CN-122022367-A - Working condition self-adaption-oriented dynamic matching method and system for oil pumping unit equipment

CN122022367ACN 122022367 ACN122022367 ACN 122022367ACN-122022367-A

Abstract

The invention discloses a dynamic matching method and a dynamic matching system for working condition-oriented self-adaptive pumping unit equipment, and belongs to the technical field of oilfield oil extraction engineering. The multi-dimensional production data of the oil pumping well are intelligently extracted, key evaluation indexes such as power utilization rate, load utilization rate and torque utilization rate are calculated, and the matching state of equipment and working conditions is judged according to a preset reasonable section. Aiming at the oil well with unreasonable matching, the optimization strategy of the oil pumping unit or the motor interchange in the well group is adopted preferentially to realize the recycling and reasonable configuration of equipment resources, and if the equipment resources still cannot meet the requirements after interchange, the measures of directly replacing the motor or the oil pumping unit are implemented. And meanwhile, an underground efficiency prediction model is established and used for evaluating the energy-saving effect after matching. The intelligent and systematic matching process obviously reduces the energy consumption of the oil pumping well, improves the system efficiency and the equipment utilization rate, and has the characteristics of strong operability, obvious energy-saving effect and easy popularization.

Inventors

YANG HUKUN
LI YANCHUN
HUANG HETING
DAI YONGQIN
LI TENGFEI
ZHAO YANQI

Assignees

东北石油大学

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (10)

1. The dynamic matching method of the pumping unit equipment facing the working condition self-adaption is characterized by comprising the following steps of: acquiring original production data of all the pumping wells of a target well group, and preprocessing the original production data to form a target data set; comparing each matching degree index with a preset reasonable interval threshold value, and judging the matching state of equipment and the current working condition; Executing a two-stage dynamic matching strategy according to the matching state judgment result, and outputting a matching decision scheme, wherein the two-stage dynamic matching strategy comprises the steps of preferentially generating an interchange matching scheme of equipment in the well group; Based on equipment parameters in the matching decision scheme, invoking an underground efficiency prediction model to obtain a matching effect quantization result; and integrating the matching state judging result, the matching decision scheme and the matching effect quantifying result, and outputting a dynamic matching decision report.
2. The method for dynamically matching pumping unit equipment for working condition self-adaptation according to claim 1, wherein the raw production data comprises equipment parameters, working condition parameters, production performance parameters and energy consumption parameters; And automatically extracting key data items from the original production data according to preset matching analysis requirements by using an intelligent screening algorithm.
3. The method for dynamically matching pumping unit equipment for operating condition adaptation according to claim 2, wherein preprocessing the raw production data to form a target data set comprises: Cleaning the key data items, and removing abnormal values and invalid data; Performing interpolation processing on missing values in the cleaned data; And carrying out normalization processing on the interpolated data to form the target data set.
4. The method for dynamically matching pumping unit equipment for working condition self-adaption according to claim 1, wherein the matching degree index comprises a motor power utilization rate Suspension point load utilization rate Torque utilization rate of reduction gearbox The calculation formula is as follows: ; In the formula, Power in motor operation; Maximum safe operating power for motor calibration; ; 。
5. the method for dynamically matching pumping unit equipment for self-adapting to working conditions according to claim 4, wherein comparing each matching degree index with a preset reasonable interval threshold value, and determining the matching state of the equipment and the current working condition comprises the following steps: comparing the power utilization rate of the motor with a first group of preset threshold intervals to obtain a first comparison result, wherein the first comparison result comprises uneconomic operation, reasonable operation, economical operation or operation overload state; Comparing the suspension point load utilization rate with a second group of preset threshold intervals to obtain a second comparison result, wherein the second comparison result comprises a load rate low-efficiency area, a qualified area, a high-efficiency area or a potential safety hazard area; and comparing the torque utilization rate of the reduction gearbox with a third group of preset threshold intervals to obtain a third comparison result, wherein the third comparison result comprises a load rate low-efficiency area, a qualified area, a high-efficiency area or a potential safety hazard area.
6. The method for dynamically matching pumping unit equipment oriented to working condition self-adaptation according to claim 5, wherein when at least one of the first comparison result, the second comparison result or the third comparison result indicates that the equipment is in a state of an operation overload, a load rate inefficiency area or a potential safety hazard area, the equipment is considered to be an object to be optimized, and the two-stage dynamic matching strategy is triggered to be executed.
7. The method for dynamically matching pumping unit equipment for self-adaptation to working conditions according to claim 5, wherein generating a well group internal equipment interchange matching scheme comprises: Assembling equipment files of all pumping units and motors in a target well group to construct an equipment resource pool, wherein the equipment files record the identification, specification, deployment well positions, operation data and matching states of equipment; The interchange rule is preset, wherein the power utilization rate of the motors of the two parties after interchange approaches to a reasonable running or economical running state, and the suspension point load utilization rate and the torque utilization rate of the reduction gearbox approach to a high-efficiency zone or a qualified zone; and searching and evaluating potential equipment interchange combinations according to the equipment resource pool and interchange rules aiming at the object to be optimized through an optimization algorithm to generate an interchange matching scheme.
8. The method for dynamically matching pumping unit equipment for operating condition adaptation according to claim 7, wherein generating an equipment replacement matching scheme comprises: For the object to be optimized which cannot generate the exchange matching scheme, calculating the target specification of the required equipment according to the working condition parameters of the object to be optimized and the key matching degree index deviating from the preset reasonable interval threshold value; And comparing the target specification with a standard equipment model database, screening out candidate equipment models, and outputting a replacement proposal.
9. The method for dynamically matching pumping unit equipment for working condition adaptation according to claim 1, wherein the underground efficiency prediction model is as follows: ; In the formula, In order to match the subsurface efficiency after the match, The power consumption corresponding to the actual liquid production amount is calculated; Is the theoretical power consumption.
10. A pumping unit equipment dynamic matching system facing working condition self-adaptation is characterized by comprising: the data processing module is used for acquiring the original production data of all the pumping wells of the target well group, and preprocessing the original production data to form a target data set; The matching state judging module is used for calculating the matching degree index of each oil pumping well based on the target data set, comparing each matching degree index with a preset reasonable interval threshold value and judging the matching state of equipment and the current working condition; The decision scheme output module executes a two-stage dynamic matching strategy according to the matching state judgment result and outputs a matching decision scheme, wherein the two-stage dynamic matching strategy comprises the steps of preferentially generating an interchange matching scheme of equipment in the well group; The matching effect quantization module is used for calling an underground efficiency prediction model based on equipment parameters in the matching decision scheme to acquire a matching effect quantization result; and the decision report output module integrates the matching state judgment result, the matching decision scheme and the matching effect quantification result and outputs a dynamic matching decision report.

