Data management system for oil and gas field fracturing process

Abstract

The invention discloses a data management system for an oil and gas field fracturing process, and relates to the technical field of oil and gas field data management, four types of historical data of consumables, equipment, monitoring and production of a complete fracturing process under different time and space are acquired and stored through a historical data unit, and a corresponding data set is established; analyzing historical data by means of a data linkage unit, processing consumables and monitoring historical data into a curve graph as a matching reference, taking production historical data as an adjustment reference, taking equipment parameters corresponding to equipment historical data as an adjustment target, and constructing a linkage relationship among four types of data; real-time consumables in the fracturing process are collected through a real-time data unit, data are monitored, a curve graph is processed, and meanwhile equipment parameters are collected; and matching the real-time curve graph with the curve graph corresponding to the historical data through an adjusting unit, and adjusting the equipment parameters in real time according to the matching result and the linkage relationship. The problems that according to an existing fracturing technology, consumables are wasted, and the fracturing effect is difficult to reach the expectation are solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field data management technology, and in particular to a data management system for oil and gas field fracturing processes. Background Technology

[0002] Fracturing technology is a key process for enhancing oil and gas well production and for the efficient development of unconventional oil and gas resources such as coalbed methane and shale gas. During underground oil and gas well operations, the production capacity of wells is limited by natural permeability due to the density of the rock and the constraints of high-permeability layers, resulting in low productivity. Fracturing technology alters the structure of the bedrock by injecting high-pressure fluids, thereby creating fractures in the rock layers, reducing the rock's permeability resistance, and increasing its permeability. This leads to higher oil and gas flow rates and production. The core principle involves injecting a liquid mixture containing proppant into the well. This mixture penetrates the bottom layer under high pressure, and as the pressure decreases, the proppant deposits in the fractures, forming a propped fracture network.

[0003] Oil and gas field fracturing technology comprises three core processes: pre-construction preparation, construction implementation, and post-construction evaluation. Pre-construction preparation primarily involves providing technical and material reserves. The construction implementation phase includes wellbore pretreatment, fracturing fluid and proppant injection, monitoring of construction parameters, displacement, and well shut-in. Finally, the post-construction evaluation phase verifies the fracturing effect and ensures production. Currently, the fracturing process involves the use of a large amount of consumables (including pre-fracturing fluid, fracturing fluid, proppant, and displacement fluid). However, because equipment and construction parameters are pre-set based on prior data and construction standards, significant waste occurs during consumable use, and the fracturing effect often fails to meet expectations. Therefore, a data management system for oil and gas field fracturing technology is urgently needed. This system should integrate and manage consumable data, equipment parameter data, and production data to ensure efficient use of consumables while guaranteeing the fracturing effect in oil and gas fields. Summary of the Invention

[0004] In view of this, this application provides a data management system for oil and gas field fracturing processes to address the shortcomings of existing technologies.

[0005] The first aspect of this application provides a data management system for oil and gas field fracturing processes, comprising: The historical data unit is used to acquire historical data of the complete fracturing process under different time and space conditions, including historical data of consumables, equipment, monitoring and production. A corresponding dataset is established and stored for the historical data of each complete fracturing process. The data linkage unit acquires and analyzes historical data of consumables, equipment, monitoring, and production from any dataset to obtain analysis results. Based on the analysis results, the historical data of consumables and the historical data of monitoring are processed into curves and used as a matching benchmark. The historical data of production is used as an adjustment benchmark, and the equipment parameters corresponding to the historical data of equipment are used as adjustment targets to build a linkage relationship between consumable data, equipment data, monitoring data, and production data. The real-time data unit collects consumable and monitoring data in the fracturing process in real time and processes it into curves to obtain consumable data curves and monitoring data curves; and collects equipment parameters in the fracturing process. The adjustment unit integrates all consumable data curves and all monitoring data curves, and matches them with all datasets to obtain matching results; based on the matching results and corresponding linkage relationships, it adjusts the corresponding equipment parameters in real time.

[0006] In one possible implementation of the first aspect, plotting the historical data of the consumables includes: Based on the historical data of consumables, obtain the historical usage data of different types of consumables within a unit of time and perform data preprocessing. With time as the horizontal axis and the historical usage data of consumables per unit time as the vertical axis after data preprocessing, a curve is constructed about the change of historical usage data of consumables per unit time over time, which is denoted as the first curve. Store all first curve graphs into the first curve graph set.

