm the repeatability of the data, supports the final measurement results, and reduces data misjudgment caused by errors.

[0122] In this embodiment of the application, after step S104, step S109 is further included to process the singular values ​​in the main measurement deep lateral curve and the main measurement shallow lateral curve.

[0123] Specifically, this can involve checking whether there are values ​​less than or equal to 0 in the main measured deep lateral curve and the main measured shallow lateral curve. If such outliers are found, an interpolation algorithm is used to smooth either the main measured deep lateral curve or the main measured shallow lateral curve to eliminate the influence of outliers on the data and ensure the accuracy and continuity of the measurement data.

[0124] In step S105 above, it is checked whether double-track anomalies occur in the main depth lateral curve and the main shallow lateral curve, and the double-track anomaly score is determined. A double-track anomaly is a manifestation of abnormal quality in dual lateral logging data, meaning that the main depth lateral curve and the main shallow lateral curve maintain a relatively stable distance, like two parallel lines. Step S105 specifically includes the following steps S1051-S1054:

[0125] S1051: Calculate the difference between the main measured deep lateral curve and the main measured shallow lateral curve at each corresponding depth point to obtain a distance array.

[0126] Specifically, this can be achieved by calculating the difference between the master measured deep lateral curve and the master measured shallow lateral curve for each depth point, resulting in a distance array representing the distance between them. This distance array represents the difference between the deep lateral data and the shallow lateral data at each depth point. The formula for calculating the difference between the master measured deep lateral curve and the master measured shallow lateral curve at each depth point is shown in Formula 2 below:

[0127] x i =|log(A i )-log(C i )| Formula 2

[0128] In the formula, x i Let A be the difference between the main measured deep lateral curve and the main measured shallow lateral curve at depth point i. i The value of the lateral curve at the i-th depth point, C, is the main measurement depth curve. i The value of the shallow lateral curve at the i-th depth point is the primary measurement.

[0129] S1052: Perform difference calculations based on the distance array to obtain the difference array.

[0130] Specifically, a difference calculation method can be used to calculate the difference between every two adjacent values ​​in the distance array, resulting in a difference array. The difference array reflects the changes in distance between the main measured depth-shallow lateral data and the main measured shallow lateral curve, helping to identify whether the distance remains stable. The formula for calculating the difference between every two adjacent values ​​in the distance array is shown in Formula 3 below:

[0131] f(x i ) = x i -xi-1 Formula 3

[0132] In the formula, x i x is the i-th value in the distance array. i-1 It is the (i-1)th value in the distance array.

[0133] S1053: Mark all depth points in the difference array whose values ​​are less than the preset difference threshold as dual-track abnormal depth points.

[0134] Specifically, it can be done by filtering out all depth points in the difference array whose values ​​are less than a preset difference threshold. These depth points represent that the distance between the main measured deep lateral curve and the main measured shallow lateral curve changes very little or is close to constant at this time, and have a similar parallel double-track anomaly feature. These points are marked as double-track anomaly depth points.

[0135] S1054: Determine the double-track anomaly score based on the connection length of the double-track anomaly depth points.

[0136] Specifically, it can be done by calculating the connection length of the points marked as double-track abnormal depths. If the connection length of the double-track abnormal depths exceeds a preset depth threshold (e.g., 10 meters), it is considered that there is a double-track abnormality, and the double-track abnormality score is determined to be -20. Otherwise, the double-track abnormality score is determined to be 0.

[0137] To facilitate understanding of this solution by those skilled in the art, the main measurement of the deep lateral curve and the main measurement of the shallow lateral curve in step S105 of this application embodiment will be explained more clearly and completely below, with reference to... Figure 4 As shown in the figure, examples of the main deep lateral curve and the main shallow lateral curve under normal conditions are presented. The blue curve is the main deep lateral curve, the green curve is the main shallow lateral curve, and the red curve is the repeated deep lateral curve. It can be seen that under normal conditions, the distance between the main deep lateral curve and the main shallow lateral curve is unstable.

[0138] In this embodiment, step S105, by checking whether the main measurement depth-shallow lateral curve shows a double-track anomaly, can promptly identify possible anomalies in the data. This helps to eliminate errors during the measurement process, ensures the accuracy of the measurement curve, thereby improving the reliability of data interpretation and providing a clear and accurate reference for subsequent analysis.

