Method for determining state of beam-pumping unit

Abstract

The invention relates to the technical field of equipment detection, in particular to a method for determining the state of a beam-pumping unit, and the method comprises the steps: constructing a unit energy consumption baseline, and calculating a deviation degree index according to the unit energy consumption baseline; generating a comprehensive risk score according to the deviation index; constructing an input-output elasticity index, and obtaining a subdivision cause code according to the input-output elasticity index and the comprehensive risk score; a suggestion is determined according to the input-output elasticity index and the subdivision cause code generation state. According to the method, the unit energy consumption baseline is established under the stable working condition, an objective and comparable energy efficiency reference standard is formed, the comprehensive risk score is generated, misjudgment caused by a single index in a traditional method is avoided, and operation and maintenance personnel can rapidly position an abnormal source and formulate a more targeted maintenance strategy through subdivision presentation of causes.

Description

Technical Field

[0001] This application relates to the field of equipment testing technology, and in particular to a method for determining the condition of a beam pumping unit. Background Technology

[0002] As the most common lifting equipment in oilfield development, the operating status of beam pumping units directly affects production and energy consumption levels.

[0003] Existing technologies typically rely on dynamometer card analysis or single energy consumption data monitoring to determine whether equipment is faulty. For example, pump efficiency and fluid level can be inferred from suspension point load and displacement curves, or motor power can be used as a reference only. However, these methods generally have two shortcomings: First, dynamometer card analysis is highly dependent on the accuracy of data acquisition and expert experience, and is prone to misjudgment due to complex downhole factors, making it difficult to establish a universally applicable judgment standard; second, relying on empirical judgment based on power or fluid production data at a single moment can easily lead to misjudgment.

[0004] Therefore, existing methods are difficult to make comparable between different oil wells and lack quantitative characterization of the causes and persistence of anomalies. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for determining the status of beam pumping units that avoids misjudgments caused by single indicators in traditional methods by generating a comprehensive risk score, and enables maintenance personnel to quickly locate the source of anomalies and formulate more targeted maintenance strategies through the detailed presentation of causes.

[0006] The technical solution of this invention is as follows: A method for determining the state of a beam pumping unit, the method comprising: Construct a unit energy consumption baseline, and calculate the deviation index based on the unit energy consumption baseline; A comprehensive risk score is generated based on the deviation index; Construct an input-output elasticity index, and obtain detailed causal codes based on the input-output elasticity index and the comprehensive risk score; Recommendations are determined based on the input-output elasticity index and the subdivision factor code generation status.

[0007] Specifically, a unit energy consumption baseline is constructed, and a deviation index is calculated based on the unit energy consumption baseline; including: Construct a short time window and construct a unit energy consumption baseline based on the short time window; The deviation index is calculated based on the unit energy consumption baseline and the preset length of the scrolling window.

[0008] Specifically, a short-time window is constructed, and a unit energy consumption baseline is constructed based on the short-time window, including: A short time window is established in response to the operating conditions reaching preset conditions; Obtain the instantaneous power of the motor and the instantaneous drainage rate corresponding to the short time window; A baseline for unit energy consumption is constructed based on the instantaneous power of the motor and the instantaneous drainage rate.

[0009] Specifically, the deviation index is calculated based on the unit energy consumption baseline and the preset length of the scrolling window, including: Calculate the unit energy consumption for scrolling based on the preset length of the scrolling window; The deviation index is calculated based on the unit energy consumption baseline and the rolling unit energy consumption.

[0010] Specifically, a comprehensive risk score is generated based on the deviation index, including: Based on the deviation index, a collaborative stability index and anomaly strength are generated; A comprehensive risk score is generated based on the deviation index, the collaborative stability index, and the anomaly intensity.

[0011] Specifically, a collaborative stability index and anomaly strength are generated based on the deviation index; including: Based on the aforementioned unit energy consumption baseline, a collaborative stability assessment is performed, and a collaborative stability index is generated. Anomaly intensity is generated based on the cooperative stability index and the deviation index.

[0012] Specifically, a comprehensive risk score is generated based on the deviation index, the collaborative stability index, and the anomaly intensity, including: Obtain the preset weight coefficients; A comprehensive risk score is generated by weighting and fusing the deviation index, collaborative stability index, and anomaly intensity based on preset weighting coefficients.

[0013] Specifically, an input-output elasticity index is constructed, and a detailed causal code is obtained based on the input-output elasticity index and the comprehensive risk score; including: An input-output elasticity index is constructed based on the magnitude of changes in liquid production rate and power. The detailed causal code is obtained by comparing the input-output elasticity index and the comprehensive risk score.