Description

Working condition self-adaption-oriented dynamic matching method and system for oil pumping unit equipment Technical Field The invention relates to the technical field of oilfield oil extraction engineering, in particular to a dynamic matching method and a dynamic matching system for working condition-oriented self-adaptive oil pumping unit equipment. Background At present, the beam pumping unit is used as the most widely used mechanical oil extraction mode in the oil fields at home and abroad, occupies the core position in the crude oil extraction system, has energy consumption which is more than 30% of the total power consumption of the oil field, and is a key link for influencing the production efficiency and the cost control of the oil field. Meanwhile, scholars at home and abroad have conducted a great deal of research on efficiency evaluation, power matching analysis, parameter optimization and the like of the pumping unit system, and related technologies are concentrated on theoretical calculation or local parameter adjustment of a single well layer, so that a certain support is provided for single well operation optimization. However, in long-term production practice, the phenomenon that the motor power and the model of the pumping unit are not matched with the actual load of an oil well still commonly exists, so that the system operation efficiency is low, the energy consumption per unit liquid production is high, the fatigue and abrasion of mechanical parts are further increased, the service period of equipment is shortened, the maintenance and replacement cost is increased, the problem of the mismatching is influenced by the dynamic change of the productivity of the oil well (formation pressure attenuation, water content rise and the like), the deviation of the initial design and the actual well condition, the limited stock model of the equipment, the unscientific allocation of resources among blocks and the like, and complex dynamic characteristics are presented. In addition, the prior art lacks overall consideration on the scale of well groups or blocks, is difficult to realize the cyclic utilization and global optimization of equipment resources, cannot meet the actual requirements of oil fields on comprehensive scheduling, working condition dynamic matching and intelligent decision making of equipment, and does not form a systematic matching technology system. Therefore, developing an intelligent matching method and system based on data driving, which can realize accurate adaptation of pumping unit ground equipment and underground working conditions and give consideration to overall well planning and equipment recycling is a problem to be solved by those skilled in the art. Disclosure of Invention In view of the above, the invention provides a dynamic matching method and a system for pumping unit equipment facing working condition self-adaptation, which are used for realizing efficient and economic matching of pumping unit well ground equipment and underground working conditions by establishing a complete technical flow from intelligent data processing, multi-parameter comprehensive diagnosis, two-stage optimization matching (preferential exchange and secondary replacement) to effect prediction, thereby achieving the aims of obviously reducing system energy consumption and improving operation efficiency. In order to achieve the above purpose, the present invention adopts the following technical scheme: on one hand, the invention provides a dynamic matching method of pumping unit equipment facing working condition self-adaption, which comprises the following steps: acquiring original production data of all the pumping wells of a target well group, and preprocessing the original production data to form a target data set; comparing each matching degree index with a preset reasonable interval threshold value, and judging the matching state of equipment and the current working condition; Executing a two-stage dynamic matching strategy according to the matching state judgment result, and outputting a matching decision scheme, wherein the two-stage dynamic matching strategy comprises the steps of preferentially generating an interchange matching scheme of equipment in the well group; Based on equipment parameters in the matching decision scheme, invoking an underground efficiency prediction model to obtain a matching effect quantization result; and integrating the matching state judging result, the matching decision scheme and the matching effect quantifying result, and outputting a dynamic matching decision report. Preferably, the raw production data comprises equipment parameters, working condition parameters, production performance parameters and energy consumption parameters; And automatically extracting key data items from the original production data according to preset matching analysis requirements by using an intelligent screening algorithm. Preferably, preprocessing the raw production data