A calibration method of a beam pumping unit suspension point force measurement system

Abstract

The application relates to the technical field of equipment calibration, in particular to a calibration method of a beam-pumping unit's suspension point stress measurement system, which comprises the following steps: obtaining a static load reference of the pumping unit, and generating a dynamic feature point according to the static load reference; constructing a rate-coupled inversion compensation model according to the dynamic feature point, and generating a force value estimation based on the inversion compensation model; constructing a joint correction model, recalibrating the force value estimation, and generating a corrected force value; and generating a final calibration output according to the corrected force value. The application can improve the stability and reliability of the suspension point stress measurement, and provide more accurate basic data support for the downhole working condition diagnosis and production optimization of the pumping unit.

Description

Technical Field

[0001] This application relates to the field of equipment calibration technology, and in particular to a calibration method for a beam pumping unit suspension point force measurement system. Background Technology

[0002] Beam pumping units are the most common mechanical oil production equipment in oilfield development. Their suspension point force measurement system is a key means of performing dynamometer card analysis, judging downhole conditions, and optimizing pumping unit operating parameters. Existing suspension point force measurement systems generally rely on strain gauge, tension / compression, or hydraulic sensors, and are calibrated through static loading or theoretical calculations.

[0003] Existing measurement methods have significant shortcomings: on the one hand, it is difficult to perform standard static load calibration by hanging weights in the field environment, especially under heavy load conditions, which is almost impossible, resulting in poor reliability of calibration results; on the other hand, existing methods mostly rely on single-point static load correction, ignoring the periodic fluctuation characteristics of the suspension load during the dynamic operation of the pumping unit, which can easily cause the calibration curve to be inconsistent with the actual stress state, thus leading to significant deviations in measurement results over time and low accuracy in long-term monitoring. Summary of the Invention

[0004] Therefore, it is necessary to provide a calibration method for the suspension point force measurement system of a beam pumping unit that can solve the problem that the measurement results of the existing suspension point force measurement deviate over time and the long-term monitoring accuracy is low.

[0005] The technical solution of this invention is as follows:

[0006] A calibration method for a suspension point force measurement system of a beam pumping unit, the method comprising:

[0007] Obtain the static load reference of the pumping unit, and generate dynamic feature points based on the static load reference;

[0008] A velocity-coupled inversion compensation model is constructed based on the dynamic feature points, and a force value estimate is generated based on the inversion compensation model.

[0009] A joint correction model is constructed to recalibrate the force value estimate and generate the corrected force value.

[0010] The final calibration output is generated based on the corrected force value.

[0011] Optionally, the static load reference of the pumping unit is obtained, and dynamic feature points are generated based on the static load reference; including:

[0012] The oil pumping unit is calibrated under static load, and the static load reference of the oil pumping unit is obtained;

[0013] Dynamic operation calibration is performed based on the static load reference to generate dynamic feature points.

[0014] Optionally, static load calibration is performed on the pumping unit, and the static load reference of the pumping unit is obtained, including:

[0015] Static load calibration was performed with the pumping unit shut down, and a basic static load model was constructed.

[0016] The total force on the suspension point under shutdown condition is obtained based on the basic static load model, and the total force is set as the static load reference of the pumping unit.

[0017] Optionally, dynamic operation calibration is performed based on the static load reference to generate dynamic feature points, including:

[0018] A dynamic correction model is constructed based on the static load criterion.

[0019] Dynamic calibration is performed based on the dynamic correction model to generate dynamic feature points.

[0020] Optionally, a rate-coupled inversion compensation model is constructed based on the dynamic feature points, and a force value estimate is generated based on the inversion compensation model; including:

[0021] A calibration function is constructed based on the dynamic feature points;

[0022] A rate-coupled inversion compensation model is constructed based on the calibration function, and a force value estimate is generated based on the inversion compensation model.

[0023] Optionally, a calibration function is constructed based on the dynamic feature points; including:

[0024] In response to acquiring the dynamic feature points, a preset zero-point correction factor and sensor sensitivity coefficient are acquired;

[0025] A calibration function is constructed based on the zero-point correction factor, the sensor sensitivity coefficient, and the dynamic feature points.

[0026] Optionally, a rate-coupled inversion compensation model is constructed based on the calibration function, and a force estimate is generated based on the inversion compensation model, including:

[0027] Obtain the preset proportional coefficient and the rate of change of sensor output over time;

[0028] The calibration function is used as the model input, and a rate-coupled inversion compensation model is constructed based on the proportional coefficient and the rate of change of the sensor output over time.

[0029] Force value estimates are generated based on the inversion compensation model.

[0030] Optionally, the final calibration output is generated based on the corrected force value, including:

[0031] A fusion model is constructed based on the corrected force values;

[0032] The final calibration output is generated based on the fusion model.

[0033] Optionally, the fusion model is as follows:

[0034]

[0035] in, For the final calibration output, As a credibility weight, To correct the force value, The actual force applied at the suspension point.