[0014] Specifically, the input-output elasticity index is generated based on the following formula:

[0015] in, It is the input-output elasticity index; The variation range of the liquid production rate; The magnitude of power change; This is the average power value; This represents the average production rate.

[0016] Specifically, a beam pumping unit status determination system is also provided, the system comprising: The deviation index calculation module is used to construct a unit energy consumption baseline and calculate the deviation index based on the unit energy consumption baseline. The risk score generation module is used to generate a comprehensive risk score based on the deviation index; The subdivision causation generation module is used to construct the input-output elasticity index and obtain the subdivision causation code based on the input-output elasticity index and the comprehensive risk score; The status determination suggestion module is used to generate status determination suggestions based on the input-output elasticity index and the subdivision causal code.

[0017] Specifically, the deviation index calculation module is also used to: construct a short time window and construct a unit energy consumption baseline based on the short time window; and calculate the deviation index based on the unit energy consumption baseline and the length of a preset rolling window.

[0018] Specifically, the deviation index calculation module is also used to: construct a short time window in response to the operating condition reaching a preset condition; obtain the instantaneous power of the motor and the instantaneous drainage rate corresponding to the short time window; and construct a unit energy consumption baseline based on the instantaneous power of the motor and the instantaneous drainage rate.

[0019] Specifically, the deviation index calculation module is also used to: calculate the scrolling unit energy consumption based on the preset length of the scrolling window; and calculate the deviation index according to the unit energy consumption baseline and the scrolling unit energy consumption.

[0020] Specifically, the risk score generation module is also used to: generate a collaborative stability index and anomaly intensity based on the deviation index; and generate a comprehensive risk score based on the deviation index, collaborative stability index, and anomaly intensity.

[0021] Specifically, the risk score generation module is also used to: perform a collaborative stability assessment based on the unit energy consumption baseline and generate a collaborative stability index; and generate anomaly intensity based on the collaborative stability index and the deviation index.

[0022] Specifically, the risk score generation module is also used to: obtain preset weight coefficients; and generate a comprehensive risk score by weighting and fusing the deviation index, the collaborative stability index, and the anomaly intensity based on the preset weight coefficients.

[0023] Specifically, the subdivision causation generation module is further configured to: construct an input-output elasticity index based on the variation range of the liquid production rate and the variation range of the power; and obtain a subdivision causation code by comparing the input-output elasticity index with the comprehensive risk score.

[0024] Specifically, the subdivision causation generation module is also used to: generate an input-output elasticity index based on the following formula:

[0025] in, It is the input-output elasticity index; The variation range of the liquid production rate; The magnitude of power change; This is the average power value; This represents the average production rate.

[0026] Optionally, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-described method for determining the state of a beam pumping unit.

[0027] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for determining the state of a beam pumping unit.

[0028] The technical effects achieved by this invention are as follows: 1. The above-mentioned method for determining the state of a beam pumping unit involves sequentially constructing a unit energy consumption baseline and calculating a deviation index based on the unit energy consumption baseline. By establishing a unit energy consumption baseline under stable operating conditions, an objective and comparable energy efficiency reference standard is formed. Based on a preset rolling window, the deviation index is calculated, which can promptly capture changes in energy consumption caused by insufficient oil layer supply, decreased pump and valve sealing performance, or increased frictional resistance. 2. Generate a comprehensive risk score based on the deviation index; construct an input-output elasticity index, and obtain a detailed causal code based on the input-output elasticity index and the comprehensive risk score; avoid misjudgment caused by a single index in traditional methods, construct an input-output elasticity index to measure whether a unitized power disturbance can be responded to by a unitized change in the liquid output, and consider the relative change in power and liquid output within the window. 3. Determine recommendations based on the input-output elasticity index and the generation status of the detailed cause codes; through the detailed presentation of causes, maintenance personnel can quickly locate the source of anomalies and formulate more targeted maintenance strategies. Attached Figure Description

[0029] Figure 1This is a flowchart illustrating a method for determining the state of a beam pumping unit in one embodiment. Figure 2 A structural block diagram of a downstream beam pumping unit state determination system in one embodiment; Figure 3 This is a structural block diagram of a computer device in one embodiment. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0031] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0032] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0034] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0036] In one embodiment, a terminal is provided, the terminal being configured to: construct a unit energy consumption baseline; calculate a deviation index based on the unit energy consumption baseline; generate a comprehensive risk score based on the deviation index; construct an input-output elasticity index; obtain a detailed causal code based on the input-output elasticity index and the comprehensive risk score; and generate a status determination recommendation based on the input-output elasticity index and the detailed causal code.

