An intelligent gas production process remote monitoring method and system integrating data transmission

Abstract

The application relates to the technical field of intelligent gas production monitoring, and discloses an intelligent gas production process remote monitoring method and system integrated with data transmission, which constructs candidate execution state data under a fixed short-time remote regulation and control window scenario, and encapsulates the candidate execution state data, a key point set, an original data segment and a cache index by using an integrated transmission package, so that a remote platform can complete rapid judgment based on core data in a current window, then establishes immediate execution reference data and delayed execution reference data by using historical regulation and control results, and performs comparison processing, so that the platform can determine a candidate time point more suitable for execution in the current window, or output a conclusion waiting for a next regulation and control window. Therefore, the problem that an existing intelligent gas production process remote monitoring method cannot accurately determine an optimal valve regulation time in the fixed short-time remote regulation and control window scenario, and causes limited regulation and control windows to be inefficiently occupied, is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent gas extraction monitoring technology, and in particular to an intelligent gas extraction process remote monitoring method and system with integrated data transmission. Background Technology

[0002] During natural gas extraction, wellheads are typically equipped with pressure sensors, flow meters, electric throttle valves, and communication terminals to collect data such as tubing pressure, casing pressure, instantaneous gas production flow, and valve opening. This data is then uploaded to a remote monitoring platform. Existing intelligent gas extraction remote monitoring methods are mostly based on fixed-cycle data collection and uploading. The remote platform displays curves, compares thresholds, allows manual viewing, or provides simple trend analysis based on the received wellhead data, thus assisting production personnel in understanding the wellhead's operational status. This approach can meet general monitoring needs in typical scenarios where remote adjustments can be performed at any time, wellhead conditions change relatively smoothly, and control actions are not limited by fixed timeframes. In other words, when the platform can issue valve adjustment commands at any time, the system can usually complete basic monitoring tasks as long as it ensures continuous data display and parameter limit alerts.

[0003] However, in actual production organization, there are scenarios involving fixed, short-term remote control windows. In these scenarios, influenced by factors such as unified well station scheduling, nighttime monitoring capacity, production shift arrangements, wellhead process stability requirements, and the need to reduce frequent valve adjustments, intelligent gas production wells are not suitable for valve adjustments at all times. Instead, remote adjustments are only permitted within a few short-term windows throughout the day. For example, a gas production well might be set to only allow remote valve adjustments during the morning inspection window, the afternoon process adjustment window, and the nighttime stable production window. Once the current short-term window is missed, even if the platform recognizes subsequent data changes, it may not be able to immediately execute adjustments and must wait for the next permitted window.

[0004] In the aforementioned fixed short-term remote control window scenario, the key task of remote monitoring is no longer simply determining whether the current wellhead parameters are within a certain range, but rather further determining whether the current window is worthwhile to utilize a valve adjustment opportunity, and which moment within the current window is more suitable for valve adjustment. Inappropriate valve adjustment timing can easily lead to two types of inefficient results. One is execution too early, where an adjustment opportunity is used before the current wellhead pressure support relationship, production response state, and time conditions have reached an optimal combination, resulting in limited production improvement after adjustment, and even requiring further correction in the next window. The other is execution too late, where the wellhead has already entered a state suitable for adjustment, but the remote platform still waits for more significant parameter changes using the traditional single-point viewing method, ultimately missing the current short-term window.

[0005] In existing technologies, remote monitoring methods typically treat tubing pressure, casing pressure, instantaneous gas production flow rate, and valve opening as independent or simply correlated monitoring objects. They lack unified processing of factors such as remaining window time, duration since the last valve adjustment, wellhead pressure support changes, flow rate response changes, and the completeness of platform data reception. Especially when the current window offers only a short time for decision-making, traditional methods cannot combine the analysis of whether the wellhead has good adjustment conditions with whether there are sufficient execution opportunities in the current window, nor can they form an orderly comparison among multiple candidate moments. Therefore, traditional methods struggle to output a clear conclusion on whether to execute the current window or wait for the next window, and even more so, they cannot provide a recommended execution time within the current window.

[0006] Therefore, how to construct candidate execution state data in a fixed short-term remote control window scenario, efficiently transmit key segments within the window to a remote platform through integrated data transmission, and then compare and evaluate multiple candidate moments within the current window in conjunction with historical control results, thereby improving the utilization efficiency of the limited control window, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0007] This invention provides a method and system for remote monitoring of intelligent gas extraction processes with integrated data transmission, which at least solves the problem that existing intelligent gas extraction process remote monitoring methods are unable to accurately determine the optimal valve adjustment timing in scenarios with fixed short-term remote control windows, resulting in the inefficient use of limited control windows.

[0008] To achieve the above objectives, the present invention provides a remote monitoring method for intelligent gas extraction processes with integrated data transmission, the method comprising the following steps: Acquire monitoring data of the intelligent gas production well within the current control window, and construct candidate execution status data based on the monitoring data; Based on the candidate execution status data, multiple candidate moments within the current control window are encapsulated and integrated into transmission packets to form a data completion degree representation result for the corresponding candidate moment. Based on historical control results, immediate execution reference data and delayed execution reference data are established respectively, and the candidate execution status data are compared with the immediate execution reference data and the delayed execution reference data. Based on the control processing results and the data completion degree characterization results, the timing evaluation results corresponding to each candidate time within the current control window are determined, and the target candidate time is selected. Based on the timing evaluation results corresponding to the target candidate time, the execution conclusion of the current control window and the recommended execution time are output.

[0009] Optionally, acquire monitoring data of the intelligent gas production well within the current control window, specifically including: At each sampling moment within the current control window, the tubing pressure, casing pressure, instantaneous gas production flow rate, electric throttle valve opening, remaining time of the current control window, and the duration since the last valve control action are acquired to form the original monitoring unit; Based on the original monitoring unit, the differential pressure, flow rate response value, and pressure opening reserve value of the casing oil are calculated. The original monitoring unit, the differential pressure of the casing oil, the flow opening response value, and the pressure opening reserve value are combined to form an extended monitoring unit; Each extended monitoring unit is assigned a sequential number according to time order to form a cache record. A cache index relationship is established based on the cache record for retrieval by time range and by sequential number.

[0010] Optionally, candidate execution status data can be constructed based on the monitoring data, specifically including: Multiple candidate moments are identified within the current control window to form a set of candidate moments. For each candidate moment, the observation segment is extracted from the cached record. Based on the observed segments, the average pressure support, average flow rate, average flow rate response, average pressure rate reserve, rate of change of differential pressure, rate of change of flow rate, rate of change of flow rate response, discrete value of flow rate, and discrete value of differential pressure are calculated respectively. By combining the remaining time of the current control window corresponding to the candidate moment with the duration since the last valve control action, candidate execution status data is formed.

