A method for evaluating stability of rescue well positioning result based on Rayleigh criterion

Abstract

The application belongs to the technical field of stability evaluation of rescue well positioning results. The application provides a method for evaluating the stability of rescue well positioning results based on the Rayleigh criterion. In the case that the distance positioning result obtained by the optimization algorithm in the accident well positioning algorithm using the improved Bezier model has singular values, resulting in low accuracy of data stability evaluation, the singular values of the distance positioning result obtained by the optimization algorithm are removed by using the Rayleigh criterion, and then the stability of the removed data set is evaluated. This method can to some extent judge the wrong optimal result and remove it, making the optimization result data more reasonable. The method has small implementation size and high implementation effect, does not need human secondary judgment on the result data, and improves the speed of processing the results of part of the optimization algorithm. The method combines relevant statistical physical quantities to analyze the stability of the processed data, and the stability result is intuitive and accurate.

Description

Technical Field

[0001] This disclosure relates to the field of stability assessment technology for rescue well positioning results, and in particular to a method for stability assessment of rescue well positioning results based on the Laida criterion. Background Technology

[0002] When using the "improved Bessel model" to locate accident wells via electromagnetic detection, the complex downhole magnetic field measurement environment presents challenges. Not only can it receive secondary magnetic fields formed by converging currents, but it can also sense various complex magnetic field signals in the environment. Therefore, the stability of this location method during distance measurement was investigated. Generally, optimization algorithms are used to solve for the distance to accident wells, often employing optimization functions. However, using optimization functions is highly likely to get stuck in local optima instead of finding the global optimum, leading to outliers in the results. Especially at low signal-to-noise ratios, the optimal solution may contain singular values, resulting in inaccurate data stability assessments.

[0003] Therefore, it is necessary to improve one or more of the 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 this disclosure as set forth in the claims. The description herein does not constitute an admission that it is prior art simply because it is included in this section. Summary of the Invention

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

[0006] According to embodiments of this disclosure, a method for evaluating the stability of rescue well location results based on the Laida criterion is provided, the method comprising:

[0007] The confidence probability is determined based on the signal-to-noise ratio of the initial result data set;

[0008] The initial result data set was calculated using the accident well location algorithm to obtain the standard error;

[0009] The confidence interval is determined based on the standard error and the confidence probability.

[0010] The initial result data set is subjected to singular value removal based on the confidence interval to obtain the target result data set;

[0011] A stability analysis is performed on the target result data set to obtain the stability analysis results.

[0012] Furthermore, the step of determining the confidence probability based on the signal-to-noise ratio of the initial result data set includes:

[0013] Based on the signal-to-noise ratio of the initial result data set, and in conjunction with the Laida criterion, the confidence probability is determined. .

[0014] Furthermore, the step of calculating the standard error using the accident well location algorithm on the initial result data set includes:

[0015] The initial result data set is calculated using the accident well location algorithm to obtain the average value and residual error of the initial result data set;

[0016] Based on the mean and residual error, the standard deviation and standard error of the initial result data set are obtained using the Bessel formula.

[0017] Furthermore, the expression for the average value is:

[0018]

[0019] The expression for the residual error is:

[0020]

[0021] The expression for the standard deviation is:

[0022]

[0023] The expression for the standard error is:

[0024]

[0025] The initial result data set is as follows: ( i =1,2,…,n), The average value of the initial result data. For residual error, S The standard deviation is... This represents the standard error.

[0026] Furthermore, the expression for the confidence interval is:

[0027]

[0028] Among them, confidence probability .

[0029] Furthermore, the step of removing outliers from the initial result data set based on the confidence interval to obtain the target result data set includes:

[0030] Based on the confidence interval, singular value discrimination is performed on each initial result data in the initial result data group, discarding gross errors and retaining normal data;

[0031] Iterate through all the initial result data and obtain the target result data group based on the retained initial result data. .

[0032] Furthermore, the step of performing singular value discrimination on each initial result data in the initial result data group based on the confidence interval, discarding gross errors and retaining normal data, includes:

[0033] when At that time, the initial result data If the error is gross, discard it;

[0034] when At that time, the initial result data If the data is normal, retain it;

[0035] in, ,for The probability of occurrence.

[0036] Furthermore, the step of performing stability analysis on the target result data set to obtain the stability analysis results includes:

[0037] Based on the target result data group after removing outliers Calculate its variance According to the variance Evaluate the stability of the relevant algorithms.

[0038] Furthermore, the expression for the difference is:

[0039]

[0040] Where m is the number of target result data in the target result data group. Yi For the first i One target result data, The average value of the target result data.

