Oil field logging perforation data compression method and system

Abstract

The invention discloses an oil field well logging perforation data compression method and system, and the method comprises the steps: obtaining oil field well logging perforation data, a three-dimensional mapping specification and a frequency spectrum compression mapping table, carrying out the dimension normalization of the oil field well logging perforation data through employing the three-dimensional mapping specification, and obtaining perforation standard data and a data recording table, performing space-time compression on the perforation standard data according to the frequency spectrum compression mapping table to obtain segmented compressed data, performing fixed-length coding on the data recording table to obtain a segmented sequence number recording table, and packaging the segmented compressed data according to the segmented sequence number recording table to obtain perforation compressed data. Space-time compression is carried out through the frequency spectrum compression mapping table, and the compression ratio is remarkably increased while millisecond-level dynamic details and micron-level geologic features of underground data are reserved; through multi-dimensional data normalization processing, redundant information among different dimensional data is eliminated, so that the compression ratio of the logging perforation data is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of compression algorithms, and in particular to an oilfield well logging perforation data compression method and system. Background Art

[0002] With the continued advancement of oilfield digital transformation, intelligent wellsite construction, and real-time reservoir dynamic analysis, the demand for real-time processing and precise analysis of logging and perforation data is becoming increasingly urgent. Efficient data compression technology has become a key breakthrough. Because applications such as real-time adjustment of drilling trajectories and dynamic optimization of perforation parameters require accurate reproduction of millisecond-level changes and micron-level details in downhole pressure and imaging logging data, the volume of oilfield logging and perforation data is exploding, posing a significant challenge to existing compression algorithms.

[0003] On the one hand, existing technologies suffer from low compression ratios, which not only easily distorts key geological features (such as fracture morphology and pore structure), affecting reservoir evaluation accuracy, but also struggles to meet storage cost controls and bandwidth requirements for remote, real-time transmission of massive amounts of data, hindering the responsiveness and stability of backend decision-making and analysis systems. Furthermore, existing technologies fail to consider the uniformity of data dimensions, employing independent compression and storage strategies when processing data of different dimensions, resulting in a significant amount of redundant information. 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 compressing oilfield logging perforation data. The technical problem to be solved by the present invention is achieved through the following technical solutions: A method for compressing oilfield well logging and perforation data, comprising: Obtaining oilfield logging perforation data, three-dimensional mapping specifications, and a spectrum compression mapping table, wherein the oilfield logging perforation data includes one-dimensional perforation data, two-dimensional perforation data, and three-dimensional perforation data, and the spectrum compression mapping table is determined according to a spectrum energy ratio; Normalizing the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table; Performing spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and performing fixed-length encoding on the data record table to obtain a segmented sequence number record table; The segmented compression data is encapsulated according to the segmented sequence number record table to obtain the perforation compression data.

[0005] In a specific embodiment, the dimensional normalization of the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table includes: Performing three-dimensional mapping on the one-dimensional perforation data and the two-dimensional perforation data to obtain first mapping data and second mapping data having a three-dimensional data structure; Performing bit width alignment on the first mapping data, the second mapping data, and the three-dimensional perforation data and then performing dimension correlation rearrangement to obtain the perforation standard data so that the perforation standard data has a preset data association relationship; A data record table is formed according to the three-dimensional mapping specification and the preset data association relationship, wherein the data record table includes record data in a three-dimensional mapping order and record data in a dimension correlation rearrangement order.

[0006] In a specific embodiment, the performing spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and performing fixed-length encoding on the data record table to obtain a segmented sequence number record table, includes: Performing spatial segmentation on the perforation standard data to obtain a plurality of perforation block data; compressing the perforation block data according to the spectrum compression mapping table to obtain perforation block compressed data; splicing the perforation block compressed data to obtain segmented compressed data so that data at adjacent physical locations are continuous in storage; The data record table is coded with fixed length to obtain a segment sequence number record table to record the mapping relationship between the original position of the perforation block and the compressed storage position.

[0007] In a specific embodiment, the spatial segmentation of the perforation standard data to obtain a plurality of perforation block data includes: Performing basic block specification segmentation on the perforation standard data to obtain basic block data, wherein the basic block data is determined according to the pixels and segmentation scale of the perforation standard data; After performing a three-dimensional Fourier transform on the basic block data, determining the data level of each basic block data by using the spectrum energy ratio; The adjacent basic block data are fused according to the data level to obtain the perforation block data. The fusion rule is: adjacent basic blocks with the same data level and basic blocks with a level difference of 1 are fused, and the pixels of the fused perforation block data are made smaller than a preset value.