[0011] Optionally, based on the candidate execution status data, multiple candidate moments within the current control window are encapsulated and integrated into transmission packets to form a data completion degree representation result for the corresponding candidate moment, specifically including: For each candidate time segment, calculate the window information density. Based on the information density of the window and the changes in flow rate, differential pressure, and flow opening response at each sampling point within the observation segment, the starting point, ending point, local extreme points, and points of significant change in the observation segment are selected to form a set of key points. The candidate execution state data, the set of key points, the original data fragments near the key points, and the cache index used for readback are encapsulated to form an integrated transmission packet corresponding to the candidate time. Based on the actual number of received data units and the planned number of received data units corresponding to the candidate time, the data completion degree characterization result is calculated.

[0012] Optionally, reference data for immediate implementation and reference data for delayed implementation can be established based on historical control results, specifically including: Extract samples from historical control results where the single adjustment return meets preset conditions after execution within the current control window, and obtain an immediate execution reference set; The immediate execution reference center is calculated based on the aforementioned immediate execution reference set, and the immediate execution reference bandwidth is calculated based on the immediate execution reference center; Extract samples from historical control results that were not executed in the current control window but whose single adjustment returns meet preset conditions after being executed in the next control window, and obtain a delayed execution reference set. The delayed execution reference center is calculated based on the delayed execution reference set, and the delayed execution reference bandwidth is calculated based on the delayed execution reference center.

[0013] Optionally, the candidate execution status data is compared with the immediate execution reference data and the delayed execution reference data, specifically including: The candidate execution status data is divided into a pressure support group, a flow response group, and a time opportunity group; The immediate execution reference center and the delayed execution reference center are split according to the same feature grouping method as the candidate execution state data; Calculate the projection values ​​between each feature group and the immediate execution reference center, as well as the projection values ​​between each feature group and the delayed execution reference center. The bandwidth consistency value of the candidate time step for the immediate execution reference pattern is calculated based on the immediate execution reference bandwidth, and the bandwidth consistency value of the candidate time step for the delayed execution reference pattern is calculated based on the delayed execution reference bandwidth.

[0014] Optionally, based on the control processing results and the data completion degree characterization results, the timing evaluation results corresponding to each candidate time point within the current control window are determined, and target candidate times are selected, specifically including: Based on the projection values ​​and bandwidth consistency values ​​corresponding to each feature group, calculate the immediate execution comprehensive value and the delayed execution comprehensive value respectively; Based on the immediate execution comprehensive value, the delayed execution comprehensive value, the remaining time of the current control window, the duration since the last valve control action, and the data completion degree characterization result, the timing evaluation result corresponding to the candidate moment is determined; The timing evaluation results corresponding to all candidate times within the current control window are sorted, and the candidate time with the largest timing evaluation result is selected as the target candidate time. The corresponding timing evaluation result is then used as the target timing evaluation result for the current control window.

[0015] Optionally, a remote monitoring method for intelligent gas extraction processes integrating data transmission, the method further includes: Determine whether the target timing evaluation result is within the preset critical interval. If so, initiate a local readback request to the wellhead based on the cache index corresponding to the target candidate time to obtain high-resolution original data fragments near the target candidate time. Update the candidate execution status data and data completion degree representation results corresponding to the target candidate time based on the high-resolution raw data fragment; Based on the updated candidate execution status data and the updated data completion degree representation results, the projection value, bandwidth consistency value, immediate execution comprehensive value, delayed execution comprehensive value, and timing evaluation result corresponding to the target candidate time are recalculated. The recalculated timing evaluation result will be used as the final target timing evaluation result for the current control window.

[0016] Optionally, based on the timing evaluation result corresponding to the target candidate time, the execution conclusion of the current control window and the recommended execution time are output, specifically including: Compare the final target timing evaluation result of the current control window with the preset execution threshold; When the final target timing evaluation result is not less than the preset execution threshold, the conclusion of the current control window execution is output, and the target candidate time is output as the recommended execution time. When the final target timing evaluation result is less than the preset execution threshold, the conclusion of waiting for the next control window is output. After completing the regulation and obtaining the actual regulation result, if the single regulation benefit after the current regulation window is executed meets the preset conditions, the candidate execution status data before this execution will be added to the immediate execution reference set. If the current control window is not executed and the single adjustment benefit after the execution of the next control window meets the preset conditions, then the candidate execution status data corresponding to the current control window is added to the delayed execution reference set, and the immediate execution reference center, the immediate execution reference bandwidth, the delayed execution reference center, and the delayed execution reference bandwidth are updated again based on the updated reference set.

[0017] Furthermore, to achieve the above objectives, the present invention also provides an intelligent gas extraction process remote monitoring system integrating data transmission, comprising: The acquisition module is used to acquire monitoring data of the intelligent gas production well within the current control window, and to construct candidate execution status data based on the monitoring data; The encapsulation module is used to encapsulate and integrate multiple candidate moments within the current control window into transmission packets based on the candidate execution status data, thereby forming a data completion degree representation result for the corresponding candidate moment. A module is established to create immediate execution reference data and delayed execution reference data based on historical control results, and to compare the candidate execution status data with the immediate execution reference data and the delayed execution reference data. The determination module is used to determine the timing evaluation result corresponding to each candidate moment within the current control window based on the control processing result and the data completion degree characterization result, and to select the target candidate moment; The output module is used to output the execution conclusion of the current control window and the recommended execution time based on the timing evaluation result corresponding to the target candidate time.

[0018] The beneficial effects of this invention are as follows: It proposes an intelligent gas production process remote monitoring method and system with integrated data transmission. By uniformly processing wellhead pressure, gas production flow rate, valve opening, remaining time of the current control window, and the duration since the last valve adjustment in a fixed short-term remote control window scenario, remote monitoring no longer focuses on viewing single-point parameters but shifts to candidate state analysis oriented towards control timing. Then, by integrating transmission packets, candidate execution state data, key point sets, original data fragments, and cache indexes are encapsulated and combined with data completion degree characterization results for subsequent evaluation. This allows the remote platform to make rapid judgments based on core data within the current window, and supplement high-resolution original data fragments through local backreading when the results are critical, thus balancing real-time performance and reliability. Finally, it establishes immediate execution reference data and delayed execution reference data using historical control results, and compares the candidate execution state data corresponding to multiple candidate times within the current window with the two types of reference data. This allows the platform to determine a more suitable candidate time for execution within the current window or output a conclusion to wait for the next control window, thereby improving the utilization efficiency of the limited control window. This solves the problem that existing intelligent gas extraction process remote monitoring methods struggle to accurately determine the optimal valve adjustment timing in fixed, short-term remote control window scenarios, leading to inefficient use of the limited control window. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the method of an embodiment of the present invention; Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] This invention provides a remote monitoring method for intelligent gas extraction processes with integrated data transmission, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the intelligent gas extraction process remote monitoring method integrating data transmission, as described in an embodiment of the present invention.

