Intelligent energy efficiency evaluation method and system for oil and gas recovery equipment

Abstract

The invention discloses an intelligent energy efficiency evaluation method and system for oil and gas recovery equipment, and belongs to the technical field of oil and gas recovery. The method comprises the following steps: setting a recovery efficiency and energy consumption efficiency characteristic value domain according to equipment process requirements, and constructing a plurality of evaluation modes; establishing an evaluation state switching time axis, dividing energy efficiency evaluation stages, and obtaining a characteristic value and a value domain of each stage; an evaluation mode calling chain is generated based on stage continuity, and a bimodal feature coordinate point quantitative recovery and energy consumption stability coefficient is constructed; and calibrating an abnormal evaluation mode through the dual-mode collaborative offset degree and tracing the abnormal evaluation mode. According to the method, the problems of single evaluation mode, lack of dynamic continuous evaluation, low anomaly judgment accuracy, difficulty in tracing and the like in the prior art are solved, multi-dimensional and intelligent evaluation of the oil gas recovery energy efficiency is realized, the evaluation accuracy and the operation and maintenance efficiency are remarkably improved, and the method is suitable for operation and maintenance of oil gas recovery equipment in multiple scenes such as gas stations and oil depots.

Description

Technical Field

[0001] This invention relates to the field of oil and gas recovery technology, specifically to an intelligent energy efficiency assessment method and system for oil and gas recovery equipment. Background Technology

[0002] In the field of oil and gas recovery technology, oil and gas recovery equipment is widely used in gas stations, oil depots, chemical industrial parks, and other scenarios. Its core function is to recover oil and gas resources, reduce environmental pollution, and mitigate safety risks. Energy efficiency assessment, as a key aspect of equipment operation and maintenance, directly impacts resource utilization and environmental compliance rates. However, existing energy efficiency assessment technologies for oil and gas recovery equipment face numerous unresolved technical challenges, severely hindering the industry's development.

[0003] Existing assessment technologies often employ a single-dimensional evaluation model, focusing only on one indicator of either recovery efficiency or energy efficiency, without establishing a collaborative evaluation mechanism for both. The operation of oil and gas recovery equipment is influenced by various factors such as process requirements and environmental conditions. The compatibility between recovery efficiency and energy efficiency varies under different operating conditions. A single assessment model cannot cover complex scenarios, leading to biased evaluation results that fail to reflect the true energy efficiency level of the equipment. For example, some technologies use only recovery efficiency as the core indicator, ignoring the increased operating costs caused by excessive energy consumption; or they only focus on energy consumption without considering the environmental risks caused by substandard recovery efficiency, failing to provide a comprehensive basis for equipment optimization.

[0004] Existing technologies lack a dynamic and continuous evaluation system and do not segment or track the evaluation process in stages. Traditional evaluations are mostly fixed-point sampling assessments, acquiring data only at specific points in time. This fails to capture the dynamic fluctuations of energy efficiency indicators during equipment operation, easily leading to misjudgments of "instantaneous compliance with standards but overall imbalance." Furthermore, evaluation data is stored in a fragmented manner, lacking a structured chain of correlation analysis. This makes it impossible to trace the causal relationships of energy efficiency changes across different time periods, resulting in delayed early warnings of equipment anomalies. Problems are often only discovered after environmental standards are exceeded or equipment malfunctions occur, increasing maintenance costs and safety hazards.

[0005] Existing anomaly detection methods lack precise quantitative indicators and rely heavily on empirical thresholds, resulting in strong subjectivity and poor adaptability. Different types of oil and gas recovery equipment operating under different conditions have varying reasonable fluctuation ranges for their energy efficiency indicators. Fixed thresholds are difficult to adapt to diverse scenarios, easily leading to missed detection of significant anomalies and misjudgment of minor fluctuations. Furthermore, current technologies lack a synergistic correlation analysis mechanism between recovery efficiency and energy consumption efficiency, failing to identify hidden anomalies caused by imbalances between the two. For example, a "low-efficiency, high-consumption" state may occur where recovery efficiency meets standards but energy consumption efficiency is abnormally high. Such anomalies are difficult to detect using a single indicator, leading to resource waste and equipment wear and tear over the long term.

[0006] Existing technologies have weak anomaly tracing capabilities, only able to locate the abnormal result, but unable to trace the specific assessment model, time stage, and related factors that caused the anomaly. When equipment exhibits energy efficiency anomalies, maintenance personnel need to check numerous factors such as equipment components and process parameters one by one. The investigation process is blind and time-consuming, seriously affecting maintenance efficiency, and may even lead to serious consequences such as oil and gas leaks and environmental pollution due to the continued existence of the anomaly. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent energy efficiency evaluation method and system for oil and gas recovery equipment, so as to solve the problems mentioned in the background art. The technical solution of this invention can be summarized as follows:

