A method and system for collecting bulk material consumption on an offshore drilling platform

Abstract

This invention provides a method and system for collecting bulk material consumption data on offshore drilling platforms, belonging to the field of marine oil and gas engineering technology. The method includes: constructing a three-dimensional consumption structure of well section-operation stage-material type, using the well as the unit; configuring a quota standard library and operation stage templates to achieve consumption baseline comparison and anomaly identification; transmitting collected data to the platform via a breakpoint resume mechanism; constructing a platform-level three-dimensional material matrix and performing multi-dimensional statistics; and finally displaying the data based on the platform structure model using heat maps and timeline views, and outputting anomaly classification statistics tables, trend change graphs, and anomaly impact analysis reports. This invention innovatively integrates data labeling modeling, visualization rendering calculation, and anomaly factor analysis, overcoming problems such as statistical lag, difficulty in tracing, and slow response of bulk materials in the offshore environment, and realizing a fully visible, diagnosable, and manageable digital data collection method.

Description

Technical Field

[0001] This invention relates to the field of marine oil and gas engineering technology, and in particular to a method and system for collecting bulk material consumption data from offshore drilling platforms. Background Technology

[0002] Offshore drilling platforms continuously consume large quantities of bulk materials such as drilling mud, cement, fracturing fluid, and weighting agents during oil and gas development. The usage of these materials typically exhibits dynamic and fluctuating characteristics depending on the stage of the operation. Due to the unique nature of the offshore operating environment, most platforms currently rely on manual recording, decentralized reports, or periodic manual reporting for material statistics. This not only suffers from problems such as information lag, record omissions, and poor data consistency, but also struggles to support real-time identification of abnormal consumption and the platform-level refined management requirements. Especially under conditions of simultaneous multi-well operation or complex geological conditions, the lack of a high-frequency, structured consumption data collection mechanism has become a significant bottleneck restricting the optimal allocation of offshore drilling materials and cost control. Summary of the Invention

[0003] In view of this, the present invention aims to provide a method and system for collecting bulk material consumption data of offshore drilling platforms, which can realize the detailed recording of material usage data at the well section level and the structured analysis of consumption behavior at the platform level, so as to improve the efficiency and transparency of material management in the offshore operating environment.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows: A method for collecting bulk material consumption data from an offshore drilling platform, comprising the following steps: Step S1, Single-well data acquisition and modeling: Taking a well as a unit, collect bulk material consumption data during the drilling, cementing, and completion stages, associate material type, time, well depth, and operation stage information, and construct a three-dimensional consumption record structure of well depth-operation stage-material type. Step S2, Standard Template Configuration: Establish a material quota standard library and operation stage templates to define the reference consumption range of various materials under different processes and well sections; Step S3, Data Acquisition and Verification Linkage: During the data acquisition process, the standard template is compared in real time to identify abnormal data, including excessive data and jump data, and prompts or markers are generated. Step S4, Data Synchronization and Storage: Upload the collected data to the platform database, and use a breakpoint resume mechanism to ensure the integrity of data transmission when the signal is unstable; Step S5, Platform Data Summary: Collect and analyze the material data of each well on the entire platform, generate a platform-level three-dimensional material matrix, and extract the consumption characteristics of key stages and regions. Step S6, graphical display generation: Based on the platform structure model, a graphical interface including heat map and time axis is built to intuitively present the changes in material consumption in the well section; Step S7, Anomaly Diagnosis Output: Analyze the anomaly records and output the well section number, time node, and abnormal material type for engineering review and control decision-making.

[0005] Furthermore, in step S1, the three-dimensional consumption record structure uses well section depth as the main axis dimension, and superimposes two label dimensions: operation stage and material type, to construct the association relationship between well section, stage, and material, thereby realizing the mapping and tracking of material consumption in spatial location and operation process. By constructing a time series consumption function Dynamic modeling is performed by introducing a time variable t and applying it to the time series consumption function. By taking the derivative over time, we obtain the function that consumes time per unit. Its function expression is: in: Indicates at a point in time well section Work phase Material type The corresponding real-time material consumption rate, L / day, m 3 / day or kg / day; Indicates the deadline Cumulative material consumption, L, m 3 or kg; The derivative with respect to time is expressed using the finite difference method. By comparing the normalized consumption index on the platform, the structural proportion of different well sections and material types can be assessed, and a normalized consumption index can be constructed. The formula is as follows: in: Indicates well section ,stage ,materials The relative consumption percentage of the combination is dimensionless; the denominator is the total consumption of all materials within the same cycle or platform. By modeling consumption intensity using a spatial density expression, the consumption intensity per unit length is defined to measure the material usage density of each well section. The formula is as follows: in: Indicates well section Average consumption density per unit length, L / m, m 3 / m or kg / m; Indicates well section The length of the corresponding depth interval, in meters.

[0006] Furthermore, in step S2, the quota standard library is generated based on historical engineering data and operation design parameters, and supports parameterized configuration according to well depth range, lithology type and construction technology; Quota standard library by well section Work phase Material type For a three-dimensional index primary key, construct a quota model in the following form, with the model formula as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; Indicates the first Each well segment is a spatial interval identifier variable, which is dimensionless; Indicates well section The corresponding lithological characteristic parameters are dimensionless. This represents parameters for different operational stages, corresponding to drilling, cementing, and completion construction techniques; it is dimensionless. This parameter represents the material type and is dimensionless. This represents the quota calculation function, used to generate corresponding standard consumption amounts based on well section, lithology, construction technology, and material type, L, m 3 or kg; Standard quota function Based on historical platform operation sample data Establish and combine expert rules with scenario correction coefficients. The adjustment is made, and its mathematical form is as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; This represents the cumulative consumption (L, m) for the corresponding well section, operational stage, and material combination in the historical sample. 3 or kg; L and m represent the expected consumption of the same well section, stage, and material combination in historical samples. 3 or kg; This is the working condition adjustment factor, which is weighted and corrected based on construction design parameters or risk prediction, and is dimensionless. For expert experience correction terms, used for deviation compensation, L, m 3 Or kg.

