Energy consumption evaluation method and device for thickened oil gathering and transportation system

Abstract

The invention discloses an energy consumption evaluation method and device for a thickened oil gathering and transportation system, and the method comprises the steps: obtaining an index value standardization matrix according to the index values, corresponding to a plurality of preset energy consumption influence indexes, of a to-be-evaluated thickened oil gathering and transportation system in a plurality of statistical time periods of a plurality of oil field oil blocks; determining a key energy consumption influence index with the maximum influence degree on the to-be-evaluated thickened oil gathering and transportation system, and dividing the key energy consumption influence index in a corresponding index value range interval; for each preset gathering and transportation process flow and each index value range interval, according to the corresponding energy consumption and the gathering and transportation quantity, obtaining corresponding reference energy consumption; determining respective corresponding key energy consumption influence index value correction coefficients according to the obtained multiple pieces of reference energy consumption; and obtaining the comparable comprehensive energy consumption corresponding to each oil field block according to the production data and the energy consumption of all the oil field blocks and the obtained multiple correction coefficients. According to the embodiment of the invention, the energy consumption level of the thickened oil gathering and transportation system under different oil field oil blocks can be scientifically and comprehensively reflected, and the comparability of the energy consumption level is realized.

Description

Technical Field

[0001] This article relates to oil and gas storage and transportation technology, and in particular to an energy consumption evaluation method and device for a heavy oil gathering and transportation system. Background Art

[0002] As the main way and link of heavy oil production, heavy oil gathering and transportation system occupies an important position in the overall heavy oil development process. Its energy application intensity and energy consumption control pressure continue to rise. Compared with conventional light oil, the production structure of heavy oil gathering and transportation system is more complex and its high energy consumption dependence is more significant. Therefore, how to scientifically grasp the production energy consumption status of heavy oil gathering and transportation system and evaluate its energy consumption level more objectively and reasonably is becoming increasingly important.

[0003] Existing energy consumption evaluation methods for heavy oil gathering and transportation systems, such as the method for calculating energy consumption per unit liquid volume (crude oil) specified in GB / T 33653-2017, can only measure the energy consumption level of the system itself, but cannot scientifically and comprehensively reflect the energy consumption level of heavy oil gathering and transportation systems under different oilfield blocks. Summary of the invention

[0004] The present application provides an energy consumption evaluation method and device for a heavy oil gathering and transportation system, which can scientifically and comprehensively reflect the energy consumption level of the heavy oil gathering and transportation system under different oilfield blocks, and achieve comparability of energy consumption levels under different oilfield blocks.

[0005] On the one hand, the present application provides an energy consumption evaluation method for a heavy oil gathering and transportation system, the method comprising:

[0006] According to the index values ​​corresponding to multiple preset energy consumption impact indicators of the heavy oil gathering and transportation system to be evaluated in multiple statistical time periods of multiple oilfield blocks, a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated is obtained; wherein each oilfield block corresponds to a preset gathering and transportation process flow;

[0007] Using the index value normalization matrix, determining the pre-energy consumption influencing index with the greatest impact on the heavy oil gathering and transportation system to be evaluated from the multiple pre-energy consumption influencing indexes as the key energy consumption influencing index, and dividing the corresponding index value range intervals for the key energy consumption influencing index to obtain multiple key energy consumption influencing index value range intervals;

[0008] For each of the preset gathering and transportation process flows and each of the key energy consumption influencing index value range intervals, according to the energy consumption and the amount of gathering and transportation liquid in all statistical time periods of the preset gathering and transportation process flows adopted by the heavy oil gathering and transportation system to be evaluated, the benchmark energy consumption of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flows and the key energy consumption influencing index value range intervals are obtained;

[0009] Determine, based on the obtained multiple benchmark energy consumptions, the corresponding correction coefficients of the key energy consumption impact index values ​​when the heavy oil gathering and transportation system to be evaluated adopts each preset gathering and transportation process flow and is within each key energy consumption impact index value range;

[0010] The comparable comprehensive energy consumption corresponding to each oilfield block is obtained according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks and the correction coefficients of the multiple key energy consumption influencing index values ​​obtained.

[0011] On the other hand, the present application provides an energy consumption evaluation device for a heavy oil gathering and transportation system, comprising: a memory and a processor, wherein the memory is used to store an executable program;

[0012] The processor is used to read and execute the executable program to implement the above-mentioned energy consumption evaluation method for the heavy oil gathering and transportation system.

[0013] Compared with the related art, the present application obtains a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated according to the index values ​​corresponding to multiple preset energy consumption influencing indicators in multiple statistical time periods of multiple oilfield blocks of the heavy oil gathering and transportation system to be evaluated; using the normalized matrix of index values, the pre-energy consumption influencing indicator with the greatest impact on the heavy oil gathering and transportation system to be evaluated is determined as the key energy consumption influencing indicator, and the key energy consumption influencing indicator is divided into corresponding index value range intervals to obtain multiple key energy consumption influencing indicator value range intervals; for each of the preset gathering and transportation process flows and each of the key energy consumption influencing indicator value range intervals, the corresponding benchmark energy consumption is obtained according to the corresponding energy consumption and gathering and transportation fluid volume; the corresponding key energy consumption influencing indicator value correction coefficients are determined according to the obtained multiple benchmark energy consumptions; and the comparable comprehensive energy consumption corresponding to each oilfield block is obtained according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks, and the obtained multiple key energy consumption influencing indicator value correction coefficients. The energy consumption evaluation method for the heavy oil gathering and transportation system provided in the embodiment of the present application provides a method for comparing the energy consumption levels of heavy oil gathering and transportation systems with different physical properties and different processes. Therefore, it is possible to more scientifically identify the energy consumption gap, thereby scientifically and comprehensively reflecting the energy consumption level of the heavy oil gathering and transportation system under different oilfield blocks, and achieving comparability of energy consumption levels under different oilfield blocks.

[0014] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0016] Figure 1 This is a flow chart of an energy consumption evaluation method for a heavy oil gathering and transportation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0018] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0019] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0020] The present application embodiment provides an energy consumption evaluation method for a heavy oil gathering and transportation system, such as Figure 1As shown, the method includes:

[0021] Step 101, obtaining a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated according to index values ​​corresponding to multiple preset energy consumption impact indexes of the heavy oil gathering and transportation system to be evaluated in multiple statistical time periods of multiple oilfield blocks; wherein each oilfield block corresponds to a preset gathering and transportation process flow;

[0022] Step 102: using the index value normalization matrix, determining the pre-energy consumption influencing index with the greatest impact on the heavy oil gathering and transportation system to be evaluated from the multiple pre-energy consumption influencing indexes as the key energy consumption influencing index, and dividing the key energy consumption influencing index into corresponding index value range intervals to obtain multiple key energy consumption influencing index value range intervals;

[0023] Step 103: for each of the preset gathering and transportation process flows and each of the key energy consumption influencing index value range intervals, according to the energy consumption and the amount of gathering and transportation liquid in all statistical time periods of the preset gathering and transportation process flows adopted by the heavy oil gathering and transportation system to be evaluated, obtain the benchmark energy consumption of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flows and the key energy consumption influencing index value range intervals;

[0024] Step 104: determining, based on the obtained multiple benchmark energy consumptions, the key energy consumption impact index value correction coefficients corresponding to each preset gathering and transportation process flow and each key energy consumption impact index value range interval of the heavy oil gathering and transportation system to be evaluated;

[0025] Step 105: Obtain the comparable comprehensive energy consumption corresponding to each oilfield block according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks and the obtained correction coefficients of the multiple key energy consumption influencing index values.

[0026] The preset energy consumption influencing index is an objective preset energy consumption influencing index that causes changes in the energy consumption of the heavy oil gathering and transportation system, such as oil viscosity, crude oil dehydration temperature, water content, etc.