[0007] In one possible implementation of the first aspect, plotting the historical monitoring data includes: Based on the historical monitoring data, different types of historical monitoring data are acquired and data preprocessing is performed. With time as the horizontal axis and the values ​​of different types of historical monitoring data as the vertical axis, construct a curve graph about the changes of different types of historical monitoring data over time, denoted as the second curve graph; Store all second curve plots into the second curve plot set.

[0008] In one possible implementation of the first aspect, the historical data of consumables and historical monitoring data after curve processing are used as matching benchmarks, including: Based on the first curve set and the second curve set, all first curves and all second curves belonging to the same complete fracturing process are placed into the corresponding registration set.

[0009] In one possible implementation of the first aspect, using the production history data as the basis for adjustment includes: Based on the production history data of multiple complete fracturing processes in different time periods, multiple data intervals were divided for different types of production data, and each data interval of all types of production data was assigned a value using a unified assignment method. Based on the production history data of a single complete fracturing process, the average value of different types of production data is extracted within a set time period; Assign values ​​to the data intervals corresponding to the average values ​​of different types of production data, and use these values ​​as the feature values ​​of the corresponding production data; The feature values ​​of all types of production data in a single complete fracturing process are integrated using a preset formula and used as the adjustment benchmark value for the corresponding historical production data.

[0010] In one possible implementation of the first aspect, the feature values ​​of all types of production data from a single complete fracturing process are integrated using a preset formula, including: To adjust the baseline value, For the feature values ​​of different types of production data, The number of types of production data.

[0011] In one possible implementation of the first aspect, the consumable data in the fracturing process is collected in real time and processed into a curve to obtain a consumable data curve, including: Real-time data collection and preprocessing of the usage of different types of consumables per unit time during the fracturing process; With time as the horizontal axis and the real-time consumption data of consumables per unit time after data preprocessing as the vertical axis, a curve is constructed on the change of real-time consumption data of consumables per unit time over time, denoted as the third curve.

[0012] In one possible implementation of the first aspect, real-time acquisition of monitoring data during the fracturing process and plotting the data to obtain monitoring data plots include: Real-time collection of different types of monitoring data and data preprocessing; With time as the horizontal axis and the values ​​of different types of real-time monitoring data as the vertical axis, construct a curve graph showing the change of different types of real-time monitoring data over time, denoted as the fourth curve graph.

[0013] In one possible implementation of the first aspect, integrating all consumable data graphs and all monitoring data graphs, and matching them with all datasets, includes: Match all third and fourth curves with curves in all registration sets. If all curves in a registration set are successfully matched, obtain the adjustment benchmark value of the production history data corresponding to the registration set, and record it as the first adjustment benchmark value. If the first adjustment benchmark value exceeds the set value, obtain the corresponding device parameters based on the historical data of the device and record them as the first device parameters; Using the first equipment parameter as the adjustment target, the equipment parameter is adjusted in real time; If the first adjustment benchmark value is less than or equal to the set value, based on the linkage relationship and in combination with all third curves, all fourth curves and real-time equipment parameters, the production data is predicted to obtain a predicted adjustment benchmark value, and it is determined whether the predicted adjustment benchmark value exceeds the set value. If it does, no action is taken; if not, an early warning is issued.

[0014] In one possible implementation of the first aspect, matching all third and fourth curve plots with curve plots in all registration sets includes: A preset number of feature points are selected in all third and fourth curves. The mean square error between the feature points of the third or fourth curve and the corresponding curve in a single registration set is calculated. If the mean square error is less than a set threshold, it is determined that the third or fourth curve is successfully matched with the corresponding curve in a single registration set.