[0037] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0038] In one embodiment, such as Figure 1 As shown, a method for determining the state of a beam pumping unit is provided, the method comprising: Step S100: Construct a unit energy consumption baseline and calculate the deviation index based on the unit energy consumption baseline; Step S200: Generate a comprehensive risk score based on the deviation index; Step S300: Construct an input-output elasticity index, and obtain a detailed causal code based on the input-output elasticity index and the comprehensive risk score; Step S400: Determine a recommendation based on the input-output elasticity index and the subdivision factor code generation status.

[0039] In this embodiment, a unit energy consumption baseline is first constructed, and a deviation index is calculated based on the unit energy consumption baseline. By establishing a unit energy consumption baseline under stable operating conditions, an objective and comparable energy efficiency reference standard is formed, and the deviation index is calculated based on a preset rolling window. This allows for timely detection of energy consumption changes caused by insufficient oil layer supply, decreased pump and valve sealing performance, or increased frictional resistance. Then, a comprehensive risk score is generated based on the deviation index; an input-output elasticity index is constructed, and a detailed causal code is obtained based on the input-output elasticity index and the comprehensive risk score; avoiding the misjudgment caused by a single index in traditional methods, the input-output elasticity index is constructed to measure whether a unitized power disturbance can be responded to by a unitized change in the liquid output, taking into account the relative change in power and liquid output within the window. Next, recommendations are determined based on the input-output elasticity index and the generation status of the detailed cause codes; through the detailed presentation of causes, maintenance personnel can quickly locate the source of the anomaly and formulate more targeted maintenance strategies.

[0040] In one embodiment, step S100: constructing a unit energy consumption baseline and calculating a deviation index based on the unit energy consumption baseline; including: Step S110: Construct a short time window and construct a unit energy consumption baseline based on the short time window; Step S120: Calculate the deviation index based on the unit energy consumption baseline and the preset length of the scrolling window.

[0041] In this embodiment, a short time window is constructed, and a unit energy consumption baseline is constructed based on the short time window; the deviation index is calculated based on the unit energy consumption baseline and the length of a preset rolling window; by establishing a unit energy consumption baseline under stable operating conditions, an objective and comparable energy efficiency reference standard is formed, and the deviation index is calculated based on the preset rolling window, which can promptly capture changes in energy consumption caused by insufficient oil layer supply, decreased pump and valve sealing performance, or increased frictional resistance.

[0042] In one embodiment, step S110: constructing a short-time window and constructing a unit energy consumption baseline based on the short-time window includes: Step S111: In response to the operating condition reaching the preset condition, a short time window is constructed; Step S112: Obtain the instantaneous power of the motor and the instantaneous drainage rate corresponding to the short time window; Step S113: Construct a unit energy consumption baseline based on the instantaneous power of the motor and the instantaneous drainage rate.

[0043] In this embodiment, a unit energy consumption baseline is constructed for the same beam pumping unit within a short time window under stable operating conditions, stable well fluid supply, and no equipment alarms. Within the baseline window, the instantaneous power sequence of the motor and the instantaneous fluid discharge rate sequence measured on the ground are selected. The power consumption and fluid production volume of the window are obtained by time integration, and a model is constructed to calculate the unit energy consumption baseline.

[0044] Stable operating conditions refer to the pumping unit's operating load and energy consumption remaining within an acceptable fluctuation range over a certain period, without significant power spikes or mechanical shocks. The detection data primarily includes the motor power curve, suspension point load curve, and current waveform. If the motor power fluctuation within the observation window does not exceed ±5% of the average value, the suspension point load curve maintains a consistent shape without significant distortion, and the current waveform shows no frequent spikes or abnormal distortion, then the equipment can be considered to be in a stable operating condition. This quantitative standard ensures that the collected data represents the typical performance of the equipment under healthy conditions.

[0045] Stable well fluid supply refers to a continuous and minimally fluctuating fluid supply within the wellbore, maintaining a stable production rate. The key monitoring data includes the real-time discharge rate from the wellhead flowmeter and the well fluid level changes from the level measurement device. If the standard deviation of the instantaneous discharge rate recorded by the flowmeter within the specified window does not exceed 10% of the average value, and the level change remains within a reasonable fluctuation range (usually no more than 1-1.5 meters) over one stroke cycle, then the well fluid supply can be considered stable. This standard ensures the stability and representativeness of the production data when establishing a baseline for unit energy consumption, thus avoiding baseline deviations caused by abnormal well fluid supply.

[0046] Unlike existing technologies that rely solely on empirical judgments based on power or production data at a single moment, this step establishes a baseline for unit energy consumption under stable operating conditions, creating an objective and comparable energy efficiency reference standard. Its advantage lies in its ability to quantify the energy efficiency level of the pumping unit under normal and healthy conditions, providing a clear reference value for subsequent anomaly assessments and avoiding the uncertainty and lack of portability inherent in traditional methods that rely on human experience.

