A downhole mass high-reliability data storage method

Abstract

The application discloses a kind of high capacity high reliability data storage methods in well, it is related to data processing technical field.The method includes: obtaining logging instrument information in logging process;Logging instrument information includes the first logging instrument information of speed evaluation dimension and the second logging instrument information of stability evaluation dimension;Determine the logging speed weight of current time based on the first logging instrument information;Determine the borehole stability coefficient of current time based on the second logging instrument information;Determine the current time of well downlink path importance based on logging speed weight, borehole stability coefficient and the first logging deviation angle;Based on well downlink path importance, the compression window size of current time is dynamically adjusted, to make based on compression window size to the logging data in logging process is compressed storage.The application can significantly improve the reliability of data compression storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a downhole large-capacity high-reliability data storage method. BACKGROUND

[0002] In the process of horizontal well oil and gas exploration, logging instruments are often used to carry out logging work on horizontal wells. The storage module of the logging instrument transmits and stores the multi-source data collected downhole to the ground end. Researchers can make adaptive adjustments to subsequent logging plans and oil and gas exploration strategies based on these data, providing strong support for efficient exploration and development of oil and gas resources.

[0003] At present, the LZ77 compression algorithm is often used to compress and store logging data. After the compression window parameters are set according to certain rules and experience, the data is compressed according to the established process, and the compressed data is stored to reduce the space required for data storage.

[0004] However, the existing method often presets a fixed compression window size, but in the actual logging process, the downhole environment is complex and variable, and the data characteristics under different depths and different geological conditions differ greatly. The fixed compression window size is difficult to adapt to such complex and variable conditions, resulting in poor reliability of data compression storage. SUMMARY

[0005] The embodiment of the present application provides a downhole large-capacity high-reliability data storage method, which can improve the reliability of data compression storage.

[0006] In a first aspect, the embodiment of the present application provides a downhole large-capacity high-reliability data storage method, comprising:

[0007] obtaining logging instrument information in the logging process; the logging instrument information includes first logging instrument information of a speed evaluation dimension and second logging instrument information of a stability evaluation dimension;

[0008] determining a logging speed weight at the current time based on the first logging instrument information; the logging speed weight is used to represent the logging specification degree evaluated based on the deviation between the actual logging speed and the theoretical logging speed;

[0009] determining a borehole stability coefficient at the current time based on the second logging instrument information; the borehole stability coefficient is used to evaluate the stability degree of the borehole;

[0010] determining a downhole travel importance at the current time based on the logging speed weight, the borehole stability coefficient and the first logging deviation angle; the first logging deviation angle is the included angle between the real-time travel direction of the logging instrument at the current time and the travel fitting straight line, and the travel fitting straight line is a straight line fitted by the positions of the logging instrument within a target time length before the current time;

[0011] Based on the downhole running importance, the compression window size at the current time is dynamically adjusted, so that the logging data in the logging process is compressed and stored based on the compression window size.

[0012] Further, the present application also proposes that based on the first logging instrument information, the logging speed weight at the current time is determined, comprising:

[0013] Based on the first logging instrument information at each time, the theoretical logging speed at each time is respectively predicted;

[0014] The theoretical logging speed at each time except the current time is fitted according to the time sequence to obtain a first theoretical speed curve; the theoretical logging speed at each time including the current time is fitted according to the time sequence to obtain a second theoretical speed curve;

[0015] The actual logging speed at each time except the current time is fitted according to the time sequence to obtain a first actual speed curve; the actual logging speed at each time including the current time is fitted according to the time sequence to obtain a second actual speed curve;

[0016] The first theoretical speed curve and the first actual speed curve are subjected to Pearson correlation coefficient calculation to obtain a first correlation coefficient; the second theoretical speed curve and the second actual speed curve are subjected to Pearson correlation coefficient calculation to obtain a second correlation coefficient;

[0017] The first correlation coefficient is divided by the second correlation coefficient to obtain the current logging speed rationality at the current time;

[0018] The current logging speed rationality and the historical logging speed rationality average are used to determine the logging speed weight at the current time.

[0019] Further, the present application also proposes that the first logging instrument information comprises pump propulsion pressure, drill bit rotation speed, mud viscosity and second logging deviation angle, and the second logging deviation angle is used to represent the included angle between the real-time running direction of the logging instrument at the current time and the gravity direction;

[0020] Based on the first logging instrument information at each time, the theoretical logging speed at each time is respectively predicted, comprising:

[0021] The pump propulsion pressure and the drill bit rotation speed at the target time are used to determine the logging propulsion force degree at the target time; the target time is any time;

[0022] The mud viscosity and the second logging deviation angle at the target time are used to determine the downhole running resistance degree at the target time;

[0023] The logging propulsion force degree and the downhole running resistance degree at the target time are used to determine the theoretical logging speed at the target time.

[0024] Furthermore, the present invention also proposes that the second logging instrument information includes annular pressure value and wellbore vibration value;

[0025] Based on the information from the second logging tool, determine the wellbore stability coefficient at the current moment, including:

[0026] Multiply the reciprocal of the annular pressure value by the reciprocal of the wellbore vibration value to obtain the wellbore stability coefficient at the current moment.

[0027] Furthermore, this invention also proposes determining the importance of the downhole path at the current moment based on the logging velocity weight, the wellbore stability coefficient, and the first logging deviation angle, including:

[0028] After phase adjustment of the first logging deviation angle, the tangent value is calculated to obtain the target deviation angle tangent value;

[0029] The initial travel weight is obtained by multiplying the target deviation angle tangent, the reciprocal of the logging velocity weight, and the reciprocal of the wellbore stability coefficient.

[0030] The initial path importance is standardized to obtain the current downhole path importance.

[0031] Furthermore, the present invention also proposes that, before dynamically adjusting the compression window size at the current moment based on the importance of the downhole path, the method further includes:

[0032] Based on the network parameters of the logging tool, determine the network evaluation index at the current moment; the network evaluation index includes at least one of the storage difficulty factor and bandwidth health presentation.

[0033] Based on network evaluation metrics and the importance of downhole paths, the current compression storage requirement is determined.

[0034] Based on the importance of the downhole path, the size of the compression window at the current moment is dynamically adjusted, including:

[0035] The size of the compression window is dynamically adjusted based on the demand for compressed storage.

[0036] Furthermore, this invention also proposes that network evaluation metrics include a storage difficulty factor;

[0037] Based on the network parameters of the logging tool, determine the network evaluation indicators for the current moment, including:

[0038] By utilizing the storage capacity of each data packet to be stored in the logging tool and the number of data packets to be stored, the current storage resource consumption coefficient at the current moment is determined.

[0039] acquire an average packet loss rate of a target historical moment; the target historical moment is a historical moment corresponding to the same historical resource consumption coefficient as the current resource consumption coefficient;

[0040] standardize a product of the current resource consumption coefficient and the average packet loss rate to obtain a storage difficulty factor of the current moment.

[0041] Further, the present application also proposes that the network evaluation index comprises a bandwidth health presentation degree;

[0042] Based on the network parameters of the logging instrument, the network evaluation index of the current moment is determined, comprising:

[0043] acquire a current storage network bandwidth value of the logging instrument at the current moment, and a change standard deviation of historical storage network bandwidth values in a time domain within a target time length before the current moment;

[0044] multiply the current storage network bandwidth value by an inverse of the change standard deviation to obtain the bandwidth health presentation degree of the current moment.

[0045] Further, the present application also proposes that the network evaluation index comprises a storage difficulty factor and a bandwidth health presentation degree;

[0046] Based on the network evaluation index and the downhole path importance degree, the compression storage demand degree of the current moment is determined, comprising:

[0047] multiply the bandwidth health presentation degree, the inverse of the storage difficulty factor, and the inverse of the downhole path importance degree to obtain an initial storage demand degree of the current moment;

[0048] standardize the initial storage demand degree to obtain the compression storage demand degree of the current moment.

[0049] Further, the present application also proposes that, based on the downhole path importance degree, the compression window size of the current moment is dynamically adjusted, and the method further comprises:

[0050] initialize a compression storage pointer; the compression storage pointer is used to point to a character position to be processed currently;

[0051] search, within the compression window size, from a data position pointed to by the compression storage pointer in a data sorting direction, to obtain a longest character sequence matched with the logging data in the forward buffer;

[0052] move the compression storage pointer by a target length in the data sorting direction to obtain an updated compression storage pointer; the target length is determined based on the longest character sequence;

[0053] In the compressed window size, the logging data is read from the data position pointed by the updated compressed storage pointer in sequence to perform the encoding operation, so as to compress and store the logging data.