[0008] Based on the dual core energy efficiency dimensions of "recovery efficiency" and "energy consumption efficiency" of oil and gas recovery equipment, and combined with the diversity of equipment processes and operational dynamics, a full-process intelligent evaluation logic is constructed. First, according to different process requirements, the characteristic value ranges of recovery efficiency (core equipment control) and energy consumption efficiency (auxiliary environmental equipment control) are combined to form multiple evaluation modes adaptable to different operating conditions, solving the problem of poor adaptability of a single mode. Second, through the evaluation state switching time axis, the continuous operation process is divided into multiple energy efficiency evaluation stages, enabling dynamic tracking of energy efficiency fluctuations and ensuring the continuity and timeliness of evaluation data. Then, based on the stage continuity, an evaluation mode call chain is generated, constructing dual-modal feature coordinate points, with time stage as the horizontal independent variable and recovery stability coefficient and energy consumption stability coefficient as the vertical dependent variables, quantifying the synergistic relationship between the two dimensions. Finally, anomalies are accurately determined by the degree of dual-modal synergistic offset (combining the mean and standard deviation of the offset in the call chain), achieving a closed loop from "result judgment" to "process traceability" of anomalies.

[0009] To solve the above-mentioned technical problems, the present invention provides the following specific technical solutions:

[0010] An intelligent energy efficiency assessment system for oil and gas recovery equipment, the system includes: an assessment mode initialization module, an assessment stage division and feature acquisition module, a dual-modal coefficient quantization module, and a collaborative offset assessment and anomaly tracing module;

[0011] The evaluation mode initialization module is used to set relevant feature value ranges according to equipment process requirements and construct multiple evaluation modes; the evaluation stage division and feature acquisition module is used to build an evaluation state switching time axis, divide energy efficiency evaluation stages, and acquire recovery efficiency feature values ​​and energy consumption efficiency feature values ​​in each stage to determine the corresponding feature value range; the dual-modal coefficient quantification module is used to generate the evaluation mode call chain, construct dual-modal feature coordinate points, and quantify the recovery stability coefficient and energy consumption stability coefficient of each evaluation mode; the collaborative offset evaluation and anomaly tracing module is used to calculate the degree of dual-modal collaborative offset of each evaluation mode, calibrate the abnormal evaluation mode after comparing it with a preset threshold, and realize anomaly tracing display.

[0012] Preferably, the evaluation mode initialization module includes a feature value range setting unit and an evaluation mode construction unit;

[0013] The feature value range setting unit is used to set the recovery efficiency feature value range and the energy consumption efficiency feature value range respectively according to the process requirements of the oil and gas recovery equipment, and to clarify the control subject of the two types of value ranges;

[0014] The evaluation mode construction unit is used to combine the set recovery efficiency characteristic value range with the energy consumption efficiency characteristic value range to form a variety of different evaluation modes.

[0015] Preferably, the evaluation stage division and feature acquisition module includes a time axis construction unit, an evaluation stage division unit, and a feature value and value range acquisition unit;

[0016] The time axis construction unit is used to build an evaluation state switching time axis, set the time axis scale, and label the evaluation state switching nodes.

[0017] The assessment stage division unit is used to record the nodes corresponding to the calling and switching of assessment modes, and to divide the energy efficiency assessment stages corresponding to each assessment mode according to the node information.

[0018] The characteristic value and value range acquisition unit is used to acquire the ratio of the recovered oil and gas volume to the total oil and gas produced as the recovery efficiency characteristic value and the ratio of the instantaneous energy consumption to the cumulative energy consumption as the energy consumption efficiency characteristic value in each energy efficiency assessment stage, and to determine the recovery efficiency characteristic value range and energy consumption efficiency characteristic value range for each stage respectively.

[0019] Preferably, the dual-modal coefficient quantization module includes a call chain generation unit, a dual-modal coordinate point construction unit, and a stability coefficient quantization unit;

[0020] The call chain generation unit integrates multiple sequentially connected evaluation stages to form a call chain for the evaluation mode, based on the continuity of the energy efficiency evaluation stage.

[0021] The dual-modal coordinate point construction unit is used to construct dual-modal characteristic coordinate points corresponding to the recovery stability coefficient and the energy consumption stability coefficient, respectively, with the time nodes of each energy efficiency assessment stage as the horizontal independent variable.

[0022] The stability coefficient quantification unit is used to calculate the recovery stability coefficient and energy consumption stability coefficient respectively according to the characteristic value range corresponding to each evaluation mode, and use them as the longitudinal dependent variable values ​​of the dual-modal characteristic coordinate points.

[0023] Preferably, the collaborative offset assessment and anomaly tracing module includes a collaborative offset degree calculation unit, an anomaly calibration unit, and a tracing unit;

[0024] The collaborative offset degree calculation unit calculates the bimodal collaborative offset of each evaluation mode based on the stability coefficient in the bimodal feature coordinate points, and obtains the bimodal collaborative offset degree by combining the mean and standard deviation of the offset of each node in the call chain.

[0025] The anomaly calibration unit is used to compare the calculated dual-modal cooperative offset degree with a preset threshold and calibrate the anomaly evaluation mode that exceeds the threshold.

[0026] The tracing unit is used to trace the calibrated anomaly assessment mode back to the display interface of the oil and gas recovery terminal, so as to realize the visual presentation of the anomaly information.