[0047] To suppress the impact of occasional spikes, the baseline window selection incorporates site experience to screen clean sections, avoiding start-up / shutdown boundaries and periods of grid fluctuation. The constructed unit energy consumption baseline model is shown below:

[0048] in, This is the baseline for energy consumption per unit, expressed in kilowatt-hours per cubic meter (kWh / m³). 3 .

[0049] The instantaneous power of the motor is measured in kilowatts (kW). Data is collected in real time by voltage and current sensors installed in the motor circuit, and the instantaneous active power is calculated in the control system.

[0050] This is the instantaneous drainage rate, expressed in cubic meters per hour (m). 3 / h; Real-time measurement at the surface outlet using a wellhead flow meter (commonly an electromagnetic flow meter or a volumetric flow meter).

[0051] This is the baseline start time; it is automatically marked by the monitoring system based on the operation log, or a "stable period" can be manually selected as the starting point. It is equivalent to a specific time on the monitoring timeline, for example, in the format of 08:00 on [Date] 20xx.

[0052] The monitoring system automatically marks the operation logs. Specifically, it uses a sliding window (updated every 5 minutes) to automatically calculate the relative fluctuations of power and drainage rate, while simultaneously checking for alarm / shutdown events. When the conditions of "power fluctuation ≤ ±5%, drainage rate standard deviation ≤ 10% of the mean, and no alarms" are met for a set minimum duration (e.g., 30-60 minutes), a "stable period" start marker and timestamp are generated in the log. If any condition is broken (threshold exceeded or alarm / shutdown occurs), the system immediately writes a "stable period ended" marker and records the cause code, forming a traceable stable segment.

[0053] The baseline time window length is manually set by the system during the setup phase, selecting a period of time during which the equipment is in a stable operating condition as the baseline window. It is usually set to 1-12 hours, but can be extended to 24 hours depending on the stability of the produced fluid, to smooth out short-term fluctuations.

[0054] The model outputs It reflects the average energy efficiency level of the equipment and wellbore in converting electrical energy into production fluid under the most normal operating conditions, and measures whether there are abnormal increases or decreases in real-time unit energy consumption at different time periods.

[0055] Unlike single-point sampling, this time-window-based calculation method effectively smooths out the effects of short-term spikes and random noise, resulting in a more stable and reliable baseline value. This baseline not only reflects the energy efficiency characteristics of the equipment itself but also serves as a basis for lateral comparisons between different well sites, improving the objectivity and universality of condition determination.

[0056] In one embodiment, step S120: calculating the deviation index based on the unit energy consumption baseline and the preset length of the scrolling window, including: Step S121: Calculate the unit energy consumption for scrolling based on the preset length of the scrolling window; Step S122: Calculate the deviation index based on the unit energy consumption baseline and the rolling unit energy consumption.

[0057] In this embodiment, during long-term operation of the equipment, the unit energy consumption will fluctuate around the baseline level due to changes in oil layer supply, gas-liquid ratio, pump and valve sealing, and transmission chain status. The rolling unit energy consumption for the current period is calculated within a rolling window and compared with the unit energy consumption baseline to obtain a deviation index, which is used to quantify whether energy efficiency deviates from a healthy level. The length of the scroll window is The energy consumption calculation model is as follows:

[0058] in, To scroll the window The unit energy consumption per cubic meter is expressed in kilowatt-hours per cubic meter (kWh / m³). 3 .

[0059] The length of the rolling window is preset by maintenance personnel or the monitoring system and is used to calculate the rolling average or total amount of real-time energy consumption and fluid production. It is generally set to 0.5–6 hours, typically 1 hour or 2 hours; for wells with large fluctuations in well fluid, it can be set to a longer time to smooth the data.

[0060] The instantaneous power of the motor within the scrolling window is expressed in kilowatts (kW), but here it specifically refers to the instantaneous power of the motor within the scrolling window. Sampling points within the time window.

[0061] The instantaneous drainage rate within the scrolling window, expressed in cubic meters per hour (m). 3 / h, but only take The data within the window.

[0062] This is a deviation index.

[0063] It should be noted that, in the description of this invention, the time symbol... and Although both indicate time, their meanings are not the same and need to be clearly distinguished: This indicates the current time or the timestamp of the monitoring system, used for outputting results or marking the "now" of the observed data; while It is the independent variable in integration or statistical operations, used only when defining the mean, standard deviation, or integral within a certain time window, and its value changes with the interval during the integration process. or Internal changes. Therefore, It is a fixed observation point, and It is an iterable variable that takes values ​​continuously within a window. Although similar in form, they serve completely different roles: the former is used for positioning, while the latter is used for calculation. For the result moment, This is the time variable for the integration process.