[0054] The present application has the following advantages:

[0055] In the downhole large-capacity high-reliability data storage method provided by the embodiment of the present application, the first logging instrument information of the logging instrument speed evaluation dimension and the second logging instrument information of the stability evaluation dimension are obtained, so as to determine the logging speed weight and the borehole stability coefficient respectively. The angle between the real-time driving direction of the logging instrument and the driving fitting straight line is combined to further determine the downhole running importance. The downhole running importance reflects the complexity of the current logging state and the degree of attention required. Based on the downhole running importance, the compressed window size is dynamically adjusted to be more suitable for the actual data characteristics. In this way, by dynamically adjusting the compressed window size, the complex and changeable situation can be more effectively adapted, and the storage space and data integrity can be effectively balanced. Therefore, by dynamically adjusting the compressed window size, the reliability of data compression and storage is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0057] Figure 1 The flowchart of the first downhole large-capacity high-reliability data storage method provided by an embodiment of the present application is shown in the figure.

[0058] Figure 2 The flowchart of S120 provided by an embodiment of the present application is shown in the figure.

[0059] Figure 3 The schematic diagram of a theoretical speed curve provided by an embodiment of the present application is shown in the figure.

[0060] Figure 4 The flowchart of the second downhole large-capacity high-reliability data storage method provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0061] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object of the application, the following describes in detail the specific implementation, structure, features and effects of a large-capacity and high-reliability data storage method in a well according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0063] It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of the present application comply with the relevant provisions of laws and regulations.

[0064] It should be noted that in the embodiments of the present application, some existing industry solutions such as software, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0065] In the conventional existing logging data compression storage technology, the LZ77 compression algorithm is usually used to compress and store the logging data. This algorithm first sets the compression window parameters according to certain rules and experience, then compresses the data according to the established process, and finally stores the compressed data, thereby reducing the required storage space. However, the existing method often presets a fixed compression window size. However, in the actual logging process, the downhole environment is complex, and the data characteristics under different depths and geological conditions vary greatly. The fixed compression window size cannot adapt to such complex and variable conditions, and it is difficult to accurately capture the characteristics and rules of the data. When compressing data, either the compression is not sufficient, wasting storage space, or the compression is excessive, losing key information, thereby resulting in poor reliability of data compression storage, affecting the subsequent analysis and use of logging data.

[0066] Faced with the aforementioned problems, this invention first recognizes that a fixed compression window cannot adapt to the differences in data characteristics caused by dynamic changes in the downhole environment. Traditional methods do not consider the impact of logging tool travel status and wellbore stability on the importance of downhole paths, resulting in low matching efficiency of compression algorithms. To address this, this invention deeply analyzes the correlation between the importance of downhole paths and the operating status of the logging tool, discovering that logging velocity fluctuations, wellbore stability, and travel trajectory deviation angle are key factors affecting the importance of downhole paths. By establishing a logging velocity weight to reflect the deviation between the actual logging trajectory and the theoretical trajectory, combining it with the wellbore stability coefficient to assess the impact of wellbore condition on data continuity, and introducing a logging deviation angle to characterize the travel direction offset, a multi-dimensional dynamic evaluation model is constructed. Based on this, the three factors are integrated to generate a downhole path importance index, quantifying in real time the timeliness requirements for logging data compression and storage at different stages, providing a precise basis for adjusting the compression window size.

[0067] In this regard, such as Figure 1 As shown in the figure, this embodiment of the invention provides a flowchart of a method for storing large-capacity, highly reliable data in a downhole well. This method can be applied to electronic devices and may include the following steps S110 to S150:

[0068] S110, acquire logging instrument information during the logging process; the logging instrument information includes first logging instrument information in the velocity assessment dimension and second logging instrument information in the stability assessment dimension.

[0069] In this step, well logging is a series of operations conducted in oil and gas exploration and development using specialized logging instruments to measure along the wellbore to obtain information such as the physical properties and geological structure of underground rock strata. Well logging provides insights into underground conditions, offering crucial data for subsequent oil and gas extraction.

[0070] Log instrument information is used to characterize the various data and parameter information collected and recorded by the log instrument during the logging process. It reflects the working status of the log instrument downhole and the measured downhole environmental characteristics.

[0071] The first logging tool information in the velocity assessment dimension is data used to evaluate the logging tool's movement speed downhole. This may include pump push pressure, bit rotation speed, mud viscosity, etc., which allows analysis of whether the logging tool's movement meets the expected logging speed requirements.

[0072] The second logging tool information in the stability assessment dimension is relevant to evaluating the stability of the wellbore. This includes data such as annular pressure and wellbore vibration, which reflect whether the wellbore can maintain stability in complex downhole environments.

[0073] The data recorded by the logging instrument can reflect the working state of the logging instrument and the environmental characteristics of the well. The first logging instrument information in the speed evaluation dimension is used to analyze whether the movement of the logging instrument meets the expected speed requirement, and the second logging instrument information in the stability evaluation dimension is used to understand the stability state of the wellbore in a complex environment, thereby providing basic data for subsequent evaluation of the logging specification degree and the wellbore stability degree.

[0074] Specifically, in the logging operation process of oil and gas exploration and development, a special logging instrument is used to measure and obtain data along the wellbore, and logging instrument information including a speed evaluation dimension (such as pump propulsion pressure, drill bit rotation speed, etc., which reflect the moving speed of the logging instrument in the well) and a stability evaluation dimension (such as annular pressure value, wellbore vibration value, etc., which reflect the stability degree of the wellbore) is collected.

[0075] In S120, a logging speed weight at the current time is determined based on the first logging instrument information. The logging speed weight is used to represent the logging specification degree evaluated based on the deviation between the actual logging speed and the theoretical logging speed.

[0076] In this step, the logging speed weight is an index for measuring the logging specification degree at the current time. It is determined based on the deviation between the actual logging speed and the theoretical logging speed. If the deviation is small, it means that the logging process is more standardized, and the logging speed weight is likely to be larger; otherwise, the deviation is larger, and the logging speed weight is smaller.

[0077] The logging speed weight is an index for measuring the logging specification degree. The smaller the deviation between the actual logging speed and the theoretical logging speed, the more standardized the logging process, and the larger the logging speed weight; the larger the deviation, the smaller the logging speed weight. In this way, the speed information is converted into a weight value that can directly reflect the logging specification degree, thereby providing a speed basis for subsequent comprehensive evaluation of the wellbore stability degree.

[0078] Specifically, based on the first logging instrument information in the speed evaluation dimension, such as the pump propulsion pressure, the drill bit rotation speed, etc., the deviation between the actual logging speed and the theoretical logging speed is calculated, and then the logging speed weight at the current time is determined.

[0079] In S130, a wellbore stability coefficient at the current time is determined based on the second logging instrument information. The wellbore stability coefficient is used to evaluate the stability degree of the wellbore.

[0080] In this step, the wellbore stability coefficient is a parameter for evaluating the stability degree of the wellbore at the current time. The wellbore is a subsurface hole formed during drilling, and its stability is affected by many factors such as formation pressure and drilling fluid performance. The wellbore stability coefficient is obtained by analyzing and calculating the related influencing factors. The larger the value, the more stable the wellbore, and the smaller the value, the worse the wellbore stability.

[0081] wherein the borehole stability is affected by multiple factors, and the borehole stability coefficient can comprehensively reflect the effects of these factors on the degree of borehole stability. The larger the value is, the more stable the borehole is, and the smaller the value is, the worse the stability is. The borehole stability coefficient provides a key parameter about the borehole stability state for subsequent evaluation of the importance of downhole travel.

[0082] Specifically, based on the second logging instrument information of the stability evaluation dimensions such as the annular pressure value and the borehole wall vibration value obtained above, the related factors affecting the borehole stability are analyzed and calculated to obtain the borehole stability coefficient at the current time.