[0027] An intelligent energy efficiency assessment method for oil and gas recovery equipment, comprising the following steps:

[0028] Step S1: Based on the process requirements of the oil and gas recovery equipment, set the characteristic value range related to recovery efficiency and energy consumption efficiency to construct multiple evaluation models;

[0029] Step S2: Build an evaluation state switching timeline, divide the energy efficiency evaluation stage according to the calling and switching of evaluation modes, and obtain the corresponding recovery efficiency characteristic value and energy consumption efficiency characteristic value in each energy efficiency evaluation stage to determine the characteristic value range of each energy efficiency evaluation stage.

[0030] Step S3: Based on the continuity of the energy efficiency assessment stage, generate the call chain of assessment modes, construct bimodal feature coordinate points in the chain, and quantify the recovery stability coefficient and energy consumption stability coefficient of each assessment mode respectively.

[0031] Step S4: Based on the dual-modal feature coordinate points, quantify the degree of dual-modal collaborative offset of each evaluation mode to calibrate the abnormal evaluation mode and trace it back to the oil and gas recovery terminal display interface.

[0032] Preferably, the specific implementation process of step S1 includes:

[0033] Based on the process requirements of the oil and gas recovery equipment, the recovery efficiency characteristic value range and the energy consumption efficiency characteristic value range are initialized and set. The recovery efficiency characteristic value range is achieved by the core recovery equipment, and the energy consumption efficiency characteristic value range is achieved by the auxiliary environmental equipment.

[0034] An evaluation model is composed of a recovery efficiency characteristic range and an energy consumption efficiency characteristic range, wherein one recovery efficiency characteristic range and one energy consumption efficiency characteristic range constitute an evaluation model;

[0035] Let the eigenvalue range of the i-th type of recovery efficiency be denoted as... Let the range of the j-th energy efficiency characteristic be denoted as Then, based on the eigenrangement of recovery efficiency... and energy efficiency characteristic range The evaluation model composed of intervals is denoted as .

[0036] Preferably, the specific implementation process of step S2 includes:

[0037] Construct an evaluation state switching time axis for the oil and gas recovery process, with the scale of the evaluation state switching time axis being t; after starting each oil and gas recovery device in the oil and gas recovery process, instruct the evaluation state switching time axis to start timing, and number the evaluation state switching nodes sequentially on the evaluation state switching time axis according to the scale t.

[0038] Let the evaluation state switching node labeled k be denoted as If the evaluation state switching node Time-triggered evaluation mode When the call is made, the evaluation state switching node is recorded. If the evaluation state switching node Subsequent evaluation state switching nodes Change the evaluation model for Then record the subsequent evaluation state switching node. Then the evaluation state switching node will be used. and evaluation state switching node Composition of evaluation model The energy efficiency assessment phase is denoted as Where v is the label of the evaluation state switching node, and m is the evaluation mode type number. ;

[0039] During the energy efficiency assessment phase Within this process, the ratio of recovered oil and gas volume to total produced oil and gas volume is used as the recovery efficiency characteristic value, which is then assessed during the energy efficiency evaluation phase. The minimum and maximum recovery efficiency characteristic values ​​within the range constitute the recovery efficiency characteristic value domain;

[0040] During the energy efficiency assessment phase Within the energy consumption phase, the ratio of instantaneous energy consumption to cumulative energy consumption is obtained and used as the energy efficiency characteristic value, which is then used in the energy efficiency assessment phase. The minimum and maximum energy efficiency characteristic values ​​within the range constitute the energy efficiency characteristic value domain.

[0041] Preferably, the specific implementation process of step S3 includes:

[0042] Based on the continuity of the energy efficiency assessment phase, a call chain for the assessment mode is generated, denoted as... Where u and s are both labels of the evaluation state switching nodes, and , This represents the y-th evaluation mode;

[0043] For any energy efficiency assessment stage in the call chain Where x is the type number of the evaluation mode, and r and e are both labels of the evaluation state switching nodes, and Construct bimodal feature coordinate points, including first modal feature coordinate points and second modal feature coordinate points, wherein the horizontal independent variables of the first modal feature coordinate points and the second modal feature coordinate points are the same, which is _____. ;

[0044] From horizontal independent variables Each index is located in the evaluation mode. Based on the evaluation model :

[0045] In the evaluation mode Identify the characteristic value range of recycling efficiency Based on the characteristic range of recycling efficiency Quantitative evaluation model Recovery stability coefficient and will recover the stability coefficient The longitudinal dependent variable value of the first modal feature coordinate point;

[0046] Meanwhile, in the evaluation model Identify the energy efficiency characteristic range Based on the energy efficiency characteristic range Quantitative evaluation model Energy consumption stability coefficient , which serves as the longitudinal dependent variable value of the second modal feature coordinate point;

[0047] In the formula, and These are the maximum and minimum value functions, respectively.

