Dynamic matching method and system for pumping unit equipment adaptive to working conditions

Abstract

The invention discloses a dynamic matching method and system for pumping unit equipment adaptive to working conditions, and belongs to the technical field of oil field oil production engineering. Key evaluation indexes such as the power utilization rate, the load utilization rate and the torque utilization rate are calculated by intelligently extracting multi-dimensional production data of a rod-pumped well, and the matching state of equipment and working conditions is judged according to a preset reasonable interval. For an unreasonably matched oil well, preferentially adopting an optimization strategy of oil pumping unit or motor interchange in a well group to realize cyclic utilization and reasonable configuration of equipment resources; and if the requirement cannot be met after interchange, the measure of directly replacing the motor or the oil pumping unit is implemented. And meanwhile, an underground efficiency prediction model is established and is used for evaluating the energy-saving effect after matching. Through the intelligent and systematic matching process, the energy consumption of the rod-pumped well is remarkably reduced, the system efficiency and the equipment utilization rate are improved, and the method has the advantages of being high in operability, remarkable in energy-saving effect and easy to popularize.

Description

Technical Field

[0001] This invention relates to the field of oilfield production engineering technology, and more specifically to a dynamic matching method and system for pumping unit equipment that is adaptable to operating conditions. Background Technology

[0002] Currently, beam pumping units are the most widely used mechanical oil recovery method in oilfields both domestically and internationally, occupying a core position in the crude oil extraction system. Their energy consumption accounts for over 30% of the total electricity consumption of an oilfield, making them a key factor affecting oilfield production energy efficiency and cost control. Meanwhile, scholars both at home and abroad have conducted extensive research on pumping unit system efficiency evaluation, power matching analysis, and parameter optimization. Related technologies are mostly focused on theoretical calculations or local parameter adjustments at the single-well level, providing some support for single-well operation optimization.

[0003] However, in long-term production practice, the mismatch between the power and model of the pumping unit motor and the actual load of the oil well remains widespread. This not only leads to low system operating efficiency and high energy consumption per unit of fluid produced, but also exacerbates fatigue wear of mechanical components, shortens equipment service life, and increases maintenance and replacement costs. Moreover, this mismatch problem is influenced by multiple factors coupled together, including dynamic changes in oil well production capacity (formation pressure decay, water cut increase, etc.), deviations between initial design and actual well conditions, limited equipment inventory, and unscientific resource allocation between blocks, exhibiting complex dynamic characteristics. In addition, existing technologies lack comprehensive consideration at the well group or block scale, making it difficult to achieve the recycling and global optimization of equipment resources. This fails to meet the actual needs of oilfields for comprehensive equipment scheduling, dynamic matching of operating conditions, and intelligent decision-making, and a systematic matching technology system has not yet been formed.

[0004] Therefore, developing a data-driven, intelligent matching method and system that takes into account both the overall planning of multiple wells and the recycling of equipment, and can achieve precise adaptation between the surface equipment of the pumping unit and the downhole operating conditions, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a dynamic matching method and system for pumping unit equipment that is adaptable to operating conditions. By establishing a complete technical process from intelligent data processing, multi-parameter comprehensive diagnosis, two-level optimization matching (priority interchange, then replacement) to effect prediction, it achieves efficient and economical matching between the surface equipment of the pumping unit well and the downhole operating conditions, thereby achieving the goal of significantly reducing system energy consumption and improving operating efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: On the one hand, this invention proposes a dynamic matching method for pumping unit equipment that is adaptable to operating conditions, including the following steps: Obtain the raw production data of all pumping wells in the target well group, and preprocess the raw production data to form the target dataset; Based on the target dataset, calculate the matching degree index for each pumping well; compare each matching degree index with a preset reasonable range threshold to determine the matching status of the equipment with the current operating conditions. Based on the matching status determination result, a two-level dynamic matching strategy is executed, and a matching decision scheme is output. The two-level dynamic matching strategy includes: firstly generating a matching scheme for equipment exchange within the well group; if there is no feasible exchange matching scheme, then generating a matching scheme for equipment replacement. Based on the equipment parameters in the matching decision scheme, the underground efficiency prediction model is invoked to obtain the quantitative results of the matching effect; Integrate the matching status determination results, matching decision schemes, and quantitative results of matching effects to output a dynamic matching decision report.

[0007] Preferably, the raw production data includes equipment parameters, operating condition parameters, production performance parameters, and energy consumption parameters; Using intelligent filtering algorithms, key data items are automatically extracted from the original production data based on preset matching analysis requirements.