[0083] S140, based on the logging speed weight, the borehole stability coefficient and the first logging deviation angle, the importance of downhole travel at the current time is determined; the first logging deviation angle is an included angle formed by the real-time travel direction of the logging instrument at the current time and a travel fitting straight line, and the travel fitting straight line is a straight line fitted by the positions of the logging instrument within a target time length before the current time.

[0084] In this step, the first logging deviation angle refers to an included angle formed by the real-time travel direction of the logging instrument at the current time and the travel fitting straight line. The real-time travel direction is the actual moving direction of the logging instrument at the current time; the travel fitting straight line is a straight line fitted by the positions of the logging instrument within a target time length before the current time. This target time length can be set according to actual conditions, and the fitting straight line reflects the average moving trend of the logging instrument within a period of time. The first logging deviation angle reflects the deviation degree between the actual moving direction of the logging instrument and the average moving trend.

[0085] The importance of downhole travel is a parameter determined by comprehensively considering factors such as logging speed weight, borehole stability coefficient and first logging deviation angle. It is used to represent the attention degree to the downhole travel of the logging instrument at the current time, and the larger the value is, the higher the importance of the current downhole travel is, and the subsequent compression window size will be adjusted accordingly according to this importance.

[0086] wherein the first logging deviation angle reflects the deviation degree between the actual moving direction of the logging instrument and the average trend. The importance of downhole travel comprehensively considers factors such as speed, borehole stability and travel deviation, and the larger the value is, the higher the attention degree to the current downhole travel is, which provides a comprehensive basis for subsequent adjustment of the compression window size.

[0087] Specifically, the included angle formed by the real-time travel direction of the logging instrument at the current time and the travel fitting straight line (fitted by the positions of the logging instrument within a target time length before the current time) is determined, that is, the first logging deviation angle, and then the importance of downhole travel at the current time is determined by comprehensively considering the logging speed weight, the borehole stability coefficient and the first logging deviation angle.

[0088] S150, based on the downhole path importance, dynamically adjusting the compression window size at the current time, so as to compress and store the logging data in the logging process based on the compression window size.

[0089] In this step, the compression window size is a parameter used to divide data segments during data compression. It determines the length range of data processing each time, and different compression window sizes will affect the effect and efficiency of data compression.

[0090] Among them, the compression window size determines the data processing length range, and affects the compression effect and efficiency. According to the downhole path importance, the size is dynamically adjusted, which can make the compression process better adapt to the complex situation underground, improve the quality and reliability of logging data compression storage, and meet the actual logging demand.

[0091] Specifically, based on the downhole path importance determined above, the compression window size in the current data compression process is dynamically adjusted, and then the logging data is compressed and stored based on the adjusted compression window size.

[0092] The present application combines logging speed weight, borehole stability coefficient and first logging deviation angle multidimensional parameters to calculate the downhole path importance in real time. And based on the downhole path importance, the compression window size is dynamically adjusted, which solves the problem that fixed compression window cannot adapt to the complex environment underground, resulting in poor storage reliability.

[0093] The present application realizes the dynamic compression storage of logging data. The logging speed weight reflects the deviation degree of the actual logging track and the theoretical track, the borehole stability coefficient evaluates the influence of the well wall state on data continuity, and the first logging deviation angle represents the deviation amount of the running direction. These three parameters together constitute the downhole path importance index, which quantifies the timeliness requirement of logging data compression storage at different stages in real time, and provides an accurate basis for compression window size adjustment. The dynamically adjusted compression window size can better adapt to the complex changes of the underground environment, and improve the reliability and efficiency of data compression storage.

[0094] As an example, in the logging process, the logging instrument acquires first logging instrument information and second logging instrument information in real time through the built-in sensor. The first logging instrument information includes pump propulsion pressure, drill bit speed, mud viscosity and other parameters, which are used for speed evaluation. The second logging instrument information includes annular pressure value, well wall vibration value and other parameters, which are used for stability evaluation.

[0095] Then, based on the pump propelling pressure, the bit rotating speed, the mud viscosity and other parameters, the logging speed weight is determined; and based on the annular pressure value, the well wall vibration value and other parameters, the wellbore stability coefficient is determined. The determination process of the first logging deviation angle is as follows: first, the driving fitting straight line is fitted by using the position data in the target time length, and then the included angle between the real-time driving direction at the current time and the straight line is calculated.

[0096] Then, the tangent value of the first logging deviation angle after phase adjustment is calculated to obtain the target deviation angle tangent value. The target deviation angle tangent value, the reciprocal of the logging speed weight and the reciprocal of the wellbore stability coefficient are multiplied to obtain the initial marching weight. The initial marching weight is standardized to obtain the downhole marching weight at the current time.

[0097] Finally, based on the downhole marching weight, the compression window size at the current time is dynamically adjusted. When the downhole marching weight is low, the requirement for the decompression speed of the real-time compression storage data of the logging instrument is lower, and at this time, the compression window size can be increased to capture more data features; when the downhole marching weight is high, the requirement for the decompression speed of the real-time compression storage data of the logging instrument is higher, and at this time, the compression window size is reduced to improve the compression efficiency. The adjusted compression window size is used for compression storage of the logging data.

[0098] Through the embodiment, the first logging instrument information of the logging instrument speed evaluation dimension and the second logging instrument information of the stability evaluation dimension are obtained, the logging speed weight and the wellbore stability coefficient can be determined respectively. The downhole marching weight is further determined by combining the included angle between the real-time driving direction of the logging instrument and the driving fitting straight line. The downhole marching weight reflects the complexity of the current logging state and the degree of attention. Based on the downhole marching weight, the compression window size is dynamically adjusted to be more suitable for the actual data features. In this way, by dynamically adjusting the compression window size, the complex and changeable situation can be more effectively adapted, and the storage space and data integrity are effectively balanced. Therefore, by dynamically adjusting the compression window size, the reliability of the data compression storage is significantly improved.

[0099] In some schemes of the present application, a method for evaluating the logging specification degree based on the logging speed weight is proposed. However, in the process of determining the logging speed weight, if the deviation between the theoretical logging speed and the actual logging speed is directly used, the dynamic change trend of the logging specification degree may not be accurately reflected, resulting in insufficient real-time and accuracy of the weight calculation result.

[0100] To this end, as shown in Figure 2 the present application further proposes that S120 specifically can include the following S121 to S126:

[0101] S121, based on the first logging instrument information at each time point, predict the theoretical logging rate at each time point;

[0102] S122, fit the theoretical logging rates at all times except the current time according to the time sequence to obtain the first theoretical rate curve; fit the theoretical logging rates at all times including the current time according to the time sequence to obtain the second theoretical rate curve;

[0103] S123, fit the actual logging velocity at each time other than the current time according to the time sequence to obtain the first actual velocity curve; fit the actual logging velocity at each time including the current time according to the time sequence to obtain the second actual velocity curve;

[0104] S124, calculate the Pearson correlation coefficient between the first theoretical speed curve and the first actual speed curve to obtain the first correlation coefficient; calculate the Pearson correlation coefficient between the second theoretical speed curve and the second actual speed curve to obtain the second correlation coefficient;

[0105] S125, divide the first correlation coefficient by the second correlation coefficient to obtain the rationality of the current logging rate at the current moment;

[0106] S126. Determine the logging speed weight at the current moment by using the current logging speed rationality and the average historical logging speed rationality.

[0107] In this embodiment, the theoretical logging speed of the logging tool should be positively correlated with the actual logging speed of the logging tool in the downhole path in the time domain. If the actual logging speed of the real-time logging tool does not meet this correlation, it indicates that the real-time status of the logging tool is more abnormal. Therefore, the urgency for decompressing the downhole multi-source sensor data that the logging tool compresses and stores in real time at the surface is higher, so as to detect the cause of the logging tool's abnormality in a timely manner. Thus, the rationality of the real-time logging speed of the logging tool is calculated.

[0108] like Figure 3 The diagram illustrates a theoretical velocity curve. The theoretical velocity curve can be obtained by fitting the theoretical logging velocity at each moment according to a time series; similarly, the actual velocity curve can be obtained by fitting the actual logging velocity at each moment according to a time series.

[0109] The difference between the first and second theoretical velocity curves reflects the impact of the theoretical logging velocity at the current moment on the overall trend; the difference between the first and second actual velocity curves reflects the dynamic change of the actual logging velocity at the current moment; the dynamic correlation between the theoretical and actual velocity curves can be quantified by calculating the Pearson correlation coefficient.