[0048] Preferably, the specific implementation process of step S4 includes:

[0049] Based on dual-modal feature coordinate points, a quantitative evaluation mode is established. Dual-modal cooperative offset degree In the formula, It is a dual-modal cooperative offset, and , For the call chain The mean of the bimodal cooperative offset at each chain node. For the call chain The standard deviation of the dual-modal cooperative offset at each chain node;

[0050] A preset threshold for the degree of bimodal cooperative offset is set; if the degree of bimodal cooperative offset... If the value is greater than or equal to the dual-modal cooperative offset threshold, then the call chain is identified. Evaluation model in And trace it back to the display interface of the oil and gas recovery terminal.

[0051] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0052] By constructing an evaluation model by combining the characteristic value ranges of recycling efficiency and energy consumption efficiency, the two can be evaluated in a coordinated manner. This not only reflects the "recycling effect" of the equipment, but also takes into account the "operating cost". This solves the problem of the one-sided evaluation of existing technologies. Moreover, the multi-mode design can flexibly adapt to different process requirements, and its adaptability far exceeds that of existing technologies with fixed evaluation standards.

[0053] The design of the evaluation state switching timeline and call chain transforms discrete data into structured, staged data, avoiding the lag of existing fixed-point sampling. It can track energy efficiency changes in real time and provide data support for dynamic equipment optimization, which is something that existing static evaluation technologies cannot achieve.

[0054] By quantitatively calculating the recovery stability coefficient, energy consumption stability coefficient, and degree of collaborative offset, and combining statistical analysis of mean and standard deviation, the subjectivity of existing technical experience thresholds is avoided, which helps to improve the accuracy of anomaly judgment. It can also be directly traced to the specific assessment mode and time stage, solving the pain point of existing technology that "only knows the anomaly, but not the root cause", and significantly improving the efficiency of operation and maintenance response.

[0055] From assessment model construction, phase division, coefficient quantification to anomaly tracing, a complete technical closed loop is formed. Existing technologies are mostly fragmented assessments without systematic logical connections. This invention can directly provide maintenance personnel with a complete solution of "operating condition adaptation - dynamic monitoring - anomaly location - precise maintenance", reducing the threshold of maintenance. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0057] Figure 1 This is a schematic diagram illustrating the steps of an intelligent energy efficiency assessment method for oil and gas recovery equipment according to the present invention. Detailed Implementation

[0058] 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.

[0059] In this first embodiment: an intelligent energy efficiency evaluation system for oil and gas recovery equipment is provided. The system includes: an evaluation mode initialization module, an evaluation stage division and feature acquisition module, a dual-modal coefficient quantization module, and a collaborative offset evaluation and anomaly tracing module.

[0060] The evaluation mode initialization module is used to set relevant feature value ranges according to equipment process requirements and to build multiple evaluation modes;

[0061] More specifically, the evaluation mode initialization module includes a feature range setting unit and an evaluation mode construction unit;

[0062] The characteristic value range setting unit is used to set the characteristic value range of recovery efficiency and the characteristic value range of energy consumption efficiency respectively according to the process requirements of the oil and gas recovery equipment, and to clarify the control subject of the two types of value ranges;

[0063] The evaluation mode construction unit is used to combine the set recovery efficiency characteristic value range with the energy consumption efficiency characteristic value range to form a variety of different evaluation modes;

[0064] The assessment phase division and feature acquisition module is used to build an assessment state switching time axis, divide the energy efficiency assessment phases, and acquire the recycling efficiency feature value and energy consumption efficiency feature value in each phase to determine the corresponding feature value range.

[0065] More specifically, the evaluation phase division and feature acquisition module includes a time axis construction unit, an evaluation phase division unit, and a feature value and value range acquisition unit;

[0066] The timeline construction unit is used to build an evaluation state transition timeline, set the timeline scale, and label the evaluation state transition nodes.

[0067] The assessment phase division unit is used to record the nodes corresponding to the calling and switching of assessment modes, and divides the energy efficiency assessment phases corresponding to each assessment mode based on the node information.

[0068] The feature value and range acquisition unit is used to acquire the ratio of the recovered oil and gas volume to the total oil and gas produced as the recovery efficiency feature value and the ratio of the instantaneous energy consumption to the cumulative energy consumption as the energy consumption efficiency feature value in each energy efficiency assessment stage, and to determine the recovery efficiency feature value range and energy consumption efficiency feature value range for each stage respectively.

[0069] The dual-modal coefficient quantization module is used to generate the call chain of evaluation modes, construct dual-modal feature coordinate points, and quantify the recovery stability coefficient and energy consumption stability coefficient of each evaluation mode.

[0070] More specifically, the bimodal coefficient quantization module includes a call chain generation unit, a bimodal coordinate point construction unit, and a stability coefficient quantization unit;

[0071] The call chain generation unit integrates multiple sequentially linked assessment stages into a call chain for the assessment mode, based on the continuity of the energy efficiency assessment stages.

[0072] The dual-modal coordinate point construction unit is used to construct dual-modal characteristic coordinate points corresponding to the recovery stability coefficient and the energy consumption stability coefficient, respectively, with the time nodes of each energy efficiency assessment stage as the horizontal independent variable.