[0027] The heavy oil gathering and transportation system is a complete set of processing procedures for the qualified oil to be transported from the oil well to the joint station after processing. Since the gathering and transportation process and the expected energy consumption influencing indicators of different oilfield blocks may be different, the actual processing process and energy consumption of the corresponding heavy oil gathering and transportation system to be evaluated are also different in different oilfield blocks. Therefore, a method is needed to compare the energy consumption of the heavy oil gathering and transportation system to be evaluated between different oilfield blocks.

[0028] The energy consumption evaluation method for a heavy oil gathering and transportation system provided in an embodiment of the present application obtains a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated according to index values ​​corresponding to multiple preset energy consumption influencing indexes in multiple statistical time periods of multiple oilfield blocks of the heavy oil gathering and transportation system to be evaluated; uses the normalized matrix of index values ​​to determine the pre-energy consumption influencing index with the greatest impact on the heavy oil gathering and transportation system to be evaluated as the key energy consumption influencing index, and divides the key energy consumption influencing index into corresponding index value range intervals to obtain multiple key energy consumption influencing index value range intervals; obtains a corresponding benchmark energy consumption according to the corresponding energy consumption and gathering and transportation fluid volume for each of the preset gathering and transportation process flows and each of the key energy consumption influencing index value range intervals; determines the corresponding key energy consumption influencing index value correction coefficients according to the obtained multiple benchmark energy consumptions; obtains the corresponding comparable comprehensive energy consumption for each oilfield block according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks and the obtained multiple key energy consumption influencing index value correction coefficients. The energy consumption evaluation method for the heavy oil gathering and transportation system provided in the embodiment of the present application provides a method for comparing the energy consumption levels of heavy oil gathering and transportation systems with different physical properties and different processes. Therefore, it is possible to more scientifically identify the energy consumption gap, thereby scientifically and comprehensively reflecting the energy consumption level of the heavy oil gathering and transportation system under different oilfield blocks, and achieving comparability of energy consumption levels under different oilfield blocks.

[0029] In an exemplary embodiment, the index values ​​corresponding to multiple preset energy consumption impact indicators of the heavy oil gathering and transportation system to be evaluated in multiple statistical time periods of multiple oilfield blocks are used to obtain a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated, including:

[0030] Using the range method, normalizing the index values ​​corresponding to the plurality of preset energy consumption influencing indexes of the heavy oil gathering and transportation system to be evaluated in a plurality of statistical time periods of a plurality of oilfield blocks, to obtain normalized index values;

[0031] The indicator value normalization matrix is ​​obtained based on the normalized indicator values.

[0032] Exemplarily, the normalized matrix of preset energy consumption influencing indicators of heavy oil gathering and transportation system energy consumption can be obtained through the following steps:

[0033] First, establish the original data matrix R of the preset energy consumption impact indicators:

[0034] Assume that the energy consumption of the heavy oil gathering and transportation system is preset with m energy consumption influencing indicators as r1, r2, …, r m , the m indicator variables of n heavy oil gathering and transportation systems constitute the original data matrix R of the preset energy consumption impact indicators:

[0035]

[0036] In the formula, r ij is the jth preset energy consumption impact index data of the i-th heavy oil gathering and transportation system (i=1,2,…,n; j=1,2,…,m).

[0037] Secondly, the original data matrix is ​​normalized to obtain the preset energy consumption impact index normalized matrix P:

[0038] Since the units and levels of the preset energy consumption impact indicators are inconsistent, there is a certain incomparability between the indicators. In order to eliminate this situation and make the calculation more accurate and convenient, the range method is used to normalize the preset energy consumption impact indicators. The specific indicator normalization is as follows:

[0039] For positive indicators (the larger the indicator value, the greater the corresponding energy consumption), the following normalization is adopted:

[0040]

[0041] For negative indicators (the larger the indicator value, the smaller the corresponding energy consumption), the following normalization is adopted:

[0042]

[0043] Then we get the normalized matrix P ij =(p ij ) n×m for:

[0044]

[0045] In an exemplary embodiment, the use of the index value normalization matrix to determine the pre-energy consumption influencing index having the greatest impact on the heavy oil gathering and transportation system to be evaluated from the plurality of pre-energy consumption influencing indexes as the key energy consumption influencing index includes:

[0046] Firstly, the subjective weights corresponding to the plurality of preset energy consumption influencing indicators are calculated by using the ordinal relationship analysis method;

[0047] Secondly, based on the index value normalization matrix, a principal component analysis method is used to calculate the first objective weight corresponding to each of the plurality of preset energy consumption influencing indicators;

[0048] Thirdly, the CRITIC weight method is used to calculate the second objective weight corresponding to each of the plurality of preset energy consumption impact indicators;

[0049] Next, the objective combined weights corresponding to each of the plurality of preset energy consumption impact indicators are calculated based on the first objective weights and the second objective weights of the plurality of preset energy consumption impact indicators, and the subjective and objective comprehensive weights corresponding to each of the plurality of preset energy consumption impact indicators are calculated based on the subjective weights and the objective combined weights of the plurality of preset energy consumption impact indicators;

[0050] Finally, the preset energy consumption influencing index having the greatest impact on the heavy oil gathering and transportation system to be evaluated is determined according to the subjective and objective comprehensive weights of the plurality of preset energy consumption influencing indexes, and the determined preset energy consumption influencing index is used as the key energy consumption influencing index.

[0051] In an exemplary embodiment, the method of using the ordinal relationship analysis method to calculate the subjective weights corresponding to the plurality of preset energy consumption impact indicators includes:

[0052] First, for a plurality of preset important indicator selection methods, a weight corresponding to each of the preset important indicator selection methods and a preset energy consumption impact indicator selected by each of the preset important indicator selection methods from a plurality of preset energy consumption impact indicators are obtained;

[0053] Secondly, a preset function is used to integrate and calculate the weights corresponding to the multiple preset important indicator selection methods and the selected preset energy consumption impact indicators, to obtain the calculation result values ​​corresponding to the multiple preset important indicator selection methods, and the obtained multiple calculation result values ​​are processed in descending order, and the multiple preset energy consumption impact indicators are sorted according to the multiple calculation result values ​​processed in descending order;

[0054] Thirdly, assigning a relative importance interval value to each of the two adjacent preset energy consumption impact indicators according to each of the preset important indicator selection methods, and establishing an adjacent indicator comparison matrix with the sorted plurality of preset energy consumption impact indicators, and obtaining the relative importance point value of each of the two adjacent preset energy consumption impact indicators based on the adjacent indicator comparison matrix;

[0055] Finally, the subjective weights corresponding to the plurality of preset energy consumption influencing indicators are obtained according to the plurality of relative importance point values ​​obtained.

[0056] The preset important indicator selection method is a known method and can be a subjective method.

[0057] First, the plurality of preset energy consumption impact indicators are sorted, that is, the sequence of the preset energy consumption impact indicators is determined:

[0058] For m preset energy consumption impact indicator sets R = {r1, r2, ..., r m}, firstly, Q preset important indicator selection methods are used to select indicators, such as the tth (1≤t≤Q) preset important indicator selection method selects s ​​(1≤s≤m) relatively important indicators in the indicator set to form an indicator subset However, considering that there may be differences in the preset important indicator selection methods, it is necessary to introduce the weights η corresponding to the preset important indicator selection methods. The weights of various preset important indicator selection methods constitute a set η=(η1,η2,…,η Q ) T , according to the above conditions and by defining g t The (r) and h(r) functions determine the indicator sequence more precisely.