[0007] Furthermore, step S3 specifically involves: during the data collection and verification process, using interval judgment and stage matching rules to automatically identify excessive consumption, stage mismatch, and data loss, and generating anomaly codes for marking; The following three core verification strategies are adopted: (1) Identify excessive consumption through an interval judgment mechanism; the cumulative material consumption collected in step S1 is used to identify the excessive consumption. In step S2 S ijk Compare the results and calculate the deviation: in, Indicates a certain well section Work phase Material type At the point of time Consumption deviation, L, m 3 or kg; Indicates the reference quota values ​​for well sections and operational phase combination units, L, m 3 or kg; Indicates the cumulative material consumption at the corresponding time point, L, m 3 or kg; Set quota tolerance threshold The corresponding upper and lower limit ranges are determined by this quota tolerance threshold: in: This is the lower limit coefficient. The upper limit coefficient, and The risk levels are dynamically adjusted according to different material types and operational stages, and are dimensionless; if Exceeding the and If the range is within a reasonable range, it is marked as an out-of-limit anomaly, and the anomaly code E_01 is recorded. The record is then pushed to the anomaly identification and diagnosis module for further analysis and processing. Indicates the reference quota upper limit, L, m 3 or kg; Indicates the lower limit of the reference quota, L, m 3 or kg; (2) Perform consistency judgment of the work process through stage matching rules; assign stage tags to each data collection record. Automatically compare the data with the current work stage status. If the work stage to which the collected data belongs does not match the current planned construction stage on the platform, it is marked as a stage mismatch and an exception code E_02 is recorded. The rules are automatically executed based on the current job stage status, and the corresponding range of legal stages is updated synchronously when a job stage is switched. (3) Continuous data collection interruption judgment is made by identifying missing data; for any combination of data collection time points If data is missing consecutively within a set time window, it is considered a data missing anomaly, based on the following criteria: , in: This indicates that the data collected was empty or had no valid values. The maximum allowable duration of missing data is set in days; if the data missing criteria are met continuously, an exception code E_03 is generated and recorded as a data missing exception; t n Let n be the time period, and n be the number of days.

[0008] Furthermore, in step S4, the breakpoint resumption mechanism includes segmented caching, local breakpoint recovery, automatic retransmission, and upload status receipt functions to ensure the integrity of data transmission under weak signal conditions at sea.

[0009] Furthermore, in step S5, each dimension of the platform-level three-dimensional material matrix corresponds to a well section. and material type It also supports statistical analysis of total usage, average daily consumption, and consumption percentage for each stage. The basic form of a platform-level 3D material matrix is ​​as follows: in: Indicates well section and material type The corresponding cumulative consumption, L, m 3 or kg; Based on this matrix structure, for any well section and work phase To calculate the total usage of all materials in a given combination, the formula is: in: Indicates well section L, m 3 Or kg, used for phased usage distribution analysis, material planning evaluation and construction consumption evaluation; Calculate the average daily material consumption for each phase, taking into account the operation duration. (Delineate well section.) During the work phase The start and end time difference is The formula for daily average consumption is: in: Indicates well section During the work phase L, m 3 or kg; This represents the time difference in days between the start and end of each construction phase extracted from the work log. In addition, the platform data analysis module normalizes the platform-level three-dimensional material matrix to evaluate the well section. and material type The proportion of modular units in total platform material consumption; The key stages and regional consumption characteristics are based on the total usage of each stage. Average daily material consumption The consumption ratio of the combined units is extracted; when the corresponding indicator exceeds a preset threshold or ranks high in the statistical ranking, the corresponding operation stage is determined to be a critical stage, or the corresponding well section is determined to be critical. Work phase With material type The combined units constitute the consumption characteristics of key areas.

[0010] Furthermore, step S6 specifically involves the graphical display interface supporting switching between heatmap and timeline overlay views, automatically binding material data for each well location based on the platform structure model, and having zoom and time-series playback functions. The core data representation structure in the display interface is a visual heat consumption function. , indicating coordinates in platform space and time point The unit area consumption intensity is given by the formula: in: Represents the platform structure's planar coordinates; Indicates well In coordinates The location function at the location, when The value is 1 when mapped to this position, and 0 otherwise. For time window functions, when time Belongs to stage time period The value is 1 if the time is within the specified range, and 0 otherwise. This refers to the data collected in step S1, uploaded and cleaned in step S4; This represents the visible area occupied by the well section, used for density normalization. This is the color depth value for the corresponding position and time node in the final display interface.

[0011] Furthermore, step S7 specifically involves generating an anomaly type classification statistics table, an anomaly trend chart, and an anomaly impact analysis report from the anomaly diagnosis output, and associating them with specific well section numbers and operation stage tags; The core of abnormal diagnosis is based on abnormal label sets. Above, defined as follows: in: Indicates the first Each well section is dimensionless. This is a task phase, dimensionless; This parameter represents the material type and is dimensionless. The time point for data collection is one day. Used as an outlier binary marker; The system generates a periodic anomaly frequency function through cumulative time statistics, with the following formula: in: Indicates well section Work phase ,materials The total number of anomalies occurring for the corresponding combination; Indicates a point in time The corresponding abnormal binary marker for the combination below takes the value of 0 or 1 and is dimensionless; Indicates the number of samples taken within the statistical period; Indicates the first There are several data collection time points; to identify abnormal fluctuation trends, an anomaly occurrence rate function is introduced, with the following formula: in: Indicates time The growth rate of previous abnormal events, times / day; used to construct anomaly trend charts to identify periods of surge, mitigation, or stabilization; supports horizontal comparison of the abnormal evolution process of different well sections and material types; Based on this, the system combines platform-level material consumption total indicators. Construct an abnormal influence factor matrix: in: This indicates the frequency of anomalies per unit of material consumption, times / day, quantified well segment. Work phase ,materials The risk intensity of the corresponding combination is dimensionless. A small positive number set to prevent division by zero errors; As a core parameter for assessing the impact of anomalies, it supports the generation of a high-risk portfolio ranking in the report.

[0012] This invention also provides a bulk material consumption collection system for offshore drilling platforms, used to implement the aforementioned bulk material consumption collection method for offshore drilling platforms, comprising: Single-well material acquisition module: used to collect real-time consumption data of bulk materials such as mud, cement, fracturing fluid, and weighting agent at each working well location during the drilling, cementing, and completion operation stages, and generate three-dimensional structured data of well section-stage-material type; Data storage and synchronization module: used to cache and upload data collected from each wellhead, with breakpoint resume, status receipt and integrity verification functions, and supports stable transmission in weak signal environment at sea; Platform data analysis module: used to summarize structured data from all wells on the platform, construct a three-dimensional material consumption matrix with well number, operation stage and material type, and output total consumption, average daily consumption and percentage indicators for each stage; Anomaly identification and diagnosis module: used to perform quota verification, phase consistency comparison and interruption detection on collected data, automatically mark anomaly codes and generate anomaly statistics and impact assessment results; Graphical display and interaction module: Used to bind the analysis results to the platform structure model in the form of heatmaps and timeline overlays, and supports zooming, switching and abnormal highlighting prompts.

[0013] Compared with existing technologies, the method and system for collecting bulk material consumption on offshore drilling platforms described in this invention have the following advantages: This invention innovatively integrates data tagging modeling, visualization rendering calculation and anomaly factor analysis, overcoming problems such as statistical lag, difficulty in tracing and slow response of bulk materials in the offshore environment, and realizing a digital collection method that is visible, diagnosable and manageable throughout the entire process.