[0015] Its beneficial effects are as follows: This invention discloses a data management system for oil and gas field fracturing processes. It acquires and stores four types of historical data—consumables, equipment, monitoring, and production—for the complete fracturing process at different times and spaces through a historical data unit, establishing corresponding datasets. Using a data linkage unit, it analyzes the historical data, processing historical consumable and monitoring data into curves as a matching benchmark, using historical production data as an adjustment benchmark, and using equipment parameters corresponding to historical equipment data as adjustment targets, thus constructing a linkage relationship between the four types of data. A real-time data unit collects real-time consumable and monitoring data from the fracturing process and processes them into third and fourth curves, while simultaneously collecting equipment parameters. Finally, an adjustment unit matches the real-time curves with the corresponding historical data curves, and adjusts the equipment parameters in real-time based on the matching results and linkage relationships. This invention solves the problems of wasteful consumables and unsatisfactory fracturing effects in existing fracturing processes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the composition of a data management system for oil and gas field fracturing process provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0020] Example In current fracturing technologies, the construction phase involves the use of a large amount of consumables (including pre-fracturing fluid, fracturing fluid, proppant, and displacement fluid). However, because equipment and construction parameters are pre-set based on prior data and construction standards, there is significant waste of consumables during use, and the fracturing effect is often difficult to achieve the expected results. Therefore, there is an urgent need for a data management system for oil and gas field fracturing processes. This system should integrate and manage consumable data, equipment parameter data, and production data to ensure efficient use of consumables while guaranteeing the fracturing effect in oil and gas fields.

[0021] Therefore, this application provides a data management system for oil and gas field fracturing processes, such as... Figure 1 As shown, it includes: The historical data unit is used to acquire historical data of the complete fracturing process under different time and space conditions, including historical data of consumables, equipment, monitoring and production. A corresponding dataset is established and stored for the historical data of each complete fracturing process. The data linkage unit acquires and analyzes historical data of consumables, equipment, monitoring, and production from any dataset to obtain analysis results. Based on the analysis results, the historical data of consumables and the historical data of monitoring are processed into curves and used as a matching benchmark. The historical data of production is used as an adjustment benchmark, and the equipment parameters corresponding to the historical data of equipment are used as adjustment targets to build a linkage relationship between consumable data, equipment data, monitoring data, and production data. The real-time data unit collects consumable and monitoring data in the fracturing process in real time and processes it into curves to obtain consumable data curves and monitoring data curves; and collects equipment parameters in the fracturing process. The adjustment unit integrates all consumable data curves and all monitoring data curves, and matches them with all datasets to obtain matching results; based on the matching results and corresponding linkage relationships, it adjusts the corresponding equipment parameters in real time.

[0022] Before explaining the working principle of this embodiment, it should be noted that this embodiment includes four major data types: consumable data, equipment data, monitoring data, and production data. Consumable data revolves around the various chemical fluids and proppant materials consumed in the fracturing process, such as fracturing fluid data, proppant data, pre-flush fluid data, and displacement fluid data. Equipment data focuses on the operating parameters of core equipment in the fracturing process, such as pressure pump parameters (pump pressure, pump speed, and discharge rate), wellhead equipment parameters (wellhead on / off status), sand mixing truck parameters (sand supply rate, sand mixing uniformity), and instrumentation and control parameters. Monitoring data is real-time feedback data reflecting the formation and construction status during fracturing, such as formation parameter monitoring data (fracture length, height, and width; formation pressure; formation permeability), construction process monitoring data (manifold pressure and temperature; fluid flow rate and density; wellhead flowback data), and equipment operating status monitoring data (vibration frequency of the fracturing pump, motor temperature, and bearing temperature). Production data reflects the final effect of the fracturing process, such as oil and gas production data (daily oil production), wellhead production parameters, and production stability data.

[0023] This embodiment discloses a data management system for oil and gas field fracturing technology, including a historical data unit, a data linkage unit, a real-time data unit, and an adjustment unit, as detailed below: Historical data unit: The historical data unit is the data foundation layer of the entire fracturing process data management system for oil and gas fields. Its core function is to provide high-quality historical data support for subsequent linkage analysis and real-time parameter adjustment, breaking the problems of scattered historical data and lack of spatio-temporal correlation in traditional fracturing processes. By collecting complete fracturing process data under different time and space scenarios, a reusable and comparable database is formed. It should be noted that the complete fracturing process specifically refers to the full-cycle process from pre-construction preparation - construction implementation - post-evaluation, ensuring that the data can reflect all aspects of a single round of fracturing process. Different spatio-temporal scenarios cover different well sites, different formation conditions, and different construction times, avoiding data limitations.