[0008] Preferably, the raw production data is preprocessed to form a target dataset, including: The key data items are cleaned to remove outliers and invalid data; Imputation is performed on missing values ​​in the cleaned data; The interpolated data is then normalized to form the target dataset.

[0009] Preferably, the matching degree index includes: motor power utilization rate. Suspension point load utilization rate Gearbox torque utilization rate The calculation formula is as follows: ; In the formula, This refers to the power output of the electric motor during operation. The maximum safe operating power specified for the electric motor; ; .

[0010] Preferably, each matching degree index is compared with a preset reasonable range threshold to determine the matching status of the equipment with the current operating conditions, including: The motor power utilization rate is compared with the first set of preset threshold ranges to obtain the first comparison result. The first comparison result includes the states of uneconomical operation, reasonable operation, economical operation, or overload operation. The utilization rate of the suspension point load is compared with the second set of preset threshold intervals to obtain the second comparison result. The second comparison result includes the inefficient load rate zone, the qualified zone, the efficient load rate zone, or the safety hazard zone. The torque utilization rate of the gearbox is compared with the third set of preset threshold ranges to obtain the third comparison result. The third comparison result includes the low load rate inefficient zone, qualified zone, high efficiency zone or safety hazard zone.

[0011] Preferably, when at least one of the first comparison result, the second comparison result, or the third comparison result indicates that the device is in an overloaded, inefficient load rate, or safety hazard zone, the device is identified as an object to be optimized, and the two-level dynamic matching strategy is triggered.

[0012] Preferred, the interchangeability and matching scheme for equipment within the well group includes: The equipment files of all pumping units and motors in the target well group are collected to build an equipment resource pool. The equipment files record the equipment identification, specifications, deployment well location, operating data and matching status. The preset interchange rules are as follows: after the interchange, the power utilization rate of the motors of both parties will approach the reasonable or economical operating state, and the suspension load utilization rate and gearbox torque utilization rate will approach the high-efficiency zone or the qualified zone. For the object to be optimized, based on the equipment resource pool and the exchange rules, the optimization algorithm searches and evaluates potential equipment exchange combinations to generate an exchange matching scheme.

[0013] Preferably, the generated equipment replacement matching scheme includes: For the object to be optimized that cannot generate an interchangeable matching scheme, the target specifications of the required equipment are calculated based on the working parameters of the object to be optimized and the key matching degree index that deviates from the preset reasonable range threshold. The target specifications are compared with a database of standard equipment models to select candidate equipment models and output replacement recommendations.

[0014] The preferred underground efficiency prediction model is as follows: ; In the formula, For the matched underground efficiency, The power consumption corresponding to the actual liquid production volume; This represents the theoretical power consumption.

[0015] On the other hand, the present invention also proposes a dynamic matching system for pumping unit equipment that is adaptable to operating conditions, comprising: The data processing module acquires the raw production data of all pumping wells in the target well group, preprocesses the raw production data, and forms the target dataset. The matching status determination module calculates the matching degree index for each pumping well based on the target dataset; compares each matching degree index with a preset reasonable range threshold to determine the matching status of the equipment with the current operating conditions. The decision scheme output module executes a two-level dynamic matching strategy based on the matching status determination result and outputs a matching decision scheme. The two-level dynamic matching strategy includes: firstly generating a matching scheme for equipment swapping within the well group; if there is no feasible swapping matching scheme, then generating a matching scheme for equipment replacement. The matching effect quantification module, based on the equipment parameters in the matching decision scheme, calls the underground efficiency prediction model to obtain the matching effect quantification results; The decision report output module integrates the matching status judgment results, matching decision schemes, and quantitative results of matching effects to output a dynamic matching decision report.

[0016] As can be seen from the above technical solution, compared with the prior art, this invention discloses a dynamic matching method and system for pumping unit equipment with adaptive operating conditions. It intelligently collects and analyzes well group production data, calculates key matching indicators such as power, load, and torque to diagnose the compatibility between the equipment and downhole operating conditions, and adopts a two-level optimization strategy of "prioritizing equipment interchange within the well group, followed by direct replacement" to achieve the recycling and precise allocation of equipment resources. Simultaneously, it quantitatively evaluates the energy-saving effect after optimization through a subsurface efficiency prediction model. This invention breaks through the limitations of traditional single-well experience-based adjustment, realizing a transformation from single-point, static adjustment to group-well, dynamic, and intelligent allocation, resulting in improved system efficiency, reduced operating costs, high equipment utilization, and scalability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 The method flowchart provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] On the one hand, such as Figure 1 As shown in the figure, this invention discloses a dynamic matching method for pumping unit equipment with adaptive operating conditions, including the following steps: S1. Data Acquisition and Intelligent Extraction.

