A method and system for evaluating the energy use of a pump unit of an oilfield gathering system

Abstract

The application provides a kind of oilfield gathering and transferring system pump unit energy evaluation method and system, this method takes oilfield gathering and transferring system pump unit as research object, establishes evaluation index data matrix in combination with the actual working condition of pump unit, adopts correlation coefficient method to screen evaluation index;Using principal component analysis method obtains the index characteristic value of each evaluation index and component matrix, calculates the coefficient of each evaluation index in comprehensive score model, obtains the weight of each index;From the selected evaluation index, the optimal value of each evaluation index of pump unit is selected as reference sequence, and the comparison sequence composed of other values of each index is compared with reference sequence, the correlation coefficient is calculated, and the correlation degree is determined;The application can reduce the correlation between indexes while ensuring the comprehensiveness of indexes, and can more scientifically and completely evaluate the energy consumption of different oilfield pump units, and provide guidance for energy saving and consumption reduction work of oilfield enterprises and related design units.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and consumption reduction technology for storage and transportation systems, specifically to a method and system for evaluating the energy consumption of pump units in oilfield gathering and transportation systems. Background Technology

[0002] Pump units are among the most widely used energy-consuming equipment in oilfields, primarily consisting of centrifugal pumps, screw pumps, and plunger pumps. They are essential production facilities in oilfield surface gathering, water injection, treatment, and transportation processes. As crucial crude oil processing equipment, pump units are a key focus for energy conservation and consumption reduction in oilfields, playing a critical role in processes such as water blending, hot washing, water-containing oil transportation from transfer stations, purified oil transportation, and water and polymer injection. Therefore, as vital crude oil transportation equipment in oilfield gathering and transportation processes, the energy consumption of pump units significantly impacts gathering and transportation efficiency, making the assessment of pump unit quality paramount.

[0003] Currently, the efficiency of some oilfield production systems is declining. The overall efficiency of these systems is closely related to their production processes, and pump units are energy-supplying equipment used in multiple production stages. Therefore, conducting energy consumption analysis on pump units, identifying weaknesses, and improving energy efficiency to ensure energy-saving operation is of great significance. Research has revealed a lack of studies on pump unit energy efficiency evaluation, and the evaluation perspectives are not comprehensive enough.

[0004] Current research on evaluating the energy consumption of pump units faces several challenges. Firstly, considering only single indicators like pump efficiency and energy consumption leads to an incomplete and inaccurate assessment of energy usage. Secondly, evaluations often focus solely on the pump itself, neglecting the impact of the motor. Pump units consume significant amounts of electricity during operation, and motor efficiency greatly influences overall energy consumption. Focusing solely on pump energy consumption results in assessments that differ considerably from actual operating conditions. Finally, while previous methods like entropy weighting have been used to objectively evaluate pump energy consumption, they overlook the correlation between evaluation indicators, leading to overlapping information and compromising the effectiveness of the evaluation criteria. Summary of the Invention

[0005] To address the problems of significant discrepancies between existing evaluation methods and actual operating conditions regarding pump unit energy consumption, as well as the overlap of evaluation indicators due to high correlation between indicators, thus affecting the effectiveness of the evaluation, this invention provides an objective and scientific method for evaluating the energy consumption of pump units in oilfield gathering and transportation systems. This method ensures comprehensiveness of indicators while reducing correlation between them. This invention also relates to an energy consumption evaluation system for pump units in oilfield gathering and transportation systems.

[0006] The technical solution of the present invention is as follows:

[0007] A method for evaluating the energy consumption of pump units in an oilfield gathering and transportation system, characterized by comprising the following steps:

[0008] The first step is to take the pump units of the oilfield gathering and transportation system as the research object, establish an evaluation index data matrix based on the actual working conditions of the pump units, and use the correlation coefficient method to screen the evaluation indicators.

[0009] The second step is to standardize the selected evaluation index data using the range method, and then use principal component analysis to obtain the index characteristic values ​​and component matrix of each evaluation index. Finally, the coefficients of each evaluation index in the comprehensive scoring model are calculated to obtain the weight of each index.

[0010] The third step is to select the optimal values ​​of each evaluation index of the pump unit from the screened evaluation indexes as the reference sequence, compare the comparison sequence composed of other values ​​of each index with the reference sequence, calculate the correlation coefficient, and combine the weights of each index obtained in the second step to calculate and determine the correlation degree.

[0011] The fourth step is to sort the correlation values ​​and determine the quality of the operating conditions based on the correlation values ​​to analyze the energy consumption of the pump unit.