[0036] Optionally, a calibration system for measuring the suspension point force of a beam pumping unit is also provided, the system comprising:

[0037] The dynamic feature generation module is used to obtain the static load reference of the pumping unit and generate dynamic feature points based on the static load reference.

[0038] The force value estimation generation module is used to construct a rate-coupled inversion compensation model based on the dynamic feature points, and generate a force value estimate based on the inversion compensation model.

[0039] The force value generation module is used to construct a joint correction model, recalibrate the force value estimate, and generate the corrected force value.

[0040] The calibration output generation module is used to generate the final calibration output based on the corrected force value.

[0041] Optionally, the dynamic feature generation module is further configured to: perform static load calibration on the pumping unit and obtain the static load reference of the pumping unit; perform dynamic operation calibration based on the static load reference and generate dynamic feature points.

[0042] Optionally, the dynamic feature generation module is further configured to: perform static load calibration in the pumping unit's shutdown state and construct a basic static load model; obtain the total force on the suspension point in the shutdown state based on the basic static load model, and set the total force as the static load reference of the pumping unit.

[0043] Optionally, the dynamic feature generation module is further configured to: construct a dynamic correction model based on the static load datum; perform dynamic operation calibration according to the dynamic correction model, and generate dynamic feature points.

[0044] Optionally, the force estimation generation module is further configured to: construct a calibration function based on the dynamic feature points; construct a rate-coupled inversion compensation model based on the calibration function; and generate a force estimate based on the inversion compensation model.

[0045] Optionally, the force estimation generation module is further configured to: in response to acquiring the dynamic feature points, acquire a preset zero-point correction factor and a sensor sensitivity coefficient; and construct a calibration function based on the zero-point correction factor, the sensor sensitivity coefficient, and the dynamic feature points.

[0046] Optionally, the force estimation generation module is further configured to: obtain a preset proportional coefficient and the rate of change of the sensor output over time; use the calibration function as a model input, and construct a rate-coupled inversion compensation model based on the proportional coefficient and the rate of change of the sensor output over time; and generate a force estimate based on the inversion compensation model.

[0047] Optionally, the calibration output generation module is further configured to: construct a fusion model based on the corrected force value; and generate a final calibration output based on the fusion model.

[0048] 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 calibration method for the suspension point force measurement system of the above-described beam pumping unit.

[0049] 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 calibration method for the suspension point force measurement system of the above-described beam pumping unit.

[0050] The technical effects achieved by this invention are as follows:

[0051] 1. The calibration method of the suspension point force measurement system of the above-mentioned beam pumping unit sequentially obtains the static load reference of the pumping unit and generates dynamic feature points based on the static load reference. Compared with the prior art, it no longer relies on hanging weights or adding external standard force measuring devices to obtain the static load reference, but directly uses the physical characteristics of the suspension point force when the pumping unit is stopped to derive the theoretical reference value, avoiding the cumbersome external loading process, and can ensure the stability and repeatability of the reference point.

[0052] 2. A rate-coupled inversion compensation model is constructed based on the dynamic feature points, and a force value estimate is generated based on the inversion compensation model. Compared with the prior art, it does not simply convert the sensor signal into force through a single ratio and bias, but rather incorporates the instantaneous change rate of the sensor output into the force value estimate based on the rate-coupled inversion compensation model constructed based on the dynamic feature points, which fits the asymmetric dynamic characteristics of the upper and lower strokes in the reciprocating motion of the walking beam.

[0053] 3. Construct a joint calibration model to recalibrate the force value estimate and generate a calibrated force value; generate the final calibration output based on the calibrated force value; solve the problem that the sensor zero point and range are affected by temperature, strain fatigue and electronic drift during long-term operation, and also take into account the good characterization of boundary conditions and structural constraints by the theoretical model, as well as the real reflection of random disturbances and individual differences of the device by field measurement. It effectively solves the technical problems of static calibration being difficult to implement, insufficient calibration accuracy under dynamic working conditions and lack of correction for long-term drift in the existing technology. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the calibration method for a suspension point force measurement system of a beam pumping unit in one embodiment.

[0055] Figure 2 This is a structural block diagram of the calibration system for a beam pumping unit suspension point force measurement system in one embodiment.

[0056] Figure 3 This is a structural block diagram of a computer device in one embodiment. Detailed Implementation

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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]."

[0061] 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.

[0062] 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.

[0063] In one embodiment, a terminal is provided, the terminal being used to: acquire a static load reference of a pumping unit and generate dynamic feature points based on the static load reference; construct a rate-coupled inversion compensation model based on the dynamic feature points and generate a force value estimate based on the inversion compensation model; construct a joint correction model, recalibrate the force value estimate, and generate a corrected force value; and generate a final calibration output based on the corrected force value.

[0064] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0065] In one embodiment, such as Figure 1 As shown, a calibration method for a beam pumping unit suspension point force measurement system is provided, the method comprising:

[0066] Step S100: Obtain the static load reference of the pumping unit, and generate dynamic feature points based on the static load reference;

[0067] Step S200: Construct a velocity-coupled inversion compensation model based on the dynamic feature points, and generate a force value estimate based on the inversion compensation model;

[0068] Step S300: Construct a joint correction model, recalibrate the force value estimate, and generate the corrected force value;

[0069] Step S400: Generate the final calibration output based on the corrected force value.