[0059] Define the function g t (r) and h(r):

[0060]

[0061]

[0062] By calculating the function h(x j ) values ​​are used to process each preset energy consumption impact index in descending order, and the preset energy consumption impact index sequence index set is obtained as follows:

[0063] Construct a comparison matrix of adjacent preset energy consumption impact indicators:

[0064] After obtaining the set of preset energy consumption impact indicator sequence indicators, an adjacent indicator comparison matrix is ​​constructed, which refers to the importance comparison value assigned to each adjacent indicator by various preset important indicator selection methods. According to various preset important indicator selection methods, adjacent preset energy consumption impact indicators are determined. and The relative importance between k , k=m,m-1,…,3,2;μ k The value of and The relative importance between them can be: k =1.0, indicating equal relative importance, μ k =1.2, indicating a slightly higher relative importance, μ k =1.4, indicating high relative importance, μ k =1.6, indicating a high relative importance, μ k =1.8, indicating that the relative importance is very high.

[0065] However, when comparing various methods for selecting preset important indicators, it is often impossible to accurately determine the values ​​of some preset energy consumption influencing indicators. In order to avoid the sharpness of the values, an appropriate fuzzy interval [μ′ R(m-1) ,μ″ R(m-1) ] as a factor of the comparison matrix, making the calculation result more accurate. Since it is rather cumbersome to solve the weight value of the preset energy consumption influencing index in interval, for the convenience of application, the comparison interval value is converted into the form of point value μ j , the specific conversion formula is as follows:

[0066]

[0067] Finally, determine the subjective weights of the preset energy consumption impact indicators:

[0068] Based on the interval point value μ obtained above j , the subjective weight set of each preset energy consumption influencing index is calculated as ω j ′=(ω z,1 ,ω z,2 ,…,ω z,m ), where ω z,j The specific calculation formula is as follows:

[0069]

[0070] The remaining m-1 preset energy consumption impact indicators can be converted and calculated according to the following formula:

[0071] ω z,j-1 =μ j ×ω z,j , j=m,m-1,…,2.

[0072] In an exemplary embodiment, the normalizing matrix based on the index value and using the principal component analysis method to calculate the first objective weights corresponding to each of the plurality of preset energy consumption influencing indexes include:

[0073] Obtaining all eigenvalues ​​of the index value normalization matrix, and calculating the variance contribution rate and cumulative contribution rate of each of the preset energy consumption impact indicators based on all eigenvalues;

[0074] Obtaining a preset energy consumption impact index corresponding to a cumulative contribution rate greater than a preset contribution rate, and using the obtained preset energy consumption impact index and all preset energy consumption impact indicators arranged before the obtained preset energy consumption impact index as target preset energy consumption impact indicators;

[0075] Establishing a principal component analysis model corresponding to all target preset energy consumption influencing indicators based on the normalized matrix of the indicator value and the eigenvector corresponding to the eigenvalue, and solving the linear combination coefficient in the principal component analysis model;

[0076] For each of the preset energy consumption impact indicators, based on the linear combination coefficient in the principal component analysis model and the variance explanation rate and cumulative explanation rate of the preset energy consumption impact indicator, a comprehensive score coefficient of the preset energy consumption impact indicator is obtained;

[0077] For each of the preset energy consumption impact indicators, a first objective weight of the preset energy consumption impact indicator is obtained based on the comprehensive score coefficients of all the preset energy consumption impact indicators and the comprehensive score coefficient of the preset energy consumption impact indicator.

[0078] Exemplarily, first, the initial eigenvalue, variance contribution rate and cumulative contribution rate of the principal component analysis model are solved:

[0079] According to the equation |εE-P|=0, calculate the index value normalization matrix P ij There are t eigenvalues ​​of , and the eigenvectors corresponding to each eigenvalue are a1, a2, …, a t .

[0080] The principal component analysis model is obtained as follows:

[0081]

[0082] In the formula, a ij are the linear combination coefficients in the principal component analysis model.

[0083] Extract m principal components from the principal component analysis model, then H i Variance contribution rate of (i=1,2,…,t) for:

[0084]

[0085] Cumulative contribution rate δ lj for:

[0086]

[0087] In the formula, H i The variance contribution rate of (i=1,2,…,t), ε f is the i-th eigenvalue; δ lj For H1, H2, …, H m The cumulative contribution rate of lj When ≥80%, extracting the first m principal components can basically explain the data information contained in the original variables.

[0088] Secondly, solve the comprehensive score coefficient of each preset energy consumption impact indicator:

[0089] The variance explanation rate and the linear combination coefficient in the principal component analysis model are multiplied and added, and then divided by the cumulative explanation rate to obtain the comprehensive score coefficient of each preset energy consumption impact indicator. The specific calculation formula is:

[0090]

[0091] Finally, calculate the first objective weight ω of the preset energy consumption impact indicator fj :

[0092] The above comprehensive score coefficients are normalized to obtain the first objective weight value of each preset energy consumption impact indicator. The calculation formula is:

[0093]

[0094] In an exemplary embodiment, the CRITIC weight method is used to calculate the second objective weight corresponding to each of the plurality of preset energy consumption impact indicators, including:

[0095] First, the variability index values ​​corresponding to each of the plurality of preset energy consumption impact indicators are calculated, and for each of the preset energy consumption impact indicators, the conflict index value between the preset energy consumption impact indicator and an adjacent preset energy consumption impact indicator is calculated as the conflict index value corresponding to the preset energy consumption impact indicator; wherein the adjacent preset energy consumption impact indicator is a preset energy consumption impact indicator adjacent to the preset energy consumption impact indicator among the plurality of sorted preset energy consumption impact indicators;

[0096] Secondly, the information amount corresponding to each of the preset energy consumption impact indicators is calculated based on the variability index value and the conflict index value corresponding to each of the preset energy consumption impact indicators;

[0097] Finally, for each of the preset energy consumption impact indicators, a second objective weight of the preset energy consumption impact indicator is obtained according to the information amount of all the preset energy consumption impact indicators and the information amount of the preset energy consumption impact indicator.

[0098] Exemplarily, first, the variability and conflict of the preset energy consumption impact indicators are calculated:

[0099] The indicator variability is expressed in the form of standard deviation τ, and the specific calculation formula is as follows:

[0100]

[0101] The conflict between two adjacent indicators is expressed as the correlation coefficient ε ij The specific calculation formula is as follows:

[0102]

[0103] Among them, Xi , Y i are sample values ​​of two preset energy consumption impact indicators, i = 1, 2, …, m; is the mean of the sample values; ε ij is the correlation coefficient between indicators; τ is the standard deviation of the preset energy consumption influencing indicators.

[0104] Secondly, calculate the information volume S of the preset energy consumption impact index j The specific calculation formula is:

[0105] S j =τ j ×ε ij

[0106] Among them, S j The larger the value is, the greater the amount of information contained in the preset energy consumption impact indicator.

[0107] Then the second objective weight ω of the jth preset energy consumption impact indicator is c,j The calculation formula is:

[0108]

[0109] In an exemplary embodiment, the objective combined weights corresponding to the plurality of preset energy consumption impact indicators are calculated based on the first objective weights and the second objective weights of the plurality of preset energy consumption impact indicators;

[0110] First, based on the first objective weight and the second objective weight corresponding to each of the preset energy consumption impact indicators, the objective weight preprocessing results corresponding to each of the preset energy consumption impact indicators are obtained;

[0111] Secondly, for each of the preset energy consumption impact indicators, the objective combined weight of the preset energy consumption impact indicator is obtained according to the objective weight preprocessing results of all the preset energy consumption impact indicators and the objective weight preprocessing result of the preset energy consumption impact indicator.