[0012] Preferably, the first step involves classifying pump units with similar performance into a class of equivalent pump units, dividing all pump units into several classes of equivalent pump units, calculating the mean of each index data of each class of equivalent pump units as the index data of the equivalent pump units, and establishing an evaluation index data matrix; the second and third steps involve selecting evaluation index data of several typical pump units from all pump units.

[0013] Preferably, the first step takes the pump unit of the oilfield gathering and transportation system as the research object, and combines the actual working conditions of the pump unit to collect evaluation indicators including power factor, pump unit efficiency, throttling loss rate, gathering and transportation unit consumption, pump output power and external load, and establishes an evaluation indicator data matrix; the correlation coefficient method is used to screen evaluation indicators, with pump unit efficiency as the reference indicator, and the correlation coefficients of the other evaluation indicators with the reference indicator are compared respectively. Evaluation indicators with large correlation coefficients are eliminated, and vice versa.

[0014] Preferably, in the first step, after establishing the evaluation index data matrix, the mean, variance, and covariance of each index are calculated using the evaluation index data in the evaluation index data matrix, and then the correlation coefficient between the two indexes is calculated based on the calculated covariance.

[0015] Preferably, in the second step, principal component analysis is used to obtain the eigenvalues ​​and component matrices of each evaluation index, calculate the original information contribution rate and cumulative contribution rate of the principal components, and sort the principal components in descending order of eigenvalues. When the cumulative contribution rate exceeds a threshold, the top few principal components are selected. Based on the coefficients of each selected evaluation index in the component matrix corresponding to the top few principal components, and combined with the original information contribution rate and cumulative contribution rate, the coefficients of each evaluation index in the comprehensive scoring model are calculated, and then the weights of each index are obtained through weighted average processing.

[0016] Preferably, the third step compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the absolute difference between the reference sequence and the comparison sequence, determines the maximum absolute difference and the minimum absolute difference, calculates the correlation coefficient of each indicator based on the absolute difference, the maximum absolute difference and the minimum absolute difference, and calculates the correlation degree by multiplying the correlation coefficient with the weight of each indicator.

[0017] An energy consumption evaluation system for pump units in an oilfield gathering and transportation system, characterized in that it includes:

[0018] The first module takes the pump units of the oilfield gathering and transportation system as the research object, and establishes an evaluation index data matrix based on the actual working conditions of the pump units, and uses the correlation coefficient method to screen the evaluation indexes.

[0019] The second module uses the range method to standardize the selected evaluation index data, uses principal component analysis to obtain the index characteristic values ​​and component matrix of each evaluation index, calculates the coefficients of each evaluation index in the comprehensive scoring model, and obtains the weight of each index.

[0020] The third module selects the optimal values ​​of each evaluation index of the pump unit from the screened evaluation indexes as a reference sequence, compares the comparison sequence composed of other values ​​of each index with the reference sequence, calculates the correlation coefficient, and combines the weights of each index obtained in the second module to calculate and determine the correlation degree.

[0021] The fourth module sorts the correlation values ​​and determines the quality of the operating conditions based on the correlation values, thereby analyzing the energy consumption of the pump unit.

[0022] Preferably, the first module takes the pump unit of the oilfield gathering and transportation system as the research object, and combines the actual working conditions of the pump unit to collect evaluation indicators including power factor, pump unit efficiency, throttling loss rate, gathering and transportation unit consumption, pump output power and external load, and establishes an evaluation indicator data matrix; the correlation coefficient method is used to screen evaluation indicators, with pump unit efficiency as the reference indicator, and the correlation coefficients of the other evaluation indicators with the reference indicator are compared respectively. Evaluation indicators with large correlation coefficients are eliminated, and vice versa.

[0023] Preferably, the second module uses principal component analysis to obtain the eigenvalues ​​and component matrices of each evaluation index, calculates the original information contribution rate and cumulative contribution rate of the principal components, sorts the principal components in descending order of eigenvalues, selects the top few principal components when the cumulative contribution rate exceeds a threshold, calculates the coefficients of each evaluation index in the comprehensive scoring model based on the coefficients of each selected evaluation index in the component matrix corresponding to the top few principal components and in combination with the original information contribution rate and cumulative contribution rate, and then obtains the weight of each index through weighted average processing.