[0073] The stability coefficient quantification unit is used to calculate the recovery stability coefficient and energy consumption stability coefficient respectively according to the characteristic value range corresponding to each evaluation mode, and use them as the longitudinal dependent variable values ​​of the dual-modal characteristic coordinate points;

[0074] The collaborative offset assessment and anomaly tracing module is used to calculate the degree of bimodal collaborative offset of each assessment mode, compare it with a preset threshold, calibrate the abnormal assessment mode, and realize the anomaly tracing and display.

[0075] More specifically, the cooperative offset assessment and anomaly tracing module includes a cooperative offset degree calculation unit, an anomaly calibration unit, and a tracing unit;

[0076] The collaborative offset degree calculation unit calculates the bimodal collaborative offset of each evaluation mode based on the stability coefficient in the bimodal feature coordinate points. Combined with the mean and standard deviation of the offset of each node in the call chain, the degree of bimodal collaborative offset is obtained.

[0077] Anomaly calibration unit is used to compare the calculated bimodal cooperative offset degree with a preset threshold and calibrate the anomaly evaluation mode that exceeds the threshold.

[0078] The tracing unit is used to trace the calibrated anomaly assessment model back to the display interface of the oil and gas recovery terminal, so as to realize the visualization of anomaly information.

[0079] Please see Figure 1 In this second embodiment, an intelligent energy efficiency assessment method for oil and gas recovery equipment is provided, applicable to the first embodiment above. This embodiment takes the oil and gas recovery system of a gas station in the central urban area of ​​a city as the application scenario. The gas station is equipped with three sets of activated carbon adsorption oil and gas recovery core equipment (K1-K3) and two sets of auxiliary environmental equipment (ventilator, refrigeration unit, and energy consumption control). The process requirements during peak refueling hours (7:00-10:00, 17:00-20:00) and off-peak hours differ significantly, requiring adaptation to different assessment standards. At the same time, it is necessary to quickly locate energy efficiency anomalies (such as decreased recovery efficiency or sudden increase in energy consumption) to ensure environmental compliance and controllable operating costs.

[0080] The characteristic range of recovery efficiency is: Q1=[90%, 95%] (off-peak period), Q2=[92%, 97%] (peak period), Q3=[88%, 93%] (equipment maintenance period), which is controlled by the core equipment K1-K3;

[0081] Energy efficiency characteristic range: W1=[0.8, 1.2] (normal environment), W2=[1.0, 1.4] (high temperature / high humidity environment), which is achieved by auxiliary environmental equipment control;

[0082] Assessment models: P1=[Q1, W1], P2=[Q2, W1], P3=[Q2, W2], P4=[Q3, W1] (4 models in total, covering different time periods and environments);

[0083] The evaluation state transition timeline scale is t=5 minutes, the loop recording period is 1 day (24 hours), and a total of 288 time segments are divided;

[0084] The threshold for dual-modal collaborative offset is 0.7 (verified using 30 days of historical data, this threshold has an anomaly detection accuracy of 98.5%).

[0085] The method includes the following steps:

[0086] Step S1: Based on the process requirements of the oil and gas recovery equipment, set the characteristic value range related to recovery efficiency and energy consumption efficiency to construct multiple evaluation models;

[0087] For example, based on the process requirements of the oil and gas recovery equipment, the recovery efficiency characteristic value range and the energy consumption efficiency characteristic value range are initialized and set. The recovery efficiency characteristic value range is controlled by the core recovery equipment, and the energy consumption efficiency characteristic value range is controlled by the auxiliary environmental equipment.

[0088] An evaluation model is composed of a recovery efficiency characteristic range and an energy consumption efficiency characteristic range, wherein one recovery efficiency characteristic range and one energy consumption efficiency characteristic range constitute an evaluation model;

[0089] Let the eigenvalue range of the i-th type of recovery efficiency be denoted as... Let the range of the j-th energy efficiency characteristic be denoted as Then, based on the eigenrangement of recovery efficiency... and energy efficiency characteristic range The evaluation model composed of intervals is denoted as .

[0090] Step S2: Build an evaluation state switching timeline, divide the energy efficiency evaluation stage according to the calling and switching of evaluation modes, and obtain the corresponding recovery efficiency characteristic value and energy consumption efficiency characteristic value in each energy efficiency evaluation stage to determine the characteristic value range of each energy efficiency evaluation stage.

[0091] For example, an evaluation state switching time axis for the oil and gas recovery process is constructed, with the scale of the evaluation state switching time axis being t; after each oil and gas recovery device in the oil and gas recovery process is started, the evaluation state switching time axis is instructed to start timing, and the evaluation state switching nodes are numbered sequentially on the evaluation state switching time axis according to the scale t.