[0024] The working principle of the historical data unit can be divided into four steps: data acquisition, data classification, data set construction, and data storage. Specifically: Data acquisition: Through system interfaces (such as docking with existing data acquisition systems in oil and gas fields, well site monitoring equipment, and production management platforms), four types of key historical data generated in the entire fracturing process are obtained定向获取压裂工艺全流程中产生的四类关键历史数据,确保数据覆盖耗材、设备、监测和生产全链条,包括耗材历史数据、设备历史数据、监测历史数据和生产历史数据。采集过程中需确保数据的完整性,即每类数据需对应单轮压裂的全流程,而非片段数据。

[0025] Data classification: To avoid confusion of historical data, the collected original historical data is classified and sorted. The core classification logic is based on the spatial dimension, time dimension, and process dimension (a single complete fracturing process).

[0026] Data set construction: This is the core link of the historical data unit and also the key differentiating it from traditional scattered data storage. For each complete fracturing process, the system automatically establishes a corresponding data set. Each data set has a unique identifier, and the data set internally contains the four types of historical data of this round of fracturing process.

[0027] Data storage: The constructed data sets are stored through storage media (such as relational databases) for subsequent units to call.

[0028] Through the working principle of data acquisition, data classification, data set construction, and data storage, the historical data unit provides a comparable and reusable historical data foundation for the entire data management system, which is a prerequisite for solving the problems of consumable waste and poor fracturing effect.

[0029] Data linkage unit: The core link of the data linkage unit's oil and gas field fracturing process data management system aims to break down the isolation of historical data across four categories: consumables, equipment, monitoring, and production. Through data analysis, format conversion, and benchmark setting, it constructs a data-benchmark-target linkage relationship, providing a logical basis for subsequent real-time data matching and equipment adjustments. Essentially, it transforms the fragmented datasets stored in historical data units into a structured logical association with comparison standards (matching benchmarks), adjustment criteria (adjustment benchmarks), and control targets (equipment parameters). Its working principle can be divided into data input, data processing, and linkage construction, specifically as follows: Data input involves extracting a dataset of any complete fracturing process from historical data units. Each dataset contains four types of historical data.

[0030] Data processing, the core component of the data linkage unit, requires processing the four types of extracted historical data from two dimensions: matching benchmarks and adjusting benchmarks, transforming the raw historical data into standard indicators that can be used for correlation.

[0031] From the perspective of matching benchmarks, historical consumable / monitoring data is processed to construct matching benchmarks. The extracted historical consumable and monitoring data are preprocessed, including data cleaning (such as removing outliers and filling in missing values) and standardization, to ensure data stability. A curve is constructed showing the change of a single type of consumable over time, with time as the horizontal axis and the preprocessed consumable usage per unit time as the vertical axis. All first-order curves are summarized into a first-order curve set. Similarly, a curve is constructed showing the change of a single type of monitoring data over time, with time as the horizontal axis and the preprocessed monitoring data values ​​as the vertical axis. All second-order curves are summarized into a second-order curve set. All first-order curves and all second-order curves under the same complete fracturing process are integrated into a registration set, such as the 2023 A well fracturing registration set, which includes the fracturing fluid curve, proppant curve, formation pressure curve, and fracture length curve for that fracturing process. Each registration set corresponds to a set of historical processes, becoming the matching template for subsequent real-time curves, i.e., the matching benchmark.

[0032] From the perspective of benchmark adjustment, the benchmark is used to judge the production effectiveness of the fracturing process and provide a benchmark for subsequent equipment adjustments. Data interval division and assignment: Based on historical production data from multiple complete fracturing processes, each type of production data (e.g., daily oil production) is divided into multiple data intervals, and a unified assignment method is used to assign values ​​to each interval, realizing the conversion of production data into quantitative scores. Average values ​​of single-process production data are extracted: For the single complete fracturing process currently being analyzed, the average value of each type of production data is extracted within a set time period (e.g., one or two weeks after stable production following fracturing). Production data feature values ​​are calculated: Based on the extracted average values, the corresponding data intervals are matched, and the assigned value for that data interval is obtained as the feature value for that type of production data. The benchmark adjustment value is calculated using a formula... By integrating the feature values ​​of all production data from a single process, the adjustment baseline value of the process is obtained, which reflects the production effect of the fracturing in that round. From the data set of the single complete fracturing process currently being analyzed, the key operating parameters of the equipment are extracted as adjustment targets.