[0112] Exemplarily, the first objective weight obtained by the principal component analysis method and the second objective weight obtained by the CRITIC method are processed, and the objective weight preprocessing result is obtained by using the geometric mean method:

[0113] First, the two are processed to obtain the objective weight preprocessing results corresponding to each of the preset energy consumption impact indicators. The specific calculation formula is:

[0114]

[0115] Secondly, the geometric mean method is used to obtain the objective combination weight ω of the preset energy consumption impact indicators k,j , the specific calculation formula is:

[0116]

[0117] In an exemplary embodiment, the calculating of the subjective and objective comprehensive weights corresponding to each of the plurality of preset energy consumption impact indicators based on the subjective weights and objective combined weights of the plurality of preset energy consumption impact indicators includes:

[0118] Firstly, based on the minimum discrimination principle, corresponding boundary conditions are set according to the subjective weights and objective combined weights of the plurality of preset energy consumption influencing indicators to establish a target optimization function;

[0119] Secondly, the target optimization function is solved to obtain the subjective and objective comprehensive weight of each preset energy consumption influencing indicator.

[0120] Exemplarily, the subjective weight and the objective combined weight are organically integrated to obtain the subjective and objective comprehensive weight of the preset energy consumption impact index. In order to make the subjective and objective comprehensive weight of the preset energy consumption impact index as close as possible to the subjective weight and the objective weight without being biased towards any one of them, based on the minimum discriminant principle and considering various boundary conditions, the target optimization function is established as follows:

[0121]

[0122] Solve this objective optimization function to obtain the final subjective and objective comprehensive weight ω of the jth preset energy consumption influencing index j for:

[0123]

[0124] Among them, ω j is the subjective and objective comprehensive weight of the preset energy consumption impact index, ω z,j is the subjective weight of the preset energy consumption influencing index, ω k,j It is the objective combination weight of preset energy consumption influencing indicators.

[0125] In this embodiment, firstly, the hierarchical analysis method based on the ordinal relationship analysis method is used as the subjective weighting method, and the principal component analysis method and the CRITIC method are used as the objective weighting method to calculate the subjective weight and objective weight of each preset energy consumption influencing indicator. Secondly, the indicator weights determined by the two objective weighting methods are processed, and the geometric mean method is used to obtain the objective combined weights of the preset energy consumption influencing indicators. The target optimization function is established according to the minimum discriminant principle to obtain the subjective and objective comprehensive weights of the final preset energy consumption influencing indicators. By calculating the size of the subjective and objective comprehensive weights, the influence of each indicator variable on the entire gathering and transportation system can be directly reflected, and then the key preset energy consumption influencing indicator variables that ultimately affect the energy consumption of the heavy oil gathering and transportation system are determined as the key preset energy consumption influencing indicators of the energy consumption target preset energy consumption influencing indicators of the heavy oil gathering and transportation system.

[0126] For example, assuming that the key energy consumption influencing indicator is determined to be oil viscosity, the oil viscosity is divided into corresponding indicator value range intervals to obtain multiple oil viscosity value range intervals. In some embodiments, the energy consumption benchmark value of each gathering and transportation process under each oil viscosity range in the heavy oil gathering and transportation system is calculated by the following expression:

[0127]

[0128] Among them, G i is the volume of liquid collected and transported in each process flow of the heavy oil gathering and transportation system under the range of oil product viscosity, the unit is 10 4 t / a; It is the energy consumption benchmark value of each gathering and transportation process in the statistical period under each oil viscosity range, in kgce / t 液 ; E i It is the comprehensive energy consumption index of crude oil gathering and transportation for each gathering and transportation process under each oil product viscosity value range during the statistical period, in kgce / t 液 .

[0129] In an exemplary embodiment, the key energy consumption impact index value correction coefficient corresponding to each preset gathering and transportation process flow and each key energy consumption impact index value range interval is determined according to the obtained multiple benchmark energy consumptions, including:

[0130] First, a benchmark energy consumption is selected from a plurality of obtained benchmark energy consumptions as a target benchmark energy consumption, and a preset benchmark coefficient is used as a correction coefficient of a key energy consumption impact index value that the target benchmark can correspond to;

[0131] Secondly, in addition to the preset gathering and transportation process flow and the key energy consumption influencing indicator value range interval corresponding to the target benchmark energy consumption, for each preset gathering and transportation process flow and in each key energy consumption influencing indicator value range interval, based on the target benchmark energy consumption, the preset benchmark coefficient, and the benchmark energy consumption of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flow is adopted and in the key energy consumption influencing indicator value range interval, the key energy consumption influencing indicator value correction coefficient when the heavy oil gathering and transportation system to be evaluated adopts the preset gathering and transportation process flow and in the key energy consumption influencing indicator value range interval is obtained.

[0132] For example, it is still assumed that the key energy consumption influencing index is determined to be oil viscosity, and the heavy oil is divided into heavy oil with a viscosity less than 10000 mPa·s, heavy oil with a viscosity between 10000 mPa·s and 50000 mPa·s, and heavy oil with a viscosity greater than 50000 mPa·s according to the viscosity range;

[0133] The energy consumption benchmark value of the single-pipe heating and gathering process of heavy oil with a viscosity of less than 10000mPa·s is used as the calculation benchmark value;

[0134] Based on the calculation reference value, the correction coefficients of the double-pipe water-mixing gathering and transportation process, the double-pipe diluting gathering and transportation process, and the three-pipe heating gathering and transportation process for heavy oil with a viscosity of less than 10000mPa·s, the correction coefficients of the single-pipe heating gathering and transportation process, the double-pipe water-mixing gathering and transportation process, the double-pipe diluting gathering and transportation process, and the three-pipe heating gathering and transportation process for heavy oil with a viscosity between 10000mPa·s and 50000mPa·s, and the correction coefficients of the single-pipe heating gathering and transportation process, the double-pipe water-mixing gathering and transportation process, the double-pipe diluting gathering and transportation process, and the three-pipe heating gathering and transportation process for heavy oil with a viscosity greater than 50000mPa·s are calculated.

[0135] In this embodiment, the energy consumption benchmark value is obtained by combining the statistical data of energy consumption of the heavy oil gathering and transportation system, taking the gathering and transportation liquid volume of each gathering and transportation process under each oil viscosity range as the weight for calculating the energy consumption, and calculating the weighted average value of the energy consumption.

[0136] In some embodiments, the correction coefficient of each gathering and transportation process flow under each oil viscosity range is determined by the following expression:

[0137]

[0138] Among them, H j is the correction coefficient of each gathering and transportation process under each oil viscosity range, and E1 is the energy consumption benchmark value of the single-pipe heating gathering and transportation process of heavy oil with a viscosity less than 10000mPa·s, in kgce / t liquid.

[0139] In an exemplary embodiment, the comparable comprehensive energy consumption includes: comparable comprehensive energy consumption per unit of liquid gathering and transportation and comparable comprehensive energy consumption per unit of crude oil gathering and transportation. The comparable comprehensive energy consumption corresponding to each oil field block is obtained according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oil field blocks, and the correction coefficients of the multiple key energy consumption influencing index values ​​obtained, including:

[0140] First, an oilfield block that adopts a preset gathering and transportation process corresponding to the target benchmark energy consumption and is within the index value range corresponding to the target benchmark energy consumption is used as the target oilfield block, and the unit liquid volume gathering and transportation comprehensive energy consumption of the target oilfield block is obtained according to the unit liquid volume gathering and transportation gas consumption and unit liquid volume gathering and transportation electricity consumption of the heavy oil gathering and transportation system to be evaluated in the target oil block, and the unit crude oil gathering and transportation comprehensive energy consumption of the target oilfield block is obtained according to the unit crude oil gathering and transportation electricity consumption and unit crude oil gathering and transportation gas consumption of the heavy oil gathering and transportation system to be evaluated in the target oil block;

[0141] Secondly, for each oil field block, perform the following operations:

[0142] Determine the correction coefficient of the key energy consumption impact index value corresponding to the oil block in the oil field according to the preset gathering and transportation process flow of the oil block in the oil field and the key energy consumption impact index value range interval in which the key energy consumption impact index value is located;

[0143] Obtain the comparable comprehensive energy consumption per unit liquid volume gathering and transportation of the oil block in the oil field according to the daily liquid production per well of the oil block in the oil field, the correction coefficient of the key energy consumption influencing index value corresponding to the oil block in the oil field, the daily liquid production per well of the target oil block in the oil field and the comprehensive energy consumption per unit liquid volume gathering and transportation of the target oil block in the oil field;

[0144] According to the daily liquid production of a single well in the oil field block, the correction coefficient of the key energy consumption influencing index value corresponding to the oil field block, the daily liquid production of a single well in the target oil field block and the comprehensive energy consumption for gathering and transporting the crude oil liquid volume of the target oil field block, the comparable comprehensive energy consumption per unit crude oil gathering and transport of the oil field block is obtained.

