Rescue well positioning result stability evaluation method based on Raida criterion

Abstract

The invention belongs to the technical field of rescue well positioning result stability evaluation. The invention provides a rescue well positioning result stability evaluation method based on a Raida criterion. According to the embodiment of the invention, for the situation that the accuracy of data stability evaluation is not high due to the fact that the singular value exists in the distance result obtained by the optimization algorithm in the accident well positioning algorithm by using the improved Bezier model, the singular value of the distance result obtained by the optimization algorithm is eliminated by using the Raida criterion; and carrying out stability evaluation on the rejected data group. According to the method, the wrong optimal result is judged to a certain extent and is eliminated, so that the data of the optimal result is more reasonable. According to the method, the implementation volume is small, the implementation effect is high, manual secondary judgment on result data is not needed, and the processing speed of partial optimization algorithm results is increased. According to the method, related statistical physical quantities are combined, stability analysis is carried out on the processed data, and the stability result is visual and accurate.

Description

Technical Field

[0001] The disclosed embodiments relate to the technical field of rescue well positioning result stability assessment, and in particular to a rescue well positioning result stability assessment method based on the Raida criterion. Background Art

[0002] When using the "improved Bessel model" to locate the accident well through electromagnetic detection, the underground magnetic field measurement environment is relatively complex. Not only can it receive the secondary magnetic field formed by the converged current, but it can also sense various complex magnetic field signals in the environment. Therefore, the stability of this positioning method during distance measurement is studied. In general, when using the optimization algorithm to solve the distance of the accident well, the optimization function is often used for solution. However, using the optimization function for solution is likely to fall into the local optimal solution and fail to find the global optimal solution, resulting in some outliers in the results. Especially when the signal-to-noise ratio is low, the optimal solution may have singular values, resulting in inaccurate data stability assessment.

[0003] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0004] It should be noted that this section is intended to provide background or context for the technical solutions of the present disclosure stated in the claims. The description herein is not admitted to be prior art by virtue of being included in this section. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a method for evaluating the stability of rescue well positioning results based on the Raida criterion, thereby overcoming one or more problems caused by the limitations and defects of related technologies at least to a certain extent.

[0006] According to an embodiment of the present disclosure, a method for evaluating the stability of a rescue well positioning result based on the Raida criterion is provided, the method comprising: Determining a confidence probability based on a signal-to-noise ratio of an initial result data set; Calculating the initial result data set using an accident well location algorithm to obtain a standard error; Determining a confidence interval based on the standard error and the confidence probability; Eliminating singular values ​​from the initial result data set according to the confidence interval to obtain a target result data set; A stability analysis is performed on the target result data group to obtain a stability analysis result.

[0007] Further, the step of determining the confidence probability according to the signal-to-noise ratio of the initial result data set includes: Based on the signal-to-noise ratio of the initial result data set, combined with the Laida criterion, the confidence probability is determined .

[0008] Furthermore, the step of calculating the initial result data set using the accident well location algorithm to obtain a standard error includes: Calculating the initial result data set using an accident well location algorithm to obtain an average value and a residual error of the initial result data set; Based on the mean value and the residual error, the standard deviation and the standard error of the initial result data set are obtained using the Bessel formula.

[0009] Furthermore, the expression of the average value is:

[0010] The expression of the residual error is:

[0011] The expression of the standard deviation is:

[0012] The expression of the standard error is:

[0013] The initial result data set is ( i =1, 2, …, n), is the average value of the initial result data, is the residual error, S is the standard deviation, is the standard error.

[0014] Furthermore, the expression of the confidence interval is:

[0015] Among them, the confidence probability .

[0016] Further, the step of removing singular values ​​from the initial result data set according to the confidence interval to obtain the target result data set includes: Based on the confidence interval, singular value discrimination is performed on each initial result data in the initial result data group, gross errors are discarded, and normal data are retained; Traverse all the initial result data, and obtain the target result data set according to the retained initial result data .

[0017] Furthermore, based on the confidence interval, singular value discrimination is performed on each initial result data in the initial result data group, and the step of discarding gross errors and retaining normal data includes: when When the initial result data If it is a gross error, it will be discarded; when When the initial result data If it is normal data, it will be retained; in, ,for Probability of occurrence.

[0018] Further, the step of performing stability analysis on the target result data set to obtain a stability analysis result includes: According to the target result data group after removing the singular value , calculate its variance , according to the variance Evaluate the stability of relevant algorithms.

[0019] Furthermore, the difference is expressed as:

[0020] Wherein, m is the number of target result data in the target result data group, Yi For the i Target result data, is the average value of the target result data.