[0110] Specifically, during the logging process, the first logging instrument information is collected every fixed period, and a theoretical logging speed prediction model is established based on historical logging instrument information. The historical theoretical logging speed before the current time is time series fitted to generate a first theoretical speed curve, and the theoretical logging speed at the current time is added to generate a second theoretical speed curve. The actual logging speed is time series fitted synchronously to generate a first actual speed curve and a second actual speed curve. The Pearson correlation coefficient of the first theoretical speed curve and the first actual speed curve is calculated to obtain a historical speed matching degree; the Pearson correlation coefficient of the second theoretical speed curve and the second actual speed curve is calculated to obtain a current speed matching degree. The ratio of the historical speed matching degree and the current speed matching degree is taken as the current logging speed rationality, for example, when the first correlation coefficient is 0.85 and the second correlation coefficient is 0.7, the current logging speed rationality is 1.214. This calculation method can dynamically reflect the influence weight of the current logging speed on the overall standard degree, and avoid misjudgment caused by single time deviation.

[0111] If the positive correlation of the two sets of change curves becomes better when the real-time points at the current time are screened out than when the real-time points at the current time are retained, it indicates that the actual speed of the real-time logging instrument is less satisfied with the correlation law; that is, the larger the first correlation coefficient is compared with the second correlation coefficient, the smaller the current logging speed rationality is.

[0112] In addition, for each historical time, the historical logging speed rationality is determined in the same way as described above, and the historical logging speed rationalities are averaged to obtain a historical logging speed rationality average. Then, the logging speed weight at the current time is determined by using the current logging speed rationality and the historical logging speed rationality average through the following formula 1:

[0113] Formula 1

[0114] In formula 1, is used to represent the logging speed weight at the current time, F is used to represent the current logging speed rationality, is used to represent the historical logging speed rationality average, and norm is used to represent the standardization processing. It should be noted that, in order to ensure that the calculation result is meaningful, when performing fractional operation, if the denominator is 0, a tuning factor greater than 0 is added to the denominator to prevent the denominator from being 0. The value of the tuning factor is set by the implementer according to the actual situation, and the present application does not make special limitations.

[0115] Wherein, the smaller the difference between the current logging speed rationality and the historical logging speed rationality average is, the more standardized the current logging is, and the larger the logging speed weight is.

[0116] As an example, based on the first logging instrument information at each time, the theoretical logging speed at each time is predicted respectively. For example, a machine learning algorithm can be used to train historical logging instrument information to establish a prediction model to predict the theoretical logging speed.

[0117] The theoretical logging speeds at each time except the current time are fitted in time sequence to obtain a first theoretical speed curve. The theoretical logging speeds at each time including the current time are fitted in time sequence to obtain a second theoretical speed curve. The fitting method can use polynomial fitting or spline interpolation method.

[0118] The actual logging speeds at each time except the current time are fitted in time sequence to obtain a first actual speed curve. The actual logging speeds at each time including the current time are fitted in time sequence to obtain a second actual speed curve. The fitting method is consistent with the fitting method of the theoretical speed curve.

[0119] The first theoretical speed curve and the first actual speed curve are subjected to Pearson correlation coefficient calculation to obtain a first correlation coefficient. The second theoretical speed curve and the second actual speed curve are subjected to Pearson correlation coefficient calculation to obtain a second correlation coefficient. The calculation of the Pearson correlation coefficient can use standard statistical methods.

[0120] The second correlation coefficient is divided by the first correlation coefficient to obtain the current logging speed rationality at the current time. At the same time, for each historical time, the same way is used to determine the historical logging speed rationality, and then the historical logging speed rationalities are subjected to mean value processing to obtain a mean value of the historical logging speed rationality.

[0121] Finally, the current logging speed rationality and the mean value of the historical logging speed rationality are used to determine the logging speed weight at the current time by the above formula 1.

[0122] Through the embodiment, the specification degree of the logging speed can be accurately evaluated. By comparing the correlation of the speed curves including and not including the current time, it can be effectively judged whether the logging speed at the current time meets the expectation. This dynamic evaluation method can adapt to the complex and variable downhole environment, and improve the reliability and efficiency of the logging data compression storage.

[0123] In some of the above schemes of the application, a method for predicting the theoretical logging speed based on the first logging instrument information is proposed. However, in this process, the logging advancing force, the downhole travel resistance, the mud viscosity and the angle between the real-time driving direction of the logging instrument and the gravity direction are not comprehensively evaluated, resulting in insufficient prediction accuracy of the theoretical logging speed.

[0124] The first logging instrument information includes a pump propulsion pressure, a bit rotation speed, a mud viscosity, and a second logging deviation angle, and the second logging deviation angle is used to represent an included angle between a real-time driving direction of the logging instrument at the current time and a gravity direction.

[0125] S121 can specifically include:

[0126] The logging propulsion force degree at the target time is determined by using the pump propulsion pressure and the bit rotation speed at the target time, and the target time is any time;

[0127] The downhole driving resistance degree at the target time is determined by using the mud viscosity and the second logging deviation angle at the target time.

[0128] The theoretical logging speed at the target time is determined by using the logging propulsion force degree and the downhole driving resistance degree at the target time.

[0129] In the embodiment, the product of the pump propulsion pressure and the bit rotation speed after standardization processing is used as the logging propulsion force degree, the product of the reciprocal of the mud viscosity and the absolute value of the sine of the second logging deviation angle is used as the downhole driving resistance degree, and the value of the ratio of the logging propulsion force degree to the downhole driving resistance degree after standardization processing is used as the theoretical logging speed.

[0130] Specifically, in the logging propulsion force degree calculation process, the product of the pump propulsion pressure and the bit rotation speed can reflect the comprehensive mechanical propulsion force, and this parameter can quantify the propulsion capability of the logging instrument in the well. In the downhole driving resistance degree calculation process, the mud viscosity directly affects the fluid resistance in the well, the sine value of the second logging deviation angle can represent the degree of deviation of the logging instrument from the gravity direction, and the product of the reciprocal of the mud viscosity and the absolute value of the sine of the second logging deviation angle can quantify the hindering effect of the well trajectory on the movement of the logging instrument. By performing a ratio operation on the logging propulsion force degree and the downhole driving resistance degree, unit differences can be eliminated to form a dimensionless theoretical logging speed parameter. This theoretical logging speed parameter can dynamically reflect the theoretical speed requirement under different downhole working conditions, provide accurate input for subsequent logging speed weight calculation, and thus improve the reliability of the compressed window size dynamic adjustment.

[0131] As an example, in the process of horizontal well logging, the logging instrument is propelled in the well by using the cooperation of the while-drilling and pump propulsion, wherein the mud pump provides continuous power to push the logging tool down the well section and overcomes the wellbore friction, while the excess mud is discharged at the wellhead. The greater the propulsion pressure of the mud pump, the greater the propulsion force strength of the logging instrument, and the faster the drilling speed of the logging instrument, and thus the logging instrument is more labor-saving. Therefore, the logging propulsion force degree is determined by using the following formula 2:

[0132] Formula 2

[0133] In formula 2, A is used to represent the logging propulsion force, P is used to represent the pump propulsion pressure, V is used to represent the bit rotation speed, and norm is used to represent the normalization processing.

[0134] Wherein, the greater the mud pump propulsion pressure, and the faster the drilling speed of the while-drilling bit, the stronger the propulsion force on the logging instrument.

[0135] In addition, the more the real-time driving direction of the logging instrument at the current moment tends to be perpendicular to the gravity direction, the more the logging instrument is affected by gravity, and the more the logging instrument position is affected by the mud viscosity, the smaller the logging resistance is, and the easier the drilling of the while-drilling bit is. Therefore, the downhole logging resistance degree is determined by the following formula 3:

[0136] Formula 3

[0137] In formula 3, G is used to represent the downhole logging resistance degree, is used to represent the sine value of the second logging deviation angle, and N is used to represent the mud viscosity.

[0138] Wherein, the closer the real-time driving direction of the logging instrument at the current moment to the gravity direction, and the lower the mud viscosity at the logging instrument position, the smaller the logging resistance of the logging instrument in the well is.