[0024] Preferably, the third module compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the absolute difference between the reference sequence and the comparison sequence, determines the maximum absolute difference and the minimum absolute difference, calculates the correlation coefficient of each indicator based on the absolute difference, the maximum absolute difference and the minimum absolute difference, and calculates the correlation degree by multiplying the correlation coefficient with the weight of each indicator.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention provides an energy consumption evaluation method for pump units in oilfield gathering and transportation systems. Taking pump units as the research object, it comprehensively considers pump efficiency and motor efficiency, and establishes an evaluation index data matrix based on the actual operating conditions of the pump units. This avoids the problem of existing technologies that only evaluate from the pump perspective, neglecting the impact of the motor on energy consumption, leading to significant discrepancies between the evaluation of pump unit energy consumption and actual operating conditions. The method determines the set of evaluation indicators based on the composition of the pump unit, ensuring comprehensive coverage of information. Combining the actual operating conditions of the pump units, the method uses correlation coefficients to screen evaluation indicators, judging the correlation between two indicators and eliminating highly correlated indicators to ensure low correlation among the selected indicators and avoid overlap. This reduces correlation while ensuring comprehensiveness of indicators. Furthermore, principal component analysis is used to determine the weight of each indicator. Principal components are extracted by calculating eigenvalues ​​and original information contribution rates, and the weight of each indicator is determined based on the coefficients in the linear combination of principal components, further eliminating overlapping information and making the results more objective and reasonable. The pump units are sorted according to their correlation degree to analyze their energy consumption and identify weaknesses. The pump unit energy consumption evaluation method of the present invention not only ensures the comprehensiveness of the index coverage information, but also ensures that the correlation between the selected indicators is small. Moreover, it can more scientifically and completely evaluate the energy consumption of pump units in different oilfields, not only obtaining the operating status of the pump units, but also understanding the usage and power consumption of the motors, providing guidance for energy conservation and consumption reduction work of oilfield enterprises and related design units.

[0027] This invention also relates to an energy consumption evaluation system for pump units in oilfield gathering and transportation systems. This system corresponds to the aforementioned energy consumption evaluation method for pump units in oilfield gathering and transportation systems. It can be understood as a system that implements the energy consumption evaluation method for pump units in oilfield gathering and transportation systems. This system works collaboratively through four sequentially executed modules, comprehensively considering pump efficiency and motor efficiency. It determines the set of evaluation indicators based on the composition of the pump unit, selects suitable evaluation indicators based on the actual working conditions of the oilfield, and adopts a multi-indicator comprehensive evaluation system to evaluate the energy consumption of different oilfield pump units. This avoids the singularity and unscientific nature of using only one indicator as an evaluation indicator. It considers not only the influence of the pump itself but also the influence of the motor, enabling a more scientific and complete evaluation of the energy consumption of different oilfield pump units. While ensuring the comprehensiveness of the indicators, it reduces the correlation between them. The range method is used to standardize the selected evaluation indicator data, eliminating differences in dimensions and orders of magnitude among the evaluation indicators. Principal component analysis is used to obtain the indicator characteristic values ​​and component matrices of each evaluation indicator, calculates the coefficients of each evaluation indicator in the comprehensive scoring model, and obtains the weight of each indicator. This further eliminates overlapping information between indicators, making the results more objective and reasonable. The optimal values ​​of each evaluation index of the pump unit are selected from the screened evaluation indicators as a reference sequence. The comparison sequence composed of other values ​​of each index is compared with the reference sequence to calculate the correlation coefficient. Combined with the weights of each index obtained in the second module, the correlation degree is calculated and determined. The larger the correlation degree, the closer the value of the oil pump evaluation index is to the optimal sequence (reference sequence), and the better the operating condition. Therefore, the pump unit can be sorted according to the size of the correlation degree to analyze the energy consumption of the pump unit. This not only determines the operating status of the pump unit, but also finds its weak links for energy-saving renovation, thereby improving the effectiveness and accuracy of the energy consumption evaluation of pump units in the oilfield gathering and transportation system. Attached Figure Description

[0028] Figure 1 This is a flowchart of the energy consumption evaluation method for pump units in oilfield gathering and transportation systems according to the present invention. Detailed Implementation

[0029] To better understand the invention, it will be described in detail with reference to the accompanying drawings and embodiments.

[0030] This invention relates to a method for evaluating the energy consumption of pump units in an oilfield gathering and transportation system, such as... Figure 1The flowchart shown includes: Step 1: Taking the pump units of the oilfield gathering and transportation system as the research object, and combining the actual operating conditions of the pump units, establish an evaluation index data matrix and use the correlation coefficient method to screen evaluation indicators; Step 2: Standardize the screened evaluation index data using the range method, and use principal component analysis to obtain the indicator characteristic values ​​and component matrices of each evaluation indicator, calculate the coefficients of each evaluation indicator in the comprehensive scoring model, and obtain the weights of each indicator; Step 3: Select the optimal values ​​of each evaluation indicator of the pump unit from the screened evaluation indicators as a reference sequence, compare the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculate the correlation coefficient, and combine it with the weights of each indicator obtained in Step 2 to calculate and determine the correlation degree; Step 4: Sort the correlation degrees according to their magnitude, and determine the quality of the operating conditions based on the magnitude of the correlation degree to analyze the energy consumption of the pump units. The objective and scientific energy consumption evaluation method for pump units in oilfield gathering and transportation systems provided by this invention not only considers the influence of the pump itself but also the influence of the motor, enabling a more scientific and complete evaluation of the energy consumption of different oilfield pump units, reducing the correlation between indicators while ensuring comprehensiveness.