[0064] when A value consistently above 1 (remaining above 1.15) indicates an increase in energy input per unit of produced liquid, commonly seen in pump leakage, decreased fill rate, or increased friction; when A value significantly below 1 (remaining below 0.85) may indicate measurement errors or an abnormal surge in production. In this way, the system can achieve accurate monitoring of energy efficiency changes, avoid misjudgments from single-point sampling, and the defined threshold range enhances the method's practicality and portability.

[0065] The values ​​of 1.15 and 0.85 are mainly derived from a large number of statistical results and engineering experience in monitoring the energy efficiency of oilfield pumping units. Under healthy operating conditions, the deviation of unit energy consumption is generally concentrated between 0.90 and 1.10. Therefore, the upper and lower limits are each relaxed by about 5% as a margin, forming a judgment range of 0.85-1.15. When the deviation exceeds this range and persists, it often corresponds to actual problems such as decreased pump efficiency, abnormal liquid level, or metering error, thus having good sensitivity and reliability.

[0066] Unlike traditional single-point monitoring, this rolling window approach effectively balances real-time performance and stability. It not only promptly captures energy consumption changes caused by insufficient reservoir supply, decreased pump and valve sealing performance, or increased frictional resistance, but also mitigates the impact of occasional fluctuations through the smoothing effect of the time window. Furthermore, the deviation is designed with dimensionlessness, avoiding incomparability caused by differences in well type, power level, or flow rate, ensuring consistent interpretation of results from different well sites and time periods.

[0067] In one embodiment, step S200: generating a comprehensive risk score based on the deviation index includes: Step S210: Generate a cooperative stability index and anomaly intensity based on the deviation index; Step S220: Generate a comprehensive risk score based on the deviation index, the collaborative stability index, and the anomaly intensity.

[0068] In this embodiment, to avoid misjudgment based on a single indicator, a collaborative stability index and anomaly intensity are generated based on the deviation index; and a comprehensive risk score is generated based on the deviation index, collaborative stability index, and anomaly intensity.

[0069] In one embodiment, step S210: generating a cooperative stability index and anomaly strength based on the deviation index; including: Step S211: Perform a collaborative stability assessment based on the unit energy consumption baseline and generate a collaborative stability index; Step S212: Generate anomaly intensity based on the cooperative stability index and the deviation index.

[0070] In this embodiment, the cooperative stability assessment and anomaly intensity calculation are mainly based on the obtained deviation index.

[0071] Collaborative stability assessment includes: Looking solely at energy efficiency deviation might misjudge "short-term fluctuations" as "structural anomalies." Therefore, this step introduces the synergistic stability of power and product output to constrain misjudgments. A synergistic stability index is constructed, incorporating the synergistic stability of power and product output. Based on the mean and standard deviation of power and product output within a rolling window, the calculation formula for the synergistic stability index is given:

[0072] in, It is a collaborative stability index; The standard deviation of power is expressed in kilowatts (kW) and is used to characterize the amplitude of power fluctuations. This is the average power, expressed in kilowatts (kW). The standard deviation of the liquid production rate is expressed in cubic meters per hour (m). 3 / h is used to measure fluctuations in drainage; This is the average production rate, expressed in cubic meters per hour (m). 3 / h; When the power fluctuation is much greater than the liquid production fluctuation, that is Greater than 1.5 An increase in output indicates unstable energy input and a disproportionately small output, consistent with gas intrusion, transmission slippage, or intermittent liquid supply; conversely, if the liquid output fluctuates significantly while the power output remains relatively stable, i.e. Less than 0.7 A decrease in flow rate indicates an abnormal metering or transient wellbore refueling.

[0073] Anomaly intensity calculation includes: Based on the output of step S120 And the calculation in this step As input, the anomaly intensity is calculated according to the following formula:

[0074] It is of abnormal strength; This is an empirical adjustment coefficient, calibrated based on historical healthy operating conditions and typical abnormal operating conditions. It is usually set by maintenance personnel or system optimizers. It is generally between 0.5 and 2.0. It can be adjusted according to the actual well conditions to balance the weight of energy efficiency deviation and coordination imbalance in anomaly identification.

[0075] Based on calculations Give a corresponding preliminary status code output. :when It is at a low level (less than 1.00) and When the value is close to 1 (0.90-1.10), it is considered "stable and normal"; when... Increase (greater than 1.20) and mainly caused by When the lift-driven efficiency is greater than 1.15, it tends to be judged as "energy efficiency degradation"; when Increase (greater than 1.20) and mainly caused by When the lift is greater than 1.30, it tends to be judged as "cooperative imbalance".

