A method and system for transmitting logging instrument data

Abstract

The present invention relates to the technical field of data processing. The present invention relates to a method and system for transmitting logging instrument data, and the method therein includes: collecting logging instrument data; calculating logging data characteristic values of the logging instrument data at each acquisition moment; generating a logging data characteristic curve according to the change of the logging data characteristic values over time, and dividing the logging data characteristic curve into multiple curve segments with inflection points as demarcation points; obtaining deviation parameters of inflection points and non-inflection points among the logging data characteristic points, and obtaining the similarity of the deviation parameters of the inflection points and non-inflection points within the adjacent curve segments thereof; using the similarities of the respective inflection points and the deviation degrees of the non-inflection points as determination parameters, clustering the determination parameters to detect normal determination parameters, and transmitting the logging instrument data corresponding to the normal determination parameters. By using the method of the present invention, the accuracy and transmission efficiency of the transmitted logging instrument data can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for transmitting logging instrument data. Background Art

[0002] In the field of coalfield exploration, it is usually necessary to use logging instruments to collect physical parameters of geological strata such as rock strata and coal seams deep underground and transmit them to the ground. The staff on the ground analyze the collected physical parameters of the geological strata to understand the distribution of coal seams and coal reserves, providing an important basis for the development of coal resources.

[0003] In the prior art, the method for transmitting logging instrument data is usually: using an intermediate device to transmit the data collected by the logging instrument to the ground through cables, wireless signals or other communication means. However, since logging operations are usually carried out in harsh environments, various factors such as temperature, pressure, and electromagnetic interference may have an impact, resulting in noise in the data collected by the logging instrument, and further resulting in poor accuracy of the logging instrument data transmitted to the ground. In addition, since the data collected by the logging instrument needs to be transferred to the ground through an intermediate device, this process may take several hours or even longer, resulting in poor transmission efficiency of the data collected by the logging instrument. For example, in a data transmission method of a logging instrument disclosed in a Chinese invention patent with the authorization announcement number CN114650082B, the data collected by the logging instrument is transmitted to the ground through an underground data transmission subsystem, a cable, and a ground data transmission subsystem. Due to various factors such as temperature, pressure, and electromagnetic interference in the underground, there is noise in the data collected by the logging instrument, resulting in noise data in the logging instrument data transmitted to the ground and poor accuracy.

[0004] In order to ensure the quality of the transmitted logging instrument data, it is usually necessary to filter and denoise the logging instrument data. The usual method for filtering and denoising the logging instrument data is to analyze various physical parameters collected by various logging instruments respectively and remove the noise in each physical parameter respectively. However, this method ignores the interaction and comprehensive effect between different physical parameters of the formation and may misjudge normal data as noise. Summary of the Invention

[0005] To solve the technical problem of poor accuracy of the logging instrument data transmitted by the method in the prior art, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for transmitting logging instrument data, including: collecting logging instrument data, where the logging instrument data includes: resistivity, density data, and temperature data; calculating the logging data characteristic values of the logging instrument data at each collection moment, and the calculation expression of the logging data characteristic values is:

[0007] ;

[0008] wherein, represents the logging data characteristic value of the data at time i, represents the resistivity data value at time i, represents the density data value at time i, represents the temperature data value at time i;

[0009] Generate a logging data characteristic curve according to the change of the logging data characteristic value with time, and divide the logging data characteristic curve into multiple curve segments with the inflection point as the demarcation point;

[0010] Obtain the deviation parameters of the inflection points and non-inflection points among the logging data characteristic points, and obtain the similarity of the deviation parameters of the inflection points and non-inflection points within the adjacent curve segments; the deviation parameter of the inflection point refers to the degree of deviation from its adjacent logging data characteristic points, and the deviation parameter of the non-inflection point refers to the degree of deviation of the non-inflection point from the curve segment it belongs to;

[0011] Take the similarity of each inflection point and the deviation degree of the non-inflection point as the determination parameters, and perform clustering on the determination parameters to detect the normal determination parameters, and transmit the logging tool data corresponding to the normal determination parameters.