[0139] In step S106 above, a sliding window is used to calculate the similarity between the main measured deep lateral curve and the main measured shallow lateral curve, and to determine the similarity anomaly score. Specifically, this includes the following steps S1061-S1063:

[0140] S1061: Calculate the similarity coefficient between the main measured deep lateral curve and the main measured shallow lateral curve within each preset sliding window, and obtain a similarity coefficient array.

[0141] Specifically, this can be achieved by determining the length of a preset sliding window (e.g., 1 meter), starting from the beginning of the main measurement of the deep lateral curve, and gradually sliding the window backward. Within each sliding window, the similarity coefficient between the deep lateral curve and the shallow lateral curve is calculated according to the following formula 4:

[0142]

[0143] In the formula, C w A is the similarity coefficient within a sliding window. i The value of the lateral curve at the i-th depth point is the main measurement depth. The average value of the main measurement deep lateral curve within this sliding window, B i The value of the shallow lateral curve at the i-th depth point is measured as the primary measurement. is the average value of the shallow lateral curve measured by the main measurement within this sliding window, and n is the total number of depth points in the deep lateral curve measured by the main measurement.

[0144] The similarity coefficients within each sliding window are calculated and arranged into a similarity coefficient array according to the sliding order of the windows.

[0145] S1062: Mark all depth points in the similarity coefficient array whose values ​​are less than the preset similarity threshold as depth points with abnormal similarity.

[0146] Specifically, this can be achieved by filtering out all depth points in the similarity coefficient array that are less than a preset similarity threshold (e.g., 0.8) and marking them as depth points with abnormal similarity. These depth points with abnormal similarity indicate that there is a significant difference in the shape of the main measured deep lateral curve and the main measured shallow lateral curve, suggesting that there may be a quality problem in this measurement segment.

[0147] S1063: Determine the similarity anomaly score based on the connection length of the similarity anomaly depth points.

[0148] Specifically, this can be done by calculating the connection length of the points at the depth of the similarity anomaly. If the connection length of the points at the depth of the similarity anomaly exceeds a certain depth (e.g., 5 meters), it is considered that there is a low similarity situation, and the similarity anomaly score is determined to be -20; otherwise, the similarity anomaly score is determined to be 0.

[0149] To facilitate understanding of this solution by those skilled in the art, the similarity between the main measured deep lateral curve and the main measured shallow lateral curve in step S106 of this application embodiment is explained more clearly and completely below, with reference to... Figure 5As shown in the figure, examples of the main measurement depth lateral curve and the main measurement shallow lateral curve under abnormal conditions are presented. The blue curve is the main measurement depth lateral curve, and the green curve is the main measurement shallow lateral curve. It can be seen that the main measurement depth lateral curve and the main measurement shallow lateral curve have extremely low similarity, indicating abnormal quality of the dual lateral logging data.

[0150] In this embodiment, step S106 above checks the similarity of the main measurement depth and shallow lateral curves through a sliding window, which can detect potential anomalies in the data, especially fluctuations or abnormal phenomena that occur during the measurement process. Timely detection of similarity anomalies helps to correct the measurement data and improve its consistency, thereby ensuring the accuracy of the final data.

[0151] In step S107 above, the overlap between the main measured deep lateral curve and the main measured shallow lateral curve in the non-permeable layer is checked to determine the non-permeable layer anomaly score. A non-permeable layer anomaly indicates that in non-permeable layers such as mudstone, the overlap between the main measured deep lateral curve and the main measured shallow lateral curve is low; ideally, they should be close to overlap. If the overlap between the two curves is significantly lower than the normal value, it is determined to be a non-permeable layer anomaly, which may reflect a quality problem in the measurement. Step S107 specifically includes the following steps S1071-S1074:

[0152] S1071: Based on the natural gamma curve, multiple non-permeable segments were obtained.

[0153] Specifically, the non-permeable layer segment can be determined based on the natural gamma curve obtained in step S101 above. If the value in the natural gamma curve is greater than the non-permeable layer threshold (e.g., 100), then that depth is in the non-permeable layer segment. Using this standard, all non-permeable layers are identified and extracted point by point.