[0031] The following is a detailed description of each step in the energy consumption evaluation method for pump units in oilfield gathering and transportation systems according to the present invention.

[0032] The first step is to take the pump units of the oilfield gathering and transportation system as the research object, establish an evaluation index data matrix based on the actual working conditions of the pump units, and use the correlation coefficient method to screen the evaluation indicators.

[0033] First, based on the actual situation of the oilfield pump unit, an evaluation index data matrix X is established.

[0034]

[0035] In the formula, x ij This refers to the j-th index data of the i-th pump unit (i = 1, 2, ..., n; j = 1, 2, ..., m).

[0036] The mean, variance, and covariance of each indicator are calculated using the data of X. Then, the correlation coefficient between the two indicators is calculated based on the calculated covariance.

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, S is the average value of the j-th indicator data; jLet be the variance of the j-th indicator data; These are the average values ​​of columns j1 and j2, respectively; x ij1 x ij2 The data represents the index data in the i-th row and j1-j2 columns; S j1j2 Let r be the covariance of columns j1 and j2; j1j2 The correlation coefficient is the relationship between the two indicators.

[0042] In other words, the mean of each indicator is calculated using the evaluation indicator data in the evaluation indicator data matrix. Variance S j and covariance S j1j2 Then, based on the calculated covariance S j1j2 Calculate the correlation coefficient r between the two indicators. j1j2 Evaluation indicators with high correlation coefficients are removed, while those with low correlation coefficients are retained.

[0043] The second step involves standardizing the evaluation index data selected in the first step using the range method. Principal component analysis (PCA) is then applied to obtain the eigenvalues ​​and component matrices of each evaluation index. The coefficients of each evaluation index in the comprehensive scoring model are calculated to obtain the weights of each index. Preferably, PCA is used to obtain the eigenvalues ​​and component matrices of each evaluation index, calculate the original information contribution rate and cumulative contribution rate of the principal components, and sort the principal components according to their eigenvalues ​​from largest to smallest. When the cumulative contribution rate exceeds a threshold, the top few principal components are selected. Based on the coefficients of each selected evaluation index in the component matrices corresponding to these top few principal components, combined with the original information contribution rate and cumulative contribution rate, the coefficients of each evaluation index in the comprehensive scoring model are calculated. Finally, a weighted average is used to obtain the weights of each index.

[0044] Let m indicators reflecting the energy consumption of the pump unit after the first step of screening be x1, x2, x3, ..., x m The original data matrix X consists of m indicators from n pump units:

[0045]

[0046] In the formula, x ij This refers to the j-th indicator data for the i-th pump unit (i = 1, 2, ..., n; j = 1, 2, ..., m).

[0047] Because the various indicators differ in dimensions and orders of magnitude, the raw data needs to be standardized. A larger indicator value indicates better energy utilization of the pump unit; a smaller indicator value indicates worse energy utilization. The indicators are then standardized according to this classification, specifically using the range method.

[0048] Positive indicators:

[0049] Negative indicators:

[0050] In the formula, y ij For the standardized data of the j-th indicator of the i-th pump unit; x j Let j be the data for the j-th indicator. The processed indicator matrix Y is:

[0051]

[0052] Calculate the correlation coefficient matrix R corresponding to Y, and then calculate the eigenvalues ​​and eigenvectors of R. From the equation |λE-R|=0, we obtain m eigenvalues, which are then sorted in ascending order as λ1>λ2>…λ m ≥0, the eigenvectors corresponding to each eigenvalue are: μ1, μ2, ..., μ m Therefore, the component matrix with m principal components is obtained as follows:

[0053]

[0054] In the formula, Z i μ is the i-th principal component (i = 1, 2, ..., m); ij These are the coefficients of the component matrix, or the coefficients in the linear combination of the principal components.

[0055] Select h principal components and calculate Z. i Information contribution rate and cumulative contribution rate of (i = 1, 2, ..., m).

[0056]

[0057]

[0058] In the formula, b i λ represents the contribution rate of the original information of the principal component, reflecting the percentage of original index information contained in that principal component; i Let be the i-th eigenvalue. h Z1, Z2, ..., Z q The cumulative contribution rate, for example, when a h When the percentage is ≥80%, select the first h principal components, which are the h comprehensive indicators.