[0033] The system establishes a linkage relationship among four types of data. After completing the processing of matching benchmarks, adjustment benchmarks, and adjustment targets, the data linkage unit binds these three data with the original data, constructing a closed-loop linkage relationship between consumable data, monitoring data, production data, and equipment data. Specifically, through the registration set of historical processes (consumables + monitoring curves) → corresponding adjustment benchmark values ​​→ corresponding adjustment targets, the system clearly defines that when a real-time consumable / monitoring curve matches a certain registration set, if the adjustment benchmark value of that set meets the standard, the equipment parameters need to be adjusted to the corresponding target value; if the adjustment benchmark value does not meet the standard, an alert or optimization is required.

[0034] The data linkage unit works by extracting historical data, transforming it into benchmarks and targets, and binding related logic. This transforms isolated historical data into structured rules, which is the core logical support for the data management system to achieve dynamic adjustments, reduce waste, and improve efficiency.

[0035] Real-time data unit: The real-time data unit is the real-time data sensing layer of the oil and gas field fracturing process data management system. Its core objective is to dynamically capture key data during the fracturing operation and transform unstructured real-time data into a standardized and comparable format. This provides immediate data input for subsequent adjustment units to perform real-time matching and parameter adjustments. Its working principle includes real-time acquisition, data preprocessing, standardization transformation, and data output, specifically: Real-time data acquisition involves real-time connection with various sensors, monitoring equipment, and data acquisition terminals at the well site to synchronously collect three types of core data during the fracturing operation: real-time consumable data, real-time monitoring data, and real-time equipment parameters. The acquisition frequency is synchronized with the construction rhythm.

[0036] Data preprocessing is necessary to ensure data quality, as real-time collected data may contain outliers, missing values, and inconsistent units.

[0037] The standardization transformation converts the preprocessed real-time consumable data and real-time monitoring data into curves that are completely consistent with the format of the first and second curves in the historical data unit, namely the third and fourth curves. At the same time, the original values ​​of the real-time equipment parameters are retained, forming a standardized output format for curves and parameter values, providing data of the same format for subsequent adjustment unit matching.

[0038] Data output involves pushing the three types of standardized data to the adjustment unit through the system interface, ensuring that the adjustment unit can synchronously obtain real-time data during fracturing operations, providing delay-free data support for subsequent real-time curve matching with historical registration sets and equipment parameter adjustment decisions.

[0039] The real-time data unit, through its working principle of real-time acquisition, data preprocessing, and standardized transformation, converts the real-time status of fracturing operations into a comparable and analyzable data format. This provides core data input for the system to achieve real-time optimization and adjustment, and is the basis for immediate perception in solving the problems of material waste and fracturing effects that are difficult to achieve as expected.

[0040] Adjustment unit: The adjustment unit is the core of the oil and gas field fracturing process data management system for decision execution. Its core objective is to dynamically determine whether equipment parameters need adjustment or early warnings need to be triggered by matching and comparing real-time and historical data, combined with the linkage relationships built by the data linkage unit. Ultimately, this aims to reduce material waste and ensure fracturing effectiveness. Its working principle includes data integration, curve matching, effect assessment, and decision execution, forming a closed-loop decision logic, specifically: Data integration begins with the simultaneous acquisition of two types of core data (real-time data and historical correlation data) to ensure the completeness and relevance of the data required for decision-making. Real-time data input includes all third-order graphs, all fourth-order graphs, and real-time device parameters; historical correlation data input includes historical registration sets, historical adjustment baseline values, and historical device parameters. During integration, it is crucial to ensure data dimension consistency to avoid matching errors caused by format differences.

[0041] Curve matching involves comparing all third and fourth curves with curves in all registration sets one by one to determine if highly similar historical scenes exist. The matching logic is based on the mean squared error (MSE) of feature points, and the steps are as follows: Select feature points: In each real-time curve, select a preset number of feature points at preset intervals. Each feature point contains time coordinates and data values. To calculate the mean square error, for a single real-time curve and the corresponding curve in the registration set, calculate the mean square error value between the feature points of the two. The formula logic is the ratio of the sum of the squares of the differences between the real-time data values ​​and the historical data values ​​of all feature points to the number of feature points, and thus obtain the mean square error value. To determine single-curve matching, if the mean square error between a real-time curve and the corresponding curve in the registration set is less than a set threshold, then the real-time curve is determined to be successfully matched with the historical curve. The matching of the registration set is determined only when all the third curves and all the fourth curves are successfully matched with the corresponding curves in a certain registration set. In other words, the current construction material consumption trend and monitoring status trend are highly consistent with a certain historical scenario.