[0139] Finally, the theoretical logging speed is determined by the following formula 4 using the logging propulsion force and the downhole logging resistance degree:

[0140] Formula 4

[0141] In formula 4, is used to represent the theoretical logging speed, A is used to represent the logging propulsion force, G is used to represent the downhole logging resistance degree, and norm is used to represent the normalization processing.

[0142] Wherein, if the logging propulsion force A is greater, and the downhole logging resistance degree G is smaller, then it reflects that the real-time theoretical logging speed of the logging instrument should be faster.

[0143] Through the embodiment, the multi-dimensional information in the logging process can be fully utilized to accurately predict the theoretical logging speed at each moment. Since the pump propulsion pressure, the bit rotation speed, the mud viscosity, and the logging instrument attitude and other factors are considered, the predicted theoretical logging speed is more close to the actual situation, providing reliable basic data for subsequent logging speed weight calculation. This theoretical speed prediction method based on multi-dimensional information can effectively improve the accuracy and reliability of logging data compression storage.

[0144] In the foregoing schemes of the present application, when determining the borehole stability coefficient based on the logging instrument information, there is a problem that the stability of the borehole cannot be accurately quantified and evaluated, resulting in insufficient basis for subsequent compression window adjustment and affecting data storage reliability.

[0145] To this end, the present application further proposes that the second logging instrument information comprises an annulus pressure value and a well wall vibration value.

[0146] S130 can specifically include:

[0147] The reciprocal of the annulus pressure value is multiplied by the reciprocal of the well wall vibration value to obtain the borehole stability coefficient at the current time.

[0148] In the present embodiment, the annulus pressure value reflects the fluid pressure state in the borehole annulus, and the well wall vibration value represents the vibration intensity of the well wall structure caused by external action. Both of them will reduce the stability of the borehole when their values increase, so the reciprocal form is used for normalization processing. For example, when the annulus pressure value is 2 MPa and the well wall vibration value is 3 mm / s, the borehole stability coefficient is (1 / 2)*(1 / 3)=0.167; if the annulus pressure value decreases to 1 MPa and the well wall vibration value decreases to 1 mm / s, the coefficient increases to 1.0. This calculation method converts the two types of physical quantities into dimensionless indexes through a linear relationship, eliminating the influence of dimension difference on the evaluation result.

[0149] Specifically, the annulus pressure value and the well wall vibration value are collected in real time by a pressure sensor and a vibration sensor. After obtaining the annulus pressure value and the well wall vibration value, their reciprocals are calculated and multiplied. The product result is positively correlated with the borehole stability degree, and when the product value increases, it indicates that the borehole is in a stable state of low pressure fluctuation and low structural vibration. For example, at a certain time, the annulus pressure value is measured to be 1.5 MPa, and the well wall vibration value is measured to be 2.5 mm / s, and the borehole stability coefficient is calculated to be (1 / 1.5)*(1 / 2.5)=0.267. The borehole stability coefficient as a quantitative index and the logging speed weight jointly participate in the dynamic adjustment of the compression window size. By converting the two types of engineering parameters into a single evaluation index, the accuracy and calculation efficiency of the borehole stability evaluation are effectively improved, providing a reliable basis for subsequent data compression storage.

[0150] As an example, first, the annulus pressure value and the well wall vibration value at the current time are obtained. The annulus pressure value is measured by a pressure sensor on the logging instrument, and the unit is MPa. The well wall vibration value is measured by a vibration sensor on the logging instrument, and the unit is mm / s.

[0151] Then, the reciprocal of the annulus pressure value is calculated. For example, if the annulus pressure value at the current time is 10 MPa, the reciprocal thereof is 0.1 MPa-1; the reciprocal of the well wall vibration value is calculated. For example, if the well wall vibration value at the current time is 2 mm / s, the reciprocal thereof is 0.5 mm / s. Finally, the reciprocal of the annulus pressure value is multiplied by the reciprocal of the well wall vibration value to obtain the borehole stability coefficient at the current time.

[0152] Through the embodiment, the borehole stability coefficient can be quickly calculated based on the annulus pressure value and the well wall vibration value, which provides an important parameter for subsequent determination of the downhole path importance. Therefore, the stability degree of the borehole can be more accurately evaluated, and a reliable basis for dynamically adjusting the compression window size is provided, thereby improving the reliability and efficiency of the logging data compression storage.

[0153] In some schemes of the present application, when the downhole path importance is determined based on the logging speed weight, the borehole stability coefficient and the first logging deviation angle, directly using the original first logging deviation angle for calculation may introduce errors, thereby affecting the accuracy of the compression window size adjustment and reducing the reliability of the data compression storage.

[0154] To this end, the present application further provides that S140 specifically can include:

[0155] The tangent value of the phase-adjusted first logging deviation angle is calculated to obtain a target deviation angle tangent value;

[0156] The target deviation angle tangent value, the reciprocal of the logging speed weight and the reciprocal of the borehole stability coefficient are multiplied to obtain an initial path importance;

[0157] The initial path importance is standardized to obtain the downhole path importance at the current time.

[0158] In the embodiment, the phase adjustment is to map the first logging deviation angle to a specific angle interval, for example, to add 90 degrees to the first logging deviation angle. The calculation of the target deviation angle tangent value further converts the angle to a dimensionless ratio, which is convenient for subsequent multiplication operation with other parameters. The reciprocal of the logging speed weight reflects the negative effect of logging specification degree on the path importance, and the reciprocal of the borehole stability coefficient represents the enhancement effect of the unstable borehole on the path importance. The multiplication operation realizes the coupling effect of multiple parameters, and the standardization processing constrains the initial path importance to a preset value interval, for example, between 0 and 1, by the normalization method.

[0159] Specifically, the phase adjustment is realized by angle translation or modulo operation, for example, when the first logging deviation angle is 45 degrees, it is added by 90 degrees, and the phase adjustment is 135 degrees. The target deviation angle tangent value eliminates the angle direction difference, converts the angle change into a monotone increasing tangent function value, and enhances the sensitivity to the angle change. The reciprocal of the logging speed weight is multiplied by the reciprocal of the wellbore stability coefficient, and the degree of logging speed deviation from the standard and the degree of wellbore instability jointly act on the marching importance. The standardization processing adopts linear normalization method, for example, the initial marching importance is divided by the historical maximum value, so that the output value adapts to the dynamic change of downhole environment. Through the above steps, the downhole marching importance can more accurately represent the abnormal degree of the current logging state, and provide a reliable basis for dynamic adjustment of the compression window size, thereby improving the reliability of data compression storage.

[0160] As an example, the downhole marching importance can be determined by the following formula 5:

[0161] Formula 5

[0162] In formula 5, W is used to represent the downhole marching importance, is used to represent the first logging deviation angle, is used to represent the logging speed weight at the current time, Q is used to represent the wellbore stability coefficient at the current time, and norm is used to represent the standardization processing.

[0163] Wherein, if the real-time driving direction of the logging instrument at the current time and the deviation of the marching route in the short time period are too large, the logging instrument may run into a complex well condition section at this time; if at the same time, the smaller the wellbore stability coefficient in the section, and the lower the logging speed weight of the logging instrument, the more attention is needed to the logging at this time, and the higher the requirement for the decompression speed of the real-time compression storage data of the logging instrument to adapt to the complex well condition logging task.

[0164] Through the embodiment, the downhole marching importance at the current time can be accurately calculated based on the logging speed weight, the wellbore stability coefficient and the first logging deviation angle. The downhole marching importance comprehensively considers the logging speed, the wellbore stability and the driving direction of the logging instrument, and can more comprehensively reflect the complexity of the downhole environment and the normalization of the logging process. This provides a reliable basis for subsequent dynamic adjustment of the compression window size, and helps to improve the reliability and efficiency of data compression storage.

[0165] In some schemes of the present application, the downhole path importance is determined based on the logging speed weight, the borehole stability coefficient and the first logging deflection angle, and then the compression window size is adjusted only according to the downhole path importance. However, the downhole storage network environment fluctuates dynamically, for example, the storage bandwidth is unstable or the storage resource is insufficient, so that it is difficult to adapt to the network fluctuation by simply relying on the downhole path importance, which may cause data storage failure or delay and affect the storage reliability.