[0059] Calculate the coefficients in the comprehensive score model, which involves taking a weighted average of the coefficients of the indicators in the principal component linear combination. Since the sum of the weights of all indicators is 1, the indicator weights need to be normalized based on the indicator coefficients in the comprehensive model to obtain the weight w.

[0060] The third step: Select the optimal values ​​of each evaluation indicator of the pump unit from the screened evaluation indicators as a reference sequence, and compare the comparison sequence composed of other values ​​of each indicator with the reference sequence to calculate the correlation coefficient. Combined with the weights of each indicator obtained in the second step, the correlation degree is calculated and determined. Preferably, the comparison sequence composed of other values ​​of each indicator is compared with the reference sequence, the absolute difference between the reference sequence and the comparison sequence is calculated, the maximum absolute difference and the minimum absolute difference are determined, and the correlation coefficient of each indicator is calculated based on the absolute difference, the maximum absolute difference and the minimum absolute difference. The correlation degree is then determined by multiplying the correlation coefficient by the weight of each indicator.

[0061] The higher the index value, the better the energy utilization of the pump unit, and the maximum value of the index; conversely, the lower the index value, the worse the energy utilization of the pump unit, and the minimum value of the index. Based on this, the optimal reference sequence is selected according to a certain order as Y0 = [y1 y2 … y m The data is processed according to the data standardization method in the second step, the absolute difference between the reference sequence and the comparison sequence is calculated, the maximum absolute difference M and the minimum absolute difference m are determined, and then the correlation coefficient is calculated.

[0062] △ i (j)=|Y0(j)-Y i (j)|

[0063]

[0064]

[0065]

[0066] In the formula, △ i (j) represents the absolute difference between the reference sequence and the comparison sequence; Y0(j) represents the index value of the reference sequence; Y i (j) represents the index value of the comparison sequence; where ρ is the discrimination coefficient (0 < ρ < 1). The smaller ρ is, the greater the difference between the correlation coefficients, and the stronger the discrimination ability. Typically, ρ is taken as 0.5; ξ i (j) is the correlation coefficient.

[0067] Calculate the correlation degree by combining the weights calculated in the second step.

[0068]

[0069] In the formula, w j The weights calculated in the second step; r i For correlation degree.

[0070] The fourth step is to sort the correlation values ​​and determine the quality of the operating conditions based on the correlation values ​​to analyze the energy consumption of the pump unit.

[0071] The energy consumption of pump units is analyzed by sorting them according to their correlation, identifying weaknesses. This method ensures comprehensive coverage of information, minimizes the correlation between selected indicators, and provides a more scientific and complete evaluation of the energy consumption of pump units in different oilfields.

[0072] The following section takes certain pump units in the Northwest Oilfield as the research object, evaluates their energy consumption, and provides a detailed explanation.

[0073] Taking a certain oilfield in Northwest China as an example, the energy consumption of 85 pump units in 6 typical metering stations was evaluated. The specific steps are as follows:

[0074] Step 1: Taking the pump units of the oilfield gathering and transportation system as the research object, and combining the actual operating conditions of the pump units, an evaluation index data matrix is ​​established, and the correlation coefficient method is used to screen the evaluation indicators. Ideally, pump units with similar performance are classified into one category of equivalent pump units. Thus, all pump units are divided into several categories of equivalent pump units. The mean values ​​of each index data for each category of equivalent pump units are calculated and used as the index data for the equivalent pump units. The results are shown in Table 1.

[0075] Table 1

[0076]

[0077] The correlation coefficient method is used to determine the correlation between two indicators, and the indicators are screened to eliminate redundant indicators. Considering the actual operating conditions of the pump unit, the evaluation indicators collected in this embodiment include six indicators: power factor, pump unit efficiency, throttling loss rate, collection and transmission unit consumption, pump output power, and external load. First, a reference indicator is determined from the six indicators. Pump unit efficiency is the most intuitive indicator reflecting the energy consumption of the pump unit, and considering the unit efficiency, it is more appropriate to use it as the reference indicator. Then, the correlation coefficients of the remaining five indicators with the reference indicator are compared. The larger the correlation coefficient, the closer the relationship between the indicator and the typical indicator, and the worse its independence; it should be eliminated. Conversely, it is retained.

[0078] Establish an indicator matrix:

[0079]

[0080] The correlation coefficient method was used to screen evaluation indicators, and the calculated correlation coefficients are shown in Table 2.