[0070] In this application, the static load reference of the pumping unit is obtained, and dynamic feature points are generated based on the static load reference. Compared with the prior art, it no longer relies on hanging weights or adding external standard force measuring devices to obtain the static load reference. Instead, it directly uses the physical characteristics of the force on the suspension point when the pumping unit is stopped to derive the theoretical reference value, avoiding the cumbersome external loading process, and ensuring the stability and repeatability of the reference point.

[0071] A rate-coupled inversion compensation model is constructed based on the dynamic feature points, and a force estimate is generated based on the inversion compensation model. Compared with the prior art, it does not simply convert the sensor signal into force through a single ratio and bias, but rather incorporates the instantaneous rate of change of the sensor output into the force estimate based on the rate-coupled inversion compensation model constructed based on the dynamic feature points, which fits the asymmetric dynamic characteristics of the upper and lower strokes in the reciprocating motion of the walking beam.

[0072] By constructing a joint calibration model, the force value estimate is recalibrated, and a corrected force value is generated. The final calibration output is generated based on the corrected force value. This solves the problem that the sensor zero point and range are affected by temperature, strain fatigue, and electronic drift during long-term operation. It also takes into account the good characterization of boundary conditions and structural constraints by the theoretical model, as well as the true reflection of random disturbances and individual differences of the device by field measurements. This effectively solves the technical problems of static calibration being difficult to implement, insufficient calibration accuracy under dynamic working conditions, and lack of correction for long-term drift in the existing technology.

[0073] In one embodiment, step S100: obtaining the static load reference of the pumping unit and generating dynamic feature points based on the static load reference; including:

[0074] Step S110: Perform static load calibration on the pumping unit and obtain the static load reference of the pumping unit;

[0075] Step S120: Perform dynamic operation calibration based on the static load reference to generate dynamic feature points.

[0076] In this embodiment, the pumping unit is statically calibrated and its static load reference is obtained; dynamic operation calibration is performed based on the static load reference to generate dynamic feature points, providing a data foundation for the subsequent construction of a rate-coupled inversion compensation model.

[0077] In one embodiment, step S110: performing static load calibration on the pumping unit and obtaining the static load reference of the pumping unit includes:

[0078] Step S111: Perform static load calibration with the pumping unit in a stopped state and construct a basic static load model;

[0079] Step S112: Obtain the total force on the suspension point under shutdown state based on the basic static load model, and set the total force as the static load reference of the pumping unit.

[0080] In this embodiment, static load calibration is performed while the pumping unit is stopped. The total force on the suspension point in the stopped state is set as the static load reference and matched with the signal collected by the sensor to form the initial calibration point of the system, thus establishing an initial reference point for the entire calibration system.

[0081] When the pumping unit stops completely, the load on the suspension point mainly comes from two parts: firstly, a constant load generated by the weight of the sucker rod string itself; and secondly, the force transmitted upwards by the hydrostatic pressure of the liquid column through the plunger rod. In this state, the sensor output signals collected by the system can be correlated with the theoretical force value, thus obtaining an initial calibration point. The initial calibration point includes a known theoretical load value, i.e., the static load reference. This refers to the actual output parameters of a sensor, but in calculations it can refer to a static load reference. .

[0082] The theoretical force value refers to the actual force at the suspension point, derived by a person skilled in the art through mechanical calculations based on known parameters such as the weight of the downhole rod string, the diameter of the pump plunger, and the hydrostatic pressure of the hydraulic column, under conditions of pump unit shutdown or specific operating conditions. This value does not rely on sensor signals but is set through design parameters and on-site operating conditions, serving as a reference benchmark for calibration; this is the value obtained in this step. .

[0083] The difference between this embodiment and existing technologies lies in that it no longer relies on hanging weights or adding external standard force measuring devices to obtain the static load benchmark. Instead, it directly derives the theoretical benchmark value by utilizing the physical characteristics of the force at the suspension point when the pumping unit is shut down. Existing methods are often difficult to implement under high load conditions due to the difficulty of hanging weights, resulting in limitations in the calibration process. This embodiment, however, establishes a basic static load model, comprehensively calculates the components of the rod weight and the hydrostatic pressure of the liquid column to obtain the total force in the static state, and establishes a corresponding relationship with the sensor output signals, thereby forming the initial calibration point. This method not only simplifies on-site operations and avoids the cumbersome external loading process, but also ensures the stability and repeatability of the benchmark point.

[0084] Static load calibration involves constructing a basic static load model to describe the relationship between the actual force on the suspension point in the stopped state and the sensor output. The total force on the suspension point in the stopped state output by the basic static load model is then mapped to the sensor output signal to obtain the initial calibration point of the system.

[0085] The basic static load model is shown below:

[0086]

[0087] in, This represents the total force (N) on the suspension point when the machine is stopped.