[0008] In a specific embodiment, compressing the perforation block data according to the spectrum compression mapping table to obtain perforation block compressed data includes: According to the spectrum complexity level, a corresponding candidate compression method is searched from the spectrum compression mapping table to compress the perforation block data according to the candidate compression method to obtain perforation block compressed data, wherein the candidate compression method is determined according to a preset prediction rule, a preset transformation rule and a preset encoding rule.

[0009] In a specific embodiment, the method for calculating the spectrum compression mapping table includes: Acquiring test data, wherein the test data is perforation standard data; Performing spectrum classification on the test data to obtain spectrum serial numbers and corresponding spectrum data; compressing the spectrum data using a candidate compression method to obtain a candidate compression ratio; The spectrum compression mapping table is obtained by setting a matching candidate compression mode for the spectrum data according to the candidate compression rate.

[0010] In a specific embodiment, the preset prediction rules include spatial correspondence prediction, spatial direction prediction, extreme value prediction and direct transmission prediction, the preset transformation rules include Haar wavelet transform, Hadamard transform and direct transformation, and the preset encoding rules include Huffman coding, Golomb coding and run-length coding.

[0011] In a specific embodiment, compressing the perforation block data according to the candidate compression method to obtain perforation block compressed data includes: dividing the perforation block data into a plurality of basic compression units, wherein the basic compression unit is a minimum processing unit having consistent spectral characteristics; Performing spectrum analysis on the basic compression unit to obtain a spectrum sequence number and a candidate spectrum compression method; Performing fixed-length encoding on the spectrum sequence number to obtain basic header compressed data; Compressing the basic unit using a spectrum candidate compression method to obtain basic information compressed data; All basic header compressed data and basic information compressed data are concatenated to obtain the perforation block compressed data.

[0012] In a specific embodiment, encapsulating the segmented compressed data according to the segmented sequence number record table to obtain the perforation compression data includes: byte-level aligning of the segmented compressed data to obtain segmented spliced ​​data, wherein the segmented spliced ​​data has a preset byte length; Separately encapsulating each segmented spliced ​​data according to the data sequence number in the segment sequence number record table to obtain segmented encapsulated data so that the access address of each segmented encapsulated data is unique; The data serial number and the component packaging data are integrally packaged to obtain perforation compression data.

[0013] In one embodiment, an oilfield logging and perforation data compression system includes: An acquisition unit, configured to acquire oilfield logging and perforation data, a three-dimensional mapping specification, and a spectrum compression mapping table, wherein the oilfield logging and perforation data includes one-dimensional perforation data, two-dimensional perforation data, and three-dimensional perforation data, and the spectrum compression mapping table is determined based on a spectrum energy ratio; a processing unit, configured to perform dimension normalization on the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table; a compression unit, configured to perform spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and perform fixed-length encoding on the data record table to obtain a segmented sequence number record table; The encapsulation unit is used to encapsulate the segmented compression data according to the segmented sequence number record table to obtain perforation compression data.

[0014] Beneficial effects of the present invention: The present invention provides a method and system for compressing oilfield logging and perforation data. This system uses a spectrum compression mapping table to perform spatiotemporal compression, significantly improving the compression ratio while retaining millisecond-level dynamic details and micron-level geological features of downhole data. Furthermore, multi-dimensional data normalization eliminates redundant information between data of different dimensions, further improving the compression rate of logging and perforation data.