[0076] This approach not only avoids misjudgments caused by a single indicator in traditional methods, but also allows for the output of preliminary status codes. At the same time, it was clearly pointed out whether the anomaly was mainly driven by energy efficiency degradation or synergistic imbalance, providing a clearer basis for subsequent graded treatment.

[0077] In one embodiment, step S220: generating a comprehensive risk score based on the deviation index, the collaborative stability index, and the anomaly intensity, including: Step S221: Obtain the preset weight coefficients; Step S222: Based on preset weighting coefficients, the deviation index, the collaborative stability index, and the anomaly intensity are weighted and fused to generate a comprehensive risk score.

[0078] In this embodiment, a comprehensive risk score is obtained by weighted fusion of three indicators, and a three-level status label is given.

[0079] To avoid misjudgments based on a single indicator, by... , , Weighted fusion is performed to form an interpretable comprehensive risk score:

[0080] in, For comprehensive risk scoring; These are the weighting coefficients, and Before the system goes live, a one-time adjustment is performed using historical health data, so that... During normal operation, the concentration is low, but it rises significantly when abnormal conditions such as decreased pump efficiency, gas intrusion, or wear of the drive chain occur.

[0081] according to Based on the numerical distribution, the system divides the state into three levels: low-order ( The median (e.g.) is "Level 1 Normal". The condition is classified as "Level II Minor Abnormality," and is located at a high level (e.g., The result is classified as "Level 3 Severe Abnormality". More importantly, the system can also identify the contribution of each factor to the risk score, for example, if... A higher weighting indicates a primary problem of "coordination imbalance between energy input and liquid production response." This result not only provides a state classification but also explains the dominant source of the anomaly for operations and maintenance personnel, laying a solid foundation for subsequent steps. This step weights and integrates the anomaly intensity, energy efficiency deviation, and coordination stability index to form a comprehensive risk score, and provides a state label through classification mapping. Its advantage lies in providing clear classification results and tracing the contribution source of each factor, avoiding the uninterpretability of black-box models.

[0082] In one embodiment, step S300: constructing an input-output elasticity index, and obtaining a detailed causal code based on the input-output elasticity index and the comprehensive risk score; including: Step S310: Construct an input-output elasticity index based on the variation range of the liquid production rate and the variation range of the power. Step S320: Compare the input-output elasticity index and the comprehensive risk score to obtain the detailed causal code.

[0083] In this embodiment, the anomaly depends not only on its intensity but also on the "response of input changes to output changes." Therefore, this step constructs an "input-output elasticity index" within a rolling window to measure whether a unitized power disturbance can be responded to by a unitized change in the product liquid. Considering the relative changes in power and product liquid within the window, the formula for calculating the input-output elasticity index is as follows:

[0084] in, This is the input-output elasticity index.

[0085] The variation in liquid production rate is expressed in cubic meters per hour (m). 3 / h; instantaneous drainage rate within the scrolling window The range or quantile difference is used to characterize the fluctuation range of the liquid production.

[0086] The power variation is expressed in kilowatts (kW); instantaneous power is expressed within a rolling window. The range (maximum value minus minimum value) or several percentile differences (such as 95%–5% percentile difference) can be used to reflect the intensity of power fluctuations.

[0087] Specifically, if Significantly lower than 1 and A high value indicates "inadequate liquid production response despite power fluctuations," suggesting potential pump leakage, air intrusion, or increased friction; if Significantly higher than 1 Moderate, possibly due to measurement fluctuations or short-term replenishment; Combination , , and The criteria are used to output the detailed causal code. The three categories are: "energy efficiency degradation as the main factor", "coordination imbalance as the main factor", and "questionable measurement".

[0088] Specifically, when And accompanied by When this occurs, it indicates that the power fluctuation is significantly higher than the liquid production response, and the system infers anomalies such as pump and valve leakage, gas intrusion, or increased friction. This type is "energy efficiency degradation-driven".

[0089] when When this occurs, it indicates that the relative fluctuations in power and product output are basically matched. If at this point... If the value remains low (less than 1.0), the system classifies it as normal or slightly abnormal. This category is "dominated by coordinated imbalance".

[0090] when A value in the median range (1.0–1.8) indicates that the fluctuation in production volume is significantly higher than the fluctuation in power output. This type of situation is usually related to flow meter malfunctions or short-term wellbore recharge. This category is "measuring questionable".

[0091] Compared to traditional single-risk assessment, this step ensures the comparability of results for different well types and power levels through dimensionless design; on the other hand, by presenting the causes in a detailed manner, it enables maintenance personnel to quickly locate the source of the anomaly and formulate more targeted maintenance strategies.