[0041] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0042] In the embodiments of this disclosure, the stability assessment method for rescue well location results based on the Laida criterion addresses the issue of singular values ​​in the distance determination results obtained by the most optimized algorithm in the accident well location algorithm using the improved Bessel model, which leads to low accuracy in data stability assessment. This is addressed by using the Laida criterion to remove singular values ​​from the distance determination results obtained by the optimized algorithm, and then performing a stability assessment on the data set after removal. This method ignores the inaccuracy caused by some inaccurate results due to the limitations of the optimization algorithm itself, thus accurately analyzing the stability of the relevant algorithm results. Furthermore, this method identifies and removes erroneous optimal results to a certain extent, making the optimized result data more reasonable. This method is small in size, highly effective, and does not require secondary manual judgment of the result data, improving the processing speed of some optimization algorithm results. By combining relevant statistical physical quantities, this method performs stability analysis on the processed data, providing intuitive and accurate stability results. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0044] Figure 1 The diagram illustrates the steps of a method for evaluating the stability of rescue well location results based on the Laida criterion in an exemplary embodiment of this disclosure.

[0045] Figure 2 This diagram illustrates a specific flowchart of the stability assessment method for rescue well location results based on the Laida criterion in an exemplary embodiment of this disclosure.

[0046] Figure 3 This illustrates the ranging results when the signal-to-noise ratio is 1 dB in an exemplary embodiment of this disclosure;

[0047] Figure 4 This illustrates the ranging results when the signal-to-noise ratio is 10 dB in an exemplary embodiment of this disclosure;

[0048] Figure 5 This illustrates the ranging results using the Laida criterion when the signal-to-noise ratio is 1 dB in an exemplary embodiment of this disclosure;

[0049] Figure 6 The distance measurement results using the Laida criterion are shown in an exemplary embodiment of this disclosure when the signal-to-noise ratio is 10 dB. Detailed Implementation

[0050] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary 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.

[0051] Furthermore, the accompanying drawings are merely illustrative diagrams of embodiments of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0052] This example implementation provides a method for evaluating the stability of rescue well location results based on the Laida criterion. (Reference) Figure 1 As shown, the stability assessment method for rescue well location results based on the Laida criterion may include steps S101 to S105.

[0053] Step S101: Determine the confidence probability based on the signal-to-noise ratio of the initial result data set;

[0054] Step S102: Calculate the standard error of the initial result data set using the accident well location algorithm;

[0055] Step S103: Determine the confidence interval based on the standard error and the confidence probability;

[0056] Step S104: Perform singular value removal on the initial result data group according to the confidence interval to obtain the target result data group;

[0057] Step S105: Perform stability analysis on the target result data set to obtain stability analysis results.

[0058] The aforementioned stability assessment method for rescue well location results based on the Laida criterion addresses two main issues. First, it addresses the issue of singular values ​​in the distance determination results obtained by the most optimized algorithm in accident well location algorithms using the improved Bessel model, which leads to low accuracy in data stability assessment. This is addressed by using the Laida criterion to remove singular values ​​from the distance determination results obtained by the optimized algorithm, and then performing a stability assessment on the remaining data set. This method ignores the inaccuracy caused by some inaccurate results due to the limitations of the optimization algorithm itself, thus accurately analyzing the stability of the relevant algorithm results. Second, this method identifies and removes erroneous optimal results to a certain extent, making the optimized results more reasonable. The method is compact, highly effective, and eliminates the need for secondary manual judgment of the results, improving the processing speed of some optimization algorithm results. Finally, by combining relevant statistical physical quantities, this method performs stability analysis on the processed data, providing intuitive and accurate stability results.

[0059] Below, we will refer to Figures 1 to 6 The steps of the stability assessment method for rescue well location results based on the Laida criterion described in this example embodiment will be explained in more detail.

[0060] In step S101, when using the improved "Bessel model" to optimize the algorithm for locating accident wells, the relevant optimization functions in the optimization toolbox are often used to find the optimal value of the objective function. However, using optimization functions may lead to getting stuck in local optima and failing to find the global optimum. Therefore, some outliers will appear in the optimization results, which will affect the accuracy and reliability of the optimization results and lead to inaccurate optimization results. Therefore, it is necessary to remove the outliers.

[0061] To improve the accuracy of optimization results, the "Layda Criterion" is proposed for data evaluation of optimization results. The calculation steps for processing and evaluating optimization results using the "Layda Criterion" are as follows:

[0062] When the optimization result data volume is large, the basic idea of ​​using the Laida criterion to identify gross errors is to use a given confidence probability. Using three times the standard error limit of the measurement series as the standard, any error exceeding this limit is considered not to fall under the category of random error, but rather a gross error. Measurement values ​​containing gross errors are called outliers, and outliers are unacceptable and should be removed from the measurement data.

