Logging instrument data transmission method and system

Abstract

The invention relates to the technical field of data processing, and relates to a logging instrument data transmission method and system, and the method comprises the steps: collecting logging instrument data; calculating a logging data characteristic value of the logging instrument data at each acquisition moment; generating a logging data characteristic curve according to the change of the logging data characteristic value along with time, and dividing the logging data characteristic curve into a plurality of curve segments by taking the inflection point as a demarcation point; deviation parameters of inflection points and non-inflection points in the logging data feature points are obtained, and the similarity between the inflection points and the deviation parameters of the non-inflection points in the curve segments adjacent to the inflection points is obtained; and taking the similarity of each inflection point and the deviation degree of the non-inflection point as judgment parameters, clustering the judgment parameters so as to detect normal judgment parameters, and transmitting logging instrument data corresponding to the normal judgment parameters. By adopting the method provided by the 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 field of data processing technology, and more specifically, to a method and system for transmitting well logging instrument data. Background Art

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

[0003] The method for transmitting the data of the logging instrument in the prior art is usually: using an intermediate device to transmit the data collected by the logging instrument to the ground through a cable, wireless signal or other communication means. However, since the logging operation is usually carried out in a harsh environment, there may be various factors such as temperature, pressure, and electromagnetic interference, which may cause the data collected by the logging instrument to have noise, and then the accuracy of the logging instrument data transmitted to the ground is poor. 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 data transmission efficiency of the data collected by the logging instrument. For example, a data transmission method for a logging instrument disclosed in a Chinese invention patent with authorization announcement number CN114650082B transmits the data collected by the logging instrument to the ground through a downhole data transmission subsystem, a cable, and a ground data transmission subsystem. Due to the influence of various factors such as temperature, pressure, and electromagnetic interference in the downhole, the data collected by the logging instrument has noise, resulting in noisy data in the logging instrument data transmitted to the ground, and the accuracy is poor.

[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 the various physical parameters collected by various logging instruments and remove the noise in the various physical parameters. However, this method ignores the interaction and comprehensive effect between different physical parameters of the formation, and will mistakenly judge normal data as noise. Summary of the invention

[0005] In order to solve the technical problem that the logging instrument data transmitted by the prior art method has poor accuracy, 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, comprising: collecting logging instrument data, the logging instrument data comprising: resistivity, density data and temperature data; calculating the logging data characteristic value of the logging instrument data at each collection time, the calculation expression of the logging data characteristic value is: ; in, represents the characteristic value of the logging 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; 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; Obtain the deviation parameters of the inflection point and the non-inflection point in the characteristic points of the logging data, and obtain the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment; the deviation parameter of the inflection point refers to the degree of deviation from its adjacent characteristic points of the logging data, 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 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.

[0007] The beneficial effect is that the 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 logging instrument data, the method of the present invention first calculates the logging data characteristic value (i.e., comprehensive index) corresponding to the resistivity data, density data and temperature data, and analyzes whether the logging data is normal based on the logging data characteristic value (i.e., comprehensive analysis of the resistivity data, density data and temperature data), thereby improving the efficiency of logging data detection and the accuracy of the detection results, thereby improving the accuracy of the transmitted logging instrument data.

[0008] Since the logging data corresponding to each measuring point in the same geological layer are basically close, the slopes of each point on the logging data curve segment corresponding to the same geological layer should be close to 0. Therefore, for the logging data feature point that is not a turning point, when it deviates from the curve segment to which it belongs to a large extent, it means that the logging data feature point is likely to be noise data; since the logging data will change greatly in the transition layer, for the inflection point data, it is likely to be the logging data feature point corresponding to the transition layer, but the neighborhood range curve of the logging data feature point corresponding to the transition layer is relatively smooth (the deviation parameters of the logging data feature point and the logging data feature point in the adjacent curve segment are relatively close), so when the deviation parameter approximation of the inflection point and the logging data feature point in the adjacent curve segment is low, it means that the inflection point is likely to be noise data; therefore, using the deviation parameter of the non-inflection point as the judgment parameter for clustering and using the approximation of the inflection point as the corresponding judgment parameter for clustering can further improve the accuracy of the detection results.

[0009] When detecting well logging data, the method of the present invention takes into account the interaction and comprehensive effects among resistivity data, density data and temperature data, and takes into account the properties of the characteristic points of the well logging data corresponding to each stratum and the properties of the characteristic points of the well logging data corresponding to the transition layer, and uses a clustering algorithm to detect normal well logging data and transmit it, thereby improving the accuracy and transmission efficiency of the transmitted logging instrument data.