[0088] The weight of the rod string (N) is calculated based on the material density, diameter, and length of the downhole sucker rod string, and then multiplied by the gravitational acceleration.

[0089] It represents the hydrostatic pressure of the liquid column (Pa), which is calculated from the liquid level height and liquid density at the well depth, i.e., liquid column height × liquid density × gravitational acceleration.

[0090] This represents the force-bearing area (m²) of the pump plunger. 2 The cross-sectional area is calculated based on the diameter of the pump plunger.

[0091] Calculated using this model This serves as the static load reference, and the signals collected by the sensors are correlated with it to establish a correspondence, thus forming the initial calibration point of the system. First, based on the known downhole rod parameters, pump plunger diameter, and hydraulic column pressure, the theoretical force value is calculated. Then, this is compared with the sensor's output signal when the machine stops, forming a mapping relationship between the two, namely, "theoretical force". "Measured signal". Through this mapping, the system obtains a clear reference point, providing a reliable basis for force correction during subsequent dynamic operation.

[0092] In one embodiment, step S120: performing dynamic operation calibration based on the static load reference to generate dynamic feature points includes:

[0093] Step S121: Construct a dynamic correction model based on the static load datum;

[0094] Step S122: Perform dynamic calibration based on the dynamic correction model to generate dynamic feature points.

[0095] In this embodiment, during normal operation of the pumping unit, the force at the suspension point fluctuates periodically with the movement of the walking beam, forming a typical suspension point load curve. For calibration, feature extraction of this curve is necessary, primarily identifying the maximum and minimum load points. This process requires linking the static load baseline with the dynamic curve. The horizontal axis of the dynamic curve describes the "motion position," and the vertical axis describes the "force magnitude." In practical applications, as the pumping unit moves up and down, the curve will exhibit a closed loop or approximately elliptical trajectory; different shapes reflect operating conditions such as pump efficiency, fluid level fluctuations, and downhole friction. Identifying the maximum and minimum load points means identifying the maximum and minimum value points.

[0096] The result obtained from step S100 By comparing the curve with the dynamic curve signal, the curve can be normalized to ensure that subsequent modeling will not be distorted due to sensor signal drift.

[0097] To achieve quantitative extraction of feature points, a dynamic correction model is constructed, transforming the suspension load curve into a structure of "static load reference + dynamic correction".

[0098] The dynamic correction model is as follows:

[0099]

[0100] in, This indicates the actual force (N) exerted on the suspension point at a certain moment during operation. This represents the dynamic fluctuation portion (N) relative to the static load reference.

[0101] This represents the instantaneous force value at the suspension point as it changes over time during operation. A complete dynamic force curve covers the mechanical state at every moment. Dynamic characteristic points are derived from the entire curve. The "limited number of key points extracted" from the curve, such as the maximum value point (i.e., the maximum load point) and minimum value point (i.e., the minimum load point) of the curve in a cycle, are special moments on the curve, and the corresponding force values ​​are the values ​​of the dynamic characteristic points.

[0102] The methods for obtaining it include the following:

[0103] 1. Based on the feature point extraction method of work map:

[0104] During a complete stroke of a pumping unit's normal operation, the stress curve at the suspension point exhibits typical periodic fluctuations. This can be analyzed by detecting the stress values ​​at the peak, trough, and transition intervals of each stroke, and then comparing them with a static load reference. The difference between the peak and trough points constitutes the main [value / value]. This method is suitable for making periodic corrections to establish the range of force fluctuations.

[0105] 2. Based on numerical filtering and separation methods:

[0106] The actual sensor output signal is low-pass filtered to extract the approximately constant component, corresponding to the static load reference. Then, the low-pass result is subtracted from the original signal to obtain the high-frequency or periodic component. This method is suitable for long-term online automatic calibration and can separate slowly varying reference values ​​from rapidly fluctuating components.

[0107] In different application scenarios, Instructions for obtaining:

[0108] In periodic operating condition analysis, method 1 is used to correct for peak and valley points to ensure... The value range is accurate. During long-term operation, method 2 can be built-in to automatically separate static load and dynamic fluctuations, thereby achieving adaptive correction.

[0109] It should be noted that, The setting method should be obtained by those skilled in the art based on actual needs, and this application does not make specific limitations.

[0110] Furthermore, this model is used to extract... The maximum and minimum values ​​are used as the maximum load point and minimum load point in the dynamic characteristic points.