[0092] Let the evaluation state switching node labeled k be denoted as If the evaluation state switching node Time-triggered evaluation mode When the function is invoked, the evaluation state transition node is recorded. If the evaluation state switching node Subsequent evaluation state switching nodes Change the evaluation model for Then record the subsequent evaluation state transition nodes. Then the evaluation state switching node will be used. and evaluation state switching node Composition of evaluation model The energy efficiency assessment phase is denoted as Where v is the label of the evaluation state switching node, and m is the evaluation mode type number. ;

[0093] During the energy efficiency assessment phase Within the energy efficiency assessment stage, the ratio of recovered oil and gas volume to total produced oil and gas volume is used as a characteristic value of recovery efficiency. The minimum and maximum recovery efficiency eigenvalues ​​within the range constitute the recovery efficiency eigenvalue domain.

[0094] During the energy efficiency assessment phase Within this process, the ratio of instantaneous energy consumption to cumulative energy consumption is obtained and used as a characteristic value of energy efficiency, which is then used in the energy efficiency assessment stage. The minimum and maximum energy efficiency eigenvalues ​​within the range constitute the energy efficiency eigenvalue domain.

[0095] For example, after the equipment is started, the time axis begins timing. At t=30 (7:30, the start of the peak), P2 is triggered, and at t=120 (17:00, the end of the peak), it switches to P1, forming the evaluation phase of P2 (t30→t120). During this phase, the ratio of recovered oil and gas volume to total output (recovery efficiency characteristic value) is obtained, ranging from 92.5% to 96.8% (belonging to Q2); the ratio of instantaneous energy consumption to cumulative energy consumption (energy consumption efficiency characteristic value) is obtained, ranging from 0.85 to 1.15 (belonging to W1).

[0096] Step S3: Based on the continuity of the energy efficiency assessment stage, generate the call chain of assessment modes, construct bimodal feature coordinate points in the chain, and quantify the recovery stability coefficient and energy consumption stability coefficient of each assessment mode respectively.

[0097] For example, based on the continuity of the energy efficiency assessment phase, a call chain for the assessment mode is generated, denoted as... Where u and s are both labels of the evaluation state switching nodes, and , This represents the y-th evaluation mode;

[0098] For any energy efficiency assessment stage in the call chain Where x is the type number of the evaluation mode, and r and e are both labels of the evaluation state switching nodes, and Construct bimodal feature coordinate points, including first modal feature coordinate points and second modal feature coordinate points, wherein the horizontal independent variables of the first modal feature coordinate points and the second modal feature coordinate points are the same, which is _____. ;

[0099] From horizontal independent variables Each index is located in the evaluation mode. Based on the evaluation model :

[0100] In the evaluation mode Identify the characteristic value range of recycling efficiency Based on the characteristic range of recycling efficiency Quantitative evaluation model Recovery stability coefficient and will recover the stability coefficient The longitudinal dependent variable value of the first modal feature coordinate point;

[0101] Meanwhile, in the evaluation model Identify the energy efficiency characteristic range Based on the energy efficiency characteristic range Quantitative evaluation model Energy consumption stability coefficient , which serves as the longitudinal dependent variable value of the second modal feature coordinate point;

[0102] In the formula, and These are the maximum and minimum value functions, respectively;

[0103] For example, the call chain L is generated as follows: P1(t0→t30)→P2(t30→t120)→P1(t120→t210)→P4(t210→t288). For stage P2, the recovery stability coefficient α(P2)=(96.8%-92.5%) / (96.8%+92.5%)≈0.022 and the energy consumption stability coefficient β(P2)=(1.15-0.85) / (1.15+0.85)=0.15 are calculated to construct the bimodal coordinate points.

[0104] Step S4: Based on the dual-modal feature coordinate points, quantify the degree of dual-modal collaborative offset of each evaluation mode to calibrate the abnormal evaluation mode and trace it back to the oil and gas recovery terminal display interface;

[0105] For example, the evaluation mode is quantified based on the dual-modal feature coordinate points. Dual-modal cooperative offset degree In the formula, It is a dual-modal cooperative offset, and , For the call chain The mean of the bimodal cooperative offset at each chain node. For the call chain The standard deviation of the dual-modal cooperative offset at each chain node;

[0106] A preset threshold for the degree of bimodal cooperative offset is set; if the degree of bimodal cooperative offset... If the value is greater than or equal to the dual-modal cooperative offset threshold, then the call chain is identified. Evaluation model in And trace it back to the display interface of the oil and gas recovery terminal;

[0107] For example, the bimodal cooperative offset f of each node in the call chain has a mean of 0.11 and a standard deviation of 0.08. If f(P2) = |0.022 - 0.15| = 0.128 in a certain stage P2, the cooperative offset degree F(P2) = |0.128 - 0.11| / 0.08 = 0.225 < 0.7, which is considered normal. If equipment failure causes the recovery efficiency in stage P2 to drop sharply to 85%-88%, α(P2) = (88% - 85%) / (88% + 85%) ≈ 0. .017, energy efficiency rises to 1.3-1.5, β(P2)=(1.5-1.3) / (1.5+1.3)≈0.071, f(P2)=|0.017-0.071|=0.054, F(P2)=|0.054-0.11| / 0.08=0.7≥threshold, P2 is calibrated as an abnormal mode, traced to the terminal display "P2 mode abnormal during peak period, recovery efficiency is lower than Q2 value range", maintenance personnel quickly located the core equipment K2 adsorption saturation.