[0022] In this embodiment, a remote monitoring method for intelligent gas extraction technology integrating data transmission includes the following steps: S1: Obtain monitoring data of the intelligent gas production well within the current control window, and construct candidate execution status data based on the monitoring data.

[0023] Specifically, at each sampling moment within the current control window, the tubing pressure, casing pressure, instantaneous gas production flow rate, electric throttle valve opening, remaining time of the current control window, and the duration since the last valve adjustment action are acquired to form an initial monitoring unit. Based on the initial monitoring unit, the casing-oil pressure difference, flow rate response value, and pressure opening reserve value are calculated. The initial monitoring unit, the casing-oil pressure difference, the flow rate response value, and the pressure opening reserve value are combined to form an extended monitoring unit. Each extended monitoring unit is assigned a sequential number according to time order to form a cache record. Based on the cache record, a cache index relationship for retrieval by time range and by sequential number is established.

[0024] Subsequently, multiple candidate moments are identified within the current control window to form a candidate moment set. For each candidate moment, observation segments are extracted from the cached records. Based on the observation segments, the average pressure support, average flow rate, average flow opening response, average pressure opening reserve, rate of change of differential pressure, rate of change of flow rate, rate of change of flow opening response, flow dispersion, and dispersion of differential pressure are calculated. Combined with the remaining time of the current control window corresponding to the candidate moment and the duration since the last valve control action, candidate execution status data is formed.

[0025] In this embodiment of the invention, step S1 is used to establish the data foundation required for all subsequent processing within the current control window. It is easy to understand that in a fixed short-term remote control window scenario, the problem the remote platform needs to solve is not simply viewing the pressure or flow rate at a certain moment, but rather determining whether a candidate moment within the current window is suitable for occupying a valve adjustment opportunity. Therefore, this step, while acquiring the tubing pressure, casing pressure, instantaneous gas production flow rate, and electric throttle valve opening, also acquires the remaining time of the current control window and the duration since the last valve adjustment action, thus forming a unified expression of the process status and the time opportunity status at the data level.

[0026] It should be noted that the raw monitoring data can be jointly provided by the wellhead sensor, the electric throttle valve actuator, the wellhead controller, and the window plan information from the remote platform. It should also be noted that the remaining time of the current control window can be calculated from the preset window plan table and the current sampling time; the duration since the last valve adjustment action can be calculated from the end time of the most recent valve adjustment action and the current sampling time.

[0027] Specifically, in the first At each sampling moment, the system acquires tubing pressure, casing pressure, instantaneous gas production flow rate, electric throttle valve opening, remaining time in the current control window, and the duration since the last valve adjustment action, forming the original monitoring unit:

[0028] in, Indicates the first The original monitoring unit at each sampling time, Indicates the first Each sampling time, Indicates the first The oil pipe pressure at each sampling time, Indicates the first The sleeve pressure at each sampling time, Indicates the first Instantaneous gas production flow rate at each sampling time, Indicates the first The opening degree of the electric throttle valve at each sampling time. Indicates the first The remaining time of the current control window at each sampling moment. Indicates the first The sampling time is the duration since the last valve adjustment action.

[0029] In the original monitoring unit data structure formed by the above formula, tubing pressure, casing pressure, instantaneous gas production flow rate, and electric throttle valve opening are used to describe the current wellhead process status; the remaining time of the current control window is used to describe the remaining operating opportunities in the current short-term window; and the duration since the last valve adjustment action is used to describe the time interval between the current action and the previous action. By incorporating the above data into the same original monitoring unit, subsequent processing can simultaneously consider whether the process conditions are mature and whether the timing is appropriate.

[0030] Furthermore, the system calculates the differential pressure, flow rate response, and pressure reserve based on the original monitoring unit:

[0031]

[0032]

[0033] in, Indicates the first The differential pressure of the casing oil at each sampling time, Indicates the first The flow opening response value at each sampling time. Indicates the first Pressure opening reserve value at each sampling time This represents a very small positive number used to avoid a denominator of zero.

[0034] Specifically, the casing pressure differential is used to characterize the current pressure support relationship of the gas production well. The flow rate opening response value is used to characterize the flow production performance corresponding to a unit opening under the current valve opening conditions. The pressure opening reserve value is used to characterize the pressure support margin corresponding to a unit opening under the current valve opening conditions. It is easy to understand that within a fixed short-term control window, the simple level of flow rate does not indicate whether adjustment is suitable. The pressure support relationship, the degree of flow rate response to opening, and the pressure margin at the current opening all jointly affect the benefits after valve adjustment. Therefore, the above three derived values ​​are key intermediate data for subsequently forming candidate execution state data.

[0035] Furthermore, the system combines the original monitoring unit with the differential pressure, flow rate response value, and pressure opening reserve value of the casing oil to form an extended monitoring unit:

[0036] in, Indicates the first An extended monitoring unit for each sampling time.

[0037] It should be noted that the extended monitoring unit combines the original measured values ​​with the process characterization values ​​calculated from the original measured values ​​into a unified data structure, enabling subsequent window segment extraction, feature calculation, and transmission encapsulation to be performed based on the same data structure. In practical applications, the extended monitoring unit can be stored in the wellhead controller, local edge computing terminal, or site server.

[0038] Furthermore, the system assigns sequential numbers to each extended monitoring unit according to time order, forming a cache record:

[0039] in, Indicates the first Cache records for each sampling time, Indicates the first The sequential number corresponding to each sampling time.

[0040] In this embodiment of the invention, cached records are used to support subsequent integrated transport packet encapsulation and partial readback. Specifically, when the platform needs to supplement the acquisition of raw data segments near a target candidate time, the wellhead can quickly locate the corresponding cached record based on the sequence number and time range, without needing to re-upload the entire control window or the entire day's data. This reduces communication overhead and improves decision-making efficiency within a short time window.

[0041] Within the current control window, the system identifies multiple candidate moments, forming a set of candidate moments:

[0042] in, Indicates the first The set of candidate times within a control window Indicates the first Within the first control window One candidate moment, Indicates the first The number of candidate moments within each control window.

[0043] For each candidate time point, the system backtracks and extracts the observed segment from the cached records: ;

[0044] in, Indicates the first Within the first control window The observation segment corresponding to each candidate time point This indicates the number of sampling points contained in the observed segment. This represents the cached record corresponding to the starting sampling point of the observed segment. This represents the cached record of the sampling point corresponding to the candidate time.

[0045] In this embodiment of the invention, the observation segment is used to describe the process over a short period of time prior to a candidate moment. It is readily understood that timing determination should not rely solely on a single instantaneous value at the candidate moment, as such an instantaneous value may be affected by short-term fluctuations and cannot reflect the changing direction of pressure support and flow response relationships. By backtracking and extracting the observation segment, the local evolution process prior to the candidate moment can be obtained, making subsequent candidate execution status data more reflective of whether the current stage is suitable for valve regulation.

[0046] Furthermore, based on the observed segments, the system calculates the average pressure support, average flow rate, average flow rate response, average pressure rate reserve, rate of change of differential pressure, rate of change of flow rate, rate of change of flow rate response, discrete value of flow rate, and discrete value of differential pressure. ; ; ; ; ; in, Indicates the first Within the first control window The average pressure support of the observed segment corresponding to each candidate time point. This represents the average flow rate. This represents the average flow opening response. This represents the average pressure opening reserve. This indicates the rate of change of the differential pressure between the casing and the oil. Indicates the rate of change of flow rate. This indicates the rate of change in flow opening response. Represents the discrete values ​​of flow rate. This represents the discrete value of the differential pressure between the casing and the oil. Indicates the first segment of the observation segment Differential pressure of the casing oil at each sampling point Indicates the first segment of the observation segment Instantaneous gas production flow rate at each sampling point Indicates the first segment of the observation segment The flow opening response value at each sampling point Indicates the first segment of the observation segment Pressure opening reserve value at each sampling point.

[0047] In this embodiment of the invention, mean-type features are used to describe the overall state within the observation segment, rate-of-change features are used to describe the direction of process evolution within the observation segment, and discrete features are used to describe the stability of data within the observation segment. For example, when the average pressure support and the average pressure opening reserve are both high within the observation segment, it indicates that the pressure conditions before the candidate time point are relatively favorable; when the rate of change of flow opening response is positive, it indicates that the flow production performance corresponding to a unit opening is improving; when the flow dispersion and the differential pressure dispersion are too high, it indicates that the fluctuations within the observation segment are large, and it may not be advisable to immediately seize the opportunity to adjust the valve. Therefore, the above features together constitute the basis for timing judgment.

[0048] Furthermore, the system combines the above features with the remaining time of the current control window and the duration since the last valve control action to form candidate execution status data: ;

[0049] in, Indicates the first Within the first control window Candidate execution status data corresponding to each candidate time point This indicates the remaining time of the current control window corresponding to the candidate time. This indicates the duration since the last valve control action, corresponding to the candidate moment.

[0050] In this embodiment of the invention, candidate execution status data serves as the unified input for subsequent integrated transmission, reference comparison, timing evaluation, and execution output. Through this data structure, different candidate moments within the current window can be transformed into data objects of the same dimension, enabling fair comparison and ranking during subsequent processing. In a specific application example, a smart gas well is configured with three fixed short-term control windows daily, each lasting ten minutes. The remote platform generates a candidate moment every thirty seconds within the current window and backtracks four minutes from each candidate moment to form an observation segment. If the pressure support is sufficient, the flow opening response shows an upward trend, the pressure opening reserve is high, and there is still sufficient remaining time in the window for a particular candidate moment, then the candidate execution status data corresponding to that candidate moment will have a higher probability of entering the target candidate moment in subsequent evaluation.

[0051] S2: Based on the candidate execution status data, encapsulate and integrate multiple candidate moments within the current control window into transmission packets to form a data completion degree representation result for the corresponding candidate moment.

[0052] Specifically, for each candidate time point corresponding to an observation segment, the window information density is calculated; based on the window information density and the changes in flow rate, differential pressure, and flow opening response at each sampling point within the observation segment, the starting point, ending point, local extreme points, and points of significant change in the observation segment are selected to form a set of key points; the candidate execution state data, the set of key points, the original data segments near the key points, and the cache index used for readback are encapsulated to form an integrated transmission packet corresponding to the candidate time point; based on the actual number of received data units and the planned number of received data units corresponding to the candidate time point, the data completion degree characterization result is calculated.

[0053] In this embodiment of the invention, step S2 is used to organize the data corresponding to each candidate moment within the current control window into an integrated transmission packet suitable for rapid processing by a remote platform. Because traditional data transmission uses a full-data-per-point upload method, the remote platform may need to wait for a large amount of raw data to arrive before making a judgment, easily missing the optimal execution moment within the window. To solve this problem, this step encapsulates data around the candidate moments, organizing the candidate execution status data, key point set, raw data fragments near the key points, and cache index together, enabling the platform to first make a judgment based on key content and then read back local details when necessary.

[0054] Specifically, the system calculates the window information density for the observation segment corresponding to each candidate time point: ;

[0055] in, Indicates the first The first regulation window Information density of the observation segment corresponding to each candidate time point , , and These represent the weighting coefficients used in the information density calculation.

[0056] In this embodiment of the invention, window information density is used to determine how much change information a given observation segment contains that is meaningful for timing selection. If the flow rate dispersion, differential pressure dispersion, flow rate change rate, or flow opening response change rate are large within the observation segment, then the information density of the observation segment is high, indicating that there are many data changes within the segment that may affect the timing decision. Conversely, if all changes within the observation segment are small, then the information density of the segment is low, and the platform can prioritize receiving its summary content instead of immediately receiving all the original details.

[0057] Furthermore, based on the window information density and the changes in flow rate, differential pressure, and flow opening response at each sampling point within the observation segment, the system filters the start point, end point, local extreme points, and points of significant change within the observation segment to form a key point set. It should be noted that the key point set is not limited to a single fixed extraction rule. In specific implementation, the system can designate the start and end points of the observation segment as mandatory points, and select the points with the largest local maximum and minimum flow rates, the local extreme points of differential pressure, and the sampling points with the largest changes in flow opening response as optional points. In this way, the key point set can retain the process information most influential on timing judgment within the observation segment with a relatively small amount of data.

[0058] Furthermore, the system encapsulates the candidate execution state data, the set of key points, the original data fragments near the key points, and the cache index for readback to form an integrated transmission packet corresponding to the candidate moment: ; in, Indicates the first The first regulation window The integrated transmission packet corresponding to each candidate time. This represents the set of key points corresponding to the candidate time. This indicates the original data segment near the key point corresponding to the candidate time. This indicates the cache index corresponding to the candidate time.

[0059] In this embodiment of the invention, the significance of the integrated transmission packet lies in changing the organizational unit of traditional point-to-point transmission. Traditional methods upload data at individual sampling points, while this invention uploads data at candidate moments, so that a single transmission packet contains a state summary of the candidate moment, key change points, local raw data, and locations that can be read back later. Thus, upon receiving the integrated transmission packet, the remote platform can directly proceed to candidate moment evaluation without needing to manually search for judgment criteria from a large number of raw curves.

[0060] Furthermore, based on the actual number of received data units and the planned number of received data units corresponding to the candidate time, the platform calculates the data completion level representation result: ; in, Indicates the first The first regulation window The data completion level representation results corresponding to each candidate time point This indicates the actual number of data units received at the corresponding candidate time. This indicates the number of planned data units to be received for the candidate time.

[0061] In this embodiment of the invention, the data completion level characterization result is used in subsequent timing evaluation. It is easy to understand that, given a limited current window time, the platform may first receive candidate execution status data and some key points, and then receive the original fragments near the key points. If the data completion level characterization result is high, it indicates that the evaluation basis for the candidate timing is relatively sufficient; if the data completion level characterization result is low, it indicates that the evaluation result for the candidate timing should be subject to certain limitations, and data can be supplemented through local readback if necessary. By introducing this result, the present invention avoids over-reliance on local information when data is incomplete, while also avoiding waiting for all original data to arrive completely.

[0062] S3: Based on historical control results, establish immediate execution reference data and delayed execution reference data respectively, and compare the candidate execution status data with the immediate execution reference data and the delayed execution reference data.

[0063] Specifically, samples from historical control results that meet preset conditions for single-time adjustment returns after execution within the current control window are extracted to obtain an immediate execution reference set; an immediate execution reference center is calculated based on the immediate execution reference set, and an immediate execution reference bandwidth is calculated based on the immediate execution reference center; samples from historical control results that do not meet preset conditions for single-time adjustment returns after execution in the next control window are extracted to obtain a delayed execution reference set; a delayed execution reference center is calculated based on the delayed execution reference set, and a delayed execution reference bandwidth is calculated based on the delayed execution reference center.

[0064] Subsequently, the candidate execution state data is divided into a pressure support group, a flow response group, and a time opportunity group; the immediate execution reference center and the delayed execution reference center are split according to the same feature grouping method as the candidate execution state data; the projection value between each feature group and the immediate execution reference center and the delayed execution reference center are calculated respectively; the bandwidth consistency value of the candidate moment to the immediate execution reference pattern is calculated based on the immediate execution reference bandwidth, and the bandwidth consistency value of the candidate moment to the delayed execution reference pattern is calculated based on the delayed execution reference bandwidth.

[0065] In this embodiment of the invention, step S3 is used to establish a comparison benchmark for evaluating the current candidate time. It should be noted that this invention does not directly use a single threshold to determine whether the current window should be executed, but rather forms two types of reference data based on historical adjustment results. One type of reference data corresponds to historical cases where execution within the current window resulted in a better single adjustment benefit, and the other type corresponds to historical cases where execution in the next window resulted in a better single adjustment benefit if the current window was not executed. Through these two types of reference data, the platform can determine whether the current candidate time is closer to the optimal state of immediate execution or closer to the optimal state of waiting for the next window.

[0066] Specifically, the system extracts samples from historical control results where the single-adjustment return meets preset conditions after execution within the current control window, and obtains an immediate execution reference set: ; in, Indicates immediate execution of the reference set. Indicates the first Candidate execution status data corresponding to each immediately executed reference sample This indicates the number of immediately executed reference samples. Furthermore, the system calculates the immediately executed reference center based on the immediately executed reference set: ; And calculate the immediate execution reference bandwidth: ; in, This indicates that the reference center will be implemented immediately. Indicates immediate execution of the reference center at the [date / time]. The values ​​in each feature dimension Indicates the first The first immediate execution reference sample in the first The values ​​in each feature dimension Indicates the first The immediate execution reference bandwidth corresponding to each feature dimension.

[0067] Similarly, the system extracts samples from historical control results that were not executed within the current control window but whose single-adjustment returns meet preset conditions after execution in the next control window, thus obtaining a delayed execution reference set: ; in, Indicates the set of references to be executed later. Indicates the first Candidate execution status data corresponding to each delayed execution reference sample This indicates the number of delayed execution reference samples. Furthermore, the system calculates the delayed execution reference center based on the delayed execution reference set: ; And calculate the reference bandwidth for delayed execution: ; in, The reference center indicates that the implementation will be postponed. The reference center indicated that the execution would be postponed in the [number]th [year]. The values ​​in each feature dimension Indicates the first The delayed execution reference sample in the first The values ​​in each feature dimension Indicates the first The delayed execution reference bandwidth corresponds to each feature dimension.

[0068] In this embodiment of the invention, the immediate execution reference center and the delayed execution reference center represent typical states of two types of historical patterns, respectively, while the immediate execution reference bandwidth and the delayed execution reference bandwidth represent the reasonable fluctuation range of the corresponding pattern across various feature dimensions. By establishing two types of reference data, this invention avoids judging solely from the perspective of whether the current window is valid or not, thus better answering the practical question of whether to execute the current window or wait for the next window.

[0069] Furthermore, to avoid features with different physical meanings from masking each other, the system divides candidate execution state data into three groups: pressure support, flow response, and time opportunity. ; in, Indicates the first Within the first control window Each candidate time point corresponds to a pressure support group in the candidate execution status data. Indicates the flow response group, Indicates a time opportunity group.

[0070] It should be noted that the pressure support group is used to characterize whether the pressure conditions before the candidate time are favorable for regulation; the flow production response group is used to characterize whether the flow production performance and flow opening response relationship before the candidate time support regulation; and the time opportunity group is used to characterize whether the remaining time in the current window is sufficient and whether the interval since the last action is suitable for taking action again.

[0071] Furthermore, the system will split the immediate execution reference center and the delayed execution reference center according to the same feature grouping method as the candidate execution state data, and calculate the projection value between each feature group and the immediate execution reference center: ; And the projection value between the reference center and the delayed execution: ; in, Indicates the feature group number, and , Indicates the first Within the first control window The candidate moment of the first The projection values ​​between each feature group and the corresponding feature group of the immediate execution reference center Indicates the first Within the first control window The candidate moment of the first The projection values ​​between each feature group and the corresponding feature group of the delayed execution reference center.

[0072] It should be noted that the projection value is used to represent the degree of proximity between the current candidate time and the two types of reference data in the corresponding feature groups. To avoid situations where the directions are similar but the specific values ​​have exceeded a reasonable range, the system further calculates a bandwidth consistency value based on the reference bandwidth.

[0073] Furthermore, the system calculates the bandwidth consistency value of the candidate time step relative to the immediate execution reference pattern based on the immediate execution reference bandwidth: ; And calculate the bandwidth consistency value of the candidate time step relative to the delayed execution reference pattern based on the delayed execution reference bandwidth: ; in, Indicates the first Within the first control window The candidate time of the first The bandwidth consistency value of each feature group for the immediately executed reference style. Indicates the first Within the first control window The candidate time of the first The bandwidth consistency value of each feature group for the delayed execution reference style. Indicates the candidate execution status data in the th... The values ​​in each feature dimension.

[0074] As is easily understood, the bandwidth consistency value is used to indicate whether the specific value at a candidate time point is still within a reasonable range of the corresponding reference data. If the value of a certain feature dimension does not exceed the reference bandwidth, then that dimension will not incur additional penalties; if the value of a certain feature dimension significantly exceeds the reference bandwidth, then the bandwidth consistency value of that feature group will decrease.

[0075] S4: Based on the control processing results and the data completion degree characterization results, determine the timing evaluation results corresponding to each candidate moment within the current control window, and select the target candidate moment.

[0076] Specifically, based on the projection value and bandwidth consistency value corresponding to each feature group, the immediate execution comprehensive value and the delayed execution comprehensive value are calculated respectively; based on the immediate execution comprehensive value, the delayed execution comprehensive value, the remaining time of the current control window, the duration since the last valve control action, and the data completion degree characterization result, the timing evaluation result corresponding to the candidate moment is determined; the timing evaluation results corresponding to all candidate moments in the current control window are sorted, the candidate moment with the largest timing evaluation result is selected as the target candidate moment, and the corresponding timing evaluation result is used as the target timing evaluation result of the current control window.

[0077] In addition, the intelligent gas production process remote monitoring method integrating data transmission further includes: determining whether the target timing evaluation result is within a preset critical interval; if so, initiating a local readback request to the wellhead based on the cache index corresponding to the target candidate time to obtain high-resolution raw data fragments near the target candidate time; updating the candidate execution status data and data completion degree representation result corresponding to the target candidate time based on the high-resolution raw data fragments; recalculating the projection value, bandwidth consistency value, immediate execution comprehensive value, delayed execution comprehensive value, and timing evaluation result corresponding to the target candidate time based on the updated candidate execution status data and updated data completion degree representation result; and using the recalculated timing evaluation result as the final target timing evaluation result of the current control window.

[0078] In this embodiment of the invention, step S4 is used to further transform the control processing results obtained in step S3 into a timing evaluation result that can be sorted, compared, and used for output decision-making. It should be noted that this invention does not base its judgment on a single pressure threshold, a single flow threshold, or a single time threshold. Instead, it incorporates immediate execution reference data, delayed execution reference data, the remaining time of the current window, the duration since the last valve control action, and the data completion degree representation results into the calculation, thereby obtaining a comprehensive evaluation of each candidate moment within the current control window. Specifically, the system calculates the immediate execution comprehensive value and the delayed execution comprehensive value based on the projection value and bandwidth consistency value corresponding to each feature group: ; ; in, Indicates the first Within the first control window The immediate execution comprehensive value corresponding to each candidate time point Indicates the first Within the first control window The total delayed execution value corresponding to each candidate time point Indicates the first The weight coefficients corresponding to each feature group, and satisfying .

[0079] The immediate execution composite value reflects the overall proximity between the candidate time and historically better-performing scenarios for the current window, while the delayed execution composite value reflects the overall proximity between the candidate time and historically better-performing scenarios for waiting for the next window. Weighting coefficients can be set based on wellhead type, control objectives, and field experience.

[0080] Furthermore, based on the comprehensive value of immediate execution, the comprehensive value of delayed execution, the remaining time of the current control window, the duration since the last valve control action, and the data completion level representation results, the system determines the timing evaluation result corresponding to the candidate moment: ; in, Indicates the first Within the first control window Timing evaluation results corresponding to each candidate moment This represents the time scale parameter of the control window. This represents the time scale parameter for the action interval.

[0081] In the above formula, the ratio between the immediate execution composite value and the delayed execution composite value describes whether the candidate timing is more inclined to execute within the current window or wait for the next window; the exponential term corresponding to the remaining time of the current control window reflects the impact of the remaining window time on the execution value; the exponential term corresponding to the duration since the last valve control action reflects whether there is a sufficient time interval for the current adjustment action; and the data completion degree characterization result reflects whether the data on which the current evaluation is based is sufficient. Through the above combination, the timing evaluation result can simultaneously reflect process conditions, window opportunities, action intervals, and data completeness.

[0082] Furthermore, the system sorts the timing evaluation results corresponding to all candidate times within the current control window and selects the candidate time with the highest timing evaluation result as the target candidate time: ; The corresponding timing evaluation results will be used as the target timing evaluation results for the current control window. ; in, This indicates the sequence number corresponding to the target candidate time. Indicates the first Evaluation results of the target timing of each regulatory window.

[0083] In this embodiment of the invention, the target candidate time is the most recommended execution time within the current control window. It is easy to understand that the target candidate time is not necessarily the moment the current window begins, nor is it necessarily the moment the current window is about to end; rather, it is the moment with the highest overall timing evaluation result within the current window.

[0084] Furthermore, when the target timing evaluation result is within a preset critical interval, the system initiates a local readback request to the wellhead based on the cache index corresponding to the target candidate time to obtain high-resolution original data fragments near the target candidate time. The preset critical interval can be understood as the range within which the target timing evaluation result approaches the execution threshold. When the target timing evaluation result is significantly higher than the execution threshold, the system can directly output an execution suggestion; when the target timing evaluation result is significantly lower than the execution threshold, the system can directly output a waiting suggestion; when the target timing evaluation result is within the critical range, it indicates that the current conclusion may be affected by data details, and in this case, supplementing key original fragments through local readback is more prudent.

[0085] After completing the partial readback, the system updates the candidate execution status data and data completion degree representation results corresponding to the target candidate time based on the high-resolution original data fragments. Based on the updated candidate execution status data and updated data completion degree representation results, it recalculates the projection value, bandwidth consistency value, immediate execution comprehensive value, delayed execution comprehensive value, and timing evaluation result corresponding to the target candidate time. The recalculated timing evaluation result serves as the final target timing evaluation result for the current control window.

[0086] S5: Based on the timing evaluation results corresponding to the target candidate time, output the execution conclusion of the current control window and the recommended execution time.

[0087] Specifically, the final target timing evaluation result of the current control window is compared with a preset execution threshold. When the final target timing evaluation result is not less than the preset execution threshold, the conclusion of the current control window execution is output, and the target candidate time is output as the recommended execution time. When the final target timing evaluation result is less than the preset execution threshold, the conclusion of waiting for the next control window is output. After the control execution is completed and the actual control result is obtained, if the single adjustment benefit after the current control window execution meets the preset conditions, the candidate execution status data before this execution is added to the immediate execution reference set. If the current control window is not executed and the single adjustment benefit after the next control window execution meets the preset conditions, the candidate execution status data corresponding to the current control window is added to the delayed execution reference set, and the immediate execution reference center, the immediate execution reference bandwidth, the delayed execution reference center, and the delayed execution reference bandwidth are updated based on the updated reference set.

[0088] In this embodiment of the invention, step S5 is used to convert the final target timing evaluation result obtained in step S4 into an execution conclusion that can be directly used by the remote monitoring platform. It is easy to understand that the remote monitoring platform ultimately needs to output a clear result to the operator or the automatic control system, namely, whether the current window should be executed and, if so, when it should be executed. Therefore, this step compares the final target timing evaluation result with a preset execution threshold.

[0089] Specifically, if the final target timing evaluation result satisfies the following formula, the system outputs the conclusion of the current control window execution and outputs the target candidate time as the recommended execution time: ; in, This indicates the preset execution threshold.

[0090] If the final target timing evaluation result does not satisfy the following formula, the system outputs a conclusion to wait for the next adjustment window: ; In this embodiment of the invention, the preset execution threshold can be set according to the process conditions, control window length, adjustment benefit requirements, and field operation strategies of different gas production wells. It should be noted that the preset execution threshold is not limited to a fixed constant and can be configured differently depending on the well group type or different time periods. As long as this threshold is used to determine whether the execution value of the current window meets the preset requirements, it satisfies the implementation needs of this invention.

[0091] Furthermore, after completing the control execution and obtaining the actual control results, if the single adjustment benefit after the current control window is executed meets the preset conditions, the system will supplement the candidate execution status data before this execution into the immediate execution reference set; if the current control window is not executed and the single adjustment benefit after the next control window is executed meets the preset conditions, the system will supplement the candidate execution status data corresponding to the current control window into the delayed execution reference set, and update the immediate execution reference center, immediate execution reference bandwidth, delayed execution reference center, and delayed execution reference bandwidth based on the updated reference set.

[0092] In this embodiment of the invention, the aforementioned reference data update process enables the platform to continuously absorb actual control results. As the number of execution cases gradually increases, the reference data for immediate execution will better reflect the actual state of the gas well suitable for execution within the current window, while the reference data for delayed execution will better reflect the actual state of the gas well suitable for waiting for the next window. Therefore, the system can continuously accumulate timing experience within a fixed short-term window during long-term operation, improving the stability of subsequent recommendations.

[0093] In a specific application example, the current control window is ten minutes. The platform evaluates multiple candidate times within the window and selects the candidate time with the highest evaluation result as the recommended time. If the final target timing evaluation result reaches the preset execution threshold, the platform outputs the conclusion of the current control window execution and sends the recommended time to the control execution unit. If the final target timing evaluation result does not reach the preset execution threshold, the platform outputs the conclusion of waiting for the next control window and retains the current window data as a reference for subsequent updates. In this way, the platform output is no longer a simple alarm or prompt, but directly addresses the control timing management of a fixed short-term window.

[0094] Reference Figure 2 , Figure 2 This is a schematic diagram of the intelligent gas extraction process remote monitoring system integrating data transmission, as described in an embodiment of the present invention.

[0095] like Figure 2 As shown, the intelligent gas extraction process remote monitoring system with integrated data transmission proposed in this embodiment of the invention includes: The acquisition module 10 is used to acquire monitoring data of the intelligent gas production well within the current control window, and to construct candidate execution status data based on the monitoring data; Encapsulation module 20 is used to encapsulate and integrate transmission packets for multiple candidate moments within the current control window based on the candidate execution status data, forming a data completion degree representation result for the corresponding candidate moment; The module 30 is used to establish immediate execution reference data and delayed execution reference data based on historical control results, and to compare the candidate execution status data with the immediate execution reference data and the delayed execution reference data. The determination module 40 is used to determine the timing evaluation result corresponding to each candidate moment within the current control window based on the control processing result and the data completion degree characterization result, and to select the target candidate moment; The output module 50 is used to output the execution conclusion of the current control window and the recommended execution time based on the timing evaluation result corresponding to the target candidate time.

[0096] Other embodiments or specific implementations of the intelligent gas extraction process remote monitoring system integrating data transmission of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0097] It is understood that in the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Nth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0099] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A remote monitoring method for intelligent gas extraction technology integrating data transmission, characterized in that, The method includes the following steps: Acquire monitoring data of the intelligent gas production well within the current control window, and construct candidate execution status data based on the monitoring data; Based on the candidate execution status data, multiple candidate moments within the current control window are encapsulated and integrated into transmission packets to form a data completion degree representation result for the corresponding candidate moment. Based on historical control results, immediate execution reference data and delayed execution reference data are established respectively, and the candidate execution status data are compared with the immediate execution reference data and the delayed execution reference data. Based on the control processing results and the data completion degree characterization results, the timing evaluation results corresponding to each candidate time within the current control window are determined, and the target candidate time is selected. Based on the timing evaluation results corresponding to the target candidate time, the execution conclusion of the current control window and the recommended execution time are output.

2. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 1, characterized in that, Acquire monitoring data of the intelligent gas production well within the current control window, specifically including: At each sampling moment within the current control window, the tubing pressure, casing pressure, instantaneous gas production flow rate, electric throttle valve opening, remaining time of the current control window, and the duration since the last valve control action are acquired to form the original monitoring unit; Based on the original monitoring unit, the differential pressure, flow rate response value, and pressure opening reserve value of the casing oil are calculated. The original monitoring unit, the differential pressure of the casing oil, the flow opening response value, and the pressure opening reserve value are combined to form an extended monitoring unit; Each extended monitoring unit is assigned a sequential number according to time order to form a cache record. A cache index relationship is established based on the cache record for retrieval by time range and by sequential number.

3. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 2, characterized in that, Based on the monitoring data, candidate execution status data is constructed, specifically including: Multiple candidate moments are identified within the current control window to form a set of candidate moments. For each candidate moment, the observation segment is extracted from the cached record. Based on the observed segments, the average pressure support, average flow rate, average flow rate response, average pressure rate reserve, rate of change of differential pressure, rate of change of flow rate, rate of change of flow rate response, discrete value of flow rate, and discrete value of differential pressure are calculated respectively. By combining the remaining time of the current control window corresponding to the candidate moment with the duration since the last valve control action, candidate execution status data is formed.

4. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 1, characterized in that, Based on the candidate execution status data, multiple candidate moments within the current control window are encapsulated and integrated into transmission packets to form a data completion degree representation result for the corresponding candidate moment, specifically including: For each candidate time segment, calculate the window information density. Based on the information density of the window and the changes in flow rate, differential pressure, and flow opening response at each sampling point within the observation segment, the starting point, ending point, local extreme points, and points of significant change in the observation segment are selected to form a set of key points. The candidate execution state data, the set of key points, the original data fragments near the key points, and the cache index used for readback are encapsulated to form an integrated transmission packet corresponding to the candidate time. Based on the actual number of received data units and the planned number of received data units corresponding to the candidate time, the data completion degree characterization result is calculated.

5. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 1, characterized in that, Based on historical control results, establish reference data for immediate implementation and reference data for delayed implementation, specifically including: Extract samples from historical control results where the single adjustment return meets preset conditions after execution within the current control window, and obtain an immediate execution reference set; The immediate execution reference center is calculated based on the aforementioned immediate execution reference set, and the immediate execution reference bandwidth is calculated based on the immediate execution reference center; Extract samples from historical control results that were not executed in the current control window but whose single adjustment returns meet preset conditions after being executed in the next control window, and obtain a delayed execution reference set. The delayed execution reference center is calculated based on the delayed execution reference set, and the delayed execution reference bandwidth is calculated based on the delayed execution reference center.

6. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 5, characterized in that, The process of comparing the candidate execution status data with the immediate execution reference data and the delayed execution reference data specifically includes: The candidate execution status data is divided into a pressure support group, a flow response group, and a time opportunity group; The immediate execution reference center and the delayed execution reference center are split according to the same feature grouping method as the candidate execution state data; Calculate the projection values ​​between each feature group and the immediate execution reference center, as well as the projection values ​​between each feature group and the delayed execution reference center. The bandwidth consistency value of the candidate time step for the immediate execution reference pattern is calculated based on the immediate execution reference bandwidth, and the bandwidth consistency value of the candidate time step for the delayed execution reference pattern is calculated based on the delayed execution reference bandwidth.

7. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 6, characterized in that, Based on the control processing results and the data completion degree characterization results, the timing evaluation results corresponding to each candidate time point within the current control window are determined, and target candidate times are selected, specifically including: Based on the projection values ​​and bandwidth consistency values ​​corresponding to each feature group, calculate the immediate execution comprehensive value and the delayed execution comprehensive value respectively; Based on the immediate execution comprehensive value, the delayed execution comprehensive value, the remaining time of the current control window, the duration since the last valve control action, and the data completion degree characterization result, the timing evaluation result corresponding to the candidate moment is determined; The timing evaluation results corresponding to all candidate times within the current control window are sorted, and the candidate time with the largest timing evaluation result is selected as the target candidate time. The corresponding timing evaluation result is then used as the target timing evaluation result for the current control window.

8. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 7, characterized in that, The method further includes: Determine whether the target timing evaluation result is within the preset critical interval. If so, initiate a local readback request to the wellhead based on the cache index corresponding to the target candidate time to obtain high-resolution original data fragments near the target candidate time. Update the candidate execution status data and data completion degree representation results corresponding to the target candidate time based on the high-resolution raw data fragment; Based on the updated candidate execution status data and the updated data completion degree representation results, the projection value, bandwidth consistency value, immediate execution comprehensive value, delayed execution comprehensive value, and timing evaluation result corresponding to the target candidate time are recalculated. The recalculated timing evaluation result will be used as the final target timing evaluation result for the current control window.

9. The intelligent gas extraction process remote monitoring method with integrated data transmission as described in claim 1, characterized in that, Based on the timing evaluation results corresponding to the target candidate time, the execution conclusion and recommended execution time of the current control window are output, specifically including: Compare the final target timing evaluation result of the current control window with the preset execution threshold; When the final target timing evaluation result is not less than the preset execution threshold, the conclusion of the current control window execution is output, and the target candidate time is output as the recommended execution time. When the final target timing evaluation result is less than the preset execution threshold, the conclusion of waiting for the next control window is output. After completing the regulation and obtaining the actual regulation result, if the single regulation benefit after the current regulation window is executed meets the preset conditions, the candidate execution status data before this execution will be added to the immediate execution reference set. If the current control window is not executed and the single adjustment benefit after the execution of the next control window meets the preset conditions, then the candidate execution status data corresponding to the current control window is added to the delayed execution reference set, and the immediate execution reference center, the immediate execution reference bandwidth, the delayed execution reference center, and the delayed execution reference bandwidth are updated again based on the updated reference set.

10. A remote monitoring system for intelligent gas extraction processes integrating data transmission, characterized in that, The system includes: The acquisition module is used to acquire monitoring data of the intelligent gas production well within the current control window, and to construct candidate execution status data based on the monitoring data; The encapsulation module is used to encapsulate and integrate multiple candidate moments within the current control window into transmission packets based on the candidate execution status data, thereby forming a data completion degree representation result for the corresponding candidate moment. A module is established to create immediate execution reference data and delayed execution reference data based on historical control results, and to compare the candidate execution status data with the immediate execution reference data and the delayed execution reference data. The determination module is used to determine the timing evaluation result corresponding to each candidate moment within the current control window based on the control processing result and the data completion degree characterization result, and to select the target candidate moment; The output module is used to output the execution conclusion of the current control window and the recommended execution time based on the timing evaluation result corresponding to the target candidate time.

Images