[0081] Table 2

[0082]

[0083] As can be seen from Table 2, the correlation coefficient between pump output power and unit efficiency is the largest, and the two contain the most overlapping information. Therefore, pump output power should be excluded to ensure the comprehensiveness of the indicators and avoid redundancy. Finally, pump unit efficiency, throttling loss rate, power factor, collection and transmission unit consumption, and external load are determined as evaluation indicators.

[0084] The second step is to select evaluation index data of several typical pump units from all pump units (85 pump units). In this example, 7 typical pump units are selected. The range method is used to standardize the index data selected in the first step. Principal component analysis is used to obtain the characteristic values ​​and component matrix of each index. The coefficients of each index in the comprehensive scoring model are calculated to obtain the weight of each index.

[0085] The original data is shown in Table 3:

[0086] Table 3

[0087]

[0088] First, the original data was standardized, as shown in Table 4.

[0089] Table 4

[0090]

[0091] The eigenvalues, original information contribution rate, and cumulative contribution rate are then calculated, as shown in Table 5.

[0092] Table 5

[0093]

[0094] As shown in Table 5, the eigenvalues ​​corresponding to the first two principal components are greater than 1, and the cumulative variance contribution rate of the first two principal components reaches 80.965%, exceeding 80%. Therefore, the first two principal components can basically reflect the information of all indicators and can replace the original five indicators (unit efficiency, throttling loss rate, power factor, transmission unit consumption, and external load) to calculate the coefficients in the comprehensive scoring model, as shown in Table 6, where each indicator has its coefficient in the linear combination of the two principal components.

[0095] Table 6

[0096]

[0097] Since the sum of the weights of all indicators is 1, the indicator weights need to be normalized based on the indicator coefficients in the comprehensive model. The final weight results are shown in Table 7.

[0098] Table 7

[0099]

[0100] The results show that the weights, in descending order, are throttling loss rate, power factor, unit consumption of power collection and transmission, external load, and unit efficiency. The throttling loss rate has the largest weight, indicating that energy loss during throttling accounts for the largest proportion of the total energy consumption of the pump unit.

[0101] The third step is to select the optimal values ​​of each indicator of the pump unit as the reference sequence, compare the comparison sequence composed of other values ​​of each indicator with the reference sequence, obtain the correlation coefficient, and combine it with the weights obtained in the second step to determine the degree of correlation.

[0102] Based on the order of power factor, pump unit efficiency, throttling loss rate, collection and transmission unit consumption, and external transmission load, the optimal reference sequence is selected as [0.8 68.45 0 0.24 64.2].

[0103] The correlation coefficient is calculated based on the standardized data in Table 4, and then the correlation degree is calculated by combining the weights, as shown in Table 8.

[0104] Table 8

[0105]

[0106] The fourth module sorts the correlation values ​​and determines the quality of the operating conditions based on the correlation values, thereby analyzing the energy consumption of the pump unit.

[0107] The greater the correlation, the closer the evaluation index of the oil pump is to the optimal sequence, and the better the operating condition. This allows for the ranking of energy consumption among the oil pumps. The results show that oil pump No. 2 has the best operating condition, operating in an energy-saving state. Pump unit No. 6 has the worst operating condition, with an index sequence of [0.76 58.32 16.23 2.656 2.3], indicating excessive throttling loss. This suggests significant energy consumption at the outlet valve of oil pump No. 6. Inspection revealed that this pump is still using a throttling valve for flow control. Therefore, optimizing the operation and adjustment of the oil pumps can reduce throttling losses. Alternatively, a more effective solution is to install a variable frequency speed control system on the oil pump unit to adjust the operating conditions by changing the speed, effectively avoiding throttling losses at the oil pump outlet valve. Secondly, the power factor is relatively small, with a normal power factor of around 0.8-0.9. Therefore, the power factor needs to be improved. This can be achieved by using reactive power compensation technology to reduce reactive power consumption and active power losses in the power grid, thereby improving power quality and reducing power consumption.

[0108] This invention also relates to an energy consumption evaluation system for pump units in an oilfield gathering and transportation system. This system corresponds to the aforementioned energy consumption evaluation method for pump units in an oilfield gathering and transportation system, and can be understood as a system for implementing the energy consumption evaluation method for pump units in an oilfield gathering and transportation system. The system includes:

[0109] The first module takes the pump units of the oilfield gathering and transportation system as the research object, and establishes an evaluation index data matrix based on the actual operating conditions of the pump units. The evaluation indexes are then selected using the correlation coefficient method. Preferably, this module takes the pump units of the oilfield gathering and transportation system as the research object, and the evaluation indexes collected based on the actual operating conditions of the pump units include power factor, pump unit efficiency, throttling loss rate, gathering and transportation unit consumption, pump output power, and external load. The evaluation index data matrix is ​​established. The correlation coefficient method is used to select evaluation indicators by setting the pump unit efficiency as the reference index, comparing the correlation coefficients of the other evaluation indicators with the reference index, and eliminating evaluation indicators with large correlation coefficients, while retaining those with small correlation coefficients.