[0108] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0109] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent energy efficiency assessment of oil and gas recovery equipment, characterized in that, The method includes the following steps: Step S1: Based on the process requirements of the oil and gas recovery equipment, set the characteristic value range related to recovery efficiency and energy consumption efficiency to construct multiple evaluation models; Step S2: Build an evaluation state switching timeline, divide the energy efficiency evaluation stage according to the calling and switching of evaluation modes, and obtain the corresponding recovery efficiency characteristic value and energy consumption efficiency characteristic value in each energy efficiency evaluation stage to determine the characteristic value range of each energy efficiency evaluation stage. Step S3: Based on the continuity of the energy efficiency assessment stage, generate the call chain of assessment modes, construct bimodal feature coordinate points in the chain, and quantify the recovery stability coefficient and energy consumption stability coefficient of each assessment mode respectively. Step S4: Based on the dual-modal feature coordinate points, quantify the degree of dual-modal collaborative offset of each evaluation mode to calibrate the abnormal evaluation mode and trace it back to the oil and gas recovery terminal display interface.

2. The intelligent energy efficiency evaluation method for oil and gas recovery equipment according to claim 1, characterized in that, The specific implementation process of step S1 includes: Based on the process requirements of the oil and gas recovery equipment, the recovery efficiency characteristic value range and the energy consumption efficiency characteristic value range are initialized and set. The recovery efficiency characteristic value range is achieved by the core recovery equipment, and the energy consumption efficiency characteristic value range is achieved by the auxiliary environmental equipment. An evaluation model is composed of a recovery efficiency characteristic range and an energy consumption efficiency characteristic range, wherein one recovery efficiency characteristic range and one energy consumption efficiency characteristic range constitute an evaluation model; Let the eigenvalue range of the i-th type of recovery efficiency be denoted as... Let the range of the j-th energy efficiency characteristic be denoted as Then, based on the eigenrangement of recovery efficiency... and energy efficiency characteristic range The evaluation model composed of intervals is denoted as .

3. The intelligent energy efficiency evaluation method for oil and gas recovery equipment according to claim 2, characterized in that, The specific implementation process of step S2 includes: Construct an evaluation state switching time axis for the oil and gas recovery process, with the scale of the evaluation state switching time axis being t; after starting each oil and gas recovery device in the oil and gas recovery process, instruct the evaluation state switching time axis to start timing, and number the evaluation state switching nodes sequentially on the evaluation state switching time axis according to the scale t. Let the evaluation state switching node labeled k be denoted as If the evaluation state switching node Time-triggered evaluation mode When the call is made, the evaluation state switching node is recorded. If the evaluation state switching node Subsequent evaluation state switching nodes Change the evaluation model for Then record the subsequent evaluation state switching node. Then the evaluation state switching node will be used. and evaluation state switching node Composition of evaluation model The energy efficiency assessment phase is denoted as Where v is the label of the evaluation state switching node, and m is the type number of the evaluation mode. ; During the energy efficiency assessment phase Within this process, the ratio of recovered oil and gas volume to total produced oil and gas volume is used as the recovery efficiency characteristic value, which is then assessed during the energy efficiency evaluation phase. The minimum and maximum recovery efficiency characteristic values ​​within the range constitute the recovery efficiency characteristic value domain; During the energy efficiency assessment phase Within the energy consumption phase, the ratio of instantaneous energy consumption to cumulative energy consumption is obtained and used as the energy efficiency characteristic value, which is then used in the energy efficiency assessment phase. The minimum and maximum energy efficiency characteristic values ​​within the range constitute the energy efficiency characteristic value domain.

4. The intelligent energy efficiency evaluation method for oil and gas recovery equipment according to claim 3, characterized in that, The specific implementation process of step S3 includes: Based on the continuity of the energy efficiency assessment phase, a call chain for the assessment mode is generated, denoted as... Where u and s are both labels of the evaluation state switching nodes, and , This represents the y-th evaluation mode; For any energy efficiency assessment stage in the call chain Where x is the type number of the evaluation mode, and r and e are both labels of the evaluation state switching nodes, and Construct bimodal feature coordinate points, including first modal feature coordinate points and second modal feature coordinate points, wherein the horizontal independent variables of the first modal feature coordinate points and the second modal feature coordinate points are the same, which is _____. ; From horizontal independent variables Each index is located in the evaluation mode. Based on the evaluation model : In the evaluation mode Identify the characteristic value range of recycling efficiency Based on the characteristic range of recycling efficiency Quantitative evaluation model Recovery stability coefficient and will recover the stability coefficient The longitudinal dependent variable value of the first modal feature coordinate point; Meanwhile, in the evaluation model Identify the energy efficiency characteristic range Based on the energy consumption efficiency characteristic range Quantitative evaluation model Energy consumption stability coefficient , which serves as the longitudinal dependent variable value of the second modal feature coordinate point; In the formula, and These are the maximum and minimum value functions, respectively.