[0166] To this end, as shown in Figure 4 the present application further provides that before S150, the downhole large-capacity high-reliability data storage method can further include the following S410 to S420:

[0167] S410, determining a network evaluation index at the current time based on the network parameters of the logging instrument; the network evaluation index includes at least one of a storage difficulty factor and a bandwidth health degree;

[0168] S420, determining a compression storage demand degree at the current time based on the network evaluation index and the downhole path importance;

[0169] S150 can specifically include:

[0170] based on the compression storage demand degree, dynamically adjusting the compression window size at the current time.

[0171] In this embodiment, the network parameters of the logging instrument refer to the network related parameters involved when the logging instrument works downhole, which reflect various state information of the logging instrument in the network environment, such as network connection quality, signal strength, etc., and are the basis for subsequent determination of the network evaluation index.

[0172] The network evaluation index refers to an index for measuring the current downhole network condition, including at least one of a storage difficulty factor and a bandwidth health degree. The storage difficulty factor reflects the difficulty of data storage in the network, and the bandwidth health degree reflects the stability and availability of the network bandwidth and other health states.

[0173] The storage difficulty factor is one of the network evaluation indexes, which specifically measures the difficulty degree faced in the data storage process in the downhole network environment, which may involve the influence of network delay, data transmission error rate and other factors on storage; the bandwidth health degree is also one of the network evaluation indexes, which is used to represent the health state of the downhole network bandwidth, such as bandwidth fluctuation, whether it reaches the normal working bandwidth range, etc., reflecting whether the bandwidth can meet the normal demand of data storage and other operations.

[0174] The compression storage demand degree is a comprehensive index determined according to the network evaluation index and the downhole path importance, which is used to measure the urgency of data compression storage at the current time, so as to guide the subsequent adjustment of the compression window size.

[0175] In the embodiment, the data compression storage process is optimized based on comprehensive evaluation of downhole network environment and operation conditions. First, network evaluation indexes are determined based on network parameters of the logging tool, which can reflect whether the downhole network is conducive to data storage from different aspects, such as a storage difficulty factor and a bandwidth health presentation degree, which respectively evaluate the storage difficulty level and the network bandwidth state. Then, the compression storage demand degree is determined by combining the downhole path importance, because the data importance and storage demand are different in different downhole path areas. Finally, the compression window size is dynamically adjusted according to the compression storage demand degree. When the compression storage demand degree is high, the compression window size can be adjusted to more efficiently perform data compression storage, and vice versa, so as to optimize the downhole large-capacity and high-reliability data storage method.

[0176] Specifically, the network status at the current time is analyzed and calculated based on the network parameters of the logging tool to determine the network evaluation indexes. Specifically, various network-related data such as signal strength, data transmission rate, error rate, etc. are collected by the logging tool, and then at least one of the storage difficulty factor and the bandwidth health presentation degree is calculated according to a preset algorithm and model.

[0177] Then, after obtaining the network evaluation indexes, the compression storage demand degree at the current time is determined by combining the pre-set downhole path importance through a specific calculation method or decision model. For example, the specific value of the compression storage demand degree can be obtained by weighted calculation or other logical judgment methods according to the specific values of the network evaluation indexes and the downhole path importance.

[0178] Finally, the compression window size at the current time is dynamically adjusted according to the compression storage demand degree determined above. For example, if the compression storage demand degree is high, the compression window size can be increased to perform compression processing in a wider data range and improve the compression efficiency; if the compression storage demand degree is low, the compression window size can be reduced to adapt to the relatively low storage demand and optimize the utilization of storage resources. Through such a dynamic adjustment process, the downhole data storage is optimized.

[0179] Through the embodiment, the compression window size can be dynamically adjusted according to the real-time network condition of the logging instrument and the downhole running condition. The adaptive compression storage method can better adapt to the complex and changeable downhole environment, and improve the reliability and efficiency of data compression storage. At the same time, by considering the storage difficulty factor and bandwidth health presentation, the current network condition can be more comprehensively evaluated, so that a more reasonable compression storage decision can be made. This not only can optimize the utilization of storage resources, but also can improve the stability of data transmission and storage, and provide a more reliable basis for subsequent data analysis and decision-making.

[0180] In some schemes of the present application, a scheme of dynamically adjusting the compression window size based on the network evaluation index and the downhole running importance is proposed. However, when determining the network evaluation index, the storage difficulty factor that accurately quantifies the storage difficulty has not been solved, which may lead to mismatch between storage resource allocation and actual demand, and further affect the reliability of data compression.

[0181] To this end, the present application further proposes that the network evaluation index comprises the storage difficulty factor.

[0182] S410 can specifically include:

[0183] The current storage resource consumption coefficient at the current time is determined by using the storage amount of each to-be-stored data packet in the logging instrument and the number of to-be-stored data packets.

[0184] The average packet loss rate at the target historical time is obtained; the target historical time is a historical time corresponding to the same historical storage resource consumption coefficient as the current storage resource consumption coefficient.

[0185] The product of the current storage resource consumption coefficient and the average packet loss rate is standardized to obtain the storage difficulty factor at the current time.

[0186] In the embodiment, the characteristics of the real-time to-be-stored data packet itself can affect the compression difficulty, and further affect the decompression speed, thereby causing certain errors in the compression storage demand judgment. That is, if the storage amount and the number of the to-be-stored data packet itself are larger, the compression demand at the real-time time is higher, the compression difficulty is larger, the influence on decompression is larger, and the packet loss rate performance of the data packet at the historical storage time under the same storage amount and number level is stronger, so the storage difficulty of the real-time to-be-stored data packet of the logging instrument is higher. Therefore, the storage difficulty factor of the real-time to-be-stored data packet of the logging instrument is analyzed.

[0187] The current storage resource consumption coefficient is calculated by the product of the storage amount and the number of the to-be-stored data packet, the storage amount reflects the storage space occupation degree of a single data packet, and the number represents the task size to be processed. The selection of the target historical moment is based on the matching of the historical storage resource consumption coefficient and the current value, to ensure that the historical data with the same storage pressure background is selected. The linear normalization method is adopted for the standardization processing, and the product result is mapped to the interval of 0 to 1, to facilitate the subsequent index fusion calculation.

[0188] Specifically, the logging instrument real-time statistics each to-be-stored data packet storage amount and count, the product of the two is obtained, the current storage resource consumption coefficient, the current storage resource consumption coefficient reflects the total amount of storage space occupation and task concurrency. Then, the time records with the same storage resource consumption coefficient in the historical database are retrieved, and the average packet loss rate data of the corresponding period is extracted, which represents the historical transmission reliability under the same storage pressure. The current storage resource consumption coefficient is multiplied by the average packet loss rate and then standardized to obtain the storage difficulty factor. This factor not only contains the instantaneous state of the current storage resource consumption, but also integrates the transmission stability information under the similar historical working conditions, and can dynamically represent the real-time pressure level of the storage module. By introducing the storage difficulty factor into the compressed storage demand degree calculation, the adjustment of the compression window size can respond to the change of the storage resource state synchronously, avoiding data loss or compression efficiency reduction caused by excessive storage pressure.

[0189] The storage difficulty factor can be determined by the following formula 6:

[0190] Formula 6

[0191] In formula 6, U represents the storage difficulty factor, D represents the current storage resource consumption coefficient, R represents the average packet loss rate, and norm represents the standardization processing.

[0192] Through the embodiment, the storage difficulty can be dynamically evaluated based on the real-time network parameters of the logging instrument, providing a basis for the adjustment of the compression window size. This method considers the current network status and historical packet loss, so that the storage difficulty factor can more accurately reflect the actual storage environment, thereby improving the reliability and efficiency of data compression storage. At the same time, through the standardization processing, the storage difficulty factor has good comparability, which is convenient for comprehensive consideration with other indicators in the subsequent steps.

[0193] In some schemes of the present application, the network evaluation index is used to dynamically adjust the compression window size to improve the reliability of data compression storage. However, only considering the storage difficulty factor in the calculation process of the network evaluation index may not fully reflect the real-time fluctuation state of the network bandwidth, resulting in insufficient accuracy of the bandwidth health state evaluation, and further affecting the rationality of the compression window size adjustment.

[0194] In this regard, the network evaluation index further includes bandwidth health presentation degree;

[0195] S410 can specifically include:

[0196] obtaining a current storage network bandwidth value of the logging instrument at the current time, and a change standard deviation in time domain of historical storage network bandwidth values within a target time length before the current time;

[0197] multiplying the current storage network bandwidth value by an inverse of the change standard deviation to obtain the bandwidth health presentation degree at the current time.