[0021] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the embodiment of the present disclosure, through the above-mentioned rescue well positioning result stability evaluation method based on the Raida criterion, on the one hand, in view of the situation that the distance result obtained by the optimization algorithm in the accident well positioning algorithm using the improved Bessel model has singular values, resulting in low accuracy of data stability evaluation, the singular values ​​of the distance result obtained by the optimization algorithm are eliminated by using the Raida criterion, and then the stability of the eliminated data group is evaluated. Ignoring the problem of inaccurate stability of the overall result caused by some inaccurate results due to the limitations of the optimization algorithm itself, the stability analysis of the relevant algorithm results can be accurately performed. On the other hand, the method judges the wrong optimal result to a certain extent and eliminates it, making the optimization result data more reasonable. The method has a small implementation volume and high implementation effect, does not require manual secondary judgment of the result data, and improves the speed of processing some optimization algorithm results. The method combines relevant statistical physical quantities to perform stability analysis on the processed data, and the stability results are intuitive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0023] Figure 1 A step diagram showing a method for evaluating the stability of a rescue well positioning result based on the Raida criterion in an exemplary embodiment of the present disclosure; Figure 2 A specific flow chart showing a method for evaluating the stability of a rescue well positioning result based on the Raida criterion in an exemplary embodiment of the present disclosure; Figure 3 The distance measurement result when the signal-to-noise ratio is 1 dB in an exemplary embodiment of the present disclosure is shown; Figure 4 The distance measurement result when the signal-to-noise ratio is 10 dB in the exemplary embodiment of the present disclosure is shown; Figure 5 The distance measurement result after using the Laida criterion when the signal-to-noise ratio is 1 dB in an exemplary embodiment of the present disclosure is shown; Figure 6 The distance measurement result after using the Laida criterion when the signal-to-noise ratio is 10 dB in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] In addition, the accompanying drawings are only schematic illustrations of the embodiments of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0026] This example implementation provides a method for evaluating the stability of rescue well positioning results based on the Raida criterion. Figure 1 As shown in , the rescue well positioning result stability assessment method based on the Raida criterion may include: steps S101 to S105.

[0027] Step S101: determining the confidence probability according to the signal-to-noise ratio of the initial result data set; Step S102: using the accident well location algorithm to calculate the initial result data set to obtain a standard error; Step S103: determining a confidence interval according to the standard error and the confidence probability; Step S104: removing singular values ​​from the initial result data set according to the confidence interval to obtain a target result data set; Step S105: performing stability analysis on the target result data group to obtain a stability analysis result.

[0028] Through the above-mentioned rescue well positioning result stability evaluation method based on the Raida criterion, on the one hand, in view of the fact that the distance results obtained by the optimization algorithm in the accident well positioning algorithm using the improved Bessel model have singular values, resulting in low accuracy of data stability evaluation, the singular values ​​of the distance results obtained by the optimization algorithm are eliminated by using the Raida criterion, and then the stability of the eliminated data group is evaluated. The problem of inaccurate stability caused by some inaccurate results on the overall results due to the limitations of the optimization algorithm itself is ignored, and the stability analysis of the relevant algorithm results can be accurately performed. On the other hand, this method judges the wrong optimal results to a certain extent and eliminates them, making the optimization result data more reasonable. This method has a small implementation volume and high implementation effect. It does not require manual secondary judgment of the result data, and improves the speed of processing some optimization algorithm results. This method combines relevant statistical physical quantities to perform stability analysis on the processed data, and the stability results are intuitive and accurate.

[0029] Next, we will refer to Figures 1 to 6 The various steps of the above-mentioned method for evaluating the stability of the rescue well positioning result based on the Raida criterion in this example implementation are described in more detail.

[0030] In step S101, when using the improved "Bessel model" to optimize the accident well location algorithm, the relevant optimization functions in the optimization toolbox are often used to solve the optimal value of the objective function. However, the use of the optimization function may fall into the local optimal solution and fail to find the global optimal solution. Therefore, some outliers will appear in the optimization results, which will affect the accuracy and reliability of the optimization results and cause inaccurate optimization results. Therefore, it is necessary to eliminate the data outliers.

[0031] In order to improve the accuracy of the optimization results, the "Laida Criterion" is proposed to evaluate the data of the optimization results. The calculation method steps for processing and evaluating the optimization results using the "Laida Criterion" are as follows: When the amount of optimization result data is large, the basic idea of ​​using the Laida criterion to judge the gross error is to use a given confidence probability The standard error limit of the three-times measurement series is used as the standard. Any error exceeding this limit is considered not to be a random error, but a gross error. Measurement values ​​containing gross errors are called outliers, which are undesirable and should be removed from the measurement data.