[0110] The second module standardizes the selected evaluation index data using the range method, and uses principal component analysis to obtain the indicator eigenvalues ​​and component matrices of each evaluation index. It then calculates the coefficients of each evaluation index in the comprehensive scoring model to obtain the weights of each index. Preferably, this module uses principal component analysis to obtain the eigenvalues ​​and component matrices of each evaluation index, calculates the original information contribution rate and cumulative contribution rate of the principal components, and sorts the principal components according to their eigenvalues ​​from largest to smallest. When the cumulative contribution rate exceeds a threshold, it selects the top few principal components. Based on the coefficients of each selected evaluation index in the component matrices corresponding to the selected top few principal components, and combined with the original information contribution rate and cumulative contribution rate, it calculates the coefficients of each evaluation index in the comprehensive scoring model, and then obtains the weights of each index through weighted averaging.

[0111] The third module selects the optimal values ​​of each evaluation indicator of the pump unit from the screened evaluation indicators as a reference sequence, compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the correlation coefficient, and calculates and determines the correlation degree by combining the weights of each indicator obtained in the second module. Preferably, this module compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the absolute difference between the reference sequence and the comparison sequence, determines the maximum absolute difference and the minimum absolute difference, calculates the correlation coefficient of each indicator based on the absolute difference, the maximum absolute difference and the minimum absolute difference, and calculates the correlation degree by multiplying the correlation coefficient with the weight of each indicator.

[0112] The fourth module sorts the correlation values ​​and determines the quality of the operating conditions based on the correlation values, thereby analyzing the energy consumption of the pump unit.

[0113] This invention provides an objective and scientific method and system for evaluating the energy consumption of pump units in oilfield gathering and transportation systems. First, a set of evaluation indicators is determined from both the pump and motor perspectives. Considering the actual operating conditions of the pump unit and parameters such as flow rate and head, the correlation coefficient method is used to screen evaluation indicators, judging the correlation between two indicators and eliminating those with high correlation to ensure low correlation among the selected indicators, avoiding overlap and ensuring comprehensive information coverage. Based on this, principal component analysis is applied to extract principal components by calculating eigenvalues ​​and the contribution rate of original information. The weights of each indicator are determined based on the coefficients in the linear combination of principal components, further eliminating overlapping information and making the results more objective and reasonable. Finally, the energy consumption of the pump is evaluated by calculating the correlation with the optimal reference sequence of the oilfield pump unit, identifying its weaknesses. This invention can more scientifically and comprehensively evaluate the energy consumption of different oilfield pump units, providing guidance for energy conservation and consumption reduction efforts by oilfield enterprises and related design units.

[0114] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail with reference to the accompanying drawings and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention patent.

Claims

1. A method for evaluating the energy consumption of pump units in an oilfield gathering and transportation system, characterized in that, Includes the following steps: The first step involves taking the pump units of the oilfield gathering and transportation system as the research object, comprehensively considering pump efficiency and motor efficiency, and combining the actual operating conditions of the pump units to establish an evaluation index data matrix including power factor, pump unit efficiency, throttling loss rate, gathering and transportation unit consumption, pump output power, and external load. The correlation coefficient method is used to screen the evaluation indexes: the pump unit efficiency is set as the reference index, and the correlation coefficients of the other evaluation indexes with the reference index are compared. Evaluation indexes with large correlation coefficients are eliminated, while those with small correlation coefficients are retained, thereby ensuring the comprehensiveness of the indicators while reducing their correlation. The second step is to standardize the selected evaluation index data using the range method. This includes classifying each index into positive and negative indicators based on the energy consumption of the pump unit and performing corresponding range standardization to eliminate differences in the dimensions and orders of magnitude of each evaluation index. Principal component analysis is then used to obtain the indicator characteristic values ​​and component matrices of each evaluation index. The coefficients of each evaluation index in the comprehensive scoring model are calculated to obtain the weight of each index. The third step is to select the optimal values ​​of each evaluation index of the pump unit from the screened evaluation indexes as the reference sequence, compare the comparison sequence composed of other values ​​of each index with the reference sequence, calculate the correlation coefficient, and combine the weights of each index obtained in the second step to calculate and determine the correlation degree. The fourth step is to sort the correlation values ​​and determine the quality of the operating conditions based on the correlation values ​​to analyze the energy consumption of the pump unit.