[0198] In this embodiment, the health performance of the bandwidth network system of the logging instrument affects the solving performance of the data storage task. For example, when dealing with a high-difficulty data storage task, the lower the network bandwidth value and the stronger the fluctuation performance, the weaker the bandwidth health state, and the worse the performance in dealing with the data storage task. Therefore, the real-time bandwidth health presentation degree is calculated.

[0199] The calculation of the bandwidth health presentation degree depends on the correlation between the current storage network bandwidth value and the historical bandwidth change standard deviation. The historical bandwidth change standard deviation is obtained by statistically analyzing the dispersion degree of the storage network bandwidth values within the target time length. The larger the standard deviation, the more intense the bandwidth fluctuation. Multiplying the current storage network bandwidth value by the inverse of the standard deviation can eliminate the interference of bandwidth fluctuation on the evaluation of bandwidth health state, so that the bandwidth health presentation degree can reflect both the absolute value of the current bandwidth and the stability of the historical bandwidth.

[0200] Specifically, during the operation of the logging instrument, the current storage network bandwidth value is collected in real time, and a historical storage network bandwidth value sequence within a target time length is extracted. The bandwidth fluctuation amplitude is quantified by calculating the time domain standard deviation of the historical bandwidth value sequence. If the standard deviation is large, it indicates that the bandwidth fluctuation is intense. At this time, the inverse of the standard deviation is small, and the bandwidth health presentation degree obtained by multiplying the current storage network bandwidth value is reduced, indicating that the network bandwidth is in an unstable state. If the standard deviation is small, it indicates that the bandwidth fluctuation is gentle, the inverse of the standard deviation is large, and the bandwidth health presentation degree is correspondingly improved, indicating that the network bandwidth is in a healthy and stable state. By incorporating the bandwidth health presentation degree into the calculation of the compression storage demand degree, the real-time health state of the network bandwidth can be dynamically perceived, and the compression window size adjustment strategy can be comprehensively determined in combination with the downhole travel importance degree and the storage difficulty factor, so as to adaptively reduce the compression window size when the bandwidth fluctuates, reduce the data transmission pressure, and improve the reliability of data compression storage.

[0201] The bandwidth health presentation degree can be determined by the following formula 7:

[0202] Formula 7

[0203] In formula 7, for representing bandwidth health presentation, for representing change standard deviation. T for representing current storage network bandwidth value.

[0204] As an example, the current storage network bandwidth value at the current time is 12Mbps. By calling the historical record data of the storage network bandwidth in the past 30 minutes, the time domain change standard deviation is calculated as 1.8Mbps. The current storage network bandwidth value 12Mbps is multiplied by the standard deviation reciprocal 0.556, and the bandwidth health presentation at the current time is 6.67.

[0205] Through the embodiment, the bandwidth stability state of the downhole storage network can be quantitatively evaluated in real time. By combining the correlation analysis of the current bandwidth value and the historical fluctuation degree, the health degree of the network transmission environment can be accurately identified. The bandwidth health presentation index established thereby provides an objective basis for dynamically adjusting the data compression strategy, effectively alleviates the influence of network fluctuation on the compression window size setting, and enhances the adaptability and reliability of data compression storage in complex downhole environments.

[0206] In some schemes of the above-mentioned embodiments of the application, a method for determining compression storage demand degree based on network evaluation index and downhole travel importance is proposed. However, when the network evaluation index includes the storage difficulty factor and the bandwidth health presentation, how to determine the compression storage demand degree is not quantified, which leads to insufficient accuracy of the evaluation of the compression storage demand degree.

[0207] To this end, the application further proposes that the network evaluation index includes the storage difficulty factor and the bandwidth health presentation.

[0208] S420 can specifically include:

[0209] The bandwidth health presentation, the reciprocal of the storage difficulty factor, and the reciprocal of the downhole travel importance are multiplied to obtain the initial storage demand degree at the current time;

[0210] The initial storage demand degree is standardized to obtain the compression storage demand degree at the current time.

[0211] In the embodiment, the compression storage demand degree can be determined by the following formula 8:

[0212] Formula 8

[0213] In formula 8, for representing compression storage demand degree, for representing bandwidth health presentation, U for representing storage difficulty factor, W for representing downhole travel importance, and norm for representing standardization.

[0214] If the downhole running importance of the logging instrument is low, the storage difficulty factor of the real-time data packet to be stored is small, and the bandwidth health degree of the storage network is high, a higher compression storage level can be applied to the real-time logging data to be stored, and thus the real-time compression storage demand degree is calculated.

[0215] As an example, when the network evaluation index of the logging instrument includes the storage difficulty factor and the bandwidth health degree, the bandwidth health degree value at the current time is 1.2, the storage difficulty factor value is 0.8, and the downhole running importance value is 0.5. The bandwidth health degree value is kept as the original value, the storage difficulty factor is taken as the reciprocal to obtain 1.25, the downhole running importance is taken as the reciprocal to obtain 2.0, and the product of 1.2, 1.25, and 2.0 is obtained to obtain the initial storage demand degree value 3.0. Further, the initial storage demand degree is standardized by using the maximum-minimum value normalization method, and when the preset compression storage demand degree threshold is 5.0, 3.0 is converted into the standardized value 0.6, which is used as the compression storage demand degree at the current time.

[0216] Through the embodiment, the relationship between the network transmission capability and the storage resource occupation can be effectively balanced, and the intelligent adjustment of the compression window size is realized in combination with the dynamic change feature of the downhole operation environment, so that the reliability of the data compression storage is significantly improved.

[0217] In some schemes of the present application, after the compression window size is dynamically adjusted, there is a challenge in how to effectively perform the compression operation to ensure data integrity and improve storage efficiency.

[0218] To this end, the present application further proposes that after S150, the downhole large-capacity high-reliability data storage method can further include:

[0219] The compression storage pointer is initialized, and the compression storage pointer is used to point to the current character position to be processed;

[0220] Within the compression window size, the longest character sequence matching the logging data in the forward buffer is obtained by searching from the data position pointed to by the compression storage pointer in the data sorting direction;

[0221] The compression storage pointer is moved by a target length in the data sorting direction to obtain an updated compression storage pointer; the target length is determined based on the longest character sequence;

[0222] Within the compression window size, the logging data is read from the data position pointed to by the updated compression storage pointer in sequence to perform the encoding operation, so as to compress and store the logging data.

[0223] In this embodiment, the initialization of the compressed storage pointer is achieved by setting the initial position as the starting point of the data stream, ensuring that the compression process starts from the correct position. When searching for the longest character sequence, a sliding window mechanism is used to traverse the data within the compression window and compare it character by character with the data in the forward buffer. During the matching process, the maximum length of continuous matching is recorded. The target length is defined as the length of the longest character sequence plus one, so that the compressed storage pointer moves to skip the processed data and avoid repeated compression. The encoding operation uses a variant of the LZ77 algorithm to convert the matching position and length information into a triple format for storage.

[0224] Specifically, after initializing the compressed storage pointer, the longest sequence matching the data in the forward buffer is searched within the dynamically adjusted compression window. For example, when the compression window size is adjusted to 512 bytes, the compressed storage pointer starts from the current position and searches for a character segment that continuously matches the first 32 bytes of data in the forward buffer within the window along the data arrangement direction. The position and length of the longest matching segment are recorded. After the matching is completed, the compressed storage pointer moves along the data arrangement direction by a position equal to the length of the longest matching plus one, ensuring that the subsequent processing skips the encoded data. Through the cooperation of dynamic window adjustment and pointer movement mechanism, the compression efficiency and data storage reliability can be improved in complex downhole environments.

[0225] As an example, after completing the dynamic adjustment of the compression window size, an initialization operation is performed on the compressed storage pointer, which is used to point to the starting position of the current logging data to be processed. Within the adjusted compression window, a forward search is performed from the initial position of the compressed storage pointer along the data arrangement direction to obtain the longest continuous character sequence matching the logging data content in the forward buffer. Then the compressed storage pointer is moved along the data ordering direction by a target step equal to the length of the longest character sequence + 1, to update the pointer position. Within the updated compression window, the logging data stream is read in order from the new pointer position, and the data content is compressed and encoded using the LZ77 encoding algorithm.