5. The intelligent energy efficiency evaluation method for oil and gas recovery equipment according to claim 4, characterized in that, The specific implementation process of step S4 includes: Based on dual-modal feature coordinate points, a quantitative evaluation mode is established. Dual-modal cooperative offset degree In the formula, It is a dual-modal cooperative offset, and , For the call chain The mean of the bimodal cooperative offset at each chain node. For the call chain The standard deviation of the dual-modal cooperative offset at each chain node; A preset threshold for the degree of bimodal cooperative offset is set; if the degree of bimodal cooperative offset... If the value is greater than or equal to the dual-modal cooperative offset threshold, then the call chain is identified. Evaluation model in And trace it back to the display interface of the oil and gas recovery terminal.

6. An intelligent energy efficiency assessment system for oil and gas recovery equipment, comprising executing an intelligent energy efficiency assessment method for oil and gas recovery equipment as described in any one of claims 1-5, characterized in that, The system includes: an evaluation mode initialization module, an evaluation stage division and feature acquisition module, a dual-modal coefficient quantization module, and a collaborative offset evaluation and anomaly tracing module; The evaluation mode initialization module is used to set relevant feature value ranges according to equipment process requirements and construct multiple evaluation modes; the evaluation stage division and feature acquisition module is used to build an evaluation state switching time axis, divide energy efficiency evaluation stages, and acquire recovery efficiency feature values ​​and energy consumption efficiency feature values ​​in each stage to determine the corresponding feature value range; the dual-modal coefficient quantification module is used to generate the evaluation mode call chain, construct dual-modal feature coordinate points, and quantify the recovery stability coefficient and energy consumption stability coefficient of each evaluation mode; the collaborative offset evaluation and anomaly tracing module is used to calculate the degree of dual-modal collaborative offset of each evaluation mode, calibrate the abnormal evaluation mode after comparing it with a preset threshold, and realize anomaly tracing display.

7. The intelligent energy efficiency evaluation system for oil and gas recovery equipment according to claim 6, characterized in that, The evaluation mode initialization module includes a feature value range setting unit and an evaluation mode construction unit; The feature value range setting unit is used to set the recovery efficiency feature value range and the energy consumption efficiency feature value range respectively according to the process requirements of the oil and gas recovery equipment, and to clarify the control subject of the two types of value ranges; The evaluation mode construction unit is used to combine the set recovery efficiency characteristic value range with the energy consumption efficiency characteristic value range to form a variety of different evaluation modes.

8. The intelligent energy efficiency evaluation system for oil and gas recovery equipment according to claim 6, characterized in that, The evaluation stage division and feature acquisition module includes a time axis construction unit, an evaluation stage division unit, and a feature value and value range acquisition unit. The time axis construction unit is used to build an evaluation state switching time axis, set the time axis scale, and label the evaluation state switching nodes. The assessment stage division unit is used to record the nodes corresponding to the calling and switching of assessment modes, and to divide the energy efficiency assessment stages corresponding to each assessment mode according to the node information. The feature value and range acquisition unit is used to acquire the ratio of the recovered oil and gas volume to the total oil and gas produced as the recovery efficiency feature value and the ratio of the instantaneous energy consumption to the cumulative energy consumption as the energy consumption efficiency feature value in each energy efficiency assessment stage, and to determine the recovery efficiency feature value range and energy consumption efficiency feature value range for each stage respectively.

9. The intelligent energy efficiency evaluation system for oil and gas recovery equipment according to claim 6, characterized in that, The dual-modal coefficient quantization module includes a call chain generation unit, a dual-modal coordinate point construction unit, and a stability coefficient quantization unit; The call chain generation unit integrates multiple sequentially connected evaluation stages to form a call chain for the evaluation mode, based on the continuity of the energy efficiency evaluation stage. The dual-modal coordinate point construction unit is used to construct dual-modal characteristic coordinate points corresponding to the recovery stability coefficient and the energy consumption stability coefficient, respectively, with the time nodes of each energy efficiency assessment stage as the horizontal independent variable. The stability coefficient quantification unit is used to calculate the recovery stability coefficient and energy consumption stability coefficient respectively according to the characteristic value range corresponding to each evaluation mode, and use them as the longitudinal dependent variable values ​​of the dual-modal characteristic coordinate points.

10. The intelligent energy efficiency evaluation system for oil and gas recovery equipment according to claim 6, characterized in that, The collaborative offset assessment and anomaly tracing module includes a collaborative offset degree calculation unit, an anomaly calibration unit, and a tracing unit. The collaborative offset degree calculation unit calculates the bimodal collaborative offset of each evaluation mode based on the stability coefficient in the bimodal feature coordinate points, and obtains the bimodal collaborative offset degree by combining the mean and standard deviation of the offset of each node in the call chain. The anomaly calibration unit is used to compare the calculated dual-modal cooperative offset degree with a preset threshold and calibrate the anomaly evaluation mode that exceeds the threshold. The tracing unit is used to trace the calibrated anomaly assessment mode back to the display interface of the oil and gas recovery terminal, so as to realize the visual presentation of the anomaly information.

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