[0012] The beneficial effects are as follows: Logging data usually includes resistivity data, density data and temperature data. For the noise data in resistivity data, density data and temperature data, the existing denoising methods analyze the resistivity data, density data and temperature data respectively, and remove the noise in the resistivity data, density data and temperature data respectively. However, this method ignores the interaction and comprehensive effect between the resistivity, density and temperature of the formation, and will misjudge normal data as noise. The method of the present invention, after collecting the logging tool data, first calculates the logging data characteristic values (i.e., comprehensive indexes) corresponding to the resistivity data, density data and temperature data, and analyzes whether the logging data is normal based on the logging data characteristic values (i.e., comprehensively analyzes the resistivity data, density data and temperature data), thereby improving the efficiency and accuracy of the detection of logging data, and further improving the accuracy of the transmitted logging tool data.

[0013] Since the logging data corresponding to each measurement point in the same geological layer are basically close, the slopes of all points on the curve segment of the logging data corresponding to the same geological layer should all be close to 0. Therefore, for the logging data characteristic points that are not inflection points, when their degree of deviation from the curve segment to which they belong is large, it indicates that the logging data characteristic points are very likely to be noise data; since in the transition layer, the logging data will change greatly, for the inflection point data, it is very likely to be the logging data characteristic point corresponding to the transition layer. However, the curve of the neighborhood range of the logging data characteristic point corresponding to the transition layer is relatively smooth (the deviation parameters between the logging data characteristic point and the logging data characteristic points in the adjacent curve segments are relatively close). Therefore, when the approximation degree of the inflection point and the logging data characteristic points in the adjacent curve segments is low, it indicates that the inflection point is very likely to be noise data; therefore, using the deviation parameter of the non-inflection point as the determination parameter for clustering and the approximation degree of the inflection point as the corresponding determination parameter for clustering can further improve the accuracy of the detection result.

[0014] When the method of the present invention detects logging data, it takes into account the interaction and comprehensive effect between resistivity data, density data and temperature data, and takes into account the nature of the logging data characteristic points corresponding to each stratum and the nature of the logging data characteristic points corresponding to the transition layer, and uses the clustering algorithm to detect normal logging data and transmit it, thereby improving the accuracy and transmission efficiency of the transmitted logging instrument data.

[0015] Preferably, for the curve segment with an overall slope less than the preset slope threshold, the calculation expression of the deviation parameter of the logging data characteristic point that is not an inflection point on it is:

[0016] ;

[0017] In the formula, represents the deviation parameter of the logging data characteristic point at time i, represents the deviation parameter of the logging data characteristic point at the previous acquisition time of time i, represents the overall slope of the curve segment to which the logging data characteristic point at time i belongs; represents the logging data characteristic value at time i, represents the jth logging data characteristic value on the curve segment to which the logging data characteristic point at time i belongs.

[0018] The beneficial effects are as follows: The greater the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve, the more likely it is that the logging data feature point at time i deviates from the corresponding curve segment. In addition, for a curve segment with an overall slope close to zero, the logging data feature value at time i is very close to the logging data feature values at other acquisition times on this curve segment. Therefore, the sum of the deviations between the logging data feature value at time i and the logging data feature values at each other acquisition time on this curve segment is used to measure the deviation parameter of the logging data feature point at time i. In this embodiment, when calculating the deviation parameter of the logging data feature point at time i, the deviation between the logging data feature value at time i and the logging data feature values at each other acquisition time on this curve segment, as well as the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve, are comprehensively considered, so as to calculate the deviation parameter of the logging data feature point more accurately.

[0019] Preferably, for a curve segment with an overall slope greater than the preset slope threshold, the calculation expression for the deviation parameter of the logging data feature point that is not an inflection point on it is:

[0020] ;

[0021] In the formula, represents the deviation parameter of the logging data feature point at time i, represents the deviation parameter of the logging data feature point at the previous acquisition time of time i, represents the overall slope of the curve segment to which the logging data feature point at time i belongs; represents the logging data feature value at time i, represents the logging data feature value at the previous acquisition time of time i, represents the logging data feature value at the next acquisition time of time i.