[0010] Preferably, for a curve segment whose overall slope is less than a 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, represents the deviation parameter of the characteristic point of the logging data at time i, represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, Represents the j-th logging data characteristic value on the curve segment to which the logging data characteristic point at time i belongs.

[0011] 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 the logging data feature point at time i is to deviate from the corresponding curve segment; in addition, for a curve segment whose overall slope is close to zero, the logging data characteristic value at time i is very close to the logging data characteristic value at other acquisition moments on the curve segment, so the deviation parameter of the logging data feature point at time i is measured by the sum of the deviations between the logging data characteristic value at time i and the logging data characteristic values ​​at other acquisition moments on the curve segment; when calculating the deviation parameter of the logging data feature point at time i, this embodiment comprehensively considers the deviation between the logging data characteristic value at time i and the logging data characteristic values ​​at other acquisition moments on the 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, thereby more accurately calculating the deviation parameter of the logging data feature point.

[0012] Preferably, for a curve segment whose overall slope is greater than a preset slope threshold, the calculation expression for the deviation parameter of the well logging data feature point that is not an inflection point is: ; In the formula, represents the deviation parameter of the characteristic point of the logging data at time i, represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, represents the characteristic value of the logging data at the last acquisition time of time i, Represents the characteristic value of the logging data at the next acquisition time of time i.

[0013] 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 the logging data feature point at time i is to deviate from the corresponding curve segment; in addition, for a curve segment with a larger overall slope, the logging data characteristic value at time i is only relatively close to the adjacent logging data characteristic value. Therefore, for a curve segment with a larger 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, thereby more accurately calculating the deviation parameter of the logging data feature point.

[0014] Preferably, the preset slope threshold is 0.1.

[0015] Preferably, the calculation expression of the deviation parameter of the inflection point is: ; In the formula, represents the deviation parameter of the kth inflection point, represents the characteristic value of the well logging data at the kth inflection point, The logging data characteristic value representing the next logging data characteristic point of the k-th inflection point, Indicates the logging data characteristic value of the previous logging data characteristic point of the k-th inflection point.

[0016] Its beneficial effect is: when calculating the deviation degree of the kth inflection point, first calculate the mean of the logging data characteristic values ​​of the two logging data characteristic points adjacent to the kth inflection point, measure the smoothness of the kth inflection point with the deviation between the logging data characteristic value of the kth inflection point and the mean, and use the smoothness of the kth inflection point as the degree of deviation of the kth inflection point from its adjacent logging data characteristic points, so that the deviation parameter of the inflection point can be calculated more accurately.

[0017] Preferably, the calculation expression for the similarity of the deviation parameter between the inflection point and the non-inflection point in the adjacent curve segment is: ; In the formula, represents the similarity corresponding to the k-th inflection point, represents the deviation parameter of the kth inflection point, 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.

[0018] Its beneficial effect is: when calculating the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment, this embodiment comprehensively considers the deviation between the deviation parameters of the inflection point and the deviation parameters of each logging data characteristic point in the adjacent curve segment, thereby more accurately calculating the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment.

[0019] Preferably, the DBSCAN algorithm is used when clustering the decision parameters.

[0020] The beneficial effects are: when using the DBSCAN algorithm for clustering, there is no need 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 clusters. Therefore, the DBSCAN algorithm can more accurately detect normal judgment parameters.

[0021] Preferably, 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.

[0022] Preferably, transmitting the logging data corresponding to the normal determination parameter includes: compressing and storing the logging data corresponding to the normal determination parameter according to the acquisition time, and transmitting the compressed logging data.

[0023] In a second aspect, the present invention provides a system for transmitting logging instrument data, including a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for transmitting logging instrument data of the present invention is implemented.

[0024] In summary, the beneficial effect of the present invention is that the method of the present invention can greatly improve the accuracy of the transmitted logging instrument data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 is a flow chart schematically showing a method for transmitting logging instrument data according to an embodiment of the present invention; Figure 2 FIG. 1 is a schematic diagram schematically showing the structure of a transmission system for logging instrument data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0028] Example of a method for transmitting logging instrument data: like Figure 1 As shown, the method for transmitting logging instrument data of the present invention comprises: S101, collecting logging data and calculating corresponding logging data characteristic values, specifically: collecting logging data, the logging data including resistivity, density data and temperature data; calculating the logging data characteristic values ​​of the logging data at each collection time, the calculation expression of the logging data characteristic value is: ; in, represents the characteristic value of the logging 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; The square root of the product of three data can reflect the interaction of the three data and better reflect the characteristics of the data.