[0111] Unlike existing technologies, this embodiment no longer relies solely on sensor output curves to identify peaks and valleys. Instead, it uses the static load reference obtained in step S100 as a reference point to normalize and correct the entire dynamic curve, ensuring that curves under different operating conditions can be compared and analyzed under a unified reference. Existing methods often directly use the original sensor curve to extract feature points, but this approach is easily affected by zero-point drift, temperature changes, and signal noise, leading to unstable peak and valley positions and affecting calibration accuracy. This embodiment, by introducing a dynamic correction model, decomposes the force on the dynamic curve at each moment into a stable static load reference and a clear dynamic fluctuation component. In terms of usage, the static load reference is first obtained using the calculation in step S100. Then, by collecting signals during operation through sensors and comparing them with this benchmark, the dynamic fluctuation component can be obtained. Finally, the goal was achieved. The resulting curve not only has a clear physical meaning but also effectively suppresses errors caused by signal drift. The extracted maximum and minimum stress points, as well as transition points, naturally transform into corresponding calibration data points, providing more reliable input for subsequent calibration curve fitting. The advantage of this step is that it preserves the true characteristics of the dynamic operating conditions while avoiding calibration curve deviations caused by unstable signal references in traditional methods, significantly improving the accuracy and robustness of the calibration process.

[0112] In one embodiment, step S200: constructing a rate-coupled inversion compensation model based on the dynamic feature points, and generating a force value estimate based on the inversion compensation model; including:

[0113] Step S210: Construct a calibration function based on the dynamic feature points;

[0114] Step S220: Construct a rate-coupled inversion compensation model based on the calibration function, and generate a force value estimate based on the inversion compensation model.

[0115] In this embodiment, a calibration function is constructed based on the dynamic feature points; a rate-coupled inversion compensation model is constructed based on the calibration function; and a force value estimate is generated based on the inversion compensation model to better fit the asymmetric dynamic characteristics of the upper and lower strokes in the reciprocating motion of the walking beam.

[0116] In one embodiment, step S210: constructing a calibration function based on the dynamic feature points; includes:

[0117] Step S211: In response to acquiring the dynamic feature points, acquire the preset zero-point correction factor and sensor sensitivity coefficient;

[0118] Step S212: Construct a calibration function based on the zero-point correction factor, the sensor sensitivity coefficient, and the dynamic feature points.

[0119] In this embodiment, a calibration function is constructed to fit dynamic feature points and generate a multi-point calibration curve, which is the curve corresponding to the calibration function mentioned below. Existing technologies often employ single-point linear correction. This method may be barely accurate in low-load areas, but in high-load areas or under conditions of severe load fluctuations, the error will be significantly amplified, failing to truly reflect the full-condition changes in the stress on the suspension point. In this embodiment, instead of relying on a single static load point to correct the sensor, the dynamic feature points extracted in step S100 are combined with the static load reference to form comparison data for multiple different load ranges, thereby establishing a calibration curve through multi-point fitting.

[0120] The calibration function is shown below:

[0121]

[0122] in, This indicates the sensor's output signal, which depends on the sensor type. Common values ​​are volts (V), milliamperes (mA), or digital count values ​​(dimensionless numbers).

[0123] This represents the sensor sensitivity coefficient, a signal / force ratio (e.g., V / N or mA / N). 1×10⁻⁶ -5 -1×10 -4 V / N or 5×10 -5 -5×10 -4 mA / N.

[0124] Zero-point correction factor, signal unit (and) Consistent, such as V, mA, or count). Generally within ±0.5V, ±1mA, or ±500 counts, depending on the sensor's zero-point stability and long-term drift.

[0125] Operators gradually corrected the coefficients by comparing the extracted maximum load point, minimum load point, and static load reference. and By correlating the force values ​​under different working conditions with the sensor output signals, a multi-point calibration curve is generated.

[0126] Sensitivity coefficient It represents the "signal increment caused by unit force", with the unit being "signal / force". It is obtained by regression analysis of multiple sets of "force-signal" pairs, such as peak values, valley values, and transition points, under different working conditions. The value is usually in the range of millivolts per Newton or milliamperes per Newton. It can be slightly corrected due to factors such as sensor model, installation pre-tightening, and temperature.

[0127] Zero-point correction factor Used to compensate for the deviation between the "theoretical static load and the measured signal" during shutdown, the unit is consistent with the signal, and it is usually automatically updated during the shutdown window, with its amplitude kept within a small percentage of full scale. After aligning the above information along the time axis, a multi-point comparison set covering low to high loads is formed, which can generate a calibration curve spanning the entire range. During operation, the real-time signal is converted into a force value based on this curve, and the zero point and sensitivity are periodically refreshed during shutdown periods, ensuring long-term stable and usable calibration.

[0128] During use, operators need to input multiple sets of data under different working conditions into the model, and ensure that the calibration curve covers the entire load range through iterative fitting, thereby avoiding the shortcomings of traditional single-point calibration that cannot take into account dynamic fluctuations. The advantage of this method is that it can provide a full-range calibration curve across the entire range, making the suspension point force measurement accurate not only under a single working condition, but also stable and reliable under complex dynamic environments, significantly improving the universality and long-term reliability of the calibration results.

[0129] In one embodiment, step S220: constructing a rate-coupled inversion compensation model based on the calibration function, and generating a force value estimate based on the inversion compensation model, includes:

[0130] Step S221: Obtain the preset proportional coefficient and the rate of change of sensor output over time;

[0131] Step S222: Use the calibration function as the model input, and construct a rate-coupled inversion compensation model based on the proportional coefficient and the rate of change of the sensor output over time;

[0132] Step S223: Generate force value estimates based on the inversion compensation model.