[0032] In step S102, the relevant statistical physical quantities of the optimization result data set are calculated It is known that by using the "improved Bessel model" to optimize the algorithm in the accident well location algorithm, the n data distance results (i.e., the initial result data group) are obtained as follows: When using the Laida criterion to judge and eliminate outliers containing gross errors, the independent measurement series with equal precision should be calculated first. The average value of the initial result data (i=1, 2, ..., n) and residual error , and calculate the standard deviation S and standard error of the measurement series according to the Bessel formula .in: (1) (2) (3) (4) In step S103 and step S104, the premise for analyzing the data stability results is to eliminate singular values ​​that appear because the optimization function cannot accurately find the global optimal solution.

[0033] According to the relevant data obtained (i.e. the average value of the initial result data , residual error , standard deviation S and standard error ), singular value identification is performed according to the following: ,but It is a gross error and should be discarded; ,but This is normal data and should be retained. (5) Among them, the standard error and confidence probability Satisfies the following relationship: (6) In step S105, the result stability analysis After removing the singular values, the resulting data Variance Calculations are performed to evaluate the stability of the relevant algorithms.

[0034] variance The calculation formula is:

[0035] The pseudo code for the stability evaluation method of the rescue well positioning result based on the Raida criterion is as follows:

[0036] In one embodiment, the present application performs singular value processing on the interval result data group obtained by optimizing the accident well positioning algorithm using the "improved Bessel model" based on the Raida criterion, and performs stability analysis on the result data group after removing the singular values, ignoring the problem of inaccurate stability of the overall result caused by some inaccurate results due to the limitations of the optimization algorithm itself, and can accurately perform stability analysis on the relevant algorithm results.

[0037] In one embodiment, the present application utilizes the Raida criterion in combination with statistical physical quantities to perform optimization analysis based on the results obtained by the optimization algorithm in the accident well locating algorithm using the improved "Bessel model", studies the stability of the results obtained using the optimization algorithm, and solves the common problem of singular values ​​that are prone to occur when using the optimization algorithm.

[0038] In one embodiment, the rescue well positioning result stability assessment method based on the Raida criterion proposed in the present application can eliminate outliers in the results obtained by the optimization algorithm, thereby effectively improving the stability of the optimization results.

[0039] The data stability analysis method of the present application is simple to implement and easy to operate, and can effectively reduce the influence of singular values ​​on the stability of the results, thereby improving the stability analysis results of the result data.

[0040] In a specific embodiment, Figure 2 As shown, this embodiment provides a method for evaluating the stability of rescue well positioning results based on the Raida criterion, comprising the following steps: Determine confidence probabilities; Calculate relevant statistics of the result data set; Combine the confidence probability and the relevant data statistics calculation results to judge abnormal data; The stability analysis is performed on the data set obtained after removing singular values.

[0041] In a specific embodiment, Figures 3 to 6 As shown, in order to verify the effectiveness, experimental comparison data of this application and existing optimization result data are provided.

[0042] Specifically, when using the optimization algorithm to perform distance tests in related scenarios, 100 sets of optimization results are obtained through the relevant optimization algorithm under different signal-to-noise ratios as actual distance measurement values, which are compared with the theoretical distances. Figure 3 and Figure 4 shown.

[0043] When the signal-to-noise ratio is low, the fmincon function in the MATLAB optimization toolbox is used to solve the minimum value of the nonlinear problem of the ranging result. The fmincon function may fall into the local optimal solution and fail to find the global optimal solution. Therefore, some outliers will appear in the ranging results, which will affect the accuracy and reliability of the ranging results, resulting in inaccurate ranging results under this algorithm. Therefore, it is necessary to remove the data outliers.

[0044] Since the number of measurements in this embodiment is large, the confidence probability is set to 99.7% as the standard, the confidence interval is determined according to formula (6), and singular values ​​are eliminated according to the confidence interval. Figure 3 The signal-to-noise ratio is 1dB and Figure 4 The ranging results when the signal-to-noise ratio is 10dB and the ranging results after removing the outliers using the Raida criterion are as follows: Figure 4 and Figure 5 shown.

[0045] When the signal-to-noise ratio level is 1dB Figure 3 and Figure 4 The ranging results and the signal-to-noise ratio level are 10dB Figure 5 and Figure 6 By comparing the ranging results of , it can be seen that under the same signal-to-noise ratio level, the ranging results after removing outliers using the Raida criterion are more stable.