[0029] In this embodiment, the collected logging data include resistivity, density data and temperature data, each of which reflects different physical properties of the formation, but analyzing each data separately may ignore the interaction and comprehensive effect between them. By creating a comprehensive index, these key parameters are integrated into a single comprehensive index to simplify the data interpretation and application process. The comprehensive index extracts the contribution of each parameter to the formation characteristics by weighting and nonlinear transformation, so that the index can more accurately reflect the overall characteristics of the formation.

[0030] S102, generating a characteristic curve of the well logging data according to the change of the characteristic value of the well logging data over time, and dividing the characteristic curve of the well logging data into multiple curve segments with the inflection point as a dividing point; The method for generating the logging data characteristic curve is: establishing a coordinate system with time as the horizontal axis and the logging data characteristic value as the vertical axis, generating logging data characteristic points in the coordinate system and then generating the logging data characteristic curve, the horizontal axis of the logging data characteristic point is the acquisition time, and the vertical axis is the corresponding logging data characteristic value.

[0031] 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; 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.

[0032] 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.

[0033] Since the logging data corresponding to each measuring point in the same geological layer are basically close, the slopes of each point on the logging data curve segment corresponding to the same geological layer should be close to 0. Therefore, for the logging data feature point that is not a turning point, when it deviates from the curve segment to which it belongs to a large extent, it means that the logging data feature point is likely to be noise data; since the logging data will change greatly in the transition layer, for the inflection point data, it is likely to be the logging data feature point corresponding to the transition layer, but the neighborhood range curve of the logging data feature point corresponding to the transition layer is relatively smooth (the deviation parameters of the logging data feature point and the logging data feature point in the adjacent curve segment are relatively close), so when the deviation parameter approximation of the inflection point and the logging data feature point in the adjacent curve segment is low, it means that the inflection point is likely to be noise data; therefore, using the deviation parameter of the non-inflection point as the judgment parameter for clustering and using the approximation of the inflection point as the corresponding judgment parameter for clustering can further improve the accuracy of the detection results. When detecting well logging data, the method of the present invention takes into account the interaction and comprehensive effects among resistivity data, density data and temperature data, and takes into account the properties of the characteristic points of the well logging data corresponding to each stratum and the properties of the characteristic points of the well logging data corresponding to the transition layer, and uses a clustering algorithm to detect normal well logging data and transmit it, thereby improving the accuracy and transmission efficiency of the transmitted logging instrument data.

[0034] In this embodiment, the inflection point refers to a coordinate point corresponding to a convex portion or a concave portion on the curve.

[0035] In one embodiment, for a curve segment whose overall slope is less than a preset slope threshold, the calculation expression of the deviation parameter of the well logging data feature point that is not an inflection point is: ; In the formula, represents the deviation parameter of the characteristic point of the logging data at time i, It represents the slope of the line connecting the characteristic point of the logging data at time i and the characteristic point of the logging data at the previous acquisition time. Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, It 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.

[0036] 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 is to deviate from the corresponding curve segment; in addition, for a curve segment with an overall slope close to zero, the logging data characteristic value at time i is very close to the logging data characteristic value at other acquisition moments on the curve segment, so the deviation parameter of the logging data feature point at time i is measured by the sum of the deviations between the logging data characteristic value at time i and the logging data characteristic values ​​at other acquisition moments on the curve segment; when calculating the deviation parameter of the logging data feature point at time i, this embodiment comprehensively considers the deviation between the logging data characteristic value at time i and the logging data characteristic values ​​at other acquisition moments on the 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, thereby more accurately calculating the deviation parameter of the logging data feature point.

[0037] The overall slope of a curve segment is the slope of the line connecting the beginning and end of the curve segment.

[0038] In one embodiment, for a curve segment whose overall slope is greater than a preset slope threshold, the calculation expression for the deviation parameter of the well logging data feature point that is not an inflection point is: ; In the formula, represents the deviation parameter of the characteristic point of the logging data at time i, represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, represents the characteristic value of the logging data at the last acquisition time of time i, Represents the characteristic value of the logging data at the next acquisition time of time i.

[0039] 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 will deviate from the corresponding curve segment; in addition, for a curve segment with a larger overall slope, the logging data characteristic value at time i is only relatively close to the adjacent logging data characteristic value. Therefore, for a curve segment with a larger 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 more accurately calculate the deviation parameter of the logging data feature point.

[0040] In one embodiment, the preset slope threshold is 0.1. In other embodiments, the preset slope threshold may also be other suitable values.

[0041] In one embodiment, the calculation expression of the deviation parameter of the inflection point is: ; In the formula, represents the deviation parameter of the kth inflection point, represents the characteristic value of the well logging data at the kth inflection point, The logging data characteristic value representing the next logging data characteristic point of the k-th inflection point, Indicates the logging data characteristic value of the previous logging data characteristic point of the k-th inflection point.