[0063] In step S102, the relevant statistical physical quantities of the optimization result data set are calculated.

[0064] It is known that the optimal algorithm for locating accident wells using the "improved Bessel model" yields n data distance results (i.e., the initial result data set). When using the Laida criterion to judge and eliminate outliers containing gross errors, the independent measurement series of equal precision should be calculated first. The average value of the initial result data (i=1,2,…,n) and residual error The standard deviation S and standard error of the measurement series were calculated using Bessel's formula. .in:

[0065] (1)

[0066] (2)

[0067] (3)

[0068] (4)

[0069] In steps S103 and S104, the premise for analyzing the data stability results is to remove singular values ​​that occur because the optimization function cannot accurately find the global optimal solution.

[0070] Based on the relevant data obtained (i.e., the average value of the initial result data) Residual error Standard deviation S and standard error Singular value detection is performed based on the following criteria:

[0071] ,but This is a gross error and should be discarded;

[0072] ,but This is normal data and should be retained. (5)

[0073] Among them, standard error and confidence probability The following relationship must be satisfied:

[0074] (6)

[0075] In step S105, the stability of the results is analyzed.

[0076] The result data after removing outliers Variance Calculations were performed to evaluate the stability of the relevant algorithms.

[0077] variance The calculation formula is:

[0078]

[0079] The pseudocode for the stability assessment method of rescue well location results based on the Laida criterion is as follows:

[0080]

[0081] In one embodiment, this application performs singular value processing on the distance result data set obtained by the optimization algorithm of the "improved Bessel model" well location algorithm based on the Laida criterion, and performs stability analysis on the result data set after removing singular values. This ignores the problem of inaccurate results caused by some inaccurate results due to the limitations of the optimization algorithm itself, which leads to inaccurate stability of the overall result, and can accurately perform stability analysis on the relevant algorithm results.

[0082] In one embodiment, this application utilizes the Laida criterion, combined with statistical physical quantities, to perform optimization analysis based on the results obtained from the most optimized algorithm in the accident well location algorithm using the improved "Bessel model". The stability of the results obtained using the optimization algorithm is studied, thus solving the common problem of singular values ​​that easily occur when using the optimization algorithm.

[0083] In one embodiment, the stability assessment method for rescue well location results based on the Laida criterion proposed in this application can remove outliers from the results obtained by the optimization algorithm, effectively improving the stability of the optimization results.

[0084] The data stability analysis method proposed in this application is simple to implement and easy to operate. It can effectively reduce the impact of outliers on the stability of the results, thereby improving the stability analysis results of the data.

[0085] In a specific embodiment, such as Figure 2 As shown, this embodiment provides a method for evaluating the stability of rescue well location results based on the Laida criterion, including the following steps:

[0086] Determine the confidence probability;

[0087] Calculate the relevant statistics for the result data groups;

[0088] Anomaly detection is performed by combining confidence probability and relevant data statistics.

[0089] Stability analysis was performed on the dataset obtained after removing outliers.

[0090] In a specific embodiment, such as Figures 3 to 6 As shown, experimental comparison data between this application and existing optimization results are provided to verify the effectiveness.

[0091] Specifically, when using optimization algorithms to test distances in relevant scenarios, 100 sets of optimized results were obtained under different signal-to-noise ratios (SNRs) as actual distance measurements, which were then compared with the theoretical distance. The comparison results of the actual and theoretical distance measurements under different SNRs are as follows: Figure 3 and Figure 4 As shown.

[0092] When the signal-to-noise ratio is low, the fmincon function in the MATLAB optimization toolbox is used to solve the nonlinear problem of the ranging results. However, the fmincon function may get stuck in a local optimum and fail to find the global optimum. Therefore, some outliers will appear in the ranging results, which will affect the accuracy and reliability of the ranging results and make the ranging results under this algorithm inaccurate. Therefore, it is necessary to remove the outliers.

[0093] Since the number of measurements in this embodiment is large, a confidence probability of 99.7% is used as the standard. The confidence interval is determined according to equation (6), and outliers are removed based on the confidence interval. Figure 3 The signal-to-noise ratio is 1dB and Figure 4 The ranging results at a signal-to-noise ratio of 10dB, and the ranging results after removing outliers using the Laida criterion, are as follows: Figure 4 and Figure 5 As shown.

[0094] When the signal-to-noise ratio level is 1dB Figure 3 and Figure 4 The ranging results and the signal-to-noise ratio level of 10dB Figure 5 and Figure 6 A comparison of the ranging results shows that, at the same signal-to-noise ratio level, the ranging results after applying the Laida criterion to remove outliers are more stable.