[0022] The beneficial effects are as follows: The greater the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve, the more likely it is that the logging data feature point at time i deviates from the corresponding curve segment. In addition, for a curve segment with a relatively large overall slope, the logging data feature value at time i is only relatively close to its adjacent logging data feature values. Therefore, for a curve segment with a relatively large overall slope, when calculating the deviation parameter of the logging data feature point at time i, only the deviation between the logging data feature point at time i and its previous logging data feature point, the deviation between the logging data feature point at time i and its next logging data feature point, and the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve are considered, so as to calculate the deviation parameter of the logging data feature point more accurately.

[0023] Preferably, the preset slope threshold is 0.1.

[0024] Preferably, the calculation expression of the deviation parameter of the inflection point is:

[0025] ;

[0026] In the formula, represents the deviation parameter of the k-th inflection point, represents the eigenvalue of the logging data of the k-th inflection point, represents the eigenvalue of the logging data of the next logging data feature point of the k-th inflection point, represents the eigenvalue of the logging data of the previous logging data feature point of the k-th inflection point.

[0027] Its beneficial effect is that when calculating the deviation degree of the k-th inflection point, first calculate the mean value of the eigenvalues of the logging data of the two logging data feature points adjacent to the k-th inflection point, and use the deviation between the eigenvalue of the logging data of the k-th inflection point and this mean value to measure the smoothness of the k-th inflection point, and use the smoothness of the k-th inflection point as the degree of deviation of the k-th inflection point from its adjacent logging data feature points, so that the deviation parameter of the inflection point can be calculated more accurately.

[0028] Preferably, the calculation expression of the similarity of the deviation parameter between the inflection point and the non-inflection points in the adjacent curve segment is:

[0029] ;

[0030] In the formula, represents the similarity corresponding to the k-th inflection point, represents the deviation parameter of the k-th inflection point, represents the deviation parameter of the w-th logging data feature point in the curve segment adjacent to the k-th inflection point, and m represents the total number of logging data feature points in the curve segment adjacent to the k-th inflection point.

[0031] Its beneficial effect is that in this embodiment, when calculating the similarity of the deviation parameter between the inflection point and the non-inflection points in the adjacent curve segment, the deviation between the deviation parameter of this inflection point and the deviation parameters of each logging data feature point in the adjacent curve segment is comprehensively considered, so that the similarity of the deviation parameter between the inflection point and the non-inflection points in the adjacent curve segment can be calculated more accurately.

[0032] Preferably, the DBSCAN algorithm is used for clustering the determination parameters.

[0033] The beneficial effects are as follows: When using the DBSCAN algorithm for clustering, it is not necessary to specify the number of clusters, and the number of clusters can be automatically determined according to the data density; it can handle clusters of any shape and can effectively remove noise data from the clustering clusters. Therefore, the DBSCAN algorithm can detect normal determination parameters more accurately.

[0034] Preferably, clustering the determination parameters to detect normal determination parameters includes: determining the determination parameters within the clustering clusters in the clustering result as normal determination parameters, and determining the determination parameters that do not belong to the clustering clusters as abnormal determination parameters.

[0035] Preferably, transmitting the logging tool data corresponding to the normal determination parameters includes: storing the logging tool data corresponding to the normal determination parameters in a mean compression manner according to the acquisition time, and transmitting the compressed logging tool data.

[0036] In a second aspect, the present invention provides a transmission system for logging tool data, including a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the transmission method of the logging tool data of the present invention is implemented.