[0042] For effect assessment, once a match is successful, the adjustment unit needs to combine historical production results with real-time parameters to determine whether the current construction production effect meets the standards in two scenarios, providing a basis for subsequent decision-making. Specifically: Scenario 1: If a matching registration set is found, the corresponding historical adjustment benchmark value is extracted and compared with the set value. If the first adjustment benchmark value is greater than the set value, it indicates that the production effect in the matched historical scenario met the standard, and the real-time equipment parameters in the current construction scenario need to be adjusted to the corresponding equipment parameters in the historical scenario to reproduce the production effect. If the first adjustment benchmark value is less than or equal to the set value, it indicates that the production effect in the matched historical scenario did not meet the standard, and it is necessary to further predict the production effect of the current fracturing construction. Based on the material-monitoring-equipment-production linkage relationship constructed by the data linkage unit, combined with the real-time curves (the third and fourth curves) and real-time equipment parameters, the predicted adjustment benchmark value for the current construction is predicted by a preset algorithm (such as regression analysis or machine learning model), and then compared with the set threshold. Since machine learning is limited by complex factors such as formation heterogeneity, construction randomness, and environmental interference, the prediction accuracy based on the linkage relationship has inherent limitations. Therefore, the linkage relationship is only used to predict the production effect when the production effect in the matched historical scenario does not meet the standard. If the predicted adjustment benchmark value is greater than the set value, it means that the production effect can be achieved by executing with the current equipment parameters, and no action is taken for the time being; if the predicted adjustment benchmark value is less than or equal to the set value, it means that the production effect cannot be achieved by executing with the current equipment parameters, triggering an early warning and prompting manual intervention.

[0043] Scenario 2: If the real-time curve does not match any of the historical registration sets, a no-match warning will be triggered directly, prompting the operator to manually adjust the equipment parameters based on prior data.

[0044] Decision execution involves adjusting the unit to execute corresponding decisions based on different results of the effect assessment, achieving dynamic intervention. Execution 1: Parameter adjustment. If it is determined that the corresponding equipment parameters under the historical scenario need to be reproduced, a parameter adjustment command is sent to the corresponding equipment through the system control interface to accurately adjust the real-time equipment parameters to meet the corresponding equipment parameters under the historical scenario. During the adjustment process, equipment parameters are collected in real time and fed back to ensure adjustment accuracy. Execution 2: Early warning trigger. If it is determined that the production effect is not up to standard or there is no matching scenario, an early warning is immediately triggered. At the same time, the current real-time data and judgment logic are recorded to provide a basis for future fracturing processes. Execution 3: Continuous monitoring. Regardless of whether an adjustment or an early warning is executed, the adjustment unit will continuously collect real-time data and repeat the "matching-judgment-execution" process at preset intervals to ensure that the construction process is always in the optimal state.

[0045] The adjustment unit combines historical experience with real-time data by accurately matching historical scenarios, judging production effects, and dynamically executing intervention decisions. This enables adaptive optimization of the fracturing process, ultimately solving the core problems of material waste and failure to achieve expected fracturing results.

[0046] In some embodiments, plotting the historical data of the consumables includes: Based on the historical data of consumables, obtain the historical usage data of different types of consumables within a unit of time and perform data preprocessing. With time as the horizontal axis and the historical usage data of consumables per unit time as the vertical axis after data preprocessing, a curve is constructed about the change of historical usage data of consumables per unit time over time, which is denoted as the first curve. Store all first curve graphs into the first curve graph set.

[0047] In some embodiments, plotting the historical monitoring data includes: Based on the historical monitoring data, different types of historical monitoring data are acquired and data preprocessing is performed. With time as the horizontal axis and the values ​​of different types of historical monitoring data as the vertical axis, construct a curve graph about the changes of different types of historical monitoring data over time, denoted as the second curve graph; Store all second curve plots into the second curve plot set.