[0095] Finally, a stability analysis was performed on the ranging results after outlier removal. The average value, percentage error, and variance of the ranging results before and after outlier removal using the Laida criterion were calculated when the signal-to-noise ratio varied from 1 dB to 10 dB. The calculation results are shown in Table 1.

[0096] The above provides a detailed description of the implementation examples of the method of this application and elaborates on the specific operation methods. Extensive experiments have demonstrated that using the Laida criterion results in smaller variance of the ranging results at the same signal-to-noise ratio, indicating that the ranging results after removing outliers are more stable. It should be noted that the data results obtained in this application are more effective at lower signal-to-noise ratios. For those skilled in the art, there will be various modifications in the specific implementation methods and application scope based on the steps of this application.

[0097] Table 1 Evaluation of ranging results under different signal-to-noise ratios

[0098]

[0099] The aforementioned stability assessment method for rescue well location results based on the Laida criterion addresses two main issues. First, it addresses the issue of singular values ​​in the distance determination results obtained by the most optimized algorithm in accident well location algorithms using the improved Bessel model, which leads to low accuracy in data stability assessment. This is addressed by using the Laida criterion to remove singular values ​​from the distance determination results obtained by the optimized algorithm, and then performing a stability assessment on the remaining data set. This method ignores the inaccuracy caused by some inaccurate results due to the limitations of the optimization algorithm itself, thus accurately analyzing the stability of the relevant algorithm results. Second, this method identifies and removes erroneous optimal results to a certain extent, making the optimized results more reasonable. The method is compact, highly effective, and eliminates the need for secondary manual judgment of the results, improving the processing speed of some optimization algorithm results. Finally, by combining relevant statistical physical quantities, this method performs stability analysis on the processed data, providing intuitive and accurate stability results.

[0100] It should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., in the above description indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this disclosure.

[0101] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0102] In the embodiments of this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0103] In embodiments of this disclosure, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0104] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0105] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for evaluating the stability of a rescue well positioning result based on the Raydist criterion, characterized in that, The method comprises: determining a confidence probability according to a signal-to-noise ratio of an initial result data set; calculating the initial result data set by using an accident well positioning algorithm to obtain a standard error; specifically comprising: calculating the initial result data set by using an improved Bessel model to obtain a mean value and a residual error of the initial result data set: the expression of the mean value is: the expression of the residual error is: based on the mean value and the residual error, obtaining a standard deviation and a standard error of the initial result data set by using a Bessel formula: the expression of the standard deviation is: the expression of the standard error is: Wherein, the initial result data group n is the n data distance results obtained by using the optimization algorithm in the blowout well positioning algorithm based on the improved Bezier model; is the average value of the initial result data, is the residual error, S is the standard deviation, is the standard error; determining a confidence interval according to the standard error and the confidence probability; performing singular value rejection on the initial result data set according to the confidence interval to obtain a target result data set; performing stability analysis on the target result data set to obtain a stability analysis result; specifically comprising: According to the target result data set after removing singular values , calculate the variance , according to the variance evaluate the stability of the correlation algorithm; the expression of the difference is: wherein m is the number of target result data in the target result data set, Y i is the first target result data, i is the nth target result data, is the average value of the target result data.

2. The method for evaluating the stability of the positioning result of the relief well according to the Raydist criterion according to claim 1, characterized in that, in the step of determining a confidence probability according to a signal-to-noise ratio of an initial result data set, comprising: determining the confidence probability based on the signal-to-noise ratio of the initial result data set in combination with a Relya criterion .

3. The method for evaluating the stability of the positioning result of the relief well according to the Raydist criterion according to claim 2, characterized in that, the expression of the confidence interval is: wherein the confidence probability .

4. The method for evaluating the stability of the positioning result of the relief well according to the Rajda criterion according to claim 3, characterized in that, in the step of performing singular value rejection on the initial result data set according to the confidence interval to obtain a target result data set, comprising: based on the confidence interval, performing singular value discrimination on each initial result data in the initial result data set, discarding gross errors and retaining normal data; traversing all the initial result data, obtaining the target result data set according to the reserved initial result data .

5. The method for evaluating the stability of the positioning result of the relief well according to the Raydist criterion according to claim 4, characterized in that, in the step of performing singular value discrimination on each initial result data in the initial result data set based on the confidence interval, discarding gross errors and retaining normal data, comprising: When the initial result data is a coarse error, it is discarded; When the initial result data is normal data, it is retained; wherein is Probability of occurrence.

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