[0037] In summary, the beneficial effect of the present invention is that: using the method of the present invention can greatly improve the accuracy of the transmitted logging tool data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By referring to the accompanying drawings and reading the following detailed description, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become easily understandable. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0039] Figure 1 is a flowchart schematically showing a method for transmitting logging tool data according to an embodiment of the present invention;

[0040] Figure 2 is a schematic structural diagram showing a transmission system for logging tool data according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0043] Embodiment of the method for transmitting well logging instrument data:

[0044] As Figure 1 shown, the method for transmitting well logging instrument data of the present invention includes:

[0045] S101. Collect well logging instrument data and calculate the corresponding well logging data characteristic values. Specifically: collect well logging instrument data, where the well logging instrument data includes resistivity, density data, and temperature data; calculate the well logging data characteristic values of the well logging instrument data at each acquisition moment, and the calculation expression of the well logging data characteristic values is:

[0046] ;

[0047] Wherein, represents the well logging data characteristic value of the data at time i, represents the resistivity data value at time i, represents the density data value at time i, represents the temperature data value at time i;

[0048] represents the square root of the product of the three data, which can reflect the interaction of the three data and better reflect the characteristics of the data.

[0049] In this embodiment, the well logging instrument data collected includes resistivity, density data, and temperature data. These data respectively reflect different physical properties of the formation, but analyzing each data separately may ignore their interaction and comprehensive effect. By creating a comprehensive index, these key parameters are integrated into a single comprehensive index, simplifying the process of data interpretation and application. The comprehensive index extracts the contribution degree of each parameter to the formation characteristics through weighted and non-linear transformation methods, so that the index can more accurately reflect the overall characteristics of the formation.

[0050] S102. Generate a well logging data characteristic curve according to the change of the well logging data characteristic value over time, and divide the well logging data characteristic curve into multiple curve segments with the inflection point as the demarcation point;

[0051] The method for generating the well logging data characteristic curve is: establish a coordinate system with time as the horizontal axis and the well logging data characteristic value as the vertical axis, generate well logging data characteristic points in the coordinate system, and then generate the well logging data characteristic curve. The abscissa of the well logging data characteristic point is the acquisition moment, and the ordinate is the corresponding well logging data characteristic value.

[0052] S103, obtaining deviation parameters of non-inflection points, and similarity of deviation parameters of inflection points and non-inflection points in adjacent curve segments, specifically: obtaining deviation parameters of inflection points and non-inflection points in characteristic points of well logging data, and obtaining similarity of deviation parameters of inflection points and non-inflection points in adjacent curve segments; the deviation parameter of an inflection point refers to the degree of deviation from adjacent characteristic points of well logging data, and the deviation parameter of a non-inflection point refers to the degree of deviation of the non-inflection point from the curve segment to which it belongs;

[0053] S104, obtaining judgment parameters, detecting normal judgment parameters, and transmitting logging instrument data corresponding to normal judgment parameters, specifically: taking the similarity of each inflection point and the degree of deviation of non-inflection points as judgment parameters, clustering the judgment parameters to detect normal judgment parameters, and transmitting logging instrument data corresponding to normal judgment parameters.

[0054] Well logging data usually includes resistivity data, density data and temperature data. For the noise data in the resistivity data, density data and temperature data, the existing denoising method is to analyze the resistivity data, density data and temperature data respectively, and remove the noise in the resistivity data, density data and temperature data respectively. However, this method ignores the interaction and comprehensive effect between the resistivity, density and temperature of the formation, and will mistakenly judge normal data as noise. After collecting the well logging instrument data, the method of the present invention first calculates the well logging data characteristic value (i.e., comprehensive index) corresponding to the resistivity data, density data and temperature data, and analyzes whether the well logging data is normal based on the well logging data characteristic value (i.e., comprehensively analyzes the resistivity data, density data and temperature data), thereby improving the efficiency of well logging data detection and the accuracy of the detection results, thereby improving the accuracy of the transmitted well logging instrument data.