[0092] In one embodiment, step S400: determining a recommendation based on the input-output elasticity index and the subdivision factor code generation status, including: To avoid frequent interventions due to transient disturbances, an anomaly persistence rate model is constructed to quantify the persistence of anomalies over a period of time, and based on this, a response strategy is triggered, outputting phased response recommendations. The abnormal persistence rate model is shown below:

[0093] in, The abnormal persistence rate is theoretically 0–1; when When, it indicates abnormally scattered; when When the time is right, it indicates that the abnormality is persistently significant.

[0094] This is an indicator function that outputs 0 or 1 for logical judgment. The value of is in the range of 0-1, representing the most recent The percentage of cases that were abnormal during the specified time period.

[0095] The length of the observation time window is preset by the operations and maintenance personnel or the monitoring system, and is generally set according to the stroke cycle or operation and maintenance strategy. It is usually 0.5-24 hours; if used for short-term analysis, it can be set to 1-2 hours; if used for daily monitoring, it can be set to 12-24 hours.

[0096] The output of step S210 , used in the integration process.

[0097] The abnormal intensity threshold is usually set to 1.2-1.5, and can be adjusted up or down according to the characteristics of different oil wells.

[0098] The output of step S220 , used in the integration process.

[0099] The comprehensive risk score threshold is typically between 1.0 and 1.8. The values ​​are for illustrative purposes only and are not intended to be limiting.

[0100] For example, in this embodiment, the handling strategy includes: when Low (less than 0.2) and When the indicator points to "Metrology Suspicious," only a prompt to verify the metering link is displayed; when... Medium (0.20–0.60) and Pointing to "coordination imbalance as the dominant factor" and If the value is below 1, it is recommended to check for air intrusion and the drive chain; when High (greater than 0.60) High level (greater than 1.80) and If the value is consistently greater than 1 (remaining at 1.15), a shutdown or maintenance recommendation will be issued.

[0101] In this embodiment, the statistical regularity of a large amount of oil well operation data and operation and maintenance experience are mainly based on the following: Under normal operating conditions, the proportion of abnormal states appearing in the observation window is usually no more than 20%, so less than 0.20 can be judged as scattered fluctuations; when the proportion is between 20% and 60%, it indicates that the abnormality has a certain degree of persistence but is unstable, and can be defined as a medium level; once it exceeds 60%, it means that it is in an abnormal state most of the time, which is highly correlated with serious operating conditions such as pump efficiency decline, gas intrusion or mechanical wear, so it is set as a high level criterion.

[0102] The final output includes Time series, phased action recommendations, and status diagnosis results , Depend on and This integration enables closed-loop management of "whether intervention is needed, how to intervene, and to what extent to intervene."

[0103] In one embodiment, such as Figure 2 As shown, a beam pumping unit status determination system is also provided, the system comprising: The deviation index calculation module is used to construct a unit energy consumption baseline and calculate the deviation index based on the unit energy consumption baseline. The risk score generation module is used to generate a comprehensive risk score based on the deviation index; The subdivision causation generation module is used to construct the input-output elasticity index and obtain the subdivision causation code based on the input-output elasticity index and the comprehensive risk score; The status determination suggestion module is used to generate status determination suggestions based on the input-output elasticity index and the subdivision causal code.

[0104] In one embodiment, the deviation index calculation module is further configured to: construct a short time window and construct a unit energy consumption baseline based on the short time window; and calculate the deviation index based on the unit energy consumption baseline and the length of a preset rolling window.

[0105] In one embodiment, the deviation index calculation module is further configured to: construct a short time window in response to the operating condition reaching a preset condition; obtain the instantaneous power of the motor and the instantaneous drainage rate corresponding to the short time window; and construct a unit energy consumption baseline based on the instantaneous power of the motor and the instantaneous drainage rate.

[0106] In one embodiment, the deviation index calculation module is further configured to: calculate the scrolling unit energy consumption based on the preset length of the scrolling window; and calculate the deviation index based on the unit energy consumption baseline and the scrolling unit energy consumption.

[0107] In one embodiment, the risk score generation module is further configured to: generate a collaborative stability index and anomaly intensity based on the deviation index; and generate a comprehensive risk score based on the deviation index, collaborative stability index, and anomaly intensity.

[0108] In one embodiment, the risk score generation module is further configured to: perform a collaborative stability assessment based on the unit energy consumption baseline and generate a collaborative stability index; and generate an anomaly intensity based on the collaborative stability index and the deviation index.

[0109] In one embodiment, the risk score generation module is further configured to: obtain preset weighting coefficients; and generate a comprehensive risk score by weighting and fusing the deviation index, the collaborative stability index, and the anomaly intensity based on the preset weighting coefficients.