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for compressing oilfield logging and perforation data provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of first mapping data of an oilfield logging and perforating data compression system provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of a compression method of an oilfield logging perforation data compression system provided by an embodiment of the present invention; Figure 4 This is a module block diagram of an oilfield logging and perforating data compression system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0018] Example 1 In one embodiment, see Figure 1 , Figure 1 This is a flow chart of an oilfield logging perforation data compression method. The specific steps are as follows: S1 obtains oilfield logging perforation data, three-dimensional mapping specifications and spectrum compression mapping table, wherein the oilfield logging perforation data includes one-dimensional perforation data, two-dimensional perforation data and three-dimensional perforation data, the spectrum compression mapping table is determined according to the spectral energy ratio; Specifically, the one-dimensional perforating data includes conventional logging curves, real-time logging while drilling (LWD) data and production monitoring data, such as natural gamma ray (GR), drilling pressure and oil well fluid production; the two-dimensional perforating data includes imaging logging plane data and two-dimensional maps of perforating process parameters, such as electrical imaging logging (FMI) images and the angular distribution of perforating bullets around the well; the three-dimensional perforating data includes three-dimensional geological model data and three-dimensional perforating data, such as a three-dimensional formation structure model and a three-dimensional erosion model of perforating holes.

[0019] The calculation method of the spectrum compression mapping table is: 1. Obtain test data, where the test data is standard perforation data. Standard perforation data is used as training samples because it has been dimensionally normalized (unified into a 3D format). Its spectral characteristics are representative and can cover typical data types used in actual operations (such as 1D curves, 2D images, and 3D models), ensuring the universality of the mapping table.

[0020] 2. Spectral classification of the test data to obtain spectrum numbers and corresponding spectrum data: Quantify features and generate spectrum numbers. The reason is that similar data can be adapted to the same compression strategy. Classification examples include: Three-dimensional FFT transform: Perform a fast Fourier transform on the test data block (such as 16×16×16 pixels) to obtain the frequency domain energy distribution.

[0021] Feature extraction: Calculate the low-frequency energy proportion (f < 10 Hz), medium-frequency energy proportion (10 ≤ f < 50 Hz), and high-frequency energy proportion (f ≥ 50 Hz).

[0022] Clustering and grouping: K-means clustering was used to divide the data into 5 spectral levels (numbered 1-5).

[0023] No. 1 (Low-Frequency Dominance): Low-frequency energy accounts for > 90%, such as density data of homogeneous formations (for example, a sandstone block has 92% low-frequency energy); No. 2 (medium-low frequency mixed): low-frequency energy accounts for more than 80%, such as density data of non-uniform formations; No. 3 (intermediate frequency mixing): The intermediate frequency energy accounts for 60%-80%, such as the acoustic transit time data of sandstone and mudstone interbeds (example: intermediate frequency energy 75%). No. 4 (mid-high frequency mixed): mid-frequency energy accounts for 30%-60%, such as non-uniform sound wave time difference data; No. 5 (High-Frequency Rich): The high-frequency energy ratio is greater than 30%, such as the pressure gradient data at the edge of the perforation hole (example: high-frequency energy 35%).

[0024] 3. Compress the spectral data using the candidate compression methods to obtain candidate compression ratios: Calculate the compression performance of each combination. To avoid deviations between theoretical derivation and practical application, it is necessary to measure the effects of different compression methods on similar spectral data.

[0025] 4. Setting a matching candidate compression mode for the spectrum data according to the candidate compression ratio to obtain the spectrum compression mapping table.

[0026] S2. Normalizing the oilfield logging perforation data using the three-dimensional mapping specifications to obtain perforation standard data and data record table; S21. See Figure 2 , Figure 2 This is a schematic diagram of first mapping data for an oilfield logging and perforation data compression system. The system performs three-dimensional mapping on one-dimensional and two-dimensional perforation data to generate first and second mapping data with three-dimensional data structures. For example, for one-dimensional perforation data (such as a pressure-time curve from a well logging while drilling), a three-dimensional grid is constructed based on the "depth-time-pressure value" dimension. For example, for a well section with a depth of 1000-1100m, pressure data is collected every 10 seconds. This data is mapped into a 100×6×1 three-dimensional matrix (100 depth points, 6 time points, and 1 pressure parameter) to form the first mapping data. For two-dimensional perforation data (such as electrical imaging logging images), the third dimension is expanded based on the "pixel coordinate - layer number" equation. For example, a 200×200 pixel electrical imaging image corresponding to the well section 2000-2001 m is mapped to a 200×200×2 3D data volume (x / y pixel coordinates, with the z axis representing the upper and lower layer numbers), forming the second mapping data.

[0027] S22. The first mapping data, the second mapping data, and the three-dimensional perforation data are aligned in bit width, and then dimensionally reordered to obtain the perforation standard data so that the perforation standard data has a preset data association relationship. For example, if the bit width of the first mapping data (pressure curve) is 16 bits, the bit width of the second mapping data (electrical imaging grayscale value) is 8 bits, and the bit width of the three-dimensional perforation data (geological model porosity) is 24 bits, the normalized bit width is the maximum of the three, 24 bits. The perforation normalized data is reordered using dimensionally reordered to obtain the perforation standard data. For example, based on the continuity of geological horizons, the three-dimensional data is reordered according to a "depth-first" principle. For example, the three-dimensional mapping data of the 1000-1100 m well section is divided into 10-m layers, and the data within the same layer is arranged in "xy coordinate" order so that similar lithologic data (e.g., density values ​​of sandstone sections) at adjacent depths are stored continuously.

[0028] The 3D pressure field data from perforating operations are rearranged according to the “time-space” correlation: the pressure values ​​of different well sections at the same time are stored together to facilitate the use of the differential characteristics of the time series during subsequent compression (for example, when the pressure fluctuation between adjacent moments is ≤5%, only the difference is stored).

[0029] S23. Form a data record table according to the three-dimensional mapping specification and the preset data association relationship, wherein the data record table includes record data in a three-dimensional mapping order and record data in a dimension correlation rearrangement order.

[0030] For example, specific record contents include: Record data of the 3D mapping sequence: Clarify how each raw data is expanded into a 3D format in the 3D mapping step (for example, the "time" dimension and step size added to the 1D pressure curve, and the "layer" dimension definition added to the 2D FMI image).

[0031] Record data in the reordering order of dimension correlation: Record the specific reordering strategy applied to each data or data block in dimension correlation (for example, indicate whether to store data in a "depth-first" hierarchical manner or to store spatial data at the same time in a "time-first" manner, and briefly describe the sorting logic).

[0032] Original data identification and location information: associate the original data entry with its corresponding data block location (starting offset, size) in the final "perforating standard data" volume.

[0033] This table serves as a "reverse operation manual." Later in the process, when the compressed and decompressed "perforation standard data" needs to be restored to its original, application-readable 1D, 2D, or 3D format, the reverse operation must be performed strictly according to the mapping rules and rearrangement order documented in this data record table. Without this table, the standard data cannot be correctly interpreted and restored. Therefore, it is critical metadata to ensure the reversible conversion and ultimate application of data.

[0034] S3. According to the spectrum compression mapping table, the perforation standard data is compressed in time and space to obtain segmented compressed data, and the data record table is fixed-length encoded to obtain a segmented serial number record table; S31. Spatially segmenting the perforation standard data to obtain a plurality of perforation block data. This is to divide the large-scale three-dimensional data into smaller blocks to facilitate parallel processing and adaptive compression (data characteristics vary significantly across regions, requiring targeted compression). S311. Segment the perforation standard data into basic block data. The basic block data is determined based on the pixels and segmentation scale of the perforation standard data. For example, the perforation standard data is evenly divided into a preset 3D grid size (e.g., 16×16×16 pixels). For example, a 3D geological model of an oilfield block (1000×800×200 pixels, corresponding to an area of ​​10 km×8 km×2 km) can be segmented into (1000 / 16)×(800 / 16)×(200 / 16)≈63×50×13=40,950 basic blocks.

[0035] S312. After performing a 3D Fourier transform on the basic block data, the data level of each basic block data is determined by the spectrum energy ratio. For example, a 3D Fourier transform is performed on the basic block, the high-frequency and low-frequency energy ratios are calculated, and the data level is set from high to low according to the ratios.

[0036] S313 fuses the adjacent basic block data according to the data level to obtain the perforation block data. The fusion rule is: fuse adjacent basic blocks with the same data level and basic blocks with a level difference of 1, and make the pixels of the fused perforation block data smaller than a preset value. Since directly compressing all basic blocks will lead to computational redundancy, fusing similar blocks can reduce the number of processing units and improve compression efficiency. Setting the fusion rule includes: Same-level priority fusion: Adjacent basic blocks with the same data level are prioritized for fusion. For example, eight consecutive "low-level" (16×16×16 pixel) basic blocks in a certain area can be fused into a single perforation block of 32×32×32 pixels.

[0037] Limited cross-level fusion: allows fusion of blocks with a level difference of 1.

[0038] The fusion termination condition is that the upper limit of the perforation block size cannot exceed 64×64×64 pixels to avoid memory overflow caused by a single block being too large.

[0039] S32. Compressing the perforation block data according to the spectrum compression mapping table to obtain perforation block compressed data, the purpose of which is to select the optimal compression algorithm based on the data spectrum characteristics (frequency distribution) to balance the compression ratio and accuracy; According to the spectrum complexity level, a corresponding candidate compression method is searched from the spectrum compression mapping table to compress the perforation block data according to the candidate compression method to obtain perforation block compressed data, wherein the candidate compression method is determined according to a preset prediction rule, a preset transformation rule and a preset encoding rule.

[0040] Specifically, for the calculation of spectral complexity levels, considering that the frequency distribution of data in different regions varies significantly, such as the low-frequency characteristics of smooth formations and the high-frequency characteristics of fracture edges, spectral analysis can quantify data complexity, providing a basis for selecting compression algorithms and avoiding "one-size-fits-all" compression that results in loss of detail or low compression efficiency. Taking a perforation block (16×16×16 pixel electrical imaging grayscale data) as an example, it includes: Three-dimensional FFT transform: Perform fast Fourier transform on the 16×16×16=4096 grayscale values ​​in the block to obtain a three-dimensional matrix in the frequency domain; Energy distribution calculation: Calculate the energy proportion in different frequency ranges (e.g. low frequency f<10, medium frequency 10≤f<50, high frequency f≥50); Complexity level classification: If the low-frequency energy ratio is greater than 90% (e.g., smooth mudstone section), it is judged as “low complexity”; If the medium-frequency energy accounts for 60%-80% (such as the interface between sandstone and mudstone), it is judged as "medium complexity"; If the high-frequency energy ratio is greater than 30% (such as a sudden change in grayscale at the crack edge), it is judged as “high complexity”.

[0041] Specifically, the compression process includes: Dividing the perforation block data into a plurality of basic compression units, wherein the basic compression unit is a minimum processing unit with consistent spectral characteristics, for example, dividing a 16×16×16 pixel perforation block into eight 8×8×8 pixel basic units; Perform spectrum analysis on the basic compression unit to obtain the spectrum number and candidate spectrum compression methods. For example, unit A (hole center pressure): the high-frequency energy accounts for 40%, the spectrum number is 3, and the candidate method is "extreme value prediction + Haar wavelet transform"; Unit B (far well pressure): low-frequency energy accounts for 95%, the spectrum number is 1, and the candidate method is "spatial correspondence prediction + run-length encoding".

[0042] Performing fixed-length encoding on the spectrum sequence number to obtain basic header compressed data, for example, 8-bit binary encoding may be used; The basic unit is compressed using a spectrum candidate compression method to obtain basic information compressed data, see Figure 3 , Figure 3 The present invention is a schematic diagram of a compression method for an oilfield logging perforation data compression system. The candidate compression methods include a cross combination of four prediction methods, three transformation methods, and three encoding methods. The prediction methods include spatial correspondence prediction, spatial direction prediction, extreme value prediction, and direct propagation prediction. The transformation methods include Haar wavelet transform, Hadamard transform, and direct propagation transform. The encoding methods include Huffman coding, Columbus coding, and run-length coding. Specifically, Spatial correspondence prediction, based on the correlation of adjacent spatial point data, uses stored point values ​​to predict the current point value, and only stores the residual (actual value - predicted value). It is suitable for data with strong spatial continuity (such as physical properties of homogeneous strata), with small prediction errors and a small amount of residual data.

[0043] Spatial direction prediction makes predictions along the main direction of the data change trend (such as the direction of stratum inclination), taking into account the directional correlation of three-dimensional space. It is suitable for data with obvious directionality (such as layered strata and fracture directions), and uses directional characteristics to improve prediction accuracy.

[0044] Extreme value prediction stores the maximum, minimum, and position of a data block, and uses extreme values ​​to replace the full amount of data. It is suitable for areas with small fluctuations and for locally stable data (such as constant temperature sections underground). It uses 2 values ​​to replace N values, with a compression ratio of N:2.

[0045] Direct transmission prediction directly transmits the original value for data that changes dramatically and cannot be effectively predicted (such as sudden pressure changes at the moment of perforation). It is suitable for non-stationary sudden change data (such as transient signals in engineering operations), sacrificing compression ratio in exchange for accuracy.

[0046] The Haar wavelet transform is suitable for data with edge features (such as imaging images and hole boundaries). The locality of the wavelet transform can accurately locate features.

[0047] The Hadamard transform is suitable for data with periodicity and symmetry (such as the periodic flow curve of production logging), and the spectrum becomes sparse after the transformation.

[0048] Direct transformation is suitable for mixed feature data (such as hole mutation + far well smoothing), balancing global transformation and local detail preservation.

[0049] Huffman coding is suitable for data with uneven probability distribution (such as imaging grayscale and discrete values ​​of logging curves), and uses frequency characteristics to reduce code length.

[0050] Golomb coding is suitable for small-range integer data (such as prediction residuals and transform coefficients). The coding efficiency increases as the numerical range decreases.

[0051] Run-length encoding is suitable for highly repetitive data (such as homogeneous formation properties and perforation phase repetitive patterns). The more repetitions, the higher the compression ratio.

[0052] Examples of cross-combination compression methods: Unit A (high complexity pressure data): Extreme value prediction: Calculate the maximum pressure (100MPa) and minimum pressure (80MPa) in the cell, use the extreme value to replace the full data, and generate the residual (actual value - extreme value); Haar wavelet transform: perform wavelet decomposition on the residual, retaining high-frequency coefficients and discarding low-frequency coefficients; Encoding: Huffman encoding is performed on the retained coefficients, and the original 512 16-bit pressure values ​​(1KB) are compressed to 256 bytes (compression ratio 4:1).

[0053] Unit B (low complexity pressure data): Spatial correspondence prediction: Use the pressure values ​​of adjacent pixels (such as 90MPa for the previous pixel) to predict the current value, and only store the difference (such as +2MPa). Run-length encoding: The difference of 10 consecutive pixels is + 2MPa, which is encoded as "10 2-bits"; The original 512 values ​​(1KB) are compressed to 128 bytes (compression ratio 8:1).

[0054] The compressed data of the perforation block is obtained by concatenating all the basic header compressed data and the basic information compressed data. For example, a perforation block contains 8 basic units, each unit has 1 byte of header data (8 bytes in total), and the total compressed information data is 1.5KB (256+128+…). The final compressed data of the perforation block is 1.5KB+8 bytes≈1.51KB (the original perforation block data is 16×16×16×2 bytes = 8KB, with an overall compression ratio of 5.3:1). S33. Splicing the compressed data of the perforation blocks to obtain segmented compressed data so that data at adjacent physical locations are stored continuously. This is to organize the independently compressed perforation blocks into a continuous data stream for easier storage and transmission, including: Byte alignment: Each perforation block's compressed data is padded to a fixed byte length (e.g., 512 bytes), with any missing bytes padded with zeros. For example, a perforation block compressed to 240 bytes is padded to 512 bytes (with 272 zeros); another perforation block compressed to 500 bytes is padded to 512 bytes (with 12 zeros).

[0055] Segment splicing rules: Perforation blocks are spliced ​​sequentially based on their spatial location (such as depth order or xyz coordinates) to ensure that data at adjacent physical locations is stored continuously. For example, the perforation blocks in the 1000-1010m section are spliced ​​first, followed by the perforation blocks in the 1010-1020m section, to facilitate subsequent rapid access by depth range.

[0056] S34. Fixed-length encoding is used on the data record table to generate a segmented sequence number record table to record the mapping relationship between the original position of the perforation block and the compressed storage location. This is to record the mapping relationship between the original position of the perforation block and the compressed storage location, supporting fast positioning during decompression. For example, the data record table may include information such as the original three-dimensional coordinates (e.g., depth range, xy plane range), size before compression, size after compression, and starting offset in the segmented compressed data for each perforation block. These variable-length fields (e.g., coordinate range, size) are converted to fixed-length encoding. Specifically, the depth range (1000-1002m) is represented by a 16-bit integer (starting depth 1000m → 0x03E8, interval length 2m → 0x0002); the compressed size (2.4KB) is represented by an 8-bit integer (precision 0.1KB, 2.4KB → 0x18); and the storage offset (0 byte) is represented by a 32-bit integer (supporting up to 4GB of data). Finally, each record is fixed to 64 bits (8 bytes), and the segmented sequence number record table of 1,000 records only requires 8KB, with a compression rate close to 100% (the original record table is about 100KB).

[0057] S4. Encapsulating the segmented compression data according to the segmented sequence number record table to obtain perforation compression data.

[0058] S41. Align the segmented compressed data at the byte level to obtain segmented spliced ​​data, wherein the segmented spliced ​​data has a preset byte length; for example, the alignment rule is: The default byte length is set to 512 bytes, and the segmented compressed data is padded: Segment A compressed data is 300 bytes: fill with 212 bytes of zeros and expand to 512 bytes; Segment B compressed data is 500 bytes: padded with 12 bytes of zeros to expand to 512 bytes; The compressed data of segment C is 512 bytes: it can be used directly without padding.

[0059] S42. Each segment of the spliced ​​data is individually encapsulated according to the data serial number in the segment serial number record table to obtain segment encapsulated data so that the access address of each segment encapsulated data is unique. Specifically, when encapsulating, an address header (8 bytes) is added to each segment: Segment 0 address header: [sequence number 0] [offset 0] [length 512], concatenated with 512-byte data to encapsulate into 520 bytes; Segment 1 address header: [sequence number 1][offset 512][length 512], encapsulated with the spliced ​​data into 520 bytes; The address header uses fixed-length encoding to ensure fast parsing during decompression (for example, a 32-bit integer is used to represent the offset, supporting a maximum of 4GB of data).

[0060] S43. The data serial number and the component packaging data are integrally packaged to obtain perforation compressed data for easy transmission and long-term storage (such as archiving to a distributed file system), while ensuring integrity verification during decompression.

[0061] The oilfield logging and perforation data compression method and system of this embodiment performs spatiotemporal compression through a spectrum compression mapping table, significantly improving the compression ratio while retaining millisecond-level dynamic details and micron-level geological features of downhole data; and further improving the compression rate of logging and perforation data by eliminating redundant information between data of different dimensions through multi-dimensional data normalization processing. This implementation also provides an oilfield logging perforation data compression system, see Figure 4 , Figure 4 This is a module block diagram of an oilfield logging and perforating data compression system, including: An acquisition unit, configured to acquire oilfield logging and perforation data, a three-dimensional mapping specification, and a spectrum compression mapping table, wherein the oilfield logging and perforation data includes one-dimensional perforation data, two-dimensional perforation data, and three-dimensional perforation data, and the spectrum compression mapping table is determined based on a spectrum energy ratio; a processing unit, configured to perform dimension normalization on the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table; a compression unit, configured to perform spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and perform fixed-length encoding on the data record table to obtain a segmented sequence number record table; The encapsulation unit is used to encapsulate the segmented compression data according to the segmented sequence number record table to obtain perforation compression data.

[0062] Although the present application is described herein with reference to various embodiments, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims in the process of implementing the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0063] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for compressing oilfield logging perforation data, characterized in that: include: Obtaining oilfield logging perforation data, three-dimensional mapping specifications, and a spectrum compression mapping table, wherein the oilfield logging perforation data includes one-dimensional perforation data, two-dimensional perforation data, and three-dimensional perforation data, and the spectrum compression mapping table is determined according to a spectrum energy ratio; Normalizing the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table; Performing spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and performing fixed-length encoding on the data record table to obtain a segmented sequence number record table; The segmented compression data is encapsulated according to the segmented sequence number record table to obtain the perforation compression data.

2. The oilfield logging perforation data compression method according to claim 1, characterized in that: The step of normalizing the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table includes: Performing three-dimensional mapping on the one-dimensional perforation data and the two-dimensional perforation data to obtain first mapping data and second mapping data having a three-dimensional data structure; Performing bit width alignment on the first mapping data, the second mapping data, and the three-dimensional perforation data and then performing dimension correlation rearrangement to obtain the perforation standard data so that the perforation standard data has a preset data association relationship; A data record table is formed according to the three-dimensional mapping specification and the preset data association relationship, wherein the data record table includes record data in a three-dimensional mapping order and record data in a dimension correlation rearrangement order.

3. The oilfield logging perforation data compression method according to claim 1, characterized in that: The performing spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and performing fixed-length encoding on the data record table to obtain a segmented sequence number record table, includes: Performing spatial segmentation on the perforation standard data to obtain a plurality of perforation block data; compressing the perforation block data according to the spectrum compression mapping table to obtain perforation block compressed data; splicing the perforation block compressed data to obtain segmented compressed data so that data at adjacent physical locations are continuous in storage; The data record table is coded with fixed length to obtain a segment sequence number record table to record the mapping relationship between the original position of the perforation block and the compressed storage position.

4. The oilfield logging perforation data compression method according to claim 3, characterized in that: The spatial segmentation of the perforation standard data to obtain a plurality of perforation block data includes: Performing basic block specification segmentation on the perforation standard data to obtain basic block data, wherein the basic block data is determined according to the pixels and segmentation scale of the perforation standard data; After performing a three-dimensional Fourier transform on the basic block data, determining the data level of each basic block data by using the spectrum energy ratio; The adjacent basic block data are fused according to the data level to obtain the perforation block data. The fusion rule is: adjacent basic blocks with the same data level and basic blocks with a level difference of 1 are fused, and the pixels of the fused perforation block data are made smaller than a preset value.

5. The oilfield logging perforation data compression method according to claim 3, characterized in that: The compressing the perforation block data according to the spectrum compression mapping table to obtain perforation block compressed data includes: According to the spectrum complexity level, a corresponding candidate compression method is searched from the spectrum compression mapping table to compress the perforation block data according to the candidate compression method to obtain perforation block compressed data, wherein the candidate compression method is determined according to a preset prediction rule, a preset transformation rule and a preset encoding rule.

6. The oilfield logging perforation data compression method according to claim 5, characterized in that: The method for calculating the spectrum compression mapping table includes: Acquiring test data, wherein the test data is perforation standard data; Performing spectrum classification on the test data to obtain spectrum serial numbers and corresponding spectrum data; compressing the spectrum data using a candidate compression method to obtain a candidate compression ratio; The spectrum compression mapping table is obtained by setting a matching candidate compression mode for the spectrum data according to the candidate compression rate.

7. The oilfield logging perforation data compression method according to claim 5, characterized in that: The preset prediction rules include spatial correspondence prediction, spatial direction prediction, extreme value prediction and direct transmission prediction; the preset transformation rules include Haar wavelet transform, Hadamard transform and direct transformation; the preset encoding rules include Huffman coding, Golomb coding and run-length coding.

8. The oilfield logging perforation data compression method according to claim 5, characterized in that: The compressing the perforation block data according to the candidate compression method to obtain perforation block compressed data includes: dividing the perforation block data into a plurality of basic compression units, wherein the basic compression unit is a minimum processing unit having consistent spectral characteristics; Performing spectrum analysis on the basic compression unit to obtain a spectrum sequence number and a candidate spectrum compression method; Performing fixed-length encoding on the spectrum sequence number to obtain basic header compressed data; Compressing the basic unit using a spectrum candidate compression method to obtain basic information compressed data; All basic header compressed data and basic information compressed data are concatenated to obtain the perforation block compressed data.

9. The oilfield logging perforation data compression method according to claim 1, characterized in that: The step of encapsulating the segmented compressed data according to the segmented sequence number record table to obtain the perforation compressed data includes: byte-level aligning of the segmented compressed data to obtain segmented spliced ​​data, wherein the segmented spliced ​​data has a preset byte length; Separately encapsulating each segmented spliced ​​data according to the data sequence number in the segment sequence number record table to obtain segmented encapsulated data so that the access address of each segmented encapsulated data is unique; The data serial number and the component packaging data are integrally packaged to obtain perforation compression data.

10. An oilfield logging and perforation data compression system, characterized in that: include: An acquisition unit, configured to acquire oilfield logging and perforation data, a three-dimensional mapping specification, and a spectrum compression mapping table, wherein the oilfield logging and perforation data includes one-dimensional perforation data, two-dimensional perforation data, and three-dimensional perforation data, and the spectrum compression mapping table is determined based on a spectrum energy ratio; a processing unit, configured to perform dimension normalization on the oilfield logging perforation data using the three-dimensional mapping specification to obtain perforation standard data and a data record table; a compression unit, configured to perform spatiotemporal compression on the perforation standard data according to the spectrum compression mapping table to obtain segmented compressed data, and perform fixed-length encoding on the data record table to obtain a segmented sequence number record table; The encapsulation unit is used to encapsulate the segmented compression data according to the segmented sequence number record table to obtain perforation compression data.

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