[0055] Since the logging data corresponding to each measurement point in the same geological layer are basically close, therefore, the slopes of all points on the logging data curve segment corresponding to the same geological layer should all be close to 0. Therefore, for the logging data characteristic points that are not inflection points, when the degree of deviation from the curve segment to which they belong is large, it indicates that the logging data characteristic points are very likely to be noise data; since in the transition layer, the logging data will change greatly, therefore, for the inflection point data, it is very likely to be the logging data characteristic point corresponding to the transition layer. However, the curve of the neighborhood range of the logging data characteristic point corresponding to the transition layer is relatively smooth (the deviation parameters between the logging data characteristic point and the logging data characteristic points in the adjacent curve segments are relatively close). Therefore, when the approximation degree of the inflection point and the logging data characteristic points in the adjacent curve segments is low, it indicates that the inflection point is very likely to be noise data; therefore, using the deviation parameter of the non-inflection point as the determination parameter during clustering and the approximation degree of the inflection point as the corresponding determination parameter during clustering can further improve the accuracy of the detection result. When the method of the present invention detects logging data, it takes into account the interaction and comprehensive effect among resistivity data, density data, and temperature data, and takes into account the properties of the logging data characteristic points corresponding to each formation and the properties of the logging data characteristic points corresponding to the transition layer, and uses the clustering algorithm to detect normal logging data and transmit it, thereby improving the accuracy and transmission efficiency of the transmitted logging tool data.

[0056] In this embodiment, the inflection point refers to the coordinate point corresponding to the convex part or concave part on the curve.

[0057] In one embodiment, for a curve segment with an overall slope less than the preset slope threshold, the calculation expression for the deviation parameter of the logging data characteristic points that are not inflection points on it is:

[0058] ;

[0059] In the formula, represents the deviation parameter of the logging data characteristic point at time i, represents the slope of the line connecting the logging data characteristic point at time i and the logging data characteristic point at the previous acquisition time, represents the overall slope of the curve segment to which the logging data characteristic point at time i belongs; represents the logging data characteristic value at time i, represents the jth logging data characteristic value on the curve segment to which the logging data characteristic point at time i belongs. The overall slope can reflect the overall performance of the geological layer corresponding to the curve segment.

[0060] The greater the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve, the more likely the logging data feature point at time i deviates from the corresponding curve segment. In addition, for a curve segment with an overall slope close to zero, the logging data feature value at time i is very close to the logging data feature values at other acquisition times on this curve segment. Therefore, the sum of the deviations between the logging data feature value at time i and the logging data feature values at each other acquisition time on this curve segment is used to measure the deviation parameter of the logging data feature point at time i. In this embodiment, when calculating the deviation parameter of the logging data feature point at time i, the deviation between the logging data feature value at time i and the logging data feature values at each other acquisition time on this curve segment, as well as the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature points and the overall slope of the curve, are comprehensively considered, so as to calculate the deviation parameter of the logging data feature point more accurately.

[0061] The overall slope of a curve segment refers to the slope of the line connecting the start point and the end point of the curve segment.

[0062] In one embodiment, for a curve segment with an overall slope greater than a preset slope threshold, the calculation expression for the deviation parameter of the logging data feature point that is not an inflection point on it is:

[0063] ;

[0064] In the formula, represents the deviation parameter of the logging data feature point at time i, represents the deviation parameter of the logging data feature point at the previous acquisition time of time i, represents the overall slope of the curve segment to which the logging data feature point at time i belongs; represents the logging data feature value at time i, represents the logging data feature value at the previous acquisition time of time i, represents the logging data feature value at the next acquisition time of time i.

[0065] The greater the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature point and the overall slope of the curve, the more likely the logging data feature point at time i is to deviate from the corresponding curve segment. In addition, for a curve segment with a relatively large overall slope, the logging data feature value at time i is only relatively close to its adjacent logging data feature values. Therefore, for a curve segment with a relatively large overall slope, when calculating the deviation parameter of the logging data feature point at time i, only consider the deviation between the logging data feature point at time i and its previous logging data feature point, the deviation between the logging data feature point at time i and its next logging data feature point, and the deviation between the slope of the line connecting the logging data feature point at time i and its adjacent logging data feature point and the overall slope of the curve, so as to calculate the deviation parameter of the logging data feature point more accurately.

[0066] In one embodiment, the preset slope threshold is 0.1. In other embodiments, the preset slope threshold can also be other appropriate values.