[0021] Obtain the raw production data of all pumping wells in the target well group, preprocess the raw production data, and form the target dataset.

[0022] The original production data of all pumping wells in the target well group were obtained from the oilfield production database. This data included equipment parameters, operating parameters, production performance parameters, and energy consumption parameters, such as stroke, number of strokes, pump diameter, sucker rod specifications, tubing specifications, motor power, power consumption, rated power, and power utilization rate, as shown in Table 1.

[0023] Table 1. List of raw production data

[0024] Using intelligent filtering algorithms, key data items are automatically extracted from raw production data according to preset matching analysis requirements to form a target dataset for matching analysis.

[0025] Preprocessing of key data items includes: S1-1. Perform preliminary screening on the extracted key data items for oil well production, delete outliers and invalid data, and re-integrate the remaining energy consumption node efficiency data. S1-2. Clean the remaining key data items for oil well production again, identify missing data values, and use linear interpolation to fill in the missing data. S1-3. Normalize the interpolated data to ensure its robustness and consistency. The normalization method is Min-Max normalization, and the process is as follows: .

[0026] S2. Calculation of key matching parameters and determination of status.

[0027] Based on the target dataset, the matching degree index of each pumping well is calculated; each matching degree index is compared with a preset reasonable range threshold to determine the matching status of the equipment with the current working conditions.

[0028] Matching indicators include: motor power utilization rate Suspension point load utilization rate Gearbox torque utilization rate The calculation formula is as follows: ; In the formula, The power of the electric motor during operation is expressed in kW. The maximum safe operating power of the electric motor, expressed in kW.

[0029] The calculation formula is In the formula: For safety, a value of 1 to 1.3 is generally acceptable, with 1 being the minimum value unless there are special circumstances. For a conventional pumping unit, the maximum output torque of the gearbox is generally assumed to be at a crank angle of 75° and the minimum torque at 255°. The corresponding torque factors are approximately 0.5S and -0.4S, where S represents the stroke. Therefore, the approximate formula for calculating the maximum output torque of the gearbox is: .

[0030] in: The maximum load at the suspension point is (kN). This represents the minimum load (kN) at the suspension point.

[0031] ; ; Under certain parameter settings and the required pump hang depth, the selection of the pumping unit is mainly determined by two indicators: suspension point load and crank torque, i.e., maximum suspension point load. and the maximum output torque of the gearbox Do not exceed their maximum allowed values: ; .

[0032] The rated load at the suspension point (kN); The rated torque of the gearbox is (kN.m).

[0033] The selected beam pumping unit should meet the long-term needs of the oilfield development plan.

[0034] The selected beam pumping unit should have high load utilization, torque utilization, and motor power utilization for most of its service life.

[0035] The designed beam pumping unit system should have good energy-saving effect.

[0036] The selection of pumping units should be coordinated regionally. For the same block, oil production plant, or oil field, the selected models should not be too varied. Wells with similar fluid properties and load requirements should, as far as possible, select pumping units of the same specifications and models.

[0037] Furthermore, each matching index is compared with a preset reasonable threshold range to determine the matching status of the equipment with the current operating conditions, including: A. Compare the motor power utilization rate with the first set of preset threshold intervals to obtain the first comparison result. The first comparison result includes uneconomical operation, reasonable operation, economical operation, or overload operation.

[0038] <20% is considered uneconomical; 20%≤ <30% is considered reasonable for operation; 30%≤ <50% is for operational economy; ≥50 indicates an overload.

[0039] List the current power utilization evaluation indicators (T, H, L, G) of the motor, and provide suggested replacement motor rated power values ​​for optimization and adjustment reference.

[0040] B. Compare the suspension point load utilization rate with the second set of preset threshold intervals to obtain the second comparison result. The second comparison result includes the load rate inefficient area, qualified area, high efficiency area or safety hazard area.

[0041] when Low load rate inefficient area; when The load rate is within acceptable limits; when High-efficiency load zone; when Areas with potential safety hazards.

[0042] C. Compare the gearbox torque utilization rate with the third set of preset threshold ranges to obtain the third comparison result. The third comparison result includes the low load rate inefficient zone, qualified zone, high efficiency zone, or safety hazard zone.