[0048] In some embodiments, the historical data of consumables and historical monitoring data after graph processing are used as a matching benchmark, including: Based on the first curve set and the second curve set, all first curves and all second curves belonging to the same complete fracturing process are placed into the corresponding registration set.

[0049] In some embodiments, using the production history data as the basis for adjustment includes: Based on the production history data of multiple complete fracturing processes in different time periods, multiple data intervals were divided for different types of production data, and each data interval of all types of production data was assigned a value using a unified assignment method. Based on the production history data of a single complete fracturing process, the average value of different types of production data is extracted within a set time period; Assign values ​​to the data intervals corresponding to the average values ​​of different types of production data, and use these values ​​as the feature values ​​of the corresponding production data; The feature values ​​of all types of production data in a single complete fracturing process are integrated using a preset formula and used as the adjustment benchmark value for the corresponding historical production data.

[0050] In some embodiments, the feature values ​​of all types of production data from a single complete fracturing process integrated using a preset formula include: To adjust the baseline value, For the feature values ​​of different types of production data, The number of types of production data.

[0051] In some embodiments, real-time acquisition of consumable data during the fracturing process and plotting it as a graph to obtain a consumable data graph include: Real-time data collection and preprocessing of the usage of different types of consumables per unit time during the fracturing process; With time as the horizontal axis and the real-time consumption data of consumables per unit time after data preprocessing as the vertical axis, a curve is constructed on the change of real-time consumption data of consumables per unit time over time, denoted as the third curve.

[0052] In some embodiments, real-time acquisition of monitoring data during the fracturing process and plotting the data to obtain a monitoring data curve include: Real-time collection of different types of monitoring data and data preprocessing; With time as the horizontal axis and the values ​​of different types of real-time monitoring data as the vertical axis, construct a curve graph showing the change of different types of real-time monitoring data over time, denoted as the fourth curve graph.

[0053] In some embodiments, integrating all consumable data graphs and all monitoring data graphs, and matching them with all datasets, includes: Match all third and fourth curves with curves in all registration sets. If all curves in a registration set are successfully matched, obtain the adjustment benchmark value of the production history data corresponding to the registration set, and record it as the first adjustment benchmark value. If the first adjustment benchmark value exceeds the set value, obtain the corresponding device parameters based on the historical data of the device and record them as the first device parameters; Using the first equipment parameter as the adjustment target, the equipment parameter is adjusted in real time; If the first adjustment benchmark value is less than or equal to the set value, based on the linkage relationship and in combination with all third curves, all fourth curves and real-time equipment parameters, the production data is predicted to obtain a predicted adjustment benchmark value, and it is determined whether the predicted adjustment benchmark value exceeds the set value. If it does, no action is taken; if not, an early warning is issued.

[0054] In some embodiments, matching all third and fourth curves with curves in all registration sets includes: A preset number of feature points are selected in all third and fourth curves. The mean square error between the feature points of the third or fourth curve and the corresponding curve in a single registration set is calculated. If the mean square error is less than a set threshold, it is determined that the third or fourth curve is successfully matched with the corresponding curve in a single registration set.

[0055] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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.

[0056] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A data management system for oil and gas field fracturing technology, characterized in that, include: The historical data unit is used to acquire historical data of the complete fracturing process under different time and space conditions, including historical data of consumables, equipment, monitoring and production. A corresponding dataset is established and stored for the historical data of each complete fracturing process. The data linkage unit acquires and analyzes historical data of consumables, equipment, monitoring, and production from any dataset to obtain analysis results. Based on the analysis results, the historical data of consumables and the historical data of monitoring are processed into curves and used as a matching benchmark. The historical data of production is used as an adjustment benchmark, and the equipment parameters corresponding to the historical data of equipment are used as adjustment targets to build a linkage relationship between consumable data, equipment data, monitoring data, and production data. The real-time data unit collects consumable and monitoring data in the fracturing process in real time and processes it into curves to obtain consumable data curves and monitoring data curves; and collects equipment parameters in the fracturing process. The adjustment unit integrates all consumable data curves and all monitoring data curves, and matches them with all datasets to obtain matching results; based on the matching results and corresponding linkage relationships, it adjusts the corresponding equipment parameters in real time.

2. The data management system for oil and gas field fracturing process according to claim 1, characterized in that, The process of creating a graph from the historical data of the consumables includes: Based on the historical data of consumables, obtain the historical usage data of different types of consumables within a unit of time and perform data preprocessing. With time as the horizontal axis and the historical usage data of consumables per unit time as the vertical axis after data preprocessing, a curve is constructed about the change of historical usage data of consumables per unit time over time, which is denoted as the first curve. Store all first curve graphs into the first curve graph set.

3. The data management system for oil and gas field fracturing technology according to claim 2, characterized in that, The process of creating a graph from the historical monitoring data includes: Based on the historical monitoring data, different types of historical monitoring data are acquired and data preprocessing is performed. With time as the horizontal axis and the values ​​of different types of historical monitoring data as the vertical axis, construct a curve graph about the changes of different types of historical monitoring data over time, denoted as the second curve graph; Store all second curve plots into the second curve plot set.

4. A data management system for oil and gas field fracturing technology according to claim 3, characterized in that, Historical consumable data and monitoring data, after being processed by graph analysis, are used as the matching benchmark, including: Based on the first curve set and the second curve set, all first curves and all second curves belonging to the same complete fracturing process are placed into the corresponding registration set.

5. A data management system for oil and gas field fracturing technology according to claim 1, characterized in that, Using the aforementioned historical production data as the basis for adjustment includes: Based on the production history data of multiple complete fracturing processes in different time periods, multiple data intervals were divided for different types of production data, and each data interval of all types of production data was assigned a value using a unified assignment method. Based on the production history data of a single complete fracturing process, the average value of different types of production data is extracted within a set time period; Assign values ​​to the data intervals corresponding to the average values ​​of different types of production data, and use these values ​​as the feature values ​​of the corresponding production data; The feature values ​​of all types of production data in a single complete fracturing process are integrated using a preset formula and used as the adjustment benchmark value for the corresponding historical production data.

6. A data management system for oil and gas field fracturing technology according to claim 5, characterized in that, The feature values ​​of all types of production data from a single complete fracturing process are integrated using a preset formula, including: To adjust the baseline value, For the feature values ​​of different types of production data, The number of types of production data.

7. A data management system for oil and gas field fracturing technology according to claim 4, characterized in that, Real-time acquisition and plotting of consumable data during the fracturing process yields consumable data plots including: Real-time data collection and preprocessing of the usage of different types of consumables per unit time during the fracturing process; With time as the horizontal axis and the real-time consumption data of consumables per unit time after data preprocessing as the vertical axis, a curve is constructed on the change of real-time consumption data of consumables per unit time over time, denoted as the third curve.

8. A data management system for oil and gas field fracturing technology according to claim 7, characterized in that, Real-time acquisition of monitoring data during the fracturing process and plotting of the data to obtain monitoring data curves include: Real-time collection of different types of monitoring data and data preprocessing; With time as the horizontal axis and the values ​​of different types of real-time monitoring data as the vertical axis, construct a curve graph showing the change of different types of real-time monitoring data over time, denoted as the fourth curve graph.

9. A data management system for oil and gas field fracturing technology according to claim 8, characterized in that, Integrate all consumable data charts and all monitoring data charts, and match them with all datasets, including: Match all third and fourth curves with curves in all registration sets. If all curves in a registration set are successfully matched, obtain the adjustment benchmark value of the production history data corresponding to the registration set, and record it as the first adjustment benchmark value. If the first adjustment benchmark value exceeds the set value, obtain the corresponding device parameters based on the historical data of the device and record them as the first device parameters; Using the first equipment parameter as the adjustment target, the equipment parameter is adjusted in real time; If the first adjustment benchmark value is less than or equal to the set value, based on the linkage relationship and in combination with all third curves, all fourth curves and real-time equipment parameters, the production data is predicted to obtain a predicted adjustment benchmark value, and it is determined whether the predicted adjustment benchmark value exceeds the set value. If it does, no action is taken; if not, an early warning is issued.

10. A data management system for oil and gas field fracturing technology according to claim 1, characterized in that, Matching all third and fourth curves with curves in all registration sets includes: A preset number of feature points are selected in all third and fourth curves. The mean square error between the feature points of the third or fourth curve and the corresponding curve in a single registration set is calculated. If the mean square error is less than a set threshold, it is determined that the third or fourth curve is successfully matched with the corresponding curve in a single registration set.

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