[0226] Through this embodiment, the technical defect of poor adaptability of fixed compression window in complex downhole environment is effectively solved. Through the adaptive compression mechanism after dynamically adjusting the window size, the encoding efficiency can be optimized according to the real-time data characteristics, and the storage space utilization is improved on the premise of ensuring data integrity. Through the intelligent pointer management and window matching mechanism, the reliability of downhole data compression storage is significantly enhanced, providing stable data support for the continuous development of subsequent logging operations.

[0227] It is to be understood that the application is not limited to particular configurations and processes described herein and shown in the drawings, as such may vary. The detailed description is not to be regarded as limiting, for the detailed description merely exemplifies a particular configuration and process. The process of the application may not be limited to the specific steps described and illustrated, as various modifications, alternative constructions, and equivalents can be apparent to those skilled in the art in view of the spirit of the application.

[0228] It is also to be understood that the application is not limited to the particular examples described herein, which are based on a series of steps or devices to describe some methods or systems. However, the application is not limited to the order of the steps described above, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0229] The above description is merely illustrative of the application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, modules and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein. It should be understood that the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the application, and these modifications or replacements should be covered within the protection scope of the application.

Claims

1. A method for storing large-capacity, highly reliable data in downhole mines, characterized in that: The method includes: Acquire logging instrument information during the logging process; the logging instrument information includes first logging instrument information in the velocity assessment dimension and second logging instrument information in the stability assessment dimension; Based on the information from the first logging instrument, the logging speed weight at the current moment is determined; the logging speed weight is used to characterize the degree of logging standardization assessed based on the deviation between the actual logging speed and the theoretical logging speed. Based on the information from the second logging tool, the wellbore stability coefficient at the current moment is determined; the wellbore stability coefficient is used to assess the stability of the wellbore. Based on the logging velocity weight, the wellbore stability coefficient, and the first logging deviation angle, the downhole trajectory weight at the current moment is determined; the first logging deviation angle is the angle formed by the real-time travel azimuth of the logging instrument at the current moment and the travel fitting line, and the travel fitting line is the straight line obtained by fitting the position of the logging instrument within the target time period before the current moment. Based on the importance of the downhole path, the size of the compression window at the current moment is dynamically adjusted so that the logging data during the logging process is compressed and stored based on the compression window size; The step of determining the downhole path importance at the current moment based on the logging velocity weight, the wellbore stability coefficient, and the first logging deviation angle includes: After phase adjustment of the first logging deviation angle, the tangent value is calculated to obtain the target deviation angle tangent value; the phase adjustment is to map the first logging deviation angle to a specific angle range. The initial travel weight is obtained by multiplying the target deviation angle tangent, the reciprocal of the logging velocity weight, and the reciprocal of the wellbore stability coefficient. The initial path importance is standardized to obtain the current well path importance.

2. The downhole high-capacity, high-reliability data storage method according to claim 1, characterized in that, The step of determining the logging velocity weight at the current moment based on the first logging instrument information includes: Based on the first logging instrument information at each time point, the theoretical logging rate at each time point is predicted. The theoretical logging rates at all times other than the current time are fitted in time sequence to obtain a first theoretical rate curve; the theoretical logging rates at all times including the current time are fitted in time sequence to obtain a second theoretical rate curve. The actual logging rates at all times other than the current time are fitted according to the time sequence to obtain the first actual rate curve; the actual logging rates at all times including the current time are fitted according to the time sequence to obtain the second actual rate curve. The first theoretical speed curve and the first actual speed curve are compared using Pearson correlation coefficient calculation to obtain a first correlation coefficient; the second theoretical speed curve and the second actual speed curve are compared using Pearson correlation coefficient calculation to obtain a second correlation coefficient. Divide the first correlation coefficient by the second correlation coefficient to obtain the rationality of the current logging rate at the current moment; The logging rate weight at the current moment is determined by using the current logging rate rationality and the average historical logging rate rationality.

3. The downhole high-capacity, high-reliability data storage method according to claim 2, characterized in that, The first logging tool information includes pump push pressure, drill bit speed, mud viscosity, and a second logging deviation angle, which is used to characterize the angle between the real-time travel azimuth of the logging tool at the current moment and the direction of gravity. The method of predicting the theoretical logging rate at each time point based on the first logging instrument information at each time point includes: The logging propulsion force at the target time is determined by using the pump propulsion pressure and the drill bit rotation speed at the target time; the target time can be any time. The downhole drag coefficient at the target time is determined by using the mud viscosity at the target time and the second logging deviation angle. The theoretical logging rate at the target time is determined by using the logging propulsion force and the downhole traverse resistance at the target time. Among them, the standardized value of the product of pump propulsion pressure and drill bit speed is used as the logging propulsion force; the product of the reciprocal of mud viscosity and the absolute value of the sine of the second logging deviation angle is used as the downhole diameter resistance; and the standardized value of the ratio of logging propulsion force to downhole diameter resistance is used as the theoretical logging speed.

4. The downhole high-capacity, high-reliability data storage method according to claim 1, characterized in that, The second logging tool information includes annular pressure values ​​and wellbore vibration values; The determination of the wellbore stability coefficient at the current moment based on the second logging tool information includes: The wellbore stability coefficient at the current moment is obtained by multiplying the reciprocal of the annular pressure value by the reciprocal of the wellbore vibration value.

5. The downhole high-capacity, high-reliability data storage method according to any one of claims 1-4, characterized in that, Before dynamically adjusting the compression window size at the current moment based on the downhole diameter importance, the method further includes: Based on the network parameters of the logging tool, determine the network evaluation index at the current moment; the network evaluation index includes at least one of storage difficulty factor and bandwidth health presentation degree. Based on the network evaluation metrics and the importance of the downhole path, the current compression storage requirement is determined. The dynamic adjustment of the compression window size at the current moment based on the importance of the downhole path includes: Based on the compression storage demand, the size of the compression window at the current moment is dynamically adjusted.

6. The downhole large-capacity, high-reliability data storage method according to claim 5, characterized in that, The network evaluation metrics include the storage difficulty factor; The determination of network evaluation indicators at the current moment based on the network parameters of the logging tool includes: The current resource consumption coefficient at the current moment is determined by using the storage capacity of each data packet to be stored in the logging tool and the number of data packets to be stored. Obtain the average packet loss rate at a target historical moment; the target historical moment is a historical moment in which the corresponding historical resource consumption coefficient is the same as the current resource consumption coefficient. The product of the current resource consumption coefficient and the average packet loss rate is standardized to obtain the storage difficulty factor at the current moment.

7. The downhole high-capacity, high-reliability data storage method according to claim 5, characterized in that, The network evaluation metrics include the bandwidth health presentation level; The determination of network evaluation indicators at the current moment based on the network parameters of the logging tool includes: Obtain the current storage network bandwidth value of the logging tool at the current moment, and the standard deviation of the historical storage network bandwidth value in the time domain within the target time period before the current moment; Multiply the current storage network bandwidth value by the reciprocal of the standard deviation of the change to obtain the bandwidth health presentation at the current moment.

8. The downhole high-capacity, high-reliability data storage method according to claim 5, characterized in that, The network evaluation metrics include storage difficulty factor and bandwidth health presentation; The determination of the current compression storage requirement based on the network evaluation metrics and the importance of the downhole path includes: The initial storage requirement at the current moment is obtained by multiplying the bandwidth health presentation degree, the reciprocal of the storage difficulty factor, and the reciprocal of the importance of the downhole path. The initial storage requirement is standardized to obtain the current compressed storage requirement.

9. The downhole high-capacity, high-reliability data storage method according to any one of claims 1-4, characterized in that, After dynamically adjusting the compression window size at the current moment based on the downhole diameter importance, the method further includes: The compressed storage pointer is initialized; the compressed storage pointer is used to point to the current character position to be processed. Within the compression window size, a search is performed along the data sorting direction starting from the data position pointed to by the compression storage pointer to obtain the longest character sequence that matches the logging data in the forward buffer; The compressed storage pointer is moved by a target length along the data sorting direction to obtain the updated compressed storage pointer; the target length is determined based on the longest character sequence. Within the compression window size, logging data is read sequentially from the data position pointed to by the updated compression storage pointer for encoding operations, so as to compress and store the logging data.

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