[0110] In one embodiment, the subdivision causation generation module is further configured to: construct an input-output elasticity index based on the variation range of the liquid production rate and the variation range of the power; and obtain a subdivision causation code by comparing the input-output elasticity index with the comprehensive risk score.

[0111] In one embodiment, the subdivision causation generation module is further configured to: generate an input-output elasticity index based on the following formula:

[0112] in, It is the input-output elasticity index; The variation range of the liquid production rate; The magnitude of power change; This is the average power value; This represents the average production rate.

[0113] In one embodiment, such as Figure 3 As shown, a computer device is also provided, including a memory and a processor. The memory stores a computer program and an operating system. When the processor executes the computer program, it implements the steps described in the above-described method for determining the state of a beam pumping unit. The computer device also includes a system bus, internal memory, a network structure, a display screen, and input devices, etc.

[0114] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for determining the state of a beam pumping unit.

[0115] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0117] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0118] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0119] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0121] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] Those skilled in the art will 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, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0126] One embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the embodiments of the above methods.

[0127] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above description is an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0128] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0129] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0130] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the state of a beam pumping unit, characterized in that, The method includes: Construct a unit energy consumption baseline, and calculate the deviation index based on the unit energy consumption baseline; A comprehensive risk score is generated based on the deviation index; Construct an input-output elasticity index, and obtain detailed causal codes based on the input-output elasticity index and the comprehensive risk score; Recommendations are determined based on the input-output elasticity index and the subdivision factor code generation status.

2. The method for determining the state of a beam pumping unit according to claim 1, characterized in that, Construct a unit energy consumption baseline, and calculate a deviation index based on the unit energy consumption baseline; including: Construct a short time window and construct a unit energy consumption baseline based on the short time window; The deviation index is calculated based on the unit energy consumption baseline and the preset length of the scrolling window.

3. The method for determining the state of a beam pumping unit according to claim 2, characterized in that, Constructing a short-time window and establishing a unit energy consumption baseline based on the short-time window, including: A short time window is established in response to the operating conditions reaching preset conditions; Obtain the instantaneous power of the motor and the instantaneous drainage rate corresponding to the short time window; A baseline for unit energy consumption is constructed based on the instantaneous power of the motor and the instantaneous drainage rate.

4. The method for determining the state of a beam pumping unit according to claim 2, characterized in that, The deviation index is calculated based on the unit energy consumption baseline and the preset length of the scrolling window, including: Calculate the unit energy consumption for scrolling based on the preset length of the scrolling window; The deviation index is calculated based on the unit energy consumption baseline and the rolling unit energy consumption.

5. The method for determining the state of a beam pumping unit according to claim 1, characterized in that, A comprehensive risk score is generated based on the deviation index, including: Based on the deviation index, a collaborative stability index and anomaly strength are generated; A comprehensive risk score is generated based on the deviation index, the collaborative stability index, and the anomaly intensity.

6. The method for determining the state of a beam pumping unit according to claim 5, characterized in that, Based on the deviation index, a collaborative stability index and anomaly strength are generated; including: Based on the aforementioned unit energy consumption baseline, a collaborative stability assessment is performed, and a collaborative stability index is generated. Anomaly intensity is generated based on the cooperative stability index and the deviation index.

7. The method for determining the state of a beam pumping unit according to claim 5, characterized in that, A comprehensive risk score is generated based on the aforementioned deviation index, collaborative stability index, and anomaly intensity, including: Obtain the preset weight coefficients; A comprehensive risk score is generated by weighting and fusing the deviation index, collaborative stability index, and anomaly intensity based on preset weighting coefficients.

8. The method for determining the state of a beam pumping unit according to claim 1, characterized in that, Construct an input-output elasticity index, and obtain detailed causal codes based on the input-output elasticity index and the comprehensive risk score; including: An input-output elasticity index is constructed based on the magnitude of changes in liquid production rate and power. The detailed causal code is obtained by comparing the input-output elasticity index and the comprehensive risk score.

9. The method for determining the state of a beam pumping unit according to claim 8, characterized in that, The input-output elasticity index is generated based on the following formula: ； in, It is the input-output elasticity index; The variation range of the liquid production rate; The magnitude of power change; This is the average power value; This represents the average production rate.

10. A state determination system for a beam pumping unit, characterized in that, The system includes: The deviation index calculation module is used to construct a unit energy consumption baseline and calculate the deviation index based on the unit energy consumption baseline. The risk score generation module is used to generate a comprehensive risk score based on the deviation index; The subdivision causation generation module is used to construct the input-output elasticity index and obtain the subdivision causation code based on the input-output elasticity index and the comprehensive risk score. The status determination suggestion module is used to generate status determination suggestions based on the input-output elasticity index and the subdivision causal code.

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