[0046] Finally, the stability analysis of the ranging results after removing the singular values ​​is carried out. The average value, error percentage and variance of the ranging results before and after the Raida criterion is applied to remove the outliers when the signal-to-noise ratio changes from 1dB to 10dB are calculated, and the calculation results are shown in Table 1.

[0047] The above is a detailed introduction to the implementation case of the method of the present application, and the specific operation method is described. After a large number of experiments, it is proved that the variance of the ranging results at the same signal-to-noise ratio level after using the Laida criterion is smaller, indicating that the ranging results after removing the outliers are more stable. It should be noted that the data results obtained by the present application at a low signal-to-noise ratio are effective. For those skilled in the art, according to the method steps of the present application, there will be changes in different ranges in the specific implementation method and application scope.

[0048] Table 1 Evaluation of ranging results under different signal-to-noise ratios

[0049] Through the above-mentioned rescue well positioning result stability evaluation method based on the Raida criterion, on the one hand, in view of the fact that the distance results obtained by the optimization algorithm in the accident well positioning algorithm using the improved Bessel model have singular values, resulting in low accuracy of data stability evaluation, the singular values ​​of the distance results obtained by the optimization algorithm are eliminated by using the Raida criterion, and then the stability of the eliminated data group is evaluated. The problem of inaccurate stability caused by some inaccurate results on the overall results due to the limitations of the optimization algorithm itself is ignored, and the stability analysis of the relevant algorithm results can be accurately performed. On the other hand, this method judges the wrong optimal results to a certain extent and eliminates them, making the optimization result data more reasonable. This method has a small implementation volume and high implementation effect. It does not require manual secondary judgment of the result data, and improves the speed of processing some optimization algorithm results. This method combines relevant statistical physical quantities to perform stability analysis on the processed data, and the stability results are intuitive and accurate.

[0050] It should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. in the above description indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present disclosure.

[0051] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0052] In the embodiments of the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0053] In the embodiments of the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features not being in direct contact but being in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. 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 disclosure. In this specification, the schematic representations of the above terms do 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. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0055] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for evaluating the stability of rescue well positioning results based on the Raida criterion, characterized in that: The method includes: Determining a confidence probability based on a signal-to-noise ratio of an initial result data set; Calculating the initial result data set using an accident well location algorithm to obtain a standard error; Determining a confidence interval based on the standard error and the confidence probability; Eliminating singular values ​​from the initial result data set according to the confidence interval to obtain a target result data set; A stability analysis is performed on the target result data group to obtain a stability analysis result.

2. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 1 is characterized in that: The step of determining the confidence probability according to the signal-to-noise ratio of the initial result data set includes: Based on the signal-to-noise ratio of the initial result data set, combined with the Laida criterion, the confidence probability is determined .

3. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 2 is characterized in that: The step of calculating the initial result data set using the accident well location algorithm to obtain the standard error includes: Calculating the initial result data set using an accident well location algorithm to obtain an average value and a residual error of the initial result data set; Based on the mean value and the residual error, the standard deviation and the standard error of the initial result data set are obtained using the Bessel formula.

4. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 3 is characterized in that: The expression of the average value is: The expression of the residual error is: The expression of the standard deviation is: The expression of the standard error is: The initial result data set is ( i =1, 2, …, n), is the average value of the initial result data, is the residual error, S is the standard deviation, is the standard error.

5. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 4 is characterized in that: The expression of the confidence interval is: Among them, the confidence probability .

6. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 5 is characterized in that: The step of removing singular values ​​from the initial result data set according to the confidence interval to obtain a target result data set includes: Based on the confidence interval, singular value discrimination is performed on each initial result data in the initial result data group, gross errors are discarded, and normal data are retained; Traverse all the initial result data, and obtain the target result data set according to the retained initial result data .

7. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 6 is characterized in that: Based on the confidence interval, the steps of performing singular value discrimination on each initial result data in the initial result data group, discarding gross errors, and retaining normal data include: when When the initial result data If it is a gross error, it will be discarded; when When the initial result data If it is normal data, it will be retained; in, ,for Probability of occurrence.

8. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 7 is characterized in that: The step of performing stability analysis on the target result data set to obtain a stability analysis result includes: According to the target result data group after removing the singular value , calculate its variance , according to the variance Evaluate the stability of relevant algorithms.

9. The method for evaluating the stability of the rescue well positioning result based on the Raida criterion according to claim 8 is characterized in that: The expression of the difference is: Wherein, m is the number of target result data in the target result data group, Yi For the i Target result data, is the average value of the target result data.

Images