[0042] When calculating the degree of deviation of the kth inflection point, first calculate the mean of the logging data characteristic values ​​of the two logging data characteristic points adjacent to the kth inflection point, and measure the smoothness of the kth inflection point with the deviation between the logging data characteristic value of the kth inflection point and the mean, and use the smoothness of the kth inflection point as the degree of deviation of the kth inflection point from its adjacent logging data characteristic points, so that the deviation parameter of the inflection point can be calculated more accurately.

[0043] In one embodiment, the calculation expression of the similarity of the deviation parameter between the inflection point and the non-inflection point in the adjacent curve segment is: ; In the formula, represents the similarity corresponding to the k-th inflection point, represents the deviation parameter of the kth inflection point, 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.

[0044] When calculating the similarity of the deviation parameters of the inflection point and the non-inflection points in the adjacent curve segment, this embodiment comprehensively considers the deviation between the deviation parameters of the inflection point and the deviation parameters of each logging data characteristic point in the adjacent curve segment, thereby more accurately calculating the similarity of the deviation parameters of the inflection point and the non-inflection points in the adjacent curve segment.

[0045] In one embodiment, the DBSCAN algorithm is used to cluster the decision parameters.

[0046] The DBSCAN algorithm is a clustering algorithm based on density space. It is widely used in the fields of machine learning and data mining. 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 results of the DBSCAN algorithm include core points, boundary points, and noise points. When using the DBSCAN algorithm for clustering, there is no need to specify the number of clusters. The number of clusters can be automatically determined based on the data density; it can handle clusters of any shape and can effectively remove noise data from the clustering cluster. Therefore, the DBSCAN algorithm can more accurately detect normal judgment parameters.

[0047] In one embodiment, 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 (ie, outliers) as abnormal determination parameters.

[0048] In one embodiment, transmitting the logging data corresponding to the normal determination parameter includes: compressing and storing the logging data corresponding to the normal determination parameter according to the acquisition time, and transmitting the compressed logging data.

[0049] By compressing the logging data corresponding to the normal judgment parameters, the transmission efficiency of the logging data can be greatly improved.

[0050] In one embodiment, the method further includes: keeping the logging instrument data corresponding to the abnormal determination parameter unchanged and transmitting the data.

[0051] The logging instrument data corresponding to the abnormal judgment parameters are transmitted to facilitate subsequent data analysis and other work.

[0052] Example of a transmission system for logging instrument data: The present invention also provides a transmission system for logging instrument data. Figure 2As shown, the transmission system of the logging instrument data includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for transmitting the logging instrument data described in the above embodiment is implemented.

[0053] The transmission system of the logging instrument data also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0054] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0055] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0056] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

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: ； in, represents the characteristic value of the logging 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; 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; Obtain the deviation parameters of the inflection point and the non-inflection point in the characteristic points of the logging data, and obtain the similarity of the deviation parameters of the inflection point and the non-inflection point in the adjacent curve segment; the deviation parameter of the inflection point refers to the degree of deviation from its adjacent characteristic points of the logging data, 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 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.

2. The method for transmitting logging instrument data according to claim 1, characterized in that: 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, represents the deviation parameter of the characteristic point of the logging data at time i, represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, Represents the j-th logging data characteristic value on the curve segment to which the logging data characteristic point at time i belongs.

3. The method for transmitting logging instrument data according to claim 2, 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: ； In the formula, represents the deviation parameter of the characteristic point of the logging data at time i, represents the deviation parameter of the characteristic point of the logging data at the previous acquisition time i, Represents the overall slope of the curve segment to which the characteristic point of the logging data at time i belongs; represents the characteristic value of the logging data at time i, represents the characteristic value of the logging data at the last acquisition time of time i, Represents the characteristic value of the logging data at the next acquisition time of time i.

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

1.

5. 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, represents the deviation parameter of the kth inflection point, represents the characteristic value of the well logging data at the kth inflection point, The logging data characteristic value representing the next logging data characteristic point of the k-th inflection point, Indicates the logging data characteristic value of the previous logging data characteristic point of the k-th inflection point.

6. The method for transmitting logging instrument data according to claim 1, characterized in that: The calculation expression of the similarity of the deviation parameter between the inflection point and the non-inflection point in the adjacent curve segment is: ； In the formula, represents the similarity corresponding to the k-th inflection point, represents the deviation parameter of the kth inflection point, 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.

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

8. 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.

9. The method for transmitting logging instrument data according to any one of claims 1 to 8, 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.

10. 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 as described in any one of claims 1 to 9 is implemented.

Images