[0133] In this embodiment, a rate-coupled inversion compensation model is constructed based on the left-hand side of the calibration function as input, incorporating the instantaneous rate of change of the sensor output into the force estimation, which fits the asymmetric dynamic characteristics of the upper and lower strokes in the reciprocating motion of the walking beam.

[0134] The difference and advantage of this step compared to the existing technology is that it does not simply convert the sensor signal into force through a single ratio and bias, but introduces a compensation mechanism that is sensitive to the motion rate. It incorporates the phase lag and amplitude deviation caused by the inertia of the mechanism, the friction of the connecting rod and the viscosity of the seal into the inversion process, so as to maintain high accuracy even when the upper and lower strokes are asymmetrical and the pump speed changes.

[0135] The inversion compensation model for rate coupling is shown below:

[0136]

[0137] in, This is the force estimate (N) output in this step. The instantaneous force at the suspension point calculated using the inversion compensation model is an approximate reconstruction of the actual force. Since the sensor directly outputs only an electrical signal, which is affected by zero-point drift, sensitivity deviation, and dynamic inertial effects, methods such as proportional correction, bias compensation, and rate coupling are needed to convert the sensor signal into a value as close as possible to the actual physical force. This "force estimate" is the calculated force at the suspension point at a certain moment, which will then be further corrected in subsequent steps (such as slow drift and thermal drift correction) to make it closer to the actual load.

[0138] A proportionality coefficient used to map electrical signals to forces, measured in force / signal (e.g., N / V or N / mA). It is obtained by recording the correspondence between sensor signals and actual forces under known loads, followed by multi-point fitting. Typically, it ranges from 0.05-0.5 kN / V or -0.1-1 kN / mA, varying depending on sensor sensitivity.

[0139] This is the static bias term (N). In the shutdown state, the difference between the theoretical static load value and the sensor output is used as the bias compensation.

[0140] This is the rate compensation coefficient, with units of force·time / signal (e.g., N·s / V). It is obtained by fitting the relationship between the rate of change of the signal and the difference in actual force under different pump speeds.

[0141] This represents the rate of change of the sensor output over time, expressed in signal / time (e.g., V / s or mA / s). The real-time acquired sensor signal is subjected to finite difference or numerical derivative, and then filtered to suppress noise.

[0142] When using this model, it is first obtained within multiple cycles of a stable operating condition. The complete cycle, then estimated using the medium pump speed range. and The initial value is finely adjusted using a combination of high and low pump speed sections. Until the residual errors of the upstroke and downstroke are within the allowable range; for A finite-window differential method, combined with low-pass filtering to remove high-frequency noise, ensures that the compensation is not amplified by noise. During online operation, this model will be used in real-time. Its rate of change is synchronously mapped to It uses a periodic error threshold and a shutdown window self-test to trigger fine-tuning, and finally outputs... This step serves as the direct input for the subsequent S300 joint calibration. Compared to the traditional approach that relies solely on linear mapping, this step significantly reduces phase error and amplitude bias near dynamic boundaries (such as upper and lower dead points) and during rapid rate changes, improving calibration stability and robustness under all operating conditions, and providing an input sequence that more closely approximates the actual physical forces for subsequent cross-cycle consistency processing.

[0143] In one embodiment, step S300 involves constructing a joint correction model, recalibrating the force value estimate, and generating the corrected force value; the specific implementation is as follows:

[0144] To address the impact of temperature, strain fatigue, and electron drift on sensor zero point and measurement range during long-term operation, this step introduces a "slow drift-thermal drift joint correction" for each natural operating condition segment. The joint correction model is shown below:

[0145]

[0146] in, This is the corrected force value (N) output in this step.

[0147] This represents a slow-time variable scaled in hours or shifts, and is a unit of time. It represents a time index scaled in hours, shifts, or days, used to track slow changes in zero point and range.

[0148] The zero-point drift (N) is the value within the stop window. Compared with static load reference The difference is achievable.

[0149] This is a dimensionless range correction factor. It is calculated by comparing the peak-to-valley amplitude difference of the force estimate within the same period with the theoretical amplitude difference, reflecting the change in sensor sensitivity. It is typically between 0.9 and 1.1, with a small deviation from 1, used for fine-tuning range consistency.

[0150] A small residual environmental correction term (N) is used to compensate for nonlinear deviations caused by gradual temperature changes. This small compensation value is estimated based on the slow trend of ambient temperature changes and the sensor's offset performance.

[0151] The usage method is as follows: refresh immediately when a shutdown or ultra-low speed segment is detected. When a complete steady-state cycle is detected, update the statistics according to peak-valley patterns. If the ambient temperature exceeds the set range, make a fine adjustment. This process eliminates slow thermal drift. After this treatment, the force curves for different dates and temperature settings can be superimposed on a unified scale at the same load level.