[0154] S1072: For each depth point in each non-permeable layer, calculate the difference between the main measurement deep lateral curve and the main measurement shallow lateral curve to obtain the spacing array.

[0155] Specifically, within each non-permeable layer, the difference between the main deep-side lateral curve and the main shallow-side lateral curve can be calculated point by point to obtain a spacing array. This spacing array reflects the variation in the spacing between the main deep-side lateral curve and the main shallow-side lateral curve at each depth point and is used to assess the overlap of the data.

[0156] S1073: Mark all depth points in the spacing array whose values ​​are greater than the preset non-permeable layer spacing threshold as non-permeable layer abnormal depth points.

[0157] Specifically, this can be achieved by filtering out all depth points in the spacing array that are greater than a preset non-permeable layer spacing threshold (e.g., 0.14). These are marked as abnormal depth points in the non-permeable layer, indicating a low degree of overlap between the deep and shallow lateral curves.

[0158] S1074: Determine the non-permeable layer anomaly score based on the connection length of the non-permeable layer anomaly depth points.

[0159] Specifically, this can be done by calculating the connection length of the abnormal depth points in the non-permeable layer. If the connection length of the abnormal depth points in the non-permeable layer exceeds a certain depth (e.g., 1 meter), it is considered that there is a quality anomaly of non-permeable layer non-overlapping, and the non-permeable layer anomaly score is determined to be -20; otherwise, the non-permeable layer anomaly score is determined to be 0.

[0160] To facilitate understanding of this solution by those skilled in the art, the following provides a clearer and more complete description of the main measured deep lateral curve and the main measured shallow lateral curve in the non-permeable layer segment in step S107 of this application embodiment, referring to... Figure 6 As shown in the figure, the figure illustrates examples of the main deep-side lateral curves and the main shallow-side lateral curves in a non-permeable layer under normal conditions. The left side shows the natural gamma curve, and the right side shows the main deep-side lateral curve and the main shallow-side lateral curve. The black curve represents the main deep-side lateral curve, and the green curve represents the main shallow-side lateral curve. In the figure, the value of the natural gamma curve at a depth of 2862-2868 meters is greater than 100, indicating that it is in a non-permeable layer. At this time, the main deep-side lateral curve and the main shallow-side lateral curve have good overlap at a depth of 2862.5-2867.5 meters, and there is no quality anomaly of non-overlapping non-permeable layers.

[0161] In this embodiment, step S107 checks the overlap of curves in the non-permeable layer, which can assess whether the measurement data reflects the true situation of the underground structure. The calculation of the anomaly score for the non-permeable layer helps to accurately identify impermeable rock formations, improves data accuracy, and provides strong support for the identification of oil and gas reservoirs.

[0162] In step S108 above, the quality score of the dual lateral logging data is calculated based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

[0163] Specifically, it can be done by setting a raw total score for the quality of the dual-lateral logging data, and adding the instrument calibration quality score, velocity quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score obtained in the above steps to the raw total score for the quality of the dual-lateral logging data to obtain the quality score of the dual-lateral logging data.

[0164] In one specific embodiment, the original total score for the quality of dual-lateral logging data is 100 points. A score greater than 80 points is considered excellent, 61-80 points is considered qualified, and 60 points or below is considered unqualified.

[0165] In this embodiment, step S108, by integrating various quality scores, can comprehensively evaluate the quality of the dual-lateral logging data, providing intuitive feedback on data quality, helping technicians quickly identify potential problems, optimize measurement workflows, and improve data utilization efficiency and accuracy.

[0166] Example 2

[0167] Based on the same inventive concept, embodiments of the present invention also provide a device for quantitatively evaluating the quality of dual-lateral logging data, referring to... Figure 7 As shown, the device includes:

[0168] The first acquisition module 101 is used to acquire dual-lateral logging data; the dual-lateral logging data includes a master measurement dataset and a repeat measurement dataset; the master measurement dataset includes an instrument calibration file, velocity curves, master measurement deep lateral curves, and master measurement shallow lateral curves; the repeat measurement dataset includes repeat measurement deep lateral curves.

[0169] The first calculation module 102 is used to calculate the relative error of the scale values ​​before and after logging based on the instrument scale file, and to obtain the instrument scale mass fraction.