[0067] In one embodiment, the calculation expression for the deviation parameter of the inflection point is:

[0068] ;

[0069] In the formula, represents the deviation parameter of the k-th inflection point, represents the logging data feature value of the k-th inflection point, represents the logging data feature value of the next logging data feature point of the k-th inflection point, represents the logging data feature value of the previous logging data feature point of the k-th inflection point.

[0070] When calculating the deviation degree of the k-th inflection point, first calculate the average value of the logging data feature values of the two adjacent logging data feature points of the k-th inflection point, and use the deviation between the logging data feature value of the k-th inflection point and this average value to measure the smoothness of the k-th inflection point, and take the smoothness of the k-th inflection point as the degree of deviation of the k-th inflection point from its adjacent logging data feature points, so as to calculate the deviation parameter of the inflection point more accurately.

[0071] In one embodiment, the calculation expression for the similarity of the deviation parameter between the inflection point and the non-inflection points within the adjacent curve segment is:

[0072] ;

[0073] In the formula, represents the similarity corresponding to the k-th inflection point, represents the deviation parameter of the k-th inflection point, The deviation parameter of the w-th logging data feature point in the curve segment adjacent to the k-th inflection point is represented, and m represents the total number of logging data feature points in the curve segment adjacent to the k-th inflection point.

[0074] In this embodiment, when calculating the similarity of the deviation parameter between the inflection point and the non-inflection points in the adjacent curve segment, the deviation between the deviation parameter of the inflection point and the deviation parameters of each logging data feature point in the adjacent curve segment is comprehensively considered, so as to calculate the similarity of the deviation parameter between the inflection point and the non-inflection points in the adjacent curve segment more accurately.

[0075] In one embodiment, the DBSCAN algorithm is used for clustering the determination parameters.

[0076] The DBSCAN algorithm is a density-based spatial clustering algorithm, which has been widely used in the fields of machine learning and data mining. Generally speaking, its clustering principle is that the density of each cluster is higher than the density around the cluster, and the density of noise is lower than the density of any cluster. The data points in the clustering result of the DBSCAN algorithm include core points, border points and noise points. When using the DBSCAN algorithm for clustering, it is not necessary to specify the number of clusters, and the number of clusters can be automatically determined according to the data density; it can handle clusters of any shape and can effectively remove noise data from the clustering clusters. Therefore, the DBSCAN algorithm can detect normal determination parameters more accurately.

[0077] In one embodiment, clustering the determination parameters to detect normal determination parameters includes: determining the determination parameters within the clustering cluster in the clustering result as normal determination parameters, and determining the determination parameters that do not belong to the clustering cluster (i.e., outliers) as abnormal determination parameters.

[0078] In one embodiment, transmitting the logging tool data corresponding to the normal determination parameters includes: storing the logging tool data corresponding to the normal determination parameters in a mean compression manner according to the acquisition time, and transmitting the compressed logging tool data.

[0079] By compressing the logging tool data corresponding to the normal determination parameters, the transmission efficiency of the logging tool data can be greatly improved.

[0080] In one embodiment, it further includes: keeping the logging tool data corresponding to the abnormal determination parameters unchanged and transmitting it.

[0081] By transmitting the logging tool data corresponding to the abnormal determination parameters, it is convenient for subsequent data analysis and other work.

[0082] Embodiment of the transmission system of logging tool data:

[0083] The present invention also provides a transmission system for logging tool data. As Figure 2As shown, the transmission system of the logging instrument data includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for transmitting logging instrument data described in the above embodiments is implemented.

[0084] The transmission system of the logging instrument data further includes a communication bus, a communication interface, and other components well known to those skilled in the art. Their settings and functions are known in the art, so they will not be elaborated here.

[0085] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or apparatus. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions stored or otherwise held by such a computer-readable medium.

[0086] In the description of this specification, the meanings of "a plurality" and "several" are at least two, such as two, three, or more, unless otherwise clearly and specifically defined.