[0014] (1) Achieve refined modeling of material consumption at the single well level: By constructing a three-dimensional data structure of well section-operation stage-material type, the whole process of bulk material consumption is recorded in a hierarchical and traceable manner, which significantly improves the data granularity and expressive ability. (2) Introduction of standardized templates and quota baselines: The system is configured with a material quota standard library and operation phase templates to match historical engineering parameters with on-site conditions, which facilitates anomaly judgment and resource allocation; (3) Support automatic anomaly identification and diagnosis: By adopting interval judgment and stage matching rules, the system can identify abnormal behaviors such as excessive consumption, stage mismatch and data missing in real time, and perform structured marking through anomaly codes; (4) Data upload mechanism adapted to weak signal environment at sea: adopt segmented caching and breakpoint retransmission strategy to ensure data integrity and continuity under unstable network conditions; (5) Achieve structured summary and analysis of platform-level consumption behavior: Construct a platform-level three-dimensional material matrix to support multi-indicator analysis such as total usage in stages, average daily consumption, and normalized percentage, and meet the needs of horizontal comparison and vertical trend analysis. (6) Provides multi-mode graphical display capabilities: The graphical interface supports heat map and time axis overlay view, automatically binds well location structure model and consumption data, and realizes spatial mapping and time-series playback of status; (7) Output structured anomaly reports to assist decision-making: The system can generate anomaly classification statistics tables, trend charts and impact analysis reports, and associate them with well sections and operation stages to support on-site intervention and management optimization; In summary, this invention achieves a closed-loop system for bulk material consumption information, from data collection, structural modeling, real-time verification, centralized aggregation to visualization and intelligent diagnosis, significantly improving the informatization, intelligence, and visualization level of material management on offshore drilling platforms. Attached Figure Description

[0015] 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 undue limitation of the invention. In the drawings: Figure 1 A flowchart of a method for collecting bulk material consumption data on an offshore drilling platform; Figure 2 This is a schematic diagram of the three-dimensional material consumption structure of a single well; Figure 3 A statistical structure diagram of the platform's three-dimensional matrix; Figure 4 This is a schematic diagram of a graphical thermal display interface. Figure 5 A framework diagram of a bulk material consumption acquisition system for an offshore drilling platform; Figure 6 This is a flowchart of the system upload mechanism. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] This invention provides a method and system for collecting bulk material consumption data on offshore drilling platforms. The method constructs a three-dimensional consumption record structure model of well section-operation stage-material type, configures standard templates, collects and uploads consumption data, and finally displays the platform's material status in a graphical manner and outputs anomaly diagnosis results, realizing refined collection and management of bulk materials from single well to platform level, covering the entire process, the entire structure, and visualization.

[0019] like Figure 1 As shown, this invention is a method for collecting bulk material consumption data on offshore drilling platforms, comprising the following steps: Step S1, Single-well data acquisition and modeling: Taking a well as a unit, collect bulk material consumption data during the drilling, cementing, and completion stages, associate material type, time, well depth, and operation stage information, and construct a three-dimensional consumption record structure of well depth-operation stage-material type. Step S2, Standard Template Configuration: Establish a material quota standard library and operation stage templates to define the reference consumption range of various materials under different processes and well sections; Step S3, Data Acquisition and Verification Linkage: During the data acquisition process, the standard template is compared in real time to identify abnormal data, including excessive data and jump data, and prompts or markers are generated. Step S4, Data Synchronization and Storage: Upload the collected data to the platform database, and use a breakpoint resume mechanism to ensure the integrity of data transmission when the signal is unstable; Step S5, Platform Data Summary: Collect and analyze the material data of each well on the entire platform, generate a platform-level three-dimensional material matrix, and extract the consumption characteristics of key stages and regions. Step S6, graphical display generation: Based on the platform structure model, a graphical interface including heat map and time axis is built to intuitively present the changes in material consumption in the well section; Step S7, Anomaly Diagnosis Output: Analyze the anomaly records and output the well section number, time node, and abnormal material type for engineering review and control decision-making.

[0020] In step S1, in order to achieve refined and dynamic tracking of bulk materials in single-well and multi-stage operation processes, the method introduces time, space and normalization processing dimensions into the three-dimensional material consumption structure to form an enhanced data expression system with dynamic expression and comparison capabilities.

[0021] The three-dimensional consumption record structure uses well section depth as the main axis dimension, superimposed with two label dimensions: operation stage and material type, to construct the correlation between well section, stage, and material, thereby achieving accurate mapping and tracking of material consumption in spatial location and operation process; (1) By constructing a time series consumption function Dynamic modeling is performed by introducing a time variable t and applying it to the time series consumption function. By taking the derivative over time, we obtain the function that consumes time per unit. Its function expression is: in: Indicates at a point in time well section Work phase Material type The corresponding real-time material consumption rate, L / day, m 3 / day or kg / day; Indicates the deadline Cumulative material consumption, L, m 3 or kg; The derivative with respect to time is expressed using the finite difference method. (2) By comparing the normalized consumption index on the platform, the structural proportion of different well sections and material types is evaluated, and a normalized consumption index is constructed. The formula is: in: Indicates well section ,stage ,materials The relative consumption percentage of the combination is dimensionless; the denominator is the total consumption of all materials within the same cycle or platform. (3) By using the spatial density expression to model the consumption intensity, the consumption intensity per unit length is defined to measure the material usage density of each well section. The formula is: in: Indicates well section Average consumption density per unit length, L / m or kg / m; Indicates well section The length of the corresponding depth interval, in meters.

[0022] (4) The data structure is designed using a three-dimensional structured data table index. To ensure the integrity of the data record structure and efficient retrieval, the method employs a structured index modeling approach for the three-dimensional consumption record structure data. Each record contains the following four-level index keys: Well section number : Represents the spatial depth range corresponding to the data record, where Indicates the first Each well section; Work phase identification : Corresponds to the specific operational stage in the construction process; Material type number : Indicates the category of materials collected; Timestamp Record the time point at which the data was collected.

[0023] The corresponding value fields include: Real-time material consumption rate ; Cumulative material consumption ; Normalized consumption index .

[0024] In step S2, to ensure the effectiveness of the on-site material collection process and data quality control, this system designs a material quota standard library driven by historical data, and configures parameterized templates based on well depth, lithology, and construction technology to support the subsequent on-the-fly collection and verification mechanism. The quota standard library is generated based on historical engineering data and operational design parameters, and supports parameterized configuration according to well depth range, lithology type, and construction technology. Quota standard library by well section Work phase Material type For a three-dimensional index primary key, construct a quota model in the following form, with the model formula as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; Indicates the first Each well segment is a spatial interval identifier variable, which is dimensionless; Indicates well section The corresponding lithological characteristic parameters are derived from well logging or well logging interpretation, such as sand-mud ratio and porosity, and are dimensionless; This represents parameters for different operational stages, corresponding to drilling, cementing, and completion construction techniques; it is dimensionless. This parameter represents the material type, corresponding to material categories such as mud, cement, and fracturing fluid; it is dimensionless. This represents the quota calculation function, used to generate corresponding standard consumption amounts based on parameters such as well section, lithology, construction technology, and material type, in terms of L and m. 3 or kg; Standard quota function Based on historical platform operation sample data Establish and combine expert rules with scenario correction coefficients. The adjustment is made, and its mathematical form is as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; This represents the cumulative consumption (L, m) for the corresponding well section, operational stage, and material combination in the historical sample. 3 or kg; L and m represent the expected consumption of the same well section, stage, and material combination in historical samples. 3 or kg; This is the working condition adjustment factor, which is weighted and corrected based on construction design parameters or risk prediction, and is dimensionless. For expert experience correction terms, used for deviation compensation, L, m 3 Or kg.