[0145] Exemplarily, the comparable comprehensive energy consumption of the heavy oil gathering and transportation system is calculated based on the sum of the daily liquid production of a single well of all different types of gathering and transportation processes included in the same system, the sum of the daily liquid production of a single well of different systems, the gathering and transportation power consumption and gas consumption per unit liquid volume of a heavy oil single-pipe heating gathering and transportation system with a viscosity of less than 10000 mPa·s, and the correction coefficient of each gathering and transportation process under each oil viscosity range.

[0146] In some embodiments, the energy consumption value of the heavy oil single-tube heating gathering and transportation process with a viscosity of less than 10000mPa·s under the same conditions is used, and the corresponding conversion is performed according to the gathering and transportation process according to the heavy oil with different viscosities, and the correction coefficient of each gathering and transportation process factor under the viscosity range of each oil product is combined to obtain a comparable comprehensive energy consumption evaluation method for the heavy oil gathering and transportation system. The specific calculation formula is:

[0147]

[0148]

[0149] Among them, M z1 is the comparable comprehensive energy consumption per unit liquid volume of heavy oil gathering and transportation system, kgce, M z2 is the comparable comprehensive energy consumption per unit crude oil of the heavy oil gathering and transportation system, kgce, M z0 The comprehensive energy consumption per unit liquid volume of a single-pipe heating and gathering system for heavy oil with a viscosity of less than 10000 mPa·s, kgce / t 液 , M′ z0 The comprehensive energy consumption per unit crude oil gathering and transportation of a single-pipe heating gathering and transportation system with a viscosity of less than 10000 mPa·s, kgce / t 油 , G t1 is the liquid volume of different gathering and transportation systems for heavy oils with different viscosities, tliquid, G t2 is the oil volume of different gathering and transportation systems for heavy oils with different viscosities, t油 , E 0e The power consumption per unit liquid volume of the heavy oil single-pipe heating gathering and transportation system with a viscosity of less than 10000mPa·s, (kW·h) / t 液 , E 0q is the gas consumption per unit liquid volume of the heavy oil single-pipe heating gathering and transportation system with viscosity less than 10000mPa·s, (m 3 ) / t 液 , E′ 0e The power consumption per unit crude oil gathering and transportation of a single-pipe heating gathering and transportation system with a viscosity of less than 10000 mPa·s, (kW·h) / t 油 , E′ 0q is the unit crude oil gathering and transportation gas consumption of the heavy oil single-pipe heating gathering and transportation system with viscosity less than 10000mPa·s, (m 3 ) / t 油 , It is the sum of the daily liquid production of a single well of all different types of gathering and transportation processes included in the same system, t / d. is the total daily fluid production of a single well in different systems, t / d, H t It is the correction coefficient of each gathering and transportation process under each oil viscosity range.

[0150] In practical applications, the following is an example of a heavy oil field gathering and transportation system to analyze a typical sample block of the heavy oil gathering and transportation system. The specific method and steps are as follows:

[0151] Step 1: Taking the heavy oil gathering and transportation system as the research object, combined with the characteristics of the system energy consumption composition and actual production, consider the objective preset energy consumption influencing indicators that can change its energy consumption, including oil viscosity, crude oil dehydration temperature, water content, etc., determine the set of influencing indicators that affect the preset energy consumption, and use the range method to normalize the indicator data to obtain the normalized matrix of influencing indicators that affect the preset energy consumption. The actual annual production data of the heavy oil gathering and transportation system from 2019 to 2021 are shown in Table 1.

[0152] Table 1

[0153]

[0154]

[0155] Based on the actual annual production data of the heavy oil gathering and transportation system in Table 1, the data matrix R of the indicators affecting the preset energy consumption is first constructed:

[0156]

[0157] Secondly, the range method is used to normalize the original data of the preset energy consumption influencing indicators. Taking the preset energy consumption influencing indicator of oil viscosity as an example, oil viscosity is a negative indicator, and its normalization process is as follows:

[0158] Maximum oil viscosity maxq ij :maxr ij =53800; minimum value minq ij :minr ij =2639

[0159]

[0160] The normalization results of the viscosity index data of other oil products are shown in Table 2.

[0161] Table 2

[0162]

[0163] Similarly, the normalized data of other preset energy consumption influencing indicators are shown in Table 3.

[0164] Table 3

[0165]

[0166]

[0167] Based on the above preset energy consumption impact index normalized processing data, the preset energy consumption impact index normalized data matrix P is obtained. ij :

[0168]

[0169] Step 2: Determine the key energy consumption influencing indicators of the heavy oil gathering and transportation system. Based on the normalized matrix of the indicator values ​​obtained in step 1, the order relationship analysis method is first used as the subjective weighting method, and the principal component analysis method and the CRITIC method are used as the objective weighting method to calculate the subjective weight and objective weight of each preset energy consumption influencing indicator. Secondly, the indicator weights determined by the two objective weighting methods are preprocessed to obtain the preprocessing results. The geometric mean method is used for the preprocessing results to obtain the objective combination weights of the preset energy consumption influencing indicators, and the target optimization function is established based on the minimum discriminant principle to obtain the final subjective and objective comprehensive weights of the preset energy consumption influencing indicators. By calculating the size of the subjective and objective comprehensive weights, the influence of each indicator variable on the entire heavy oil gathering and transportation system can be directly reflected, and then the key influence preset energy consumption influencing indicator variables that ultimately affect the energy consumption of the heavy oil gathering and transportation system can be determined.

[0170] First, the ordinal relationship analysis method is used to determine the subjective weights of the preset energy consumption influencing indicators. First, six preset important indicator selection methods are used to comprehensively evaluate the weights of the preset energy consumption influencing indicators of the heavy oil gathering and transportation system, and the subjective weights of the six preset important indicator selection methods are assigned as η=(0.10, 0.15, 0.25, 0.30, 0.15, 0.05) in turn. According to the six preset important indicator selection methods, the five most important indicator variables are selected from the six preset energy consumption influencing indicators, and the five most important indicator variables are defined according to the function g. t (r) and h(r) are used to calculate the subjective weights of the experts, and the weighted results of the preset energy consumption impact indicators are shown in Table 4.