[0152] Instead of fixing the results after a single calibration, this method introduces the concept of "slow drift-thermal drift joint correction," enabling the calibration results to be automatically updated as time and environmental conditions change. Traditional methods typically perform calibration only once during sensor installation or periodic maintenance, failing to address zero-point and range offsets caused by strain fatigue, electronic component drift, or temperature fluctuations during long-term operation, leading to a gradual accumulation of measurement data deviations. This method, however, establishes a cross-cycle correction mechanism, using the static load reference during shutdown as the zero-point anchor point, and utilizes... As an amplitude reference, zero drift, range variation, and residual environmental error are estimated separately, and then the output from the previous step is compared with the value. Recalibration is performed to obtain a more stable and consistent force curve. During use, the system automatically refreshes the zero-point drift when stopped or running at low speed, updates the range correction factor after detecting a complete steady-state cycle, and fine-tunes the correction amount when there are significant changes in ambient temperature, thus ensuring that the force results under different dates and operating conditions remain consistent on the same scale. Compared with existing technologies, the advantage of this step is that the calibration can dynamically correct itself in real time according to changes in the environment and time, effectively overcoming the problem of accuracy decay after a single calibration, thereby ensuring continuous consistency and reliability in long-term monitoring.

[0153] In one embodiment, step S400: generating the final calibration output based on the corrected force value includes:

[0154] Step S410: Construct a fusion model based on the corrected force values;

[0155] Step S420: Generate the final calibration output based on the fusion model.

[0156] In one embodiment, to balance the theoretical model's good characterization of boundary conditions and structural constraints with the actual field measurement's accurate reflection of random disturbances and individual device differences, and considering that the acceleration and reaction force characteristics of the walking beam mechanism near the upper and lower dead points are more consistent with the theoretical solution, while the mid-stroke section relies more on sensors to capture actual friction and liquid surface disturbances, a fusion model is constructed.

[0157] The fusion model is as follows:

[0158]

[0159] in, This is the final calibration output.

[0160] The confidence weight varies between [0,1] along the travel phase. The weight is set by giving a small weight threshold near the top and bottom dead centers, so that the theoretical term... The dominant factor is used to ensure consistent boundary mechanics; in the mid-stroke section, a larger weight is determined by comprehensively considering the signal-to-noise ratio, cycle stability, and rate term amplitude, so that... The dominant factor is used to preserve the true characteristics of on-site friction and liquid column disturbance. The weight is only used as a dimensionless proportionality coefficient and does not change the unit of force value. To correct the force value, The actual force applied at the suspension point.

[0161] When using this model, the weight distribution is first roughly determined through one or two stable cycles, and then gradually refined over three to five cycles based on the error envelope width. When changes in well conditions cause a decrease in cycle stability, the proportion of theoretical channels is automatically increased to maintain robustness. The final calibration result is directly used for subsequent working condition identification and diagnostic analysis.

[0162] In another embodiment, a verification mechanism is provided after the calibration method outputs the final calibration result. During the intervals or downtime of each operating cycle, the system automatically enters a self-verification window. Within this window, the system compares the sensor output signal in the downtime state with the static load reference. If the difference exceeds the preset threshold, it indicates that the calibration curve may be distorted due to sensor loosening, circuit aging, sudden temperature changes, or other abnormal conditions. The system will automatically trigger an anomaly rejection mechanism.

[0163] All dynamic feature point data of the abnormal period are removed from the calibration dataset to prevent erroneous data from entering the multi-point fitting process.

[0164] Record the ambient temperature, equipment start / stop status, and liquid level when the anomaly occurs, and store them as anomaly samples for reference during subsequent manual inspections.

[0165] If an anomaly occurs more than a preset number of times consecutively, the system will prompt maintenance personnel to conduct on-site inspections to ensure that the sensor installation and signal link are normal.

[0166] This expands the shutdown window from "only for baseline correction" to a means of "anomaly self-diagnosis and data quality control." Unlike existing methods that rely solely on shutdown static load points for one-time calibration, this self-verification and anomaly removal mechanism significantly improves the reliability of long-term calibration, preventing continuous deviations in calibration curves due to single erroneous data. Furthermore, it can be implemented solely through the logical utilization of existing data and anomaly detection, offering advantages such as low cost and high robustness.

[0167] In one embodiment, such as Figure 2 As shown, a calibration system for a beam pumping unit suspension point force measurement system is also provided, the system comprising:

[0168] The dynamic feature generation module is used to obtain the static load reference of the pumping unit and generate dynamic feature points based on the static load reference.

[0169] The force value estimation generation module is used to construct a rate-coupled inversion compensation model based on the dynamic feature points, and generate a force value estimate based on the inversion compensation model.

[0170] The force value generation module is used to construct a joint correction model, recalibrate the force value estimate, and generate the corrected force value.

[0171] The calibration output generation module is used to generate the final calibration output based on the corrected force value.

[0172] In one embodiment, the dynamic feature generation module is further configured to: perform static load calibration on the pumping unit and obtain the static load reference of the pumping unit; perform dynamic operation calibration based on the static load reference and generate dynamic feature points.