[0170] The second calculation module 103 is used to check the overspeed situation in the speed measurement curve and obtain the speed measurement quality score.

[0171] The third calculation module 104 is used to calculate the repeated measurement error value and determine the repeated measurement error fraction based on the main measurement depth lateral curve and the repeated measurement depth lateral curve.

[0172] The fourth calculation module 105 is used to check whether there is a double track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and to determine the double track anomaly score.

[0173] The fifth calculation module 106 is used to calculate the similarity between the main measured deep lateral curve and the main measured shallow lateral curve using a sliding window, and to determine the similarity anomaly score.

[0174] The sixth calculation module 107 is used to check the overlap between the main measurement deep lateral curve and the main measurement shallow lateral curve in the non-permeable layer section and determine the non-permeable layer anomaly score.

[0175] The total score calculation module 108 is used to calculate the quality score of the dual lateral logging data based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

[0176] Example 3

[0177] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the method for quantitative evaluation of the quality of dual-lateral logging data as described in Embodiment 1 above.

[0178] Example 4

[0179] Based on the same inventive concept, this embodiment of the invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for quantitative evaluation of the quality of dual-lateral logging data as described in Embodiment 1 above.

[0180] Example 5

[0181] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the method for quantitative evaluation of the quality of dual-lateral logging data as described in Embodiment 1 above.

[0182] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0183] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for quantitatively evaluating the quality of dual-lateral logging acquisition data, characterized in that, include: Acquire dual lateral logging data; the dual lateral logging data includes a master measurement dataset and a repeat measurement dataset; the master measurement dataset includes instrument calibration files, velocity curves, master measurement deep lateral curves, and master measurement shallow lateral curves; the repeat measurement dataset includes repeat measurement deep lateral curves; Based on the instrument calibration file, the relative error of the calibration values ​​before and after logging is calculated, and the instrument calibration mass fraction is obtained. Check the overspeed situation in the speed measurement curve to obtain the speed measurement mass fraction; Based on the main measurement depth lateral curve and the repeated measurement depth lateral curve, the repeated measurement error value is calculated, and the repeated measurement error fraction is determined; Check whether there is a double-track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and determine the double-track anomaly score; The similarity between the main measured deep lateral curve and the main measured shallow lateral curve is calculated using a sliding window to determine the similarity anomaly score; Check the overlap between the main deep lateral curve and the main shallow lateral curve in the non-permeable layer to determine the anomaly score of the non-permeable layer; The quality score of the dual lateral logging data is calculated based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

2. The method according to claim 1, characterized in that, The process of checking whether dual-track anomalies occur in the main measurement deep lateral curve and the main measurement shallow lateral curve, and determining the dual-track anomaly score, includes: Calculate the difference between the main measured depth lateral curve and the main measured shallow lateral curve at each corresponding depth point to obtain a distance array; The difference array is obtained by performing a difference calculation based on the distance array. All depth points in the difference array whose values ​​are less than a preset difference threshold are marked as dual-track abnormal depth points; The dual-track anomaly score is determined based on the connection length of the dual-track anomaly depth points.

3. The method according to claim 1, characterized in that, The step of calculating the repeat measurement error value and determining the repeat measurement error fraction based on the main measurement depth lateral curve and the repeat measurement depth lateral curve includes: Depth correction is performed on the main measured depth lateral curve and the repeated measured depth lateral curve; Based on the corrected main measurement depth lateral curve and the repeated measurement depth lateral curve, the repeated measurement error value is calculated using the following formula: In the formula, A i B is the value of the corrected master depth lateral curve at the i-th depth point. i The value of the corrected repeated deep lateral curve at the i-th depth point is given by n, where n is the total number of depth points in the repeated deep lateral curve. The repeatability error fraction is determined based on the corrected repeatability error value.

4. The method according to claim 1, characterized in that, The master measurement dataset includes natural gamma curves; The process of checking the overlap between the main measured deep lateral curve and the main measured shallow lateral curve in the non-permeable layer segment to determine the non-permeable layer anomaly score includes: Based on the natural gamma curve, multiple non-permeable segments were obtained; For each depth point in each non-permeable layer, the difference between the main measured deep lateral curve and the main measured shallow lateral curve is calculated to obtain the spacing array; All depth points in the spacing array whose values ​​are greater than the preset non-permeable layer spacing threshold are marked as non-permeable layer abnormal depth points; The non-permeable layer anomaly score is determined based on the connection length of the abnormal depth points of the non-permeable layer.