[0171] Table 4

[0172]

[0173]

[0174] According to the expert weighting results in the above table, each preset energy consumption impact indicator is processed in descending order, and the preset energy consumption impact indicator sequence indicator set is obtained as follows:

[0175] Secondly, establish a comparison matrix of adjacent preset energy consumption impact indicators, and according to the formula The point values ​​of each comparison interval are obtained by solving, and the comparison intervals and point values ​​of adjacent preset energy consumption impact indicators are shown in Table 5:

[0176] Table 5

[0177] Preset important indicator selection method <![CDATA[r1~r2]]> <![CDATA[r3]]> <![CDATA[r4]]> <![CDATA[r5]]> <![CDATA[r6]]> <![CDATA[Q1]]> [1.0， [1.0，1.2] [1.2，1.4] [1.0，1.2] [1.2，1.4] <![CDATA[Q2]]> [1.4， [1.2，1.4] [1.2，1.4] [1.2，1.4] [1.2，1.4] <![CDATA[Q3]]> [1.4， [1.4，1.6] [1.6，1.8] [1.4，1.6] [1.6，1.8] <![CDATA[Q4]]> [1.6， [1.4，1.6] [1.6，1.8] [1.4，1.6] [1.4，1.6] <![CDATA[Q5]]> [1.2， [1.2，1.4] [1.4，1.6] [1.2，1.4] [1.0，1.2] <![CDATA[Q6]]> [1.0， [1.0，1.2] [1.0，1.2] [1.0，1.2] [1.0，1.2] Compare interval points 1.47 1.37 1.54 1.37 1.44

[0178] According to the interval point values ​​in Table 5, the subjective weight set of each influencing index of preset energy consumption is calculated as ω j ′=(ω z,1 ,ω z,2 ,…,ω z,m ) T =(0.346,0.234,0.172,0.111,0.081,0.056) T .

[0179] Secondly, the principal component analysis method is used to determine the objective weights of the preset energy consumption influencing indicators. First, the initial eigenvalues, variance explanation rates and cumulative contribution rates of the principal components of the preset energy consumption influencing indicators are calculated.

[0180] From the characteristic equation:

[0181] The calculated eigenvalues ​​of the six principal components are: ε1=2.394, ε2=1.759, ε3=1.195, ε4=0.423, ε5=0.180, ε6=0.049.

[0182] Taking principal component 1 as an example, the variance explained for:

[0183]

[0184] The cumulative contribution rate δ lj for:

[0185] Similarly, the variance explanation rate and cumulative contribution rate of the remaining principal components are shown in Table 6.

[0186] Table 6

[0187]

[0188] According to the extraction of principal components and the amount of information extracted in Table 6, a total of three principal components were extracted, and the initial characteristic root values ​​were all greater than 1. The variance explanation rates of these three principal components were 39.901%, 29.319% and 19.919%, respectively, and the cumulative contribution rate was 89.139%. Therefore, the first three were extracted as principal components, and the information extraction of the remaining factor research items was analyzed.

[0189] According to the principal component analysis model The calculated linear combination coefficients of each factor variable for the three principal components are shown in Table 7.

[0190] Table 7

[0191]

[0192] The variance explanation rate of each factor and the linear combination coefficient in the principal component analysis model are multiplied and added together, and then divided by the cumulative explanation rate to obtain the comprehensive score coefficient of each preset energy consumption impact indicator.

[0193] Taking oil viscosity as an example, its comprehensive score coefficient κ1 is:

[0194]

[0195] The oil viscosity index weight ω f1 for:

[0196]

[0197] Similarly, the calculation results of the comprehensive score coefficients and weights of each preset energy consumption impact indicator are shown in Table 8.

[0198] Table 8

[0199] Preset energy consumption impact indicator name <![CDATA[Comprehensive score coefficient κ j > <![CDATA[Preset energy consumption impact index weight ω fj > Oil viscosity 0.3570 0.269 Daily liquid production per well 0.1748 0.132 Moisture content 0.1677 0.126 Oil Pour Point 0.3348 0.252 Crude oil dehydration temperature 0.2395 0.180 Purified oil output pressure 0.0542 0.041

[0200] It can be seen from Table 8 that the weights of the preset energy consumption influencing indicators of the heavy oil gathering and transportation system determined by the principal component analysis method are in the following order: oil viscosity > oil solidification point > crude oil dehydration temperature > daily liquid production of a single well > water content > purified oil transmission pressure, among which the weights of oil viscosity and oil solidification point account for the largest proportion, indicating that these two factor variables have a greater impact on the energy consumption of the heavy oil gathering and transportation system.

[0201] Secondly, the CRITIC method is used to solve the weights of the preset energy consumption influencing indicators. First, the variability and conflict of the preset energy consumption influencing indicators are calculated. Taking the two preset energy consumption influencing indicators, oil viscosity and daily liquid production per well, as examples, the variability of the indicators is:

[0202] Oil viscosity:

[0203] Daily liquid production per well:

[0204] The index conflict between the two is:

[0205]

[0206] Similarly, the calculation results of the variability and conflict of each preset energy consumption impact indicator (for each preset energy consumption impact indicator, the conflict between the preset energy consumption impact indicator and the adjacent preset energy consumption impact indicator is taken as the conflict of the preset energy consumption impact indicator) are shown in Table 9.

[0207] Table 9

[0208] Preset energy consumption impact indicator name <![CDATA[Indicator variability τ j > <![CDATA[Indicator conflict ε ij > Oil viscosity 0.307 3.824 Daily liquid production per well 0.247 4.040 Moisture content 0.270 3.994 Oil Pour Point 0.303 3.434 Crude oil dehydration temperature 0.322 5.030 Purified oil output pressure 0.316 3.145

[0209] Secondly, calculate the information content and objective weight of the preset energy consumption influencing indicators. Taking oil viscosity as an example, the information content of the indicator is:

[0210] S1=τ1×ε 12 =0.307×3.824=1.173

[0211] Then the objective weight of oil viscosity index ω c,1 for:

[0212]

[0213] Similarly, the information content and indicator weight results of each preset energy consumption influencing indicator variable are shown in Table 10.

[0214] Table 10

[0215] Preset energy consumption impact indicator name <![CDATA[Index information quantity S j > <![CDATA[Index weight ω c,j > Oil viscosity 1.173 0.170 Daily liquid production per well 0.999 0.145 Moisture content 1.078 0.156 Oil Pour Point 1.042 0.151 Crude oil dehydration temperature 1.620 0.234 Purified oil output pressure 0.995 0.144

[0216] It can be seen from Table 10 that the weights of the preset energy consumption influencing indicators of the heavy oil gathering and transportation system determined by the CRITIC method are as follows: crude oil dehydration temperature > oil viscosity > water content > oil solidification point > daily liquid production of a single well > purified oil transmission pressure, among which the weights of crude oil dehydration temperature and oil viscosity account for the largest proportion, indicating that these two factor variables have a greater impact on the energy consumption of the heavy oil gathering and transportation system.

[0217] Calculate the objective combination weights of the preset energy consumption impact indicators. Process the objective weights obtained by the principal component analysis method and the CRITIC method to obtain the preprocessing results, and use the geometric mean method to obtain the objective combination weights of the preset energy consumption impact indicators.

[0218] Taking the preset energy consumption influencing index of oil viscosity as an example, the objective weights obtained from the two are preprocessed to obtain the preprocessing results:

[0219]

[0220] The objective combination weight ω is obtained by the geometric mean method k,1 for:

[0221]

[0222] Similarly, the preprocessing results and combined weight results of each preset energy consumption influencing indicator variable are shown in Table 11.

[0223] Table 11

[0224] Preset energy consumption impact indicator name <![CDATA[Preprocessing result fω j > <![CDATA[Combined weight ω k,j > Oil viscosity 0.214 0.221 Daily liquid production per well 0.138 0.142 Moisture content 0.140 0.144 Oil Pour Point 0.195 0.201 Crude oil dehydration temperature 0.205 0.212 Purified oil output pressure 0.077 0.079

[0225] It can be seen from Table 11 that the objective combination weights of the preset energy consumption influencing indicators of the heavy oil gathering and transportation system obtained by the principal component analysis method and the CRITIC method are in the following order: oil viscosity > crude oil dehydration temperature > oil solidification point > water content > daily liquid production of a single well > purified oil transmission pressure, among which the weights of oil viscosity and crude oil dehydration temperature account for the largest proportion, indicating that these two factor variables have a greater impact on the energy consumption of the heavy oil gathering and transportation system, among which the oil viscosity has the highest impact on the energy consumption of the heavy oil gathering and transportation system.