[0173] In one embodiment, the dynamic feature generation module is further configured to: perform static load calibration in the pumping unit shutdown state and construct a basic static load model; obtain the total force on the suspension point in the shutdown state according to the basic static load model, and set the total force as the static load reference of the pumping unit.

[0174] In one embodiment, the dynamic feature generation module is further configured to: construct a dynamic correction model based on the static load datum; perform dynamic operation calibration according to the dynamic correction model, and generate dynamic feature points.

[0175] In one embodiment, the force estimation generation module is further configured to: construct a calibration function based on the dynamic feature points; construct a rate-coupled inversion compensation model based on the calibration function; and generate a force estimate based on the inversion compensation model.

[0176] In one embodiment, the force estimation generation module is further configured to: in response to acquiring the dynamic feature points, acquire a preset zero-point correction factor and a sensor sensitivity coefficient; and construct a calibration function based on the zero-point correction factor, the sensor sensitivity coefficient, and the dynamic feature points.

[0177] In one embodiment, the force estimation generation module is further configured to: obtain a preset proportional coefficient and the rate of change of the sensor output over time; use the calibration function as a model input, and construct a rate-coupled inversion compensation model based on the proportional coefficient and the rate of change of the sensor output over time; and generate a force estimate based on the inversion compensation model.

[0178] In one embodiment, the calibration output generation module is further configured to: construct a fusion model based on the corrected force value; and generate a final calibration output based on the fusion model.

[0179] 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 calibration method for the suspension point force measurement system of the beam pumping unit. The computer device also includes a system bus, internal memory, network structure, display screen, and input devices.

[0180] 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 calibration method for the suspension point force measurement system of the above-described beam pumping unit.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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 above-described methods.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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 calibration method for a suspension point force measurement system of a beam pumping unit, characterized in that, The method includes: Obtain the static load reference of the pumping unit, and generate dynamic feature points based on the static load reference; including: The oil pumping unit is calibrated under static load, and the static load reference of the oil pumping unit is obtained; Dynamic operation calibration is performed based on the static load reference to generate dynamic feature points; Static load calibration was performed with the pumping unit shut down, and a basic static load model was constructed. The total force on the suspension point under shutdown state is obtained based on the basic static load model, and the total force is set as the static load reference of the pumping unit. A velocity-coupled inversion compensation model is constructed based on the dynamic feature points, and a force value estimate is generated based on the inversion compensation model. A joint correction model is constructed to recalibrate the force value estimate and generate the corrected force value. The final calibration output is generated based on the corrected force value.

2. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 1, characterized in that, Dynamic operation calibration is performed based on the static load reference to generate dynamic feature points, including: A dynamic correction model is constructed based on the static load criterion. Dynamic calibration is performed based on the dynamic correction model to generate dynamic feature points.

3. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 1, characterized in that, A velocity-coupled inversion compensation model is constructed based on the dynamic feature points, and a force value estimate is generated based on the inversion compensation model; including: A calibration function is constructed based on the dynamic feature points; A rate-coupled inversion compensation model is constructed based on the calibration function, and a force value estimate is generated based on the inversion compensation model.

4. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 3, characterized in that, Constructing a calibration function based on the dynamic feature points; including: In response to acquiring the dynamic feature points, a preset zero-point correction factor and sensor sensitivity coefficient are acquired; A calibration function is constructed based on the zero-point correction factor, the sensor sensitivity coefficient, and the dynamic feature points.

5. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 3, characterized in that, A rate-coupled inversion compensation model is constructed based on the calibration function, and a force value estimate is generated based on the inversion compensation model, including: Obtain the preset proportional coefficient and the rate of change of sensor output over time; The calibration function is used as the model input, and a rate-coupled inversion compensation model is constructed based on the proportional coefficient and the rate of change of the sensor output over time. Force value estimates are generated based on the inversion compensation model.

6. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 1, characterized in that, The final calibration output is generated based on the corrected force value, including: A fusion model is constructed based on the corrected force values; The final calibration output is generated based on the fusion model.

7. The calibration method for the suspension point force measurement system of the beam pumping unit according to claim 6, characterized in that, The fusion model is as follows: ； in, For the final calibration output, As a credibility weight, To correct the force value, The actual force applied at the suspension point.

8. A calibration system for a beam pumping unit suspension point force measurement system, characterized in that, The system includes: A dynamic feature generation module is used to obtain the static load reference of the pumping unit and generate dynamic feature points based on the static load reference; including: The oil pumping unit is calibrated under static load, and the static load reference of the oil pumping unit is obtained; Dynamic operation calibration is performed based on the static load reference to generate dynamic feature points; Static load calibration was performed with the pumping unit shut down, and a basic static load model was constructed. The total force on the suspension point under shutdown state is obtained based on the basic static load model, and the total force is set as the static load reference of the pumping unit. The force value estimation generation module is used to construct a rate-coupled inversion compensation model based on the dynamic feature points, and generate a force value estimate based on the inversion compensation model. The force value generation module is used to construct a joint correction model, recalibrate the force value estimate, and generate the corrected force value. The calibration output generation module is used to generate the final calibration output based on the corrected force value.

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