2. The energy consumption evaluation method for pump units in an oilfield gathering and transportation system according to claim 1, characterized in that, The first step is to classify pump units with similar performance into one type of equivalent pump unit. All pump units are divided into several types of equivalent pump units. The mean value of each index data of each type of equivalent pump unit is calculated as the index data of the equivalent pump unit, and an evaluation index data matrix is ​​established. The second and third steps are to select the evaluation index data of several typical pump units from all pump units.

3. The energy consumption evaluation method for pump units in an oilfield gathering and transportation system according to claim 1 or 2, characterized in that, In the first step, after establishing the evaluation index data matrix, the mean, variance, and covariance of each index are calculated using the evaluation index data in the evaluation index data matrix, and then the correlation coefficient between the two indexes is calculated based on the calculated covariance.

4. The energy consumption evaluation method for pump units in oilfield gathering and transportation systems according to claim 1, characterized in that, The second step uses principal component analysis to obtain the eigenvalues ​​and component matrices of each evaluation index, calculates the original information contribution rate and cumulative contribution rate of the principal components, and sorts the principal components in descending order of eigenvalues. When the cumulative contribution rate exceeds the threshold, the top few principal components are selected. Based on the coefficients of each selected evaluation index in the component matrix corresponding to the selected top few principal components, and combined with the original information contribution rate and cumulative contribution rate, the coefficients of each evaluation index in the comprehensive scoring model are calculated. Then, the weights of each index are obtained through weighted averaging.

5. The energy consumption evaluation method for pump units in oilfield gathering and transportation systems according to claim 1, characterized in that, The third step compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the absolute difference between the reference sequence and the comparison sequence, determines the maximum absolute difference and the minimum absolute difference, and calculates the correlation coefficient of each indicator based on the absolute difference, the maximum absolute difference and the minimum absolute difference. The correlation coefficient is then multiplied by the weight of each indicator to determine the degree of correlation.

6. An energy consumption evaluation system for pump units in an oilfield gathering and transportation system, characterized in that, include: The first module takes the pump units of the oilfield gathering and transportation system as the research object, comprehensively considers pump efficiency and motor efficiency, and combines the actual working conditions of the pump units to establish an evaluation index data matrix including power factor, pump unit efficiency, throttling loss rate, gathering and transportation unit consumption, pump output power and external load; the correlation coefficient method is used to screen the evaluation indexes: the pump unit efficiency is set as the reference index, and the correlation coefficients of the other evaluation indexes with the reference index are compared. Evaluation indexes with large correlation coefficients are eliminated, while those with small correlation coefficients are retained, thereby ensuring the comprehensiveness of the indicators while reducing their correlation. The second module uses the range method to standardize the selected evaluation index data. This includes classifying each index into positive and negative indicators based on the energy consumption of the pump unit and performing corresponding range standardization to eliminate differences in the dimensions and orders of magnitude of each evaluation index. Principal component analysis is used to obtain the indicator characteristic values ​​and component matrices of each evaluation index, and the coefficients of each evaluation index in the comprehensive scoring model are calculated to obtain the weight of each index. The third module selects the optimal values ​​of each evaluation index of the pump unit from the screened evaluation indexes as a reference sequence, compares the comparison sequence composed of other values ​​of each index with the reference sequence, calculates the correlation coefficient, and combines the weights of each index obtained in the second module to calculate and determine the correlation degree. The fourth module sorts the correlation values ​​and determines the quality of the operating conditions based on the correlation values, thereby analyzing the energy consumption of the pump unit.

7. The energy consumption evaluation system for pump units in an oilfield gathering and transportation system according to claim 6, characterized in that, The second module uses principal component analysis to obtain the eigenvalues ​​and component matrices of each evaluation index, calculates the original information contribution rate and cumulative contribution rate of the principal components, and sorts the principal components in descending order of eigenvalues. When the cumulative contribution rate exceeds a threshold, the top few principal components are selected. Based on the coefficients of each evaluation index in the component matrix corresponding to the selected top few principal components, and combined with the original information contribution rate and cumulative contribution rate, the coefficients of each evaluation index in the comprehensive scoring model are calculated, and then the weights of each index are obtained through weighted averaging.

8. The energy consumption evaluation system for pump units in an oilfield gathering and transportation system according to claim 6, characterized in that, The third module compares the comparison sequence composed of other values ​​of each indicator with the reference sequence, calculates the absolute difference between the reference sequence and the comparison sequence, determines the maximum absolute difference and the minimum absolute difference, and calculates the correlation coefficient of each indicator based on the absolute difference, the maximum absolute difference and the minimum absolute difference. The correlation coefficient is then multiplied by the weight of each indicator to determine the degree of correlation.

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