[0226] In order to intuitively reflect the weights and impact differences of indicators obtained by various objective methods,

[0227] The weights and trends of the preset energy consumption influencing indicators calculated by principal component analysis, CRITIC method and integrated combination method show great differences. This is because each evaluation method has certain limitations in describing the information of preset energy consumption influencing indicators. Although the principal component analysis method can effectively solve the multicollinearity problem among the factor variables, it does not take into account the objective influence of the factor independent variables on the dependent variable; although the CRITIC method can effectively consider the conflict and volatility among the indicator data, it does not take into account the discrete degree among the indicator data; however, by integrating and combining the two to obtain the objective combination weight, the advantages of the two evaluation methods are complemented, so that the obtained preset energy consumption influencing indicator proportion of the heavy oil gathering and transportation system energy consumption is more accurate and reasonable, and the objective rationality of the indicator weight is further guaranteed.

[0228] Finally, the subjective and objective comprehensive weights of the preset energy consumption influencing indicators are calculated. The subjective weights obtained based on the above-mentioned ordinal relationship analysis method and the objective combination weights obtained by the principal component analysis method and the CRITIC method are organically integrated to obtain the final subjective and objective comprehensive weights of the preset energy consumption influencing indicators.

[0229] In this process, in order to make the subjective and objective comprehensive weights of the preset energy consumption influencing indicators as close as possible to the subjective weights and objective weights without being biased towards any one of them, based on the minimum discriminant principle and considering various boundary conditions, the objective optimization function is established as follows:

[0230]

[0231] Solve this objective optimization function to obtain the final subjective and objective comprehensive weight ω of the jth preset energy consumption influencing index j for:

[0232]

[0233] Taking the preset energy consumption impact index of oil viscosity as an example, the final subjective and objective comprehensive weights are obtained according to the above formula:

[0234]

[0235] Similarly, the final subjective and objective comprehensive weights of each preset energy consumption influencing indicator are shown in Table 12.

[0236] Table 12

[0237]

[0238]

[0239] It can be seen from Table 12 that the subjective and objective comprehensive weights of the final preset energy consumption influencing indicators of the heavy oil gathering and transportation system are oil viscosity>oil solidification point>single well daily liquid production>crude oil dehydration temperature>water content>purified oil transmission pressure, among which the weight of oil viscosity accounts for the largest proportion and is highly feedback, indicating that this factor variable has the greatest impact on the energy consumption of the heavy oil gathering and transportation system. Therefore, oil viscosity is determined as the key energy consumption influencing indicator of the heavy oil gathering and transportation system, which provides a basis for determining the factor correction coefficient.

[0240] Step 3: Refine the gathering and transportation process flow and oil viscosity range of the heavy oil gathering and transportation system, and determine the energy consumption benchmark value of each gathering and transportation process flow under each oil viscosity range. First, refine the different gathering and transportation process flows of the heavy oil gathering and transportation system, including the single-tube heating gathering and transportation process, the double-tube water-blending gathering and transportation process, the double-tube dilution gathering and transportation process, and the three-tube heating gathering and transportation process. Then, the viscosity is divided according to the viscosity of heavy oil, super heavy oil and extra heavy oil with a viscosity less than 10000mPa·s, and the corresponding viscosity ranges are <10000mPa·s, 10000mPa·s~50000mPa·s, and >50000mPa·s. Combined with the energy consumption statistical data of the heavy oil gathering and transportation system, the gathering and transportation liquid volume of each gathering and transportation process flow under each oil viscosity range is used as the weight for calculating the energy consumption, and the weighted average value of the energy consumption obtained is used as its energy consumption benchmark value.

[0241] Based on the energy consumption statistics of the heavy oil gathering and transportation system in Table 1, the energy consumption benchmark values ​​of each gathering and transportation process under each oil viscosity range are first calculated. Taking the 2019-2021 data of the single-pipe heating gathering and transportation system for heavy oil with a viscosity of less than 10000mPa·s as an example, the calculation process of the energy consumption benchmark value is explained.

[0242] The energy consumption benchmark value for the single-pipe heating and gathering process of heavy oil with a viscosity of less than 10000mPa·s is:

[0243]

[0244] Similarly, the calculation results of the energy consumption benchmark values ​​of each gathering and transportation process under each oil viscosity range are shown in Table 13.

[0245] Table 13

[0246]

[0247]

[0248] Step 4: Determine the correction factor H for each gathering and transportation process under each oil viscosity range j Based on the key preset energy consumption influencing indicators of heavy oil gathering and transportation system, the correction coefficient H of each gathering and transportation process under each oil viscosity range is calculated. jSince the single-pipe heating and gathering process of heavy oil with a viscosity of less than 10000mPa·s has relatively low energy consumption and a relatively simple process, the energy consumption benchmark value of the single-pipe heating and gathering process of heavy oil with a viscosity of less than 10000mPa·s is selected as the calculation benchmark value, and the correction coefficient is set to 1. Then the correction coefficient H of each gathering and transportation process under the viscosity range of other oil products is j It is the ratio of each energy consumption baseline value to the calculated baseline value.

[0249] From step 2, we know that oil viscosity is the key preset energy consumption influencing index affecting the heavy oil gathering and transportation system, so the correction coefficient of this factor is determined. Taking the single-pipe heating gathering and transportation system of super heavy oil as an example, the calculation process of the viscosity correction coefficient is explained.

[0250] The viscosity correction coefficient of the single-pipe heating gathering and transportation system for extra-heavy oil is:

[0251]

[0252] Similarly, the calculation results of the viscosity correction coefficient of each gathering and transportation process under each oil viscosity range are shown in Table 14.

[0253] Table 14

[0254]

[0255] Step 5: Determine the comparable comprehensive energy consumption evaluation method for heavy oil gathering and transportation system. Using the production energy consumption value of the single-tube heating gathering and transportation process of heavy oil with a viscosity less than 10000mPa·s under the same conditions, the corresponding conversion is performed according to the gathering and transportation process of heavy oil with different viscosities, and the correction coefficient of each gathering and transportation process factor under the viscosity range of each oil product is combined to obtain the comparable comprehensive energy consumption evaluation method for heavy oil gathering and transportation system.

[0256] Taking the energy consumption data of the heavy oil gathering and transportation system of Blocks A1 and A2 of Oilfield A in 2019 as an example, the calculation process of the comparable comprehensive energy consumption evaluation method for heavy oil is explained. The energy consumption calculation data of Blocks A1 and A2 are shown in Table 15.

[0257] Table 15

[0258]

[0259]

[0260] First, the comprehensive energy consumption per unit liquid volume M of Block A1 (heavy oil with viscosity less than 10000 mPa·s) is calculated. z0 and the comprehensive energy consumption per unit of crude oil gathering and transportation M′ z0 :

[0261] The comprehensive energy consumption per unit liquid volume of A1 block is M z0 :

[0262] M z0 =0.1229×E 0e +1.33×E 0q

[0263] =0.1229×(1074.2÷292.5)+1.33×(1146.3÷292.5)

[0264] =5.66(kgce / t 液 )

[0265] Comprehensive energy consumption per unit of crude oil gathering and transportation M′ z0 :

[0266] M′ z0 =0.1229×E′ 0e +1.33×E′ 0q

[0267] =0.1229×(1074.2÷51.7)+1.33×(1146.3÷51.7)

[0268] =32.04(kgce / t 油 )

[0269] Among them, 0.1229 and 1.33 represent the standard coal conversion coefficients. Specifically, 0.1229 means that 1kWh of electricity can be converted to 0.1229kgce, and 1.33 means that 1m3 of natural gas can be converted to 1.33kgce.