5. The method according to claim 1, characterized in that, The main measurement depth is calculated using a sliding window. The similarity between the lateral curve and the main measured shallow lateral curve is used to determine the similarity anomaly score, including: Calculate the similarity coefficient between the main measured deep lateral curve and the main measured shallow lateral curve within each preset sliding window to obtain a similarity coefficient array; All depth points in the similarity coefficient array whose values ​​are less than a preset similarity threshold are marked as depth points with abnormal similarity. The similarity anomaly score is determined based on the connection length of the similarity anomaly depth points.

6. The method according to claim 1, characterized in that, The instrument calibration file includes standard calibration values, pre-test calibration values, and post-test calibration values ​​corresponding to various calibration methods; the dual-lateral logging data also includes the validity period of the instrument's main calibration. The step of calculating the relative error of the calibration values ​​before and after logging based on the instrument calibration file, and obtaining the instrument calibration quality fraction, includes: For each calibration method in the instrument calibration file, the pre-measurement relative error and the post-measurement relative error are calculated based on the standard calibration value, the pre-measurement calibration value, and the post-measurement calibration value of the calibration method. The validity period of the instrument's main scale is used to determine the validity of the instrument's scale. Check whether the pre-measurement relative error and post-measurement relative error exceed the corresponding preset scale error threshold, and determine the total error result; The instrument scale mass fraction is calculated by combining the validity of the instrument scale and the total error result.

7. The method according to claim 1, characterized in that, The process of checking for overspeeding in the speed measurement curve to obtain a speed measurement quality score includes: Mark all depth points in the speed measurement curve whose values ​​are greater than the first preset overspeed threshold as speed anomaly points; The velocity measurement quality fraction is determined based on the connection length of the velocity anomaly points.

8. The method according to claim 1, characterized in that, After calculating the repeatability error value and determining the repeatability error fraction based on the main measurement depth lateral curve and the repeatability measurement depth lateral curve, the method further includes: Check whether there are any values ​​not greater than 0 in the main measurement deep lateral curve and the main measurement shallow lateral curve; If so, an interpolation algorithm is used to smooth the main measurement deep lateral curve or the main measurement shallow lateral curve.

9. A device for quantitatively evaluating the quality of dual-lateral logging data, characterized in that, include: The first acquisition module is used to acquire dual-lateral logging data; The dual lateral logging data includes a master measurement dataset and a repeat measurement dataset; the master measurement dataset includes instrument calibration files, velocity curves, master measurement deep lateral curves, and master measurement shallow lateral curves; the repeat measurement dataset includes repeat measurement deep lateral curves. The first calculation module is used to calculate the relative error of the scale values ​​before and after logging based on the instrument calibration file, and to obtain the instrument calibration mass fraction. The second calculation module is used to check for overspeeding in the speed measurement curve and obtain the speed measurement quality score. The third calculation module is used to calculate the repeat measurement error value and determine the repeat measurement error fraction based on the main measurement depth lateral curve and the repeat measurement depth lateral curve; The fourth calculation module is used to check whether there is a double-track anomaly in the main measurement deep lateral curve and the main measurement shallow lateral curve, and to determine the double-track anomaly score. The fifth calculation module is used to calculate the similarity between the main measured deep lateral curve and the main measured shallow lateral curve using a sliding window, and to determine the similarity anomaly score; The sixth calculation module is used to check the overlap between the main measurement deep lateral curve and the main measurement shallow lateral curve in the non-permeable layer section, and to determine the non-permeable layer anomaly score. The total score calculation module is used to calculate the quality score of the dual lateral logging data based on the instrument calibration quality score, velocity measurement quality score, repeatability error score, non-permeable layer anomaly score, dual-track anomaly score, and similarity anomaly score.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for quantitative evaluation of the quality of dual-lateral logging data as described in any one of claims 1-8.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for quantitative evaluation of the quality of dual-lateral logging data as described in any one of claims 1-8.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method for quantitative evaluation of the quality of dual-lateral logging data as described in any one of claims 1-8.

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