[0043] when Low load rate inefficient area; when The load rate is within acceptable limits; when High-efficiency load zone; when Areas with potential safety hazards.

[0044] S3. Two-level optimized matching strategy. Based on the matching status determination result, a two-level dynamic matching strategy is executed, and a matching decision scheme is output.

[0045] When at least one of the first comparison result, the second comparison result, or the third comparison result indicates that the equipment is in an overloaded, inefficient load rate, or safety hazard zone, the equipment is identified as an object to be optimized, and the two-level dynamic matching strategy is triggered.

[0046] The two-level dynamic matching strategy includes: firstly generating equipment interchange matching schemes within the well group; if no feasible interchange matching scheme is available, then generating equipment replacement matching schemes.

[0047] Specifically, the first level of optimization involves the interchangeability and matching of equipment within the well cluster. This includes constructing a well cluster equipment resource pool and recording the model, specifications, current well location, and historical operating data of each pumping unit and motor. A matching rule library is established, with rules including: after the interchange, the key parameters of both parties should approach the middle of a reasonable range (such as reasonable / economical operation, high-efficiency / qualified range); the equipment specifications should match the production capacity requirements of the target well; and the models within the block should be simplified and unified. An optimization algorithm is used to search for equipment interchangeable pairs that meet the rules in the resource pool and to evaluate the overall benefit improvement after the interchange.

[0048] If equipment cannot be interchanged within the well group, a second level of optimization is performed, which involves direct equipment replacement: Based on the operating data of the target oil well and the key parameters that deviate from the reasonable range, the target equipment specifications required to meet its efficient and safe operation are accurately calculated; the target specifications are matched with a standard equipment model database to select equipment models that meet the specifications, have high energy efficiency levels, and are compatible with the on-site installation conditions as replacement recommendations; a replacement plan recommendation including a comparison of the parameters of the old and new equipment and the expected energy-saving effect is output.

[0049] S4. Efficiency Prediction and Effect Evaluation.

[0050] Based on the equipment parameters in the matching decision scheme, the underground efficiency prediction model is invoked to obtain the quantitative results of the matching effect.

[0051] By analyzing factors such as oil well production, dynamic fluid level, and energy consumption, a subsurface efficiency prediction model is established. This model can be used to calculate the subsurface efficiency after matching the dynamometer or motor based on production parameters such as dynamometer area, stroke frequency, daily production, and dynamic fluid level.

[0052] In the formula, Wactual represents the power consumption corresponding to the actual liquid production; Wtheoretical represents the theoretical power consumption.

[0053] S5. Output intelligent matching analysis report.

[0054] The system integrates matching status assessment results, matching decision-making schemes, and quantitative results of matching effects to output a dynamic matching decision report. The report clearly presents the current status of each problematic well, recommended measures (specific replacement objects or models), expected energy-saving effects, and economic benefits, guiding efficient on-site implementation.

[0055] On the other hand, the present invention also proposes a dynamic matching system for pumping unit equipment that is adaptable to operating conditions, comprising: The data processing module acquires the raw production data of all pumping wells in the target well group, preprocesses the raw production data, and forms the target dataset. The matching status determination module calculates the matching degree index for each pumping well based on the target dataset; it compares each matching degree index with a preset reasonable range threshold to determine the matching status of the equipment with the current operating conditions. The decision-making scheme output module executes a two-level dynamic matching strategy based on the matching status determination result and outputs a matching decision scheme. The two-level dynamic matching strategy includes: firstly generating a matching scheme for equipment swapping within the well group; if there is no feasible swapping matching scheme, then generating a matching scheme for equipment replacement. The matching effect quantification module, based on the equipment parameters in the matching decision scheme, calls the underground efficiency prediction model to obtain the matching effect quantification results; The decision report output module integrates the matching status judgment results, matching decision schemes, and quantitative results of matching effects to output a dynamic matching decision report.

[0056] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0057] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic matching method for pumping unit equipment with adaptive operating conditions, characterized in that, Includes the following steps: Obtain the raw production data of all pumping wells in the target well group, and preprocess the raw production data to form the target dataset; Based on the target dataset, calculate the matching degree index for each pumping well; Each matching index is compared with a preset reasonable range threshold to determine the matching status of the equipment with the current working condition; Based on the matching status determination result, a two-level dynamic matching strategy is executed, and a matching decision scheme is output. The two-level dynamic matching strategy includes: prioritizing the generation of equipment swapping matching schemes within the well group; if no feasible swapping matching scheme is available, then generating equipment replacement matching schemes. Based on the equipment parameters in the matching decision scheme, the underground efficiency prediction model is invoked to obtain the quantitative results of the matching effect; Integrate the matching status determination results, matching decision schemes, and quantitative results of matching effects to output a dynamic matching decision report.

2. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 1, characterized in that, The original production data includes equipment parameters, operating condition parameters, production performance parameters, and energy consumption parameters; Using intelligent filtering algorithms, key data items are automatically extracted from the original production data based on preset matching analysis requirements.

3. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 2, characterized in that, The raw production data is preprocessed to form the target dataset, including: The key data items are cleaned to remove outliers and invalid data; Imputation is performed on missing values ​​in the cleaned data; The interpolated data is then normalized to form the target dataset.

4. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 1, characterized in that, The matching degree index includes: motor power utilization rate. Suspension point load utilization rate Gearbox torque utilization rate The calculation formula is as follows: ； In the formula, This refers to the power output of the electric motor during operation. The maximum safe operating power specified for the electric motor; ； 。 5. The dynamic matching method for oil pumping unit equipment based on operating condition adaptation according to claim 4, characterized in that, Each matching index is compared with a preset reasonable threshold range to determine the matching status of the equipment with the current operating conditions, including: The motor power utilization rate is compared with the first set of preset threshold ranges to obtain the first comparison result. The first comparison result includes the states of uneconomical operation, reasonable operation, economical operation, or overload operation. The utilization rate of the suspension point load is compared with the second set of preset threshold intervals to obtain the second comparison result. The second comparison result includes the inefficient load rate zone, the qualified zone, the efficient load rate zone, or the safety hazard zone. The torque utilization rate of the gearbox is compared with the third set of preset threshold ranges to obtain the third comparison result. The third comparison result includes the low load rate inefficient zone, qualified zone, high efficiency zone or safety hazard zone.

6. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 5, characterized in that, When at least one of the first comparison result, the second comparison result, or the third comparison result indicates that the device is in an overloaded, inefficient load rate, or safety hazard zone, the device is identified as an object to be optimized, and the two-level dynamic matching strategy is triggered.

7. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 5, characterized in that, Generate an interchangeable and compatible equipment scheme for the well group, including: The equipment files of all pumping units and motors in the target well group are collected to build an equipment resource pool. The equipment files record the equipment identification, specifications, deployment well location, operating data and matching status. The preset interchange rules are as follows: after the interchange, the power utilization rate of the motors of both parties will approach the reasonable or economical operating state, and the suspension load utilization rate and gearbox torque utilization rate will approach the high-efficiency zone or the qualified zone. For the object to be optimized, based on the equipment resource pool and the exchange rules, the optimization algorithm searches and evaluates potential equipment exchange combinations to generate an exchange matching scheme.

8. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 7, characterized in that, Generate a device replacement matching plan, including: For the object to be optimized that cannot generate an interchangeable matching scheme, the target specifications of the required equipment are calculated based on the working parameters of the object to be optimized and the key matching degree index that deviates from the preset reasonable range threshold. The target specifications are compared with a database of standard equipment models to select candidate equipment models and output replacement recommendations.

9. The dynamic matching method for pumping unit equipment based on operating condition adaptation according to claim 1, characterized in that, The underground efficiency prediction model is as follows: ； In the formula, For the matched underground efficiency, The power consumption corresponding to the actual liquid production volume; This represents the theoretical power consumption.

10. A dynamic matching system for pumping unit equipment with adaptive operating conditions, characterized in that, include: The data processing module acquires the raw production data of all pumping wells in the target well group, preprocesses the raw production data, and forms the target dataset. The matching status determination module calculates the matching degree index for each pumping well based on the target dataset; compares each matching degree index with a preset reasonable range threshold to determine the matching status of the equipment with the current operating conditions. The decision-making scheme output module executes a two-level dynamic matching strategy based on the matching status determination result and outputs a matching decision scheme. The two-level dynamic matching strategy includes: prioritizing the generation of equipment swapping matching schemes within the well group; if no feasible swapping matching scheme is available, then generating equipment replacement matching schemes. The matching effect quantification module, based on the equipment parameters in the matching decision scheme, calls the underground efficiency prediction model to obtain the matching effect quantification results; The decision report output module integrates the matching status judgment results, matching decision schemes, and quantitative results of matching effects to output a dynamic matching decision report.

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