[0270] Secondly, the comparable comprehensive energy consumption of each block is calculated, that is, the comparable comprehensive energy consumption per unit of gathering and transportation of Block A1 (heavy oil with viscosity less than 10000mPa·s) is converted according to the gathering and transportation process of heavy oil with different viscosities.

[0271] First, the comparable comprehensive energy consumption per unit liquid volume of Block A1:

[0272] M 11 =5.66×[6.44 / (6.44+4.50)]×1.00=3.33(kgce / t 液 )

[0273] Comparable comprehensive energy consumption per unit of crude oil gathering and transportation in Block A1:

[0274] M 12 =32.04×[6.44 / (6.44+4.50)]×1.00=18.86(kgce / t 油 )

[0275] Second, the comparable comprehensive energy consumption per unit liquid volume of the A2 block:

[0276] M 21 =5.66×[4.50 / (6.44+4.50)]×1.33=3.10(kgce / t 液 )

[0277] Comparable comprehensive energy consumption per unit of crude oil gathering and transportation in Block A2:

[0278] M 12 =32.04×[4.50 / (6.44+4.50)]×1.33=17.53(kgce / t 油 )

[0279] In order to analyze the difference and applicability of the unit comprehensive energy consumption of gathering and transmission of the two blocks before and after the comparison, the calculation results of the unit comprehensive energy consumption of gathering and transmission of blocks A1 and A2 before and after the comparison are summarized as shown in Table 16.

[0280] Table 16

[0281]

[0282]

[0283] From the calculation results in Table 16, it can be seen that the comprehensive energy consumption per unit liquid volume of the heavy oil gathering and transportation systems in Blocks A1 and A2 before comparison is 5.66 kgce / t 液 and 7.38kgce / t 液 The comprehensive energy consumption per unit of crude oil gathering and transportation is 32.04kgce / t 油 and 36.23kgce / t 油 The absolute difference between the two is 1.72 and 4.19 respectively. After comparison, the comprehensive energy consumption per unit liquid volume is 3.33 kgce / t. 液 and 3.10kgce / t 液 The comprehensive energy consumption per unit of crude oil gathering and transportation is 18.86kgce / t 油 and 17.53kgce / t 油, the absolute difference between the two is 0.23 and 1.33 respectively. From the perspective of energy consumption index and absolute difference, the gap in comprehensive energy consumption index between heavy oil gathering and transportation systems in blocks A1 and A2 is significantly reduced. This shows that by using the comparable comprehensive energy consumption per unit liquid volume gathering and transportation and the comparable comprehensive energy consumption per unit crude oil gathering and transportation to calculate, the comparable gap in comprehensive energy consumption between the two at the macro level is significantly weakened, and the influence of objective factors such as viscosity gradient and different gathering and transportation process flow of heavy oil gathering and transportation systems in the two blocks is eliminated, making the comprehensive energy consumption per unit liquid volume gathering and transportation and the comprehensive energy consumption per unit crude oil gathering and transportation of the two comparable to a certain extent. The application of this evaluation method to compare and evaluate the energy consumption levels of different heavy oil gathering and transportation systems is obviously scientific, comprehensive and reasonable, and it can more scientifically and objectively realize the comparison of energy consumption levels of different heavy oil gathering and transportation systems within the scope of the oil field, which has certain engineering practical significance.

[0284] The embodiment of the present application refines the various gathering and transportation process flows of the heavy oil gathering and transportation system under different oil viscosity ranges, and combines the correction coefficient of the key preset energy consumption influencing index of the heavy oil gathering and transportation system to obtain an evaluation method for the comparable energy consumption of unit liquid volume (crude oil) gathering and transportation. The method balances the differences between the heavy oil gathering and transportation system in different gathering and transportation process flows, and also weakens the influence of objective factors such as the physical properties of the oil products on the system. It can not only compare the energy consumption level of the heavy oil gathering and transportation system with the same physical properties and the same process, but also compare the energy consumption level of the heavy oil gathering and transportation system with different physical properties and different processes, and more scientifically realize the comparability of the energy consumption levels of different heavy oil gathering and transportation systems within the scope of the heavy oil field, and identify the energy consumption gap. Among them, the geometric mean method is used to process the principal component analysis method and the CRITIC method to obtain the objective combination weights of the factors, and the target optimization function is established according to the minimum discriminant principle, and the subjective weights of the factors obtained by the ordinal relationship analysis method are organically integrated with the objective combination weights to obtain the final preset energy consumption influencing index subjective and objective comprehensive weights, which to a certain extent realizes the complementary advantages of multiple evaluation methods and makes the overall evaluation results more scientific and reasonable.

[0285] The embodiment of the present application also provides an energy consumption evaluation device for a heavy oil gathering and transportation system, comprising: a memory and a processor, wherein the memory is used to store an executable program;

[0286] The processor is used to read and execute the executable program to implement the energy consumption evaluation method for the heavy oil gathering and transportation system described in any one of the above embodiments.

[0287] The energy consumption evaluation device for a heavy oil gathering and transportation system provided in an embodiment of the present application obtains a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated according to index values ​​corresponding to multiple preset energy consumption influencing indexes in multiple statistical time periods of multiple oilfield blocks of the heavy oil gathering and transportation system to be evaluated; uses the normalized matrix of index values ​​to determine the pre-energy consumption influencing index with the greatest impact on the heavy oil gathering and transportation system to be evaluated as the key energy consumption influencing index, and divides the key energy consumption influencing index into corresponding index value range intervals to obtain multiple key energy consumption influencing index value range intervals; obtains a corresponding benchmark energy consumption according to the corresponding energy consumption and gathering and transportation fluid volume for each of the preset gathering and transportation process flows and each of the key energy consumption influencing index value range intervals; determines the corresponding key energy consumption influencing index value correction coefficients according to the obtained multiple benchmark energy consumptions; obtains the comparable comprehensive energy consumption corresponding to each oilfield block according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks and the obtained multiple key energy consumption influencing index value correction coefficients. The energy consumption evaluation method for the heavy oil gathering and transportation system provided in the embodiment of the present application provides a method for comparing the energy consumption levels of heavy oil gathering and transportation systems with different physical properties and different processes. Therefore, it is possible to more scientifically identify the energy consumption gap, thereby scientifically and comprehensively reflecting the energy consumption level of the heavy oil gathering and transportation system under different oilfield blocks, and achieving comparability of energy consumption levels under different oilfield blocks.

[0288] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0289] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0290] In the implementation process, the processing performed by the terminal device can be completed by the hardware integrated logic circuit in the processor or the instruction in the form of software. That is, the steps of the method disclosed in the embodiment of the present application can be embodied as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0291] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0292] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0293] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

Claims

1. A method for evaluating energy consumption of a heavy oil gathering and transportation system, characterized in that: The method comprises: According to the index values ​​corresponding to multiple preset energy consumption impact indicators of the heavy oil gathering and transportation system to be evaluated in multiple statistical time periods of multiple oilfield blocks, a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated is obtained; wherein each oilfield block corresponds to a preset gathering and transportation process flow; Using the index value normalization matrix, determining the pre-energy consumption influencing index with the greatest impact on the heavy oil gathering and transportation system to be evaluated from the multiple pre-energy consumption influencing indexes as the key energy consumption influencing index, and dividing the corresponding index value range intervals for the key energy consumption influencing index to obtain multiple key energy consumption influencing index value range intervals; For each of the preset gathering and transportation process flows and each of the key energy consumption influencing index value range intervals, according to the energy consumption and the amount of gathering and transportation liquid in all statistical time periods of the preset gathering and transportation process flows adopted by the heavy oil gathering and transportation system to be evaluated, the benchmark energy consumption of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flows and the key energy consumption influencing index value range intervals are obtained; Determine, based on the obtained multiple benchmark energy consumptions, the corresponding correction coefficients of the key energy consumption impