Oil field logging data management method and system based on big data

Abstract

The invention discloses an oil field logging data management method and system based on big data. The method comprises the steps of obtaining oil field logging encapsulation data; performing big data analysis on the oil field logging encapsulation data to obtain spliced data, data attributes, data types and flow types; performing data compression on the spliced data and the data attributes to obtain attribute compressed data; performing bit compression on the attribute compressed data and the data type to obtain type compressed data; and obtaining a data storage position according to the type compressed data and the traffic type. According to the method, through decoding and big data classification of various oil field logging encapsulation data formats, the uniformity of the data is ensured; through data compression and bit compression, the compression performance and read-write efficiency of the oil field logging data are improved, and finally the logging data management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly relates to a method and system for managing oilfield logging data based on big data. Background Art

[0002] With the continuous growth of global energy demand and the progress of exploration and development technologies, logging data, as key information in oil and gas exploration and development, is applied throughout the entire process of oil and gas field exploration and development, with a wide application range and high usage frequency.

[0003] However, due to the non-uniformity of the encapsulation format of oilfield logging data, it is not conducive to the management and analysis of big data. At the same time, due to the poor compression storage performance of oilfield logging data, it is not conducive to the storage and reading of big data, ultimately resulting in low efficiency in logging data management. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a method and system for managing oilfield logging data based on big data. The technical problems to be solved by the present invention are achieved through the following technical solutions:

[0005] A method for managing oilfield logging data based on big data includes:

[0006] Obtaining oilfield logging encapsulated data;

[0007] Performing big data analysis on the oilfield logging encapsulated data to obtain spliced data, data attributes, data types, and flow types;

[0008] Performing data compression on the spliced data and the data attributes to obtain attribute compressed data;

[0009] Performing bit compression on the attribute compressed data and the data types to obtain type compressed data;

[0010] Obtaining the data storage location according to the type compressed data and the flow types.

[0011] In a specific embodiment, the performing big data analysis on the oilfield logging encapsulated data to obtain spliced data, data attributes, data types, and flow types includes:

[0012] Obtaining original data by using a corresponding decoder according to the oilfield logging encapsulated data format;

[0013] Performing big data analysis on the original data to obtain data attributes and flow types;

[0014] Performing big data classification on the data attributes to obtain data types;

[0015] Concatenate the original data according to the data attributes to obtain concatenated data.

[0016] In a specific embodiment, the data compression of the concatenated data and the data attributes to obtain attribute-compressed data includes:

[0017] Obtain a prediction operator, a transformation operator, and an encoding operator according to the concatenated data and the data attributes;

[0018] Obtain a data compression method according to the concatenated data, the prediction operator, the transformation operator, and the encoding operator;

[0019] Use the data compression method for the concatenated data to obtain type-compressed data.

[0020] In a specific embodiment, the obtaining of the prediction operator, the transformation operator, and the encoding operator according to the concatenated data and the data attributes includes:

[0021] Obtain the data dimension according to the concatenated data;

[0022] Obtain the corresponding prediction method, transformation method, or encoding method according to the data dimension;

[0023] Obtain a prediction unit, a transformation unit, and an encoding unit according to the data attributes and the data dimension;

[0024] Obtain a prediction operator according to the prediction method and the prediction unit;

[0025] Obtain a transformation operator according to the transformation method and the transformation unit;

[0026] Obtain an encoding operator according to the encoding method and the encoding unit.

[0027] In a specific embodiment, the obtaining of the data compression method according to the concatenated data, the prediction operator, the transformation operator, and the encoding operator:

[0028] Perform data analysis on the concatenated data to obtain a data analysis result;

[0029] Obtain a prediction type, a transformation type, and an encoding type according to the data analysis result;

[0030] Obtain a prediction compression method according to the prediction type and the prediction operator;

[0031] Obtain a transformation compression method according to the transformation type and the transformation operator;

[0032] Obtain an encoding compression method according to the encoding type and the encoding operator;

[0033] A data compression method is obtained according to the prediction compression method, the transform compression method, and the encoding compression method.

[0034] In a specific embodiment, obtaining type compression data by bit-compressing the attribute compression data and the data type includes:

[0035] Obtaining an index encoding operator and a bit encoding operator according to the data type;

[0036] Obtaining a bit compression method according to the attribute compression data, the index encoding operator, and the bit encoding operator;

[0037] Performing the bit compression method on the attribute compression data to obtain type compression data.

[0038] In a specific embodiment, obtaining the index encoding operator and the bit encoding operator according to the data type includes:

[0039] Obtaining the number of data attributes and the data attribute characteristics according to the data type;

[0040] Obtaining an index encoding method and a bit encoding method according to the number of data attributes and the data attribute characteristics;

[0041] Obtaining a bit encoding unit according to the data type and the number of data attributes;

[0042] Obtaining an index encoding operator according to the index encoding method;

[0043] Obtaining a bit encoding operator according to the bit encoding method and the bit encoding unit.

[0044] In a specific embodiment, obtaining the bit compression method according to the attribute compression data, the index encoding operator, and the bit encoding operator includes:

[0045] Performing compression analysis on the attribute compression data to obtain a compression analysis result;

[0046] Obtaining an index encoding type and a bit encoding type according to the compression analysis result;

[0047] Obtaining a bit compression method according to the index encoding type, the index encoding operator, the bit encoding type, and the bit encoding operator.

[0048] In a specific embodiment, obtaining the data storage location according to the type compression data and the traffic type includes:

[0049] Obtaining a first-level index according to the traffic type;

[0050] Compress the data according to the type to obtain a secondary index;

[0051] Obtain the data storage location according to the primary index and the secondary index.

[0052] 10. A big data-based oilfield logging data management system, characterized by comprising:

[0053] An acquisition unit for acquiring oilfield logging encapsulated data;

[0054] An analysis unit for performing big data analysis on the oilfield logging encapsulated data to obtain spliced data, data attributes, data types, and flow types;

[0055] A data compression unit for compressing the spliced data and the data attributes to obtain attribute-compressed data;

[0056] A bit compression unit for performing bit compression on the attribute-compressed data and the data types to obtain type-compressed data;

[0057] A storage unit for obtaining the data storage location according to the type-compressed data and the flow type.

[0058] Advantages of the present invention:

[0059] The big data-based oilfield logging data management method of the present invention ensures the unity of data through the decoding and big data classification of various oilfield logging encapsulated data formats; through data compression and bit compression, the compression performance and read / write efficiency of oilfield logging data are improved, and finally the efficiency of logging data management is improved.

[0060] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0061] Figure 1 is a flowchart of a big data-based oilfield logging data management method provided by an embodiment of the present invention;

[0062] Figure 2 is a data splicing schematic diagram of a big data-based oilfield logging data management method provided by an embodiment of the present invention;

[0063] Figure 3 is a big data analysis schematic diagram of a big data-based oilfield logging data management method provided by an embodiment of the present invention;

[0064] Figure 4 is a schematic diagram of a bit encoding unit of a big data-based oilfield logging data management method provided by an embodiment of the present invention;

[0065] Figure 5 It is a module block diagram of a method for managing oilfield logging data based on big data provided by an embodiment of the present invention. Detailed implementation manners

[0066] The present invention will be further described in detail below in conjunction with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0067] Embodiment 1

[0068] In a specific implementation manner, please refer to Figure 1 , Figure 1 which is a flowchart of a method for managing oilfield logging data based on big data. The specific steps are as follows:

[0069] S1: Oilfield logging data is key information in oil and gas exploration and development, which contains physical property data of underground rock formations and is crucial for identifying oil and gas reservoirs. With the progress of exploration technologies, a large amount of logging data has been accumulated, and effective methods are needed to manage and utilize this data. In order to establish a complete data foundation for subsequent data analysis and processing, ensure the integrity and accuracy of the data, and provide reliable data support for the exploration and development of oilfields, oilfield logging encapsulated data is thus obtained.

[0070] S2: Since the logging data is large in quantity and complex, big data analysis technologies are needed to integrate and analyze it to extract valuable information. Through big data analysis, the integration of logging data is realized, and the efficiency and accuracy of data processing are improved. In a specific implementation manner, please refer to Figure 2 , Figure 2 which is a schematic diagram of big data analysis of a method for managing oilfield logging data based on big data. Therefore, big data analysis is performed on the oilfield logging encapsulated data to obtain splicing data, data attributes, data types, and flow types.

[0071] S21: Oilfield logging encapsulated data is usually stored in a specific format to ensure the integrity and security of the data. In order to obtain the original data, a corresponding decoder needs to be used according to the oilfield logging encapsulated data format to obtain the original data. In a specific implementation manner, for LAS format, DLIS format, WIS format, and other data formats, professional software tools such as LISview and Geolog are directly used for decoding to obtain the original data.

[0072] S22: Big data analysis is performed on the original data to obtain data attributes and flow types. In a specific implementation manner, the specific steps are as follows:

[0073] The data attributes include multiple original data attributes and multiple processed data attributes;

[0074] Multiple original data attributes are obtained through big data analysis techniques, such as traffic data of underlying test data, acoustic travel time of conventional logging curves, bulk density of conventional logging curves, etc.;

[0075] Multiple processed data attributes are obtained by applying big data analysis techniques to the original data. For example, through statistical analysis of the original data, distribution characteristics, mean, and variance attributes of the data are obtained;

[0076] The read and write data volumes of each network port targeted by applying big data analysis techniques to the original data are analyzed to obtain different data traffic types. For example, corresponding data traffic types are obtained by analyzing the read and write data types of different ports at different times according to the data volume.

[0077] S23: Data types are obtained by classifying the data attributes using big data. In a specific embodiment, the specific steps are as follows:

[0078] Principal component analysis is used to extract the main features of the data attributes, reducing the dimension of the data, thus facilitating classification;

[0079] The clustering analysis method is used to classify according to the main features of the data attributes. Through this method, data attributes with similar features can be grouped into one category, thus forming different data types;

[0080] To improve the accuracy and robustness of classification and ensure that the model can still make correct classifications when facing new and unseen data, the relationship between the data attributes obtained by the neural network algorithm and the data types is used to optimize the mapping relationship between the data attributes and the data types.

[0081] S24: Stitching data is obtained based on the data attributes and the original data. In a specific embodiment, please refer to Figure 3 , Figure 3 is a schematic diagram of data stitching for an oilfield logging data management method based on big data. The specific steps are as follows:

[0082] The dimension of the data is determined according to the provided data attributes to determine whether the data is single-point data, one-dimensional data, or two-dimensional data;

[0083] If it is single-point data, it means that each data point is independent and does not form a sequence or matrix with other data points. Therefore, the original data can be directly stitched according to a preset order or rule to form the stitched data. This kind of stitching is usually linear and does not need to consider the correlation between data points;

[0084] For one-dimensional data, such as time series or linearly arranged data points, it is necessary to further distinguish the correlation of the original data. If there is a correlation between data points, one can choose to first perform a single-point processing on the one-dimensional data, that is, maintain the linear structure of the data and further form spliced data through simple concatenation. On the contrary, if there is no correlation between data points, or if it is necessary to analyze the data from different perspectives, one can choose to two-dimensionalize the one-dimensional data. By constructing a data matrix, the time series data can be converted into a two-dimensional representation of state-time;

[0085] For two-dimensional data, distinguish the correlation of the original data. If the correlation of the original data indicates that the data should be maintained on a two-dimensional plane, expand the scale of the data by splicing rows or columns to form a larger two-dimensional data set. On the contrary, if it is necessary to extract information from a more complex structure, or to better represent the depth and hierarchy of the data, choose to three-dimensionalize the two-dimensional data. By adding a new dimension to represent different attributes or features, a three-dimensional data structure is thus formed.

[0086] S3: Since the amount of oilfield logging data is huge, the direct storage and transmission costs are high and the efficiency is low. Therefore, under limited transmission bandwidth, it is necessary to compress the data to improve the transmission efficiency. Since compression technology can reduce the storage space and transmission bandwidth requirements of data, improve the economy and efficiency of data processing, maintain the integrity and availability of data, and at the same time reduce the loss of data during transmission, data compression is performed on the spliced data and the data attributes to obtain attribute-compressed data.

[0087] S31: Obtain a prediction operator, a transformation operator, and an encoding operator according to the spliced data and the data attributes;

[0088] S311: Obtain the data dimension according to the spliced data. In a specific embodiment, the data dimension includes one-dimensional data, two-dimensional data, and three-dimensional data;

[0089] S312: Obtain a prediction method, a transformation method, and an encoding method according to the data dimension. In a specific embodiment,

[0090] The prediction method is to remove spatial correlation according to the data dimension and the spatial data correlation analysis, including a direction prediction method, a maximum value prediction method, a dictionary prediction method, and a clustering prediction method;

[0091] The transformation method is to combine the prediction result and the statistical characteristics of the data, and adopt a method of frequency concentration to remove the data correlation in the frequency domain, specifically including discrete cosine transform, discrete sine transform, and HAAR wavelet transform;

[0092] The encoding method combines the results of prediction and transformation and the statistical characteristics of the data. For coefficients with a higher frequency after transformation, shorter codewords are assigned, while for coefficients with a lower frequency, longer codewords are assigned, thereby achieving data compression, including exponential Golomb coding, run-length coding, Huffman coding, and direct transfer coding.

[0093] S313: Obtain a prediction unit, a transformation unit, and a coding unit based on the data attributes and the data dimension. In a specific embodiment, the specific steps are as follows:

[0094] Obtain the data space correlation based on the data attributes and the data dimension. Through statistical analysis and data mining techniques, potential correlation patterns in the data can be discovered. For example, certain features may exhibit strong correlations in time or space, and this information is crucial for the division of subsequent units;

[0095] The prediction unit is used to obtain the size of the prediction unit and the division of prediction sub-units based on the data space correlation;

[0096] The transformation unit is used to obtain the size of the transformation unit and the division of transformation sub-units based on the size of the prediction unit and the division of prediction sub-units;

[0097] The coding unit is used to obtain the size of the coding unit and the division of coding sub-units based on the size of the transformation unit and the division of transformation sub-units during the data compression or encoding stage.

[0098] S314: Obtain a prediction operator based on the prediction method and the prediction unit. In a specific embodiment, the specific steps of the prediction operator are as follows:

[0099] Adopt different prediction methods for each prediction sub-unit to obtain the first-level prediction residual of the prediction unit;

[0100] Adopt a prediction method for the first-level prediction residual of the prediction unit to obtain the prediction residual.

[0101] S315: Obtain a transformation operator based on the transformation method and the transformation unit. In a specific embodiment, the specific steps of the transformation operator are as follows:

[0102] Adopt a transformation method for each transformation sub-unit to obtain the first-level transformation residual of the transformation unit;

[0103] Adopt a transformation method for the first-level transformation residual of the transformation unit to obtain the transformation residual.

[0104] S316: Obtain a coding operator based on the coding method and the coding unit. In a specific embodiment, the transformation operator is to adopt different coding methods for each coding sub-unit to obtain a bitstream.

[0105] S32: Obtain a data compression method based on the splicing data, the prediction operator, the transformation operator, and the encoding operator.

[0106] S321: Perform data analysis on the splicing data to obtain a data analysis result. In a specific embodiment, the data analysis result includes the strength and range of data correlation.

[0107] S322: Obtain a prediction type, a transformation type, and an encoding type based on the data analysis result. Preferably, in a specific embodiment, the specific steps are as follows:

[0108] Obtain multiple alternative prediction methods based on the strength of data correlation. For example, for strongly correlated data, it is more suitable to use the maximum / minimum value prediction method, while for weakly correlated data, the direct transmission prediction method needs to be considered. Obtain multiple alternative prediction units based on the data correlation range, and obtain multiple prediction types based on the multiple alternative prediction methods and the multiple alternative prediction units;

[0109] Obtain multiple alternative transformation methods based on the strength of data correlation, obtain multiple alternative transformation units based on the data correlation range, and obtain multiple transformation types based on the multiple alternative transformation methods and the multiple alternative transformation units;

[0110] Obtain multiple alternative encoding methods based on the strength of data correlation, obtain multiple alternative encoding units based on the data correlation range, and obtain multiple encoding types based on the multiple alternative encoding methods and the multiple alternative encoding units.

[0111] S323: Obtain a prediction compression method based on the prediction type and the prediction operator. In a specific embodiment, the specific steps are as follows:

[0112] Perform a preliminary selection among the multiple prediction operators according to the multiple prediction types to obtain a backup prediction compression method;

[0113] Use the backup prediction compression method to perform a trial run on the data to obtain the backup prediction compression method with the smallest prediction residual, and use this backup prediction compression method as the prediction compression method.

[0114] S324: Obtain a transformation compression method based on the transformation type and the transformation operator. In a specific embodiment, the specific steps are as follows:

[0115] Perform a preliminary selection among the multiple transformation operators according to the multiple transformation types to obtain a backup transformation compression method;

[0116] Using the spare transform compression method to perform a trial run on the data to obtain one of the spare transform compression methods with the minimum transform residual, and using this spare transform compression method as the transform compression method.

[0117] S325: Obtain the encoding compression method according to the encoding type and the encoding operator. In a specific embodiment, the specific steps are as follows:

[0118] Perform a primary selection among multiple encoding operators according to multiple encoding types to obtain a spare encoding compression method;

[0119] Use the spare encoding compression method to compress the test data to obtain a spare encoding compression method with the minimum code stream, and use this spare encoding compression method as the encoding compression method.

[0120] S326: Obtain the data compression method according to the prediction compression method, the transform compression method, and the encoding compression method. In a specific embodiment, the data compression method includes the prediction compression method, the transform compression method, and the encoding compression method connected in sequence.

[0121] S33: Use the data compression method to compress the spliced data to obtain type-compressed data.

[0122] S4: In order to further optimize the data storage and transmission efficiency, especially in scenarios with limited bandwidth or limited storage space, bit compression can more finely control the degree of data compression, adapt to different data types and application requirements, achieve efficient data storage and fast access, and improve the overall performance of data processing. Therefore, perform bit compression on the attribute-compressed data and the data type to obtain type-compressed data;

[0123] S41: Obtain the index encoding operator and the bit encoding operator according to the data type;

[0124] S411: Obtain the number of data attributes and the data attribute characteristics according to the data type;

[0125] S412: Obtain the index encoding method and the bit encoding method according to the number of data attributes and the data attribute characteristics. In a specific embodiment, the specific steps are as follows:

[0126] Obtain the index encoding method for each attribute according to the number of data attributes, including the equal-bit encoding method and the exponential encoding method;

[0127] Obtain the bit encoding method according to the number of data attributes and the data attribute characteristics, including adaptive binary arithmetic coding and context-adaptive variable-length coding.

[0128] S413: Obtain a bit encoding unit based on the data type and the number of data attributes. In a specific embodiment, please refer to Figure 4 , Figure 4 which is a schematic diagram of a bit encoding unit for an oilfield logging data management method based on big data. The specific steps are as follows:

[0129] Obtain multiple minimum bit encoding units according to multiple numbers of the data attributes;

[0130] Based on the data type and multiple minimum bit encoding units, obtain a bit encoding unit by using the principle of minimum multiples and feature analysis.

[0131] S414: Obtain an index encoding operator according to the index encoding method. In a specific embodiment, each index encoding method corresponds to an index encoding operator.

[0132] S415: Obtain a bit encoding operator according to the bit encoding method and the bit encoding unit. In a specific embodiment, the specific steps of the bit encoding operator are as follows:

[0133] Apply a bit encoding method to each of the bit encoding units to obtain a first-level code stream;

[0134] Apply one of the bit encoding methods to the first-level code stream to obtain a storage code stream.

[0135] S42: Obtain a bit compression method according to the attribute-compressed data, the index encoding operator, and the bit encoding operator;

[0136] S421: Perform compression analysis on the attribute-compressed data to obtain a compression analysis result. In a specific embodiment, the compression analysis result includes the size of the compressed data and the strength of bit correlation;

[0137] S422: Obtain an index encoding type and a bit encoding type according to the compression analysis result. In a specific embodiment, the specific steps are as follows:

[0138] Obtain the corresponding index encoding type according to the size of the compressed data;

[0139] Obtain multiple alternative bit encoding methods according to the strength of bit correlation. For example, for continuous data with strong correlation, run-length encoding may be more suitable; while for discrete data with weak correlation, Huffman coding may be more appropriate. In this way, the most suitable bit encoding type can be selected for different data characteristics and application scenarios, so as to achieve efficient data compression and transmission;

[0140] Obtain multiple bit encoding types according to multiple alternative bit encoding methods and the bit encoding unit.

[0141] S423: Obtain a bit compression method based on the index coding type, the index coding operator, the bit coding type, and the bit coding operator. In a specific embodiment, the specific steps are as follows:

[0142] Select from multiple index coding operators according to the index coding type. The selection process usually involves manual decision-making, which may be based on experience, previous research results, or specific application requirements, to obtain an index coding compression method;

[0143] Perform a preliminary selection from multiple bit coding operators according to multiple bit coding types to obtain a backup bit coding compression method;

[0144] Use the backup bit coding compression method to conduct a trial run on the data to obtain the backup bit coding compression method with the smallest actual generated code stream;

[0145] Combining the index coding compression method and the backup bit coding compression method with the smallest code stream during the trial run, the final bit compression method can be obtained. This method combines the advantages of index coding and bit coding, aiming to achieve efficient data compression. Index coding provides fast data access and retrieval capabilities, while bit coding is responsible for minimizing the data storage space. Through this comprehensive method, high compression ratio of data can be achieved while ensuring data accessibility, meeting the efficiency requirements of storage and transmission.

[0146] S43: Use the bit compression method for the attribute compressed data to obtain type compressed data.

[0147] S5: Since the storage location of data directly affects the data access speed and processing efficiency, it is necessary to optimize the storage location according to the characteristics and access patterns of the data. Different storage strategies are required for different types of data to achieve the best performance and cost-effectiveness. Considering that the optimal data storage location can be determined according to the traffic type of the data and the characteristics of the compressed data, which can not only improve the data access speed and processing efficiency, but also ensure the secure storage of the data, ultimately reducing the storage cost and improving the flexibility of data management. Therefore, obtain the data storage location according to the type compressed data and the traffic type.

[0148] S51: Obtain a primary index according to the traffic type. In a specific embodiment, according to the traffic type of data transmission, such as ascending index the traffic type according to the sorting of IP traffic and port traffic.

[0149] S52: Obtain a secondary index according to the type compressed data. In a specific embodiment, according to the compression and decompression complexity and energy consumption of the compressed data, perform an ascending index on the type compressed data in ascending order.

[0150] S53: Obtain the data storage location according to the first-level index and the second-level index. In a specific embodiment, according to the physical layout of the storage system, the organization method of data, and the access mode, the data corresponding to the first-level index and the second-level index is distributedly stored. At the same time, as the data access mode changes, the first-level index and the second-level index need to be dynamically updated to optimize the storage and access efficiency of data.

[0151] A method for managing oilfield logging data based on big data in this embodiment ensures the unity of data through the decoding of various oilfield logging encapsulated data formats and big data classification; improves the compression performance and reading and writing efficiency of oilfield logging data through data compression and bit compression, and finally improves the efficiency of logging data management.

[0152] In a specific embodiment, please refer to Figure 5 , Figure 5 is a block diagram of a module of a method for managing oilfield logging data based on big data. A system for managing oilfield logging data based on big data includes:

[0153] An acquisition unit for obtaining oilfield logging encapsulated data, and this unit includes modern network communication and data storage systems.

[0154] An analysis unit for obtaining spliced data, data attributes, data types, and traffic types by using big data analysis on the oilfield logging encapsulated data. This unit integrates high-performance FPGA and SOC chips to achieve hardware acceleration of data analysis and effectively process the big data stream of oilfield logging encapsulated data.

[0155] A data compression unit for compressing the spliced data and the data attributes to obtain attribute compressed data. The hardware architecture of this unit includes a compression module and a decompression module, which can quickly process parallel data compression at the write port and parallel data decompression at the read port.

[0156] A bit compression unit for compressing the attribute compressed data and the data types to obtain type compressed data. The hardware architecture of this unit includes a compression module and a decompression module, which can process data processing in the data center in parallel.

[0157] A storage unit for obtaining the data storage location according to the type compressed data and the traffic type.

[0158] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0159] Although the present application has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of cases.

[0160] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for managing oilfield logging data based on big data, characterized in that: include: Obtain oilfield logging package data; Performing big data analysis on the oilfield logging package data to obtain spliced ​​data, data attributes, data types and flow types; Compressing the spliced ​​data and the data attributes to obtain attribute compressed data; Performing bit compression on the attribute compression data and the data type to obtain type compression data; A data storage location is obtained according to the type of compressed data and the traffic type.

2. The oilfield logging data management method based on big data according to claim 1, characterized in that: The big data analysis of the oilfield logging package data to obtain the spliced ​​data, data attributes, data types and flow types includes: According to the oilfield logging package data format, a corresponding decoder is used to obtain the original data; Using big data analysis to obtain data attributes and traffic types from the raw data; Using big data classification to obtain data types for the data attributes; The original data are spliced ​​according to the data attributes to obtain spliced ​​data.

3. The oilfield logging data management method based on big data according to claim 1, characterized in that: The step of compressing the spliced ​​data and the data attributes to obtain attribute compressed data includes: Obtaining a prediction operator, a transformation operator and a coding operator according to the spliced ​​data and the data attributes; Obtaining a data compression method according to the spliced ​​data, the prediction operator, the transformation operator and the encoding operator; A data compression method is applied to the spliced ​​data to obtain type compressed data.

4. The oilfield logging data management method based on big data according to claim 3 is characterized in that: The step of obtaining a prediction operator, a transformation operator and a coding operator according to the spliced ​​data and the data attributes includes: Obtaining data dimension according to the spliced ​​data; Acquire a corresponding prediction method, transformation method or encoding method according to the data dimension; Obtain a prediction unit, a transform unit, and a coding unit according to the data attribute and the data dimension; Obtaining a prediction operator according to the prediction method and the prediction unit; Obtaining a transformation operator according to the transformation method and the transformation unit; A coding operator is obtained according to the coding method and the coding unit.

5. The oilfield logging data management method based on big data according to claim 3 is characterized in that: The data compression method is obtained according to the spliced ​​data, the prediction operator, the transformation operator and the encoding operator: Performing data analysis on the spliced ​​data to obtain a data analysis result; Obtaining a prediction type, a transformation type, and a coding type according to the data analysis result; Obtaining a prediction compression method according to the prediction type and the prediction operator; Obtaining a transformation compression method according to the transformation type and the transformation operator; Obtaining a coding compression method according to the coding type and the coding operator; A data compression method is obtained according to the prediction compression method, the transformation compression method and the encoding compression method.

6. The oilfield logging data management method based on big data according to claim 1, characterized in that: The step of applying bit compression to the attribute compressed data and the data type to obtain type compressed data includes: Obtaining an index coding operator and a bit coding operator according to the data type; Obtaining a bit compression method according to the attribute compression data, the index encoding operator and the bit encoding operator; The attribute compressed data is subjected to the bit compression method to obtain type compressed data.

7. The oilfield logging data management method based on big data according to claim 6, characterized in that: The obtaining of the index coding operator and the bit coding operator according to the data type includes: Obtaining the number of data attributes and data attribute characteristics according to the data type; Obtaining an index encoding method and a bit encoding method according to the number of data attributes and the characteristics of the data attributes; Obtaining a bit encoding unit according to the data type and the number of data attributes; Obtaining an index coding operator according to the index coding method; A bit encoding operator is obtained according to the bit encoding method and the bit encoding unit.

8. The oilfield logging data management method based on big data according to claim 6, characterized in that: The obtaining of the bit compression method according to the attribute compression data, the index encoding operator and the bit encoding operator comprises: Applying compression analysis to the attribute compression data to obtain a compression analysis result; Obtaining an index coding type and a bit coding type according to the compression analysis result; A bit compression method is obtained according to the index coding type, the index coding operator, the bit coding type and the bit coding operator.

9. The oilfield logging data management method based on big data according to claim 1, characterized in that: The obtaining of a data storage location according to the type of compressed data and the traffic type includes: Obtaining a primary index according to the traffic type; Compressing data according to the type to obtain a secondary index; A data storage location is obtained according to the primary index and the secondary index.

10. An oilfield logging data management system based on big data, characterized in that: include: An acquisition unit, used to obtain oilfield logging package data; An analysis unit, used for performing big data analysis on the oilfield logging packaged data to obtain spliced ​​data, data attributes, data types and flow types; A data compression unit, used for compressing the spliced ​​data and the data attributes to obtain attribute compressed data; A bit compression unit, used for performing bit compression on the attribute compression data and the data type to obtain type compression data; A storage unit is used to obtain a data storage location according to the type of compressed data and the traffic type.

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