[0087] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for transmitting logging instrument data, characterized in that: include: Collecting logging data, the logging data includes resistivity, density data and temperature data; calculating the logging data characteristic value of the logging data at each acquisition time, the calculation expression of the logging data characteristic value is: Among them, F i represents the characteristic value of the logging data at time i, K i Represents the resistivity data value at time i, Q i represents the density data value at time i, R i Represents the temperature data value at time i; Generate a characteristic curve of the well logging data according to the change of the characteristic value of the well logging data over time, and divide the characteristic curve of the well logging data into multiple curve segments with the inflection point as the dividing point; The deviation parameters of the inflection point and the non-inflection point in the characteristic points of the well logging data are obtained, and the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment is obtained; the deviation parameter of the inflection point refers to the degree of deviation from its adjacent well logging data characteristic point, and the deviation parameter of the non-inflection point refers to the degree of deviation of the non-inflection point from the curve segment to which it belongs; the calculation expression of the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment is: Where, T k represents the similarity corresponding to the kth inflection point, C k represents the deviation parameter of the kth inflection point, C kw represents the deviation parameter of the wth logging data characteristic point in the curve segment adjacent to the kth inflection point, and m represents the total number of logging data characteristic points in the curve segment adjacent to the kth inflection point; The similarity of each inflection point and the degree of deviation of the non-inflection point are used as determination parameters, and the determination parameters are clustered to detect normal determination parameters, and the logging instrument data corresponding to the normal determination parameters are transmitted; For a curve segment whose overall slope is less than the preset slope threshold, the calculation expression of the deviation parameter of the logging data feature point that is not an inflection point is: In the formula, C i A represents the deviation parameter of the characteristic point of the logging data at time i, i represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, B i Indicates the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; F i represents the characteristic value of the logging data at time i, F ij Represents the j-th logging data characteristic value on the curve segment to which the logging data characteristic point at time i belongs.

2. The method for transmitting logging instrument data according to claim 1, characterized in that: For the curve segment whose overall slope is greater than the preset slope threshold, the calculation expression of the deviation parameter of the logging data feature point that is not an inflection point is: C i =|A i -B i |×|(F i -F i-1 )+(F i -F i+1 )|; In the formula, C i A represents the deviation parameter of the characteristic point of the logging data at time i, i represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, B i Indicates the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; F i represents the characteristic value of the logging data at time i, F i-1 represents the characteristic value of the logging data at the last acquisition time of time i, F i+1 Represents the characteristic value of the logging data at the next acquisition time of time i.

3. The method for transmitting logging instrument data according to claim 2, characterized in that: The preset slope threshold is 0.

1.

4. The method for transmitting logging instrument data according to claim 1, characterized in that: The calculation expression of the deviation parameter of the inflection point is: In the formula, C k represents the deviation parameter of the kth inflection point, F k represents the characteristic value of the well logging data at the kth inflection point, H k+1 The logging data characteristic value of the next logging data characteristic point of the k-th inflection point, H k-1 Indicates the logging data characteristic value of the previous logging data characteristic point of the k-th inflection point.

5. The method for transmitting logging instrument data according to claim 1, characterized in that: The DBSCAN algorithm is used to cluster the decision parameters.

6. The method for transmitting logging instrument data according to claim 1, characterized in that: Clustering the determination parameters to detect normal determination parameters includes: determining the determination parameters within the cluster in the clustering result as normal determination parameters, and determining the determination parameters that do not belong to the cluster as abnormal determination parameters.

7. The method for transmitting logging instrument data according to any one of claims 1 to 6, characterized in that: Transmitting the logging instrument data corresponding to the normal determination parameters includes: compressing and storing the logging instrument data corresponding to the normal determination parameters according to the acquisition time, and transmitting the compressed logging instrument data.

8. A transmission system for logging instrument data, comprising a memory and a processor, wherein the memory stores computer program instructions, characterized in that: When the computer program instructions are executed by the processor, the method for transmitting logging instrument data according to any one of claims 1 to 7 is implemented.

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