[0025] Quota standard library by well section Work phase Material type For a three-dimensional index primary key, construct a quota model in the following form, with the model formula as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; It is a standard quota function that integrates historical data modeling and rule definition; Indicates the corresponding well section The lithological characteristic parameters are derived from well logging or well logging interpretation, such as sand-to-soil ratio and porosity. For the corresponding stage Construction process codes, such as control parameters like cement return height, fracturing fluid system, and circulation discharge rate; For the corresponding material types, such as weighting agents, corrosion inhibitors, thickeners, etc.; Standard quota function Based on historical platform operation sample data Establish and combine expert rules with scenario correction coefficients. The adjustment is made, and its mathematical form is as follows: in: This represents the expected consumption of the same well section, stage, and material combination in historical samples. This is the working condition adjustment factor, which is weighted and corrected based on construction design parameters or risk prediction. This is an expert experience correction item used for bias compensation.

[0026] The material quota standard library has the following characteristics: The quota system is structured in layers according to well sections to achieve precise control of quotas in a spatial dimension. Quota adjustments are made based on lithological characteristic parameters to reflect the impact of reservoir properties on material consumption; Adaptive configuration is performed based on construction process parameters so that different combinations of operational parameters correspond to differentiated benchmark values. It supports real-time invocation and dynamic threshold generation, providing a basis for quantitative boundary judgment in the subsequent step S3.

[0027] In step S3, in order to achieve real-time compliance judgment and data integrity assurance of on-site material collection, the system is designed with a multi-dimensional anomaly identification mechanism based on quota standards, which is used to automatically determine whether there are problems such as excessive consumption, inconsistency in operation stages, or missing continuous data.

[0028] During the data collection and verification process, interval judgment and stage matching rules are used to automatically identify excessive consumption, stage mismatch, and missing data, and generate anomaly codes for marking. The following three core verification strategies are adopted: (1) Identify excessive consumption through an interval judgment mechanism; the cumulative material consumption collected in step S1 is used to identify the excessive consumption. Reference quota value in step S2 S ijk Compare the results and calculate the deviation: in, Indicates a certain well section Work phase Material type At the point of time Consumption deviation, L, m 3 or kg; Indicates the reference quota values ​​for well sections and operational phase combination units, L, m 3 or kg; Indicates the cumulative material consumption at the corresponding time point, L, m 3 or kg; Set quota tolerance threshold The corresponding upper and lower limit ranges are determined by this quota tolerance threshold: in: This is the lower limit coefficient. The upper limit coefficient, and The risk levels are dynamically adjusted according to different material types and operational stages, and are dimensionless; if Exceeding the and If the range is within a reasonable range, it is marked as an out-of-limit anomaly, and the anomaly code E_01 is recorded. The record is then pushed to the anomaly identification and diagnosis module for further analysis and processing. Indicates the reference quota upper limit, L, m 3 or kg; Indicates the lower limit of the reference quota, L, m 3 or kg; (2) Perform consistency judgment of the work process through stage matching rules; assign stage tags to each data collection record. Automatically compare the data with the current work stage status. If the work stage to which the collected data belongs does not match the current planned construction stage on the platform, it is marked as a stage mismatch and an exception code E_02 is recorded. The rules are automatically executed based on the current job stage status, and the corresponding range of legal stages is updated synchronously when a job stage is switched. (3) Continuous data collection is judged by identifying missing data; for any combination of time points If data is missing consecutively within a set time window, it is considered a data missing anomaly, based on the following criteria: , in: This indicates that the data collected was empty or had no valid values. The maximum allowable missing duration window is set for the system, in days; if the data missing criteria are met continuously, an exception code E_03 is generated and recorded as a data missing exception. Let n be the time period, and n be the number of days.

[0029] Exception code marking and system response: Upon detecting any exception, the system associates the exception information with the corresponding record and outputs structured marking information, including: key: ; Error codes: E_01 (out of limit), E_02 (stage mismatch), E_03 (data missing); Anomaly level: can be set to alert level / early warning level / alarm level; System response actions include: generating prompts, freezing abnormal data, and issuing confirmation requests.

[0030] In step S4, the data upload adopts a breakpoint resumption mechanism, which includes segmented caching, local breakpoint recovery, automatic retransmission, and upload status receipt functions to ensure the integrity of data transmission under weak signal conditions at sea.

[0031] To ensure that bulk material acquisition data from offshore drilling platforms can be uploaded stably, completely, and with low latency in weak signal environments, the system adopts a breakpoint resumption mechanism that combines segmented caching, local breakpoint recovery, automatic backhaul, and upload status receipts. This constructs a robust data transmission channel to ensure that critical consumption data will not be lost or duplicated due to network interruptions or fluctuations.

[0032] The segmented caching mechanism is used to design a cache writing layer between the single-well material acquisition module and the upload channel, storing the raw data stream. The cache is segmented and cached based on a fixed time window or data block size. The formula for defining a cache segment is: in: Indicates the first Each cache segment is divided into time windows (e.g., every 5 seconds); each cache segment generates a unique identifier 'BlockID_n' locally and associates it with a record key. The cached segment enters the "upload queue" and waits for network availability or an upload trigger event.

[0033] A local breakpoint recovery mechanism allows for incremental resume downloads. If an upload is interrupted or fails, the system automatically determines the latest synchronization point based on the largest 'BlockID_m' of the uploaded records and retransmits from there. All uncommitted segments at the beginning.

[0034] If the number of upload failures exceeds the set retry threshold (e.g., 3 times); or if the remote status does not cover the latest 'BlockID' after network recovery, all unacknowledged segments will be automatically retransmitted. The system employs a status confirmation receipt mechanism to record the upload completion status, ensuring that each data segment is successfully written only once, thus avoiding redundant data or write conflicts caused by repeated uploads.

[0035] An upload status receipt mechanism (confirmation marker) is implemented. After each successful upload, the platform returns a 'BlockID' receipt and updates the local record status. The receipt information includes: The range of segment numbers successfully written; Receive timestamp; Receipt signature (tamper-proof verification); If packet loss or an error occurs, an error code will be returned (such as 'U_ERR_02' indicating segment corruption).

[0036] Based on this feedback, the data storage and synchronization module updates the local cache status table and marks the successful segments as "cleared" to release cache space.

[0037] The overall characteristics of this mechanism are shown in the table below: Table 1. Composition and Functional Description of the Breakpoint Resume Mechanism In step S5, after completing the collection, verification, and uploading of material consumption data for each individual well, the system calls the platform's data analysis module to aggregate the structured data from all wellheads and construct a unified three-dimensional material consumption matrix. This matrix is ​​organized by well section. stage and material type It uses three-dimensional coordinates to form a consumption data structure that supports multi-level statistics.

[0038] In the platform-level three-dimensional material matrix, each dimension corresponds to a well section. and material type It also supports statistical analysis of total usage, average daily consumption, and consumption percentage for each stage. The basic form of a platform-level 3D material matrix is ​​as follows: in: Indicates well section and material type The corresponding cumulative consumption, L, m 3 or kg; Based on this matrix structure, for any well section and work phase To calculate the total usage of all materials in a given combination, the formula is: in: Indicates well section L, m 3 Or kg, used for phased usage distribution analysis, material planning evaluation and construction consumption evaluation; Calculate the average daily material consumption for each phase, taking into account the operation duration. (Delineate well section.) During the work phase The start and end time difference is The formula for daily average consumption is: in: Indicates well section During the work phase L, m 3 or kg; This indicates the time difference between the start and end of each construction phase extracted from the work log, in days; it helps to compare the material usage efficiency of different wells and different phases, and supports the identification of abnormally high or fluctuating situations.

[0039] In addition, the platform data analysis module normalizes the platform-level three-dimensional material matrix to evaluate the well section. and material type The proportion of modular units in total platform material consumption; The key stages and regional consumption characteristics are based on the total usage of each stage. Average daily material consumption The consumption ratio of the combined units is extracted; when the corresponding indicator exceeds a preset threshold or ranks high in the statistical ranking, the corresponding operation stage is determined to be a critical stage, or the corresponding well section is determined to be critical. Work phase With material type The combined units constitute key area consumption characteristics. The preset threshold can be set based on historical engineering data, platform operation experience, or management needs.

[0040] Based on the above statistical analysis, the system further extracts consumption characteristics for key stages and regions. Specifically, the platform's data analysis module first extracts characteristics based on the total usage of each stage. Daily average material consumption Statistical comparisons are performed on each operational stage; when a corresponding indicator exceeds a preset threshold, or ranks highly in similar statistical results, the corresponding operational stage is determined to be a critical stage. Subsequently, the system analyzes the well section. Work phase With material type The combined units are used to analyze the consumption ratio; when the proportion of the combined unit in the total material consumption of the platform exceeds a preset threshold, or when it ranks high in the statistical ranking, the combined unit is determined to be a key area consumption feature.

[0041] The preset thresholds can be pre-set based on historical engineering data, platform operation experience, or on-site management requirements, or dynamically adjusted according to different platforms, well types, and construction conditions. Through these methods, the system can automatically identify high-consumption phases, high-proportion areas, and key material distribution characteristics, providing fundamental data support for subsequent graphical displays and anomaly diagnosis.

[0042] In step S6, the system summarizes the three-dimensional material consumption matrix generated in step S5. Based on this foundation, a graphical visual display interface is constructed using the platform's well location structure model to achieve spatial mapping, temporal evolution presentation, and dynamic interactive operation of well section consumption information. The graphical display interface supports switching between heatmap and timeline overlay views, automatically binds material data for each well location based on the platform structure model, and has zoom and time-series playback functions. The core data representation structure in the display interface is a visual heat consumption function. , indicating coordinates in platform space and time point The unit area consumption intensity is given by the formula: in: Represents the platform structure's planar coordinates; Indicates well In coordinates The location function at the location, when The value is 1 when mapped to this position, and 0 otherwise. For time window functions, when time Belongs to stage time period The value is 1 if the time is within the specified range, and 0 otherwise. This refers to the data collected in step S1, uploaded and cleaned in step S4; This represents the visible area occupied by the well section, used for density normalization. This is the color depth value (i.e., heat value) of the corresponding position and time node in the final display interface.

[0043] This visual heat consumption function expression structure realizes the linkage mapping of the collected data in the spatial and temporal dimensions, and is the underlying driving logic for the dynamic color changes of the graphics layer.

[0044] The display interface supports two visual view switching modes: heatmap mode and timeline overlay mode.

[0045] In heatmap mode, the system uses time points of On the distributed rendering platform model, the color intensity of each well segment reflects its current consumption density, enabling real-time visualization of the spatial status. This mode is used to identify high-consumption areas, differences between well segments, and concentrated risk areas.

[0046] In the time-axis overlay mode, the system uses stages as the unit of time progression, combining different... Moment The system loads materials sequentially, enabling a dynamic evolution of consumption status. Users can view the temporal evolution of material usage in a well section via a timeline slider or automatic playback, helping to identify abrupt changes or peak periods in consumption.

[0047] In addition, the system supports material type-based... The optional layer overlay formula is: It can render the consumption status of a certain type of material at different well locations, which is convenient for assessing the usage distribution and allocation pressure of specific materials.

[0048] The interface offers zoom and focus interaction functions, allowing users to zoom in on any well location, retrieve material curves for each stage, or freeze data layers at a specific time point for detailed comparison. The dynamic responsiveness of the graphics system, combined with its data binding structure, forms a real-time visualization analysis platform that supports decision-making, source tracing, and anomaly tracking.

[0049] In summary, the graphical display interaction module uses a structure mapping function. Stage window function and consumption intensity function Using this as the core modeling method, a material consumption expression model with spatial-temporal dual-dimensional dynamic response characteristics is constructed, which is a key technical component of this invention in the visual perception layer.

[0050] In step S7, the system performs anomaly identification and diagnosis processing for management and evaluation based on the anomaly marker data identified in step S3, the platform-level three-dimensional material matrix constructed in step S5, and the graphical display results generated in step S6.

[0051] The anomaly identification and diagnosis module automatically collects various anomaly events, generates structured diagnostic results including anomaly type classification statistics tables, anomaly trend charts, and anomaly impact analysis reports, and links them to specific well sections through indexing. With the work phase Tags support subsequent review analysis and decision-making for task optimization.

[0052] The core of abnormal diagnosis is based on abnormal label sets. Above, defined as follows: in: Indicates the first Each well section is dimensionless. For operational phases (drilling, cementing, completion, etc.), dimensionless; This parameter represents the material type and is dimensionless. The time point for data collection is one day. Anomaly binary marker (automatically generated by step S3).

[0053] The system generates a periodic anomaly frequency function through cumulative time statistics, with the following formula: Among them: Among them: Indicates well section Work phase ,materials The total number of anomalies occurring for the corresponding combination; Indicates a point in time The corresponding abnormal binary marker for the combination below takes the value of 0 or 1 and is dimensionless; Indicates the number of samples taken within the statistical period; Indicates the first The system collects data at specific time points; to identify abnormal fluctuation trends, it introduces an anomaly occurrence rate function, the formula of which is: in: Indicates time The growth rate of previous abnormal events, times / day; used to construct anomaly trend charts to identify periods of surge, mitigation, or stabilization; supports horizontal comparison of the abnormal evolution process of different well sections and material types.

[0054] Based on this, the system combines platform-level material consumption total indicators. Construct an abnormal influence factor matrix: in: This indicates the frequency of anomalies per unit of material consumption, times / day, quantified well segment. Work phase ,materials The risk intensity of the corresponding combination is dimensionless. Small positive numbers (such as) are set to prevent division by zero errors. ); As a core parameter for assessing the impact of anomalies, it supports the generation of a high-risk portfolio ranking in the report.

[0055] The output structure of the anomaly identification and diagnosis module includes: Anomaly Classification Statistics Table: Based on Build by grouping by material type and stage label; Abnormal trend chart: based on The time evolution curves of each combination are displayed; Anomaly Impact Analysis Report: Using the core indicators, a risk scoring ranking table of a three-dimensional matrix of well section-stage-material is generated, and highly sensitive blocks that need to be reviewed are selected according to the threshold.

[0056] Meanwhile, all diagnostic results are linked to the corresponding well section number through a data index structure. With work phase label By binding the data, the specific location of the anomaly can be directly located in the platform model and graphical interface, enabling a graphical and text-based project review capability.

[0057] This invention also provides a bulk material consumption collection system for offshore drilling platforms, used to implement the aforementioned bulk material consumption collection method for offshore drilling platforms, comprising: Single-well material acquisition module: used to collect real-time consumption data of bulk materials such as mud, cement, fracturing fluid, and weighting agent at each working well location during drilling, cementing, and completion operations, and generate three-dimensional structured data of "well section-stage-material type"; Data storage and synchronization module: used to cache and upload data collected from each wellhead, with breakpoint resume, status receipt and integrity verification functions, and supports stable transmission in weak signal environment at sea; Platform data analysis module: used to summarize structured data from all wells on the platform, construct a three-dimensional material consumption matrix with well number, operation stage and material type, and output total consumption, average daily consumption and percentage indicators for each stage; Anomaly identification and diagnosis module: used to perform quota verification, phase consistency comparison and interruption detection on collected data, automatically mark anomaly codes and generate anomaly statistics and impact assessment results; Graphical display and interaction module: Used to bind the analysis results to the platform structure model in the form of heatmaps, timeline overlays, etc., and supports zooming, switching and abnormal highlighting prompts.

[0058] This invention innovatively integrates data tagging modeling, visualization rendering calculation, and anomaly factor analysis, overcoming problems such as statistical lag, difficulty in tracing, and slow response of bulk materials in the marine environment, and realizing a digital data collection method that is visible, diagnosable, and manageable throughout the entire process.

[0059] To verify the engineering adaptability of the method and system described in this invention in actual drilling scenarios, the system was deployed on the "NH deepwater drilling platform" in Block A oilfield. This platform is operated by an oil and gas company, with an operating area water depth of approximately 1050 meters. The wells are densely spaced, and operations are frequently interleaved, exhibiting typical characteristics of high-frequency material consumption in offshore multi-well operations. Well "NH-8H" was selected as a demonstration well location. This well is a horizontal well with a target formation depth of 1720 meters, traversing multiple layers of tight sandstone and limestone interbedded layers. The staged fracturing process is complex, the operation cycle is long, and the material usage phases are frequent, making it highly representative.

[0060] Step S1, single-well data acquisition and modeling; Multiple types of data acquisition and sensing terminals are deployed at the well site, corresponding to the mud pump inlet / outlet, cement mixing tank, fracturing fluid injection pipeline, and auxiliary feeding system, respectively, to collect data including: Well section intervals: Z1=0m-500m, Z2=500m-1200m, Z3=1200m-1800m; Operational phase labels: P1 (drilling), P2 (cementing), P3 (fracturing); Material type labels: M1 (mud), M2 (cement), M3 (fracturing fluid), M4 (corrosion inhibitor); The data record structure strictly adopts the three-dimensional tensor structure defined in step S1: Used to indicate the well section at time t. Work phase With material type The corresponding cumulative material consumption.

[0061] like Figure 2 As shown, the three-dimensional structure tensor collected from the "NH-8H well" is displayed. The organization method is as follows. In the figure, the well section (Z1-Z3), operation stage (drilling, cementing, fracturing) and material type (mud, cement, fracturing fluid, corrosion inhibitor) form a three-dimensional coordinate axis in the tensor cube, which is used to realize the structured recording and multi-dimensional correlation expression of single-well material consumption data.

[0062] Step S2: Standard template configuration; The platform's engineering database contains material consumption records for 36 wells in this work area from 2020 to 2024. Combining well logging results with construction design parameters, a quota standard library is constructed. The core formula is as follows: ,in, Indicates well section Work phase Material type Reference quota value; Indicates the lithological characteristic parameters of the corresponding well section; This indicates the construction process parameters for the corresponding work stage.

[0063] In this embodiment, the design quota for the fracturing stage corresponding to well section Z3 is 21.5m. 3 The lithology is classified as "medium-grained sandstone + calcareous mudstone interlayers"; the process parameters include 7 fracture segments and a pumping rate of 2.5m. 3 The construction time is estimated at 90 minutes per minute. The quota standard is stored in the system database in tabular form as the data verification benchmark for step S3.

[0064] Step S3: Data collection and verification linkage; The system will collect the actual values Compared with reference quota value A comparison was made, and the following deviation formula was used for verification. Simultaneously, the system sets a quota tolerance threshold, and determines the corresponding upper and lower limit ranges based on the quota tolerance threshold, using the following formula: ,when Exceeding the and When the data falls within a defined reasonable range, it is considered an out-of-limit anomaly and an anomaly code E_01 is recorded; if the operation stage label in the collected records is inconsistent with the current platform's planned construction stage, for example, fracturing fluid is present during the cementing stage, then E_02 is triggered; if data discontinuity occurs... If so, it is determined that the data is missing and E_03 is triggered.

[0065] In this embodiment, the system detected that the cumulative consumption exceeded the standard value of 3.8m during fracturing operations in well section Z3. 3 →Mark E_01; At the same time, one stage level error record was found in the initial stage →Mark E_02.

[0066] Step S4: Data synchronization and storage; In the mud chamber and living quarters areas of the platform, the signal is unstable, and the data acquisition terminal activates the breakpoint buffer mode: Each upload task is accompanied by a unique 'Block_ID' and a status identifier; the current block transmission status is identified by the status word 'S∈Ready,Sent,Confirmed', where Ready indicates that it is waiting to be uploaded, Sent indicates that it has been sent, and Confirmed indicates that the platform has confirmed receipt. If the upload process is interrupted, the upload scheduling unit in the data storage and synchronization module automatically performs breakpoint recovery and retransmission based on the most recently confirmed Block_ID; in this embodiment, data transmission integrity reaches 100%.

[0067] Step S5: Platform data aggregation; The system will consolidate the material data from 8 wells on the NH platform, such as... Figure 3 As shown, Figure 3 The figure presents a structured summary of material consumption data for eight wells on the NH platform. A three-dimensional statistical matrix is ​​constructed based on well section, operation stage, and material type. And derive the total usage of the calculation stage. Daily usage, normalized index The matrix formula is as follows: ,in: Indicates well section Material type The cumulative consumption; Real-time data collected from step S1; after being uploaded in step S4, the collected data is merged into the platform database for structured calculation.

[0068] For phase-based total consumption analysis, the platform system first sums the three-dimensional matrix according to the material type dimension to obtain the total material consumption for each phase in the well-phase two-dimensional matrix, expressed as: On the NH platform, well 3 (denoted as...) During the fracturing stage ( The material consumption in this section is particularly concentrated. According to statistics, the fracturing fluid consumption in this section reached 48,120L, which is the highest value for a single section.

[0069] Analysis of average daily usage per stage, combined with the duration of fracturing stages extracted from the platform's operation logs. The system calculates the average daily consumption during this period: .

[0070] Consumption ratio analysis: To analyze the platform's resource allocation structure, the system normalizes the three-dimensional matrix to obtain the ratio indicators. After normalization, it was found that Well 3 consumed 38.7% of the platform's total materials during the fracturing stage, which is the highest proportion among all "well-stage-material" combinations. The system marks this as a high-load section and recommends further evaluation of the lithological structure and construction design parameters of this section.

[0071] Step S6: Graphical display generation; The interface is bound to the platform's 3D structural model, and the heat map is bound to the well section consumption index. The stacked view is dynamically rendered with the time axis t as the horizontal axis. For example... Figure 4 As shown, Figure 4 The system's graphical interface demonstrates how it binds collected data to the platform's 3D structural model and renders a heatmap. Heat color mapping maps well section energy consumption, and it supports features such as overlay views and abnormal flickering. The following display modes are enabled: Color coding: Red (high consumption) → Green (medium) → Blue (low) Layer switching: Supports filtering by material type or stage. The error code area flashes as a warning (autofocus).

[0072] Step S7: Anomaly diagnosis output; The system follows the definition of the anomaly impact matrix in the instruction manual, formula The abnormal output impact value for Z3-fracturing-mud is 0.194, indicating that the pump speed regulation mechanism needs to be monitored.

[0073] The final report includes: Categorical statistics table (E_01: 6 times; E_02: 1 time); Trend chart: Significant abnormal increase in the later stages of fracturing; Abnormal segment details binding: well segment Z3+P3 tag. Example 2

[0074] like Figure 5 The diagram shows a framework of a bulk material consumption acquisition system for an offshore drilling platform. Figure 6 The flowchart shown is a system upload mechanism flowchart.

[0075] This embodiment provides a bulk material consumption collection system for offshore drilling platforms, including: Single-well material acquisition module: used to collect real-time consumption data of bulk materials such as mud, cement, fracturing fluid, and weighting agent at each working well location during drilling, cementing, and completion operations, and generate three-dimensional structured data of "well section-stage-material type"; Data storage and synchronization module: used to cache and upload data collected from each wellhead, with breakpoint resume, status receipt and integrity verification functions, and supports stable transmission in weak signal environment at sea; Platform data analysis module: used to summarize structured data from all wells on the platform, construct a three-dimensional material consumption matrix with well number, operation stage and material type, and output total consumption, average daily consumption and percentage indicators for each stage; Anomaly identification and diagnosis module: used to perform quota verification, phase consistency comparison and interruption detection on collected data, automatically mark anomaly codes and generate anomaly statistics and impact assessment results; Graphical display and interaction module: Used to bind the analysis results to the platform structure model in the form of heatmaps, timeline overlays, etc., and supports zooming, switching and abnormal highlighting prompts.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for collecting bulk material consumptions on an offshore drilling platform, the method comprising: Includes the following steps: Step S1, Single-well data acquisition and modeling: Taking a well as a unit, collect bulk material consumption data during the drilling, cementing, and completion stages, associate material type, time, well depth, and operation stage information, and construct a three-dimensional consumption record structure of well depth-operation stage-material type. Step S2, Standard Template Configuration: Establish a material quota standard library and operation stage templates to define the reference consumption range of various materials under different processes and well sections; Step S3, Data Acquisition and Verification Linkage: During the data acquisition process, the standard template is compared in real time to identify abnormal data, including excessive data and jump data, and prompts or markers are generated. Step S4, Data Synchronization and Storage: Upload the collected data to the platform database, and use a breakpoint resume mechanism to ensure the integrity of data transmission when the signal is unstable; Step S5, Platform Data Summary: Collect and analyze the material data of each well on the entire platform, generate a platform-level three-dimensional material matrix, and extract the consumption characteristics of key stages and regions. Step S6, graphical display generation: Based on the platform structure model, a graphical interface including heat map and time axis is built to intuitively present the changes in material consumption in the well section; Step S7, Anomaly Diagnosis Output: Analyze the anomaly records and output the well section number, time node, and abnormal material type for engineering review and control decision-making.

2. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: In step S1, the three-dimensional consumption record structure uses well section depth as the main axis dimension and superimposes two label dimensions: operation stage and material type, to construct the association relationship between well section, stage, and material, thereby realizing the mapping and tracking of material consumption in spatial location and operation process. By constructing a time series consumption function Dynamic modeling is performed by introducing a time variable t and applying it to the time series consumption function. By taking the derivative over time, we obtain the function that consumes time per unit. Its function expression is: in: Indicates at a point in time well section Work phase Material type The corresponding real-time material consumption rate, L / day, m 3 / day or kg / day; Indicates the deadline Cumulative material consumption, L, m 3 or kg; The derivative with respect to time is expressed using the finite difference method. By comparing the normalized consumption index on the platform, the structural proportion of different well sections and material types can be assessed, and a normalized consumption index can be constructed. The formula is as follows: in: Indicates well section ,stage ,materials The relative consumption percentage of the combination is dimensionless; the denominator is the total consumption of all materials within the same cycle or platform. By modeling consumption intensity using a spatial density expression, the consumption intensity per unit length is defined to measure the material usage density of each well section. The formula is as follows: in: Indicates well section Average consumption density per unit length, L / m or kg / m; Indicates well section The length of the corresponding depth interval, in meters.

3. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: In step S2, the quota standard library is generated based on historical engineering data and operation design parameters, and supports parameterized configuration according to well depth range, lithology type and construction technology. Quota standard library by well section Work phase Material type For a three-dimensional index primary key, construct a quota model in the following form, with the model formula as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; Indicates the first Each well segment is a spatial interval identifier variable, which is dimensionless; Indicates well section The corresponding lithological characteristic parameters are dimensionless. This represents parameters for different operational stages, corresponding to drilling, cementing, and completion construction techniques; it is dimensionless. This parameter represents the material type and is dimensionless. This represents the quota calculation function, used to generate corresponding standard consumption amounts based on well section, lithology, construction technology, and material type, L, m 3 or kg; Standard quota function Based on historical platform operation sample data Establish and combine expert rules with scenario correction coefficients. The adjustment is made, and its mathematical form is as follows: in: Indicates well section Work phase Material type Reference quota values, L, m 3 or kg; This represents the cumulative consumption (L, m) for the corresponding well section, operational stage, and material combination in the historical sample. 3 or kg; L and m represent the expected consumption of the same well section, stage, and material combination in historical samples. 3 or kg; This is the working condition adjustment factor, which is weighted and corrected based on construction design parameters or risk prediction, and is dimensionless. For expert experience correction terms, used for deviation compensation, L, m 3 Or kg.

4. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that, The specific steps of step S3 are as follows: During the data collection and verification process, interval judgment and stage matching rules are used to automatically identify excessive consumption, stage mismatch and data missing, and anomaly codes are generated for marking. The following three core verification strategies are adopted: (1) Identify excessive consumption through an interval judgment mechanism; the cumulative material consumption collected in step S1 is used to identify the excessive consumption. In step S2 S ijk Compare the results and calculate the deviation: in, Indicates a certain well section Work phase Material type At the point of time Consumption deviation, L, m 3 or kg; Indicates the reference quota values ​​for well sections and operational phase combination units, L, m 3 or kg; Indicates the cumulative material consumption at the corresponding time point, L, m 3 or kg; Set quota tolerance threshold The corresponding upper and lower limit ranges are determined by this quota tolerance threshold: in: This is the lower limit coefficient. The upper limit coefficient, and The risk levels are dynamically adjusted according to different material types and operational stages, and are dimensionless; if Exceeding the and If the range is within a reasonable range, it is marked as an out-of-limit anomaly, and the anomaly code E_01 is recorded. The record is then pushed to the anomaly identification and diagnosis module for further analysis and processing. Indicates the reference quota upper limit, L, m 3 or kg; Indicates the lower limit of the reference quota, L, m 3 or kg; (2) Perform consistency judgment of the work process through stage matching rules; assign stage tags to each data collection record. Automatically compare the data with the current work stage status. If the work stage to which the collected data belongs does not match the current planned construction stage on the platform, it is marked as a stage mismatch and an exception code E_02 is recorded. The rules are automatically executed based on the current job stage status, and the corresponding range of legal stages is updated synchronously when a job stage is switched. (3) Continuous data collection interruption judgment is made by identifying missing data; for any combination of data collection time points If data is missing consecutively within a set time window, it is considered a data missing anomaly, based on the following criteria: ， in: This indicates that the data collected was empty or had no valid values. The maximum allowable duration of missing data is set in days; if the data missing criteria are met continuously, an exception code E_03 is generated and recorded as a data missing exception; t n Let n be the time period, and n be the number of days.

5. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: In step S4, the breakpoint resumption mechanism includes segmented caching, local breakpoint recovery, automatic retransmission, and upload status receipt functions to ensure the integrity of data transmission under weak signal conditions at sea.

6. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: In step S5, each dimension of the platform-level three-dimensional material matrix corresponds to a well section. and material type It also supports statistical analysis of total usage, average daily consumption, and consumption percentage for each stage. The basic form of a platform-level 3D material matrix is ​​as follows: in: Indicates well section and material type The corresponding cumulative consumption, L, m 3 or kg; Based on this matrix structure, for any well section and work phase To calculate the total usage of all materials in a given combination, the formula is: in: Indicates well section L, m 3 Or kg, used for phased usage distribution analysis, material planning evaluation and construction consumption evaluation; Based on the duration of the operation, calculate the average daily material consumption for each stage and define the well section. During the work phase The start and end time difference is The formula for daily average consumption is: in: Indicates well section During the work phase L, m 3 or kg; This represents the time difference in days between the start and end of each construction phase extracted from the work log. In addition, the platform data analysis module normalizes the platform-level three-dimensional material matrix to evaluate the well section. and material type The proportion of modular units in total platform material consumption; The key stages and regional consumption characteristics are based on the total usage of each stage. Average daily material consumption The consumption ratio of the combined units is extracted; when the corresponding indicator exceeds a preset threshold or ranks high in the statistical ranking, the corresponding operation stage is determined to be a critical stage, or the corresponding well section is determined to be critical. Work phase With material type The combined units constitute the consumption characteristics of key areas.

7. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: Specifically, step S6 involves a graphical display interface that supports switching between heatmap and timeline overlay views, automatically binding material data for each well location based on the platform structure model, and having zoom and time-series playback functions. The core data representation structure in the display interface is a visual heat consumption function. , representing the coordinates in the platform structure model plane. and time point The unit area consumption intensity is given by the formula: in: Represents the planar coordinates of the platform structure model; Indicates well location In the platform structure model plane coordinates The location function at the well location The display position and the plane coordinates The value is 1 when the condition is met, and 0 otherwise. For time window functions, when time Belongs to the work phase Corresponding time interval The value is 1 if the time is within the specified range, and 0 otherwise. This represents the real-time material consumption rate data collected in step S1 and uploaded and processed in step S4, expressed in L / day and m. 3 / day or kg / day; This represents the visible area occupied by the well section, in meters (m). 2 , used for density normalization; For the final planar coordinates on the display interface and time point The color depth value at that location is dimensionless.

8. The method for collecting bulk material consumption data for offshore drilling platforms according to claim 1, characterized in that: Specifically, step S7 involves generating an anomaly type classification statistics table, an anomaly trend chart, and an anomaly impact analysis report from the anomaly diagnosis output, and associating them with specific well section numbers and operation stage tags. The core of abnormal diagnosis is based on abnormal label sets. Above, defined as follows: in: Indicates the first Each well section is dimensionless. This is a task phase, dimensionless; This parameter represents the material type and is dimensionless. The time point for data collection is one day. Used as an outlier binary marker; The system generates a periodic anomaly frequency function through cumulative time statistics, with the following formula: in: Indicates well section Work phase ,materials The total number of anomalies occurring for the corresponding combination; Indicates a point in time The corresponding abnormal binary marker for the combination below takes the value of 0 or 1 and is dimensionless; Indicates the number of samples taken within the statistical period; Indicates the first There are several data collection time points; to identify abnormal fluctuation trends, an anomaly occurrence rate function is introduced, with the following formula: in: Indicates time The growth rate of previous abnormal events, times / day; used to construct anomaly trend charts to identify periods of surge, mitigation, or stabilization; supports horizontal comparison of the abnormal evolution process of different well sections and material types; Based on this, the system combines platform-level material consumption total indicators. Construct an abnormal influence factor matrix: in: This indicates the frequency of anomalies corresponding to unit material consumption, quantified well segment. Work phase ,materials The risk intensity of the corresponding combination is dimensionless. A small positive number set to prevent division by zero errors; As a core parameter for assessing the impact of anomalies, it supports the generation of a high-risk portfolio ranking in the report.

9. A bulk material consumption collection system for offshore drilling platforms, used to implement the bulk material consumption collection method for offshore drilling platforms as described in any one of claims 1-8, characterized in that, include: Single-well material acquisition module: used to collect real-time consumption data of bulk materials such as mud, cement, fracturing fluid, and weighting agent at each working well location during the drilling, cementing, and completion stages, and generate three-dimensional structured data of well section-stage-material type; Data storage and synchronization module: used to cache and upload data collected from each wellhead, with breakpoint resume, status receipt and integrity verification functions, and supports stable transmission in weak signal environment at sea; Platform data analysis module: used to summarize structured data from all wells on the platform, construct a three-dimensional material consumption matrix with well number, operation stage and material type, and output total consumption, average daily consumption and percentage indicators for each stage; Anomaly identification and diagnosis module: used to perform quota verification, phase consistency comparison and interruption detection on collected data, automatically mark anomaly codes and generate anomaly statistics and impact assessment results; Graphical display and interaction module: Used to bind the analysis results to the platform structure model in at least one of the following ways: heat map, time axis overlay diagram, and supports zooming, switching and abnormal highlighting prompts.

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