Drilling histogram structured processing method and system based on multi-source data fusion

Abstract

The invention belongs to the technical field related to borehole histograms, and provides a borehole histogram structured processing method and system based on multi-source data fusion, for accessed multi-source heterogeneous data including stratum basic data, logging curve data and map type data, non-structured and / or semi-structured data is converted into structured fields, and the structured fields are converted into structured fields; the professional format data are analyzed into standard fields, data types and codes are unified, and field mapping and format conversion of the multi-source heterogeneous data are achieved; and associating the data subjected to field mapping and format conversion with a preset database, and forming a hole-stratum-logging-quality integrated data model after final check and stratum-logging data depth alignment and collaborative verification. According to the method, structured processing and quality control of the drilling data can be rapidly and accurately realized.

Description

Technical Field

[0001] This invention belongs to the technical field of borehole columnar plots, and particularly relates to a method and system for structured processing of borehole columnar plots based on multi-source data fusion. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Geological borehole data serves as core foundational data in numerous fields, including geological exploration, geothermal resource development, hydrogeological assessment, and mine engineering design. Its accuracy and usability directly impact the quality of related work. Borehole columnar sections, a crucial presentation format of geological borehole data, are typically available in DWG, PDF, or image formats. They comprehensively record key information such as stratigraphic layering, lithological characteristics, geological age, borehole depth parameters, sampling horizons, and corresponding textual descriptions. These sections are essential for revealing stratigraphic structure, analyzing lithological variation patterns, and deducing geological evolution processes.

[0004] Under the major trend of the geological industry's transformation towards informatization and digitalization, the structured processing and database management of borehole columnar section data has become a key bottleneck restricting the industry's development. Currently, most geological exploration, geothermal, and hydrological projects still rely on traditional drawing software such as AutoCAD, Surfer, and CorelDRAW to draw two-dimensional composite columnar sections. While this method has the advantages of being intuitive, easy to understand, and convenient for on-site reporting, it has significant drawbacks: First, stratigraphic information is stored in a scattered manner in the form of graphic elements, making it impossible to directly query attributes and perform statistical analysis; second, data modification and summarization require manual operation, which is inefficient and prone to errors, making it difficult to meet the needs of large-scale data processing; third, there is a lack of standardized interfaces between CAD drawings and subsequent database systems and modeling software, resulting in poor data flow.

[0005] In addition, existing technologies also have the following prominent problems: On the one hand, formation layering data and well logging curve data (such as gamma logging GR, density logging data, resistivity logging data, etc.) are independent of each other, lacking effective automatic correlation analysis mechanisms and data consistency verification methods, making it difficult to ensure the collaborative effectiveness of multi-source data; on the other hand, data quality control methods are singular, mainly relying on manual inspection, lacking multi-dimensional and automated quality control mechanisms, and unable to comprehensively and efficiently identify data errors and anomalies; at the same time, the plotting software and database system are disconnected, resulting in asynchronous data updates and chaotic version management, further affecting the reliability and application value of the data.

[0006] Therefore, there is an urgent need for an integrated method that can connect the entire process of comprehensive bar chart data, which can not only meet the actual needs of rapid processing, batch operation and full traceability in engineering sites, but also provide a reliable and standardized data foundation for subsequent numerical simulation (including reservoir modeling), exploration report preparation and supervision and reporting. This has become a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a method and system for structured processing of borehole column charts based on multi-source data fusion. It effectively combines standardized data modeling technology, multi-level verification technology, and multi-source data fusion verification technology, which can quickly and accurately achieve structured processing and quality control of borehole data.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for structured processing of borehole columnar section diagrams based on multi-source data fusion, comprising: Acquire multi-source heterogeneous data, including formation data, well logging data, and map data; Convert unstructured and / or semi-structured data into structured fields, parse professionally formatted data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The data after field mapping and format conversion is associated with the preset database. After final inspection and verification, as well as depth alignment and collaborative verification of formation-logging data, an integrated data model of borehole-formation-logging-quality is formed. The preset database contains data structures including a lithology dictionary table, a geological timescale, a borehole base table, and a formation stratification table.

[0009] Secondly, the present invention provides a borehole columnar section structured processing system based on multi-source data fusion, comprising: The acquisition module is configured to acquire multi-source heterogeneous data, including formation data, well logging data, and map data. The field mapping and data conversion module is configured to: convert unstructured and / or semi-structured data into structured fields, parse professional format data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The model building module is configured to associate the data after field mapping and format conversion with the preset database, and after final verification and formation-logging data depth alignment and collaborative verification, form an integrated data model of borehole-formation-logging-quality; wherein, the preset database contains data structures of lithology dictionary table, geological time scale, borehole base table and formation layer table.

[0010] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0011] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0012] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0013] The above one or more technical solutions have the following beneficial effects: In this invention, multi-source heterogeneous data, including formation data, well logging data, and map data, is processed by converting unstructured and / or semi-structured data into structured fields, parsing professionally formatted data into standard fields, and unifying data types and encoding. This achieves field mapping and format conversion of the multi-source heterogeneous data. The mapped and format-converted data is then associated with a pre-set database. After final verification and formation-well logging data depth alignment and collaborative validation, an integrated borehole-formation-well logging-quality data model is formed. This invention's method can quickly and accurately achieve structured processing and quality control of borehole data.

[0014] This invention effectively combines standardized data modeling technology, multi-level automated verification technology, and multi-source data fusion verification technology to achieve full lifecycle tracking and recording of data operations, meeting the requirements for long-term archiving and compliance management of geological data. Through deep coupling analysis of stratigraphic data and well logging data, it effectively identifies logical conflicts and anomalies that are difficult to detect from a single data source, significantly improving the level of consistency verification of multi-source data.

[0015] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 This is an overall flowchart of the borehole columnar section structured processing method based on multi-source data fusion provided in an embodiment of the present invention. Figure 2 A database model relationship diagram provided for embodiments of the present invention; Figure 3 A quality verification flowchart provided for embodiments of the present invention; Figure 4 A collaborative inspection logic diagram provided for embodiments of the present invention; Figure 5 The overall system architecture diagram provided for embodiments of the present invention; Figure 6 This is an example of a data quality report provided for an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, 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 invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0020] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0021] Example 1 This embodiment discloses a method for structured processing of borehole column charts based on multi-source data fusion, including: Acquire multi-source heterogeneous data, including formation data, well logging data, and map data; Convert unstructured and / or semi-structured data into structured fields, parse professionally formatted data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The data after field mapping and format conversion is associated with the preset database. After final inspection and verification, as well as depth alignment and collaborative verification of formation-logging data, an integrated data model of borehole-formation-logging-quality is formed. The preset database contains data structures including a lithology dictionary table, a geological timescale, a borehole base table, and a formation stratification table.

[0022] The following is a detailed description of the borehole column chart structure processing method based on multi-source data fusion proposed in this embodiment: Step 1: Establish a preset database schema and construct a data structure including a lithology dictionary table, geological timescale, borehole base table, stratigraphic layer table, and lithology logging standard table; define lithology codes, age codes, borehole metadata, and layer fields, and set the layer thickness field as a generated column, which is automatically calculated by the difference between the bottom depth and the top depth.

[0023] The relationships between the data tables are as follows: Figure 2As shown, the data tables are linked through a primary key-foreign key mechanism: the lithology dictionary table uses lithology codes as the primary key, which is referenced by the stratigraphic stratification table and the lithology logging standard table; the geological timeline uses age codes as the primary key, which is referenced by the stratigraphic stratification table; the borehole base table uses borehole identifiers as the primary key, and the stratigraphic stratification table, logging data table, and quality inspection log table are all linked to it through foreign keys; the stratigraphic stratification table, as the core data table, is also linked to the lithology dictionary table, the geological timeline, and the borehole base table, forming a complete geological data association system; in addition, the lithology logging standard table is linked to the lithology dictionary table through lithology codes and is used to store the logging response standard range corresponding to each lithology.

[0024] In this step, the lithology dictionary table contains lithology codes, Chinese names, and English names; the geological timeline table contains geological time codes, Chinese names, English names, and information on the corresponding era, period, and epoch; the borehole foundation table contains project name, borehole number, coordinates, elevation, final borehole depth, and construction date; the stratigraphic layer table is linked to the borehole table and dictionary table via foreign keys, with the layer thickness field automatically calculated using database-generated columns; the well logging data table contains well logging curve data fields such as borehole identifier, depth, GR value, density value, and resistivity value, and is linked to the borehole foundation table via a foreign key for borehole identifier; the quality inspection log table contains fields such as log identifier, borehole identifier, inspection type, anomaly description, severity, and inspection time, and is linked to the borehole foundation table via a foreign key for borehole identifier.

[0025] Step 2: Provide a multi-channel data import mechanism, including an interactive input channel and a batch import channel; at the same time, integrate well logging data interface and map parsing module to realize unified access and format conversion of multi-source heterogeneous data.

[0026] In this step, an SQL direct-insert channel is provided for small-scale verification data entry, and a CSV batch channel is provided for large-scale data import. At the same time, a well logging data interface is integrated to support LAS / DLIS format data reading, and CAD / PDF map parsing function is provided, which can extract primitive information and convert it into structured data.

[0027] The direct SQL input method enables rapid insertion via predefined SQL templates. These templates use the INSERTINTO statement format and include the target table name, field list, and parameter placeholders. The field list is arranged according to the database table structure and includes information such as stratigraphic name, top depth, bottom depth, lithology code, geological age code, and description. The system automatically fills in the borehole ID parameter and calculates the layer thickness field based on the predefined SQL template. The system also automatically associates borehole IDs and calculates layer thickness. The CSV batch import method creates a temporary table structure and performs data conversion and field mapping. Data type conversion includes converting text-based depth values ​​to numeric values, converting date strings to standard date formats, and converting Chinese lithology names to standard lithology codes via dictionary lookup. Field mapping includes mapping stratigraphic names in the CSV file to the stratigraphic name field, top depth to the top depth field, bottom depth to the bottom depth field, lithology to the lithology code field via lithology dictionary lookup, geological age to the age code field via age dictionary lookup, and description information to the description field. UTF-8 encoding is supported. BOM encoding and Chinese description processing; the well logging data interface reads LAS and DLIS files through open-source well logging data parsing libraries (such as the lasio library for LAS format parsing and the dlisio library for DLIS format parsing); the CAD / PDF parsing module uses primitive recognition algorithms to extract layer lines, lithological filling patterns and text annotations.

[0028] Step 3: Perform multi-dimensional data verification, including continuity checks, logical checks, integrity checks, and statistical verification, and generate a verification report.

[0029] Continuity checks are used to detect whether the top and bottom depths of adjacent strata are connected; logical checks are used to identify unreasonable situations such as depth reversal and negative thickness; foreign key integrity checks are used to ensure that lithology codes and geological age codes exist in the dictionary table; thickness statistics verification outputs the total thickness and stratification statistics of each geological age and generates a verification report. The specific process of quality verification is as follows: Figure 3 As shown.

[0030] Specifically, the data verification mechanism adopts a multi-layered defense strategy: continuous verification introduces tolerance thresholds to handle micron-level depth errors; logical verification embeds geological regularity constraints, such as detecting whether the top depth of the stratum is less than the bottom depth and whether the layer thickness is positive; foreign key integrity verification realizes real-time dictionary verification to ensure that all lithology codes and geological age codes are defined in the dictionary table; thickness statistics verification summarizes the cumulative thickness of each geological age and generates a detailed verification report file.

[0031] Step 4: Provide a data correction mechanism to correct errors based on the verification report; use transaction control technology to ensure the consistency of data operations and establish operation logs to achieve full traceability.

[0032] Based on the error list generated by the verification report, users can make corrections using template SQL. The system adopts a database transaction mechanism to ensure the atomicity, consistency, isolation, and durability of data operations. All modification operations record the import batch, operator, timestamp, and SQL version number, achieving full traceability.

[0033] Specifically, the system provides standardized correction templates, supporting both single-item correction and batch correction modes; the database transaction mechanism ensures that correction operations either succeed entirely or are rolled back entirely, avoiding data inconsistency; all data modification operations are recorded in the audit log table through database triggers, including complete operation batches, operator identities, timestamps, and SQL version numbers, supporting full traceability and auditing of data changes.

[0034] Step 5: Perform batch import and final verification of the entire dataset, output standardized data tables and verification logs, and form a standardized database structure.

[0035] Complete layered data from all boreholes is imported in batches sequentially. The correspondence between multi-source heterogeneous data and the tables in the preset database is achieved through the following mechanism: First, a unique borehole identifier is generated for each borehole, serving as the primary index for all subsequent associated data. Second, when importing stratigraphic layered data, the corresponding lithology code is obtained by querying the lithology dictionary table based on the Chinese lithology name, and the corresponding age code is obtained by querying the geological timescale based on the geological timescale name. The foreign key fields are then filled into the stratigraphic layered table. When importing well logging data, the borehole identifier in the borehole base table is matched based on the borehole number, and the data is written to the well logging data table according to the depth sequence. All anomalies found during the verification process are associated with the corresponding borehole identifier and layer identifier and written to the quality inspection log table. The system performs a final verification and outputs the final stratigraphic details table, geological time and thickness statistics table, and verification log, ensuring that the data is free of duplication, gaps, and boundaries, forming a standardized database structure.

[0036] Specifically, the final inspection and verification includes full-depth cover integrity check, stratigraphic sequence continuity verification, and data uniqueness constraint check. The full-depth cover integrity check is achieved by comparing the minimum top depth value of the borehole in the stratigraphic stratification table with the maximum bottom depth value in the borehole base table using an SQL query. The stratigraphic sequence continuity verification is performed by sorting the stratigraphic stratification data by top depth and then using a window function to compare the top depth of each record with the bottom depth of the previous record. If the deviation exceeds a preset tolerance threshold, it is marked as a gap or overlap. The data uniqueness constraint check is performed by checking the stratigraphic depth cover integrity. The system sets up a unique index for borehole identification, top depth, and bottom depth on the stratification table and performs duplicate record queries. The geological chronology sequence rationality check verifies whether the geological age of deep strata is earlier than or equal to that of shallow strata by sorting the stratification data by depth and verifying the epochal, periodic, and epochal relationships in the geological timeline. If an inverted age is found, it is marked as an anomaly. The verification log records the inspection results, anomaly locations, and severity levels for each inspection item. The system outputs standardized strata details, geological age and thickness statistics, and a summary table of strata quantities for easy subsequent auditing and traceability.

[0037] Step 6: Perform multi-source data collaborative verification, check the depth alignment and response consistency of formation data and well logging data, and identify anomalies based on the knowledge base.

[0038] The process involves depth alignment of formation and well logging data, followed by lithology-well logging response consistency verification, i.e., checking whether the actual well logging values ​​fall within the standard response range of the lithology. Simultaneously, by analyzing the gradient abrupt change points of the well logging curves and performing correlation analysis with the formation interface depth, significant deviations are identified. Finally, intelligent anomaly identification is performed based on the geological knowledge base to detect problems such as geological age reversal, abnormally thin interbedded layers, and abnormal responses of specific lithologies (such as coal seams).

[0039] The logical flow of collaborative inspection is as follows: Figure 4As shown, the collaborative inspection includes the following steps: (1) Depth benchmark unification: Using formation data and well logging data as input, a depth mapping model is established to unify the depth coordinate system; elevation correction and depth alignment are performed to eliminate the depth benchmark differences between different data sources; sampling interval standardization is performed to unify well logging data with different sampling densities to the standard depth sequence. (2) Lithology-well logging response consistency verification: Based on the preset well logging response range of each lithology in the lithology well logging standard table, the matching degree of the well logging value in each formation depth segment is judged. If the well logging value exceeds the standard response range of the corresponding lithology, it is marked as abnormal. (3) Formation interface-curve mutation correlation analysis: The depth gradient value of the well logging curve is calculated, and the gradient mutation point is identified by the differential algorithm; the mutation point depth is correlated and matched with the formation interface depth, the depth deviation is calculated and the matching degree is evaluated. (4) Intelligent anomaly identification engine: Based on the above inspection results, the system automatically identifies and classifies anomalies, generates anomaly types and correction suggestions. (5) Collaborative inspection result output: The system outputs consistency score, anomaly record details and targeted suggested correction measures.

[0040] Specifically, depth alignment uses an interpolation algorithm to unify data from different sampling intervals; the lithology-logging response standard range is based on a pre-defined geological knowledge base and supports user-defined expansion; the gradient abrupt change analysis of logging curves uses a differential algorithm to identify abnormal jump points in the curves and performs correlation analysis with formation interface depths; anomaly identification results are categorized into three levels of severity: error, warning, and alert, facilitating the prioritization of critical issues. The overall system architecture is as follows: Figure 5 As shown.

[0041] Step 7: Automatically generate a multi-dimensional quality assessment report, establish a quality level evaluation system, and provide improvement suggestions.

[0042] The system automatically generates a multi-dimensional quality report (PDF format). The report includes basic borehole information, a summary of quality inspection results, detailed anomaly records, statistical charts, and specific improvement suggestions. Simultaneously, the system establishes a quality grading system, calculating a comprehensive quality score based on the number of errors and warnings and assigning grades. An example of a data quality report is shown below. Figure 6 As shown.

[0043] Specifically, the quality report includes basic drilling information, a summary of quality inspection results, a detailed list of anomaly records, statistical charts, and specific improvement suggestions; the quality grading system calculates a comprehensive quality score based on the number of errors and warnings, and divides it into four levels: A (Excellent), B (Good), C (Pass), and D (Fail); the report supports PDF output, making it convenient for users to archive, distribute, or print.

[0044] Step 8: Visualize the data based on the standardized data, including drawing two-dimensional maps, building three-dimensional models, and performing comparative analysis of porous structures.

[0045] Based on standardized and quality-tested data, a two-dimensional comprehensive columnar section is drawn, a three-dimensional geological body model is constructed, and the overlay display of well logging curves and columnar section is realized. It also supports the generation of multi-well profile comparison diagrams, providing intuitive and diverse visualization results for geological analysis.

[0046] Specifically, the 2D bar chart supports customizable scale, legend style, and annotation content, and can be exported as a vector or raster format; the 3D geological body model performs formation interpolation based on stratigraphic data, supports spatial sectioning and arbitrary angle rotation display; the well logging curve-bar chart overlay display enables on-screen comparison of well logging data and stratigraphic layers; the multi-well profile comparison chart supports arbitrary borehole combinations and comparison benchmark settings, automatically generates stratigraphic comparison lines, and meets the visualization needs of different application scenarios.

[0047] The following uses data from the DR1 borehole at Anju Coal Mine as an example to illustrate the method of this embodiment in detail, specifically including: Step 1: Establish a dedicated data model for borehole DR1: The lithology dictionary defines 28 lithology codes, including sandstone, mudstone, and coal seam, and associates each lithology with the standard logging response range; the geological timescale uses 10 standard age codes, from Quaternary (Q) to Cambrian (…). Enter the basic information of borehole DR1 in the borehole table: borehole coordinates (X:39457961.605, Y: 3913384.332), elevation +36.78m, final borehole depth 2309m, construction date 2024-04-30; set the layer thickness as a generated column in the stratum layer table, and automatically calculate "bottom plate depth - top plate depth" through database triggers or calculation columns.

[0048] Step 2: Import the verification data of the first 10 layers of DR1 borehole in batches using CSV. The file is encoded in UTF-8 with BOM and includes borehole name, formation name, top plate depth, bottom plate depth, lithology code, geological age code, formation description information, data import or entry time, and most recent update time period. The system creates a temporary table to receive the data and performs data type conversion and field mapping. At the same time, the corresponding well logging LAS file is imported, which includes GR, density, and resistivity curve data, to achieve synchronous access of formation data and well logging data, providing multi-source data support for subsequent collaborative analysis.

[0049] Step 3: The system automatically performs four types of checks: continuity check finds a tiny gap of 0.02m in layers 3-4; logic check verifies that the thickness of all layers is positive; foreign key integrity confirms that all lithological codes and age codes are valid; thickness statistics display the total thickness of the first 160 layers and generate a check report.

[0050] Step 4: Based on the generated verification report, correct the depth of the third-layer base plate using the template SQL. The system synchronously records the operator, timestamp, and SQL version to establish a complete data modification traceability chain.

[0051] Step 5: The system imports all 160 layers of DR1 borehole data, precisely controls the stratigraphic sequence according to the stratigraphic sequence field, performs final inspection and verification to confirm no duplication or gaps, and outputs a complete stratigraphic detail table and thickness statistics for each age, forming a standardized borehole stratigraphic database structure, realizing the standardization, traceability and efficient storage of DR1 borehole data.

[0052] Step 6: The system performs formation-logging data co-verification: the formation layer data and logging curve data are depth-aligned to verify whether the measured values ​​of GR, density, and resistivity of each lithological layer fall within the standard response range; the correspondence between the gradient abrupt change points of the logging curve and the depth of the formation interface is analyzed, and abnormal locations with deviations exceeding 0.5m are identified; based on the geological knowledge base, problems such as geological age inversion and coal seam response anomalies are detected, and a total of 2 lithology-logging response mismatch records are identified.

[0053] Step 7: The system automatically generates a multi-dimensional quality report for DR1 wells, with a comprehensive quality score of 99 points and a rating of "A (Excellent)". It accurately identifies four logical errors and provides specific correction suggestions and optimization measures, forming a complete closed-loop management of data quality.

[0054] Step 8: Based on the qualified quality inspection data, draw a two-dimensional comprehensive columnar section to clearly show the stratigraphic sequence and lithological distribution, construct a three-dimensional geological body model to visualize the spatial morphology of the stratigraphy, generate well logging curves-columnar section overlay display to intuitively reflect the rock-electric relationship, and output multi-well profile comparison map to support regional stratigraphic comparison analysis, and finally form a complete geological data result system to meet the visualization needs of different application scenarios.

[0055] This embodiment establishes a lithology dictionary, geological timescale, borehole base table, and stratigraphic layer table; it provides SQL direct insertion and CSV batch channels for data import, and integrates well logging data interface and CAD / PDF drawing parsing functions; the system automatically performs four types of basic checks: continuity, logical consistency, foreign key integrity, and thickness statistics; it corrects data based on the check reports, and uses a database transaction mechanism to ensure the atomicity, consistency, isolation, and durability of data operations; it batch imports complete borehole layer data and performs final verification; it performs depth alignment of formation-well logging data and lithology-well logging response consistency verification; it automatically generates multi-dimensional quality reports; and it provides visualization based on standardized data. This embodiment effectively combines standardized data modeling technology, multi-level automated verification technology, and multi-source data fusion verification technology, enabling rapid and accurate structured processing and quality control of borehole data. Furthermore, this invention constructs a complete operational traceability system to achieve full lifecycle tracking and recording of data operations, meeting the requirements for long-term archiving and compliance management of geological data. Through deep coupling analysis of stratigraphic data and well logging data, it effectively identifies logical conflicts and anomalies that are difficult to detect from a single data source, significantly improving the consistency verification level of multi-source data. Therefore, compared with existing technologies, the borehole columnar section multi-source data fusion processing method provided by this invention has the advantages of accuracy, efficiency, flexibility, and low cost.

[0056] In addition to borehole formation data, the method in this embodiment is also applicable to data management in multiple fields such as hydrogeology, oil drilling, and geothermal resource exploration. This embodiment can optionally include a simulation coupling interface to automatically generate three-dimensional meshes or element attribute tables based on standardized formation data, enabling data interaction with numerical simulation platforms (such as COMSOL, TOUGH2, MODFLOW, etc.).

[0057] Example 2 The purpose of this embodiment is to provide a borehole column chart structured processing system based on multi-source data fusion, including: The acquisition module is configured to acquire multi-source heterogeneous data, including formation data, well logging data, and map data. The field mapping and data conversion module is configured to: convert unstructured and / or semi-structured data into structured fields, parse professional format data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The model building module is configured to associate the data after field mapping and format conversion with the preset database, and after final verification and formation-logging data depth alignment and collaborative verification, form an integrated data model of borehole-formation-logging-quality; wherein, the preset database contains data structures of lithology dictionary table, geological time scale, borehole base table and formation layer table.

[0058] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0059] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0060] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0061] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0062] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0063] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0064] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0065] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0066] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0067] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for structured processing of borehole columnar section diagrams based on multi-source data fusion, characterized in that, include: Acquire multi-source heterogeneous data, including formation data, well logging data, and map data; Convert unstructured and / or semi-structured data into structured fields, parse professionally formatted data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The data after field mapping and format conversion is associated with the preset database. After final inspection and verification, as well as depth alignment and collaborative verification of formation-logging data, an integrated data model of borehole-formation-logging-quality is formed. The preset database contains data structures including a lithology dictionary table, a geological timescale, a borehole base table, and a formation stratification table.

2. The borehole columnar section structured processing method based on multi-source data fusion as described in claim 1, characterized in that, Also includes: The acquired multi-source heterogeneous data is subjected to multi-dimensional verification, which includes continuity checks, logical checks, foreign key integrity checks, and thickness statistical checks. Among them, the continuity check adopts tolerance threshold processing to detect the connection between the top and bottom depths of adjacent strata; the logical check embeds geological regularity constraints to identify depth inversion and unreasonable negative thickness; the foreign key integrity check implements dictionary verification to ensure that lithology codes and geological age codes exist in the corresponding dictionary tables of the preset database. The thickness statistical verification summarizes the cumulative thickness of strata according to geological time and generates a verification report containing stratification statistics.

3. The borehole columnar section structured processing method based on multi-source data fusion as described in claim 1, characterized in that, Data type conversion and field mapping are achieved for batch import of CSV data by creating a temporary table structure; the map data uses a primitive recognition algorithm to extract layer lines, lithological filling patterns and text annotations; well logging data is read from LAS and DLIS files using a dedicated parsing library.

4. The borehole columnar section structured processing method based on multi-source data fusion as described in claim 1, characterized in that, The formation-logging data depth alignment and collaborative verification specifically refers to: Interpolation algorithms were used to align the formation layer data with the well logging curve data at depth, and to verify whether the measured values ​​of GR, density, and resistivity of each lithology layer fell within the standard response range. Analyze the correspondence between gradient abrupt change points in well logging curves and formation interface depths to identify abnormal locations where deviations exceed set values; Anomalies caused by mismatch between lithology and well logging response are detected using a geological knowledge base.

5. The borehole columnar section structured processing method based on multi-source data fusion as described in claim 1, characterized in that, The lithology dictionary table includes lithology codes, Chinese names, and English names; the geological timeline includes geological time codes, Chinese names, English names, and information on the corresponding era, period, and epoch; the borehole foundation table includes project name, borehole number, coordinates, elevation, final borehole depth, and construction date; the stratigraphic layer table is linked to the borehole foundation table, lithology dictionary table, and geological timeline table via foreign keys, and the layer thickness field is automatically calculated using database-generated columns.

6. The borehole columnar section structured processing method based on multi-source data fusion as described in claim 1, characterized in that, The final inspection and verification includes full-depth cover integrity check, stratigraphic sequence continuity verification, and data uniqueness constraint check; and records the inspection results, anomaly locations, and severity classifications of each inspection item, resulting in a standardized stratigraphic detail table, geological age thickness statistics table, and stratification quantity summary table.

7. A borehole columnar section structured processing system based on multi-source data fusion, characterized in that, include: The acquisition module is configured to acquire multi-source heterogeneous data, including formation data, well logging data, and map data. The field mapping and data conversion module is configured to: convert unstructured and / or semi-structured data into structured fields, parse professional format data into standard fields, and unify data types and encoding to achieve field mapping and format conversion of multi-source heterogeneous data; The model building module is configured to associate the data after field mapping and format conversion with the preset database, and after final verification and formation-logging data depth alignment and collaborative verification, form an integrated data model of borehole-formation-logging-quality; wherein, the preset database contains data structures of lithology dictionary table, geological time scale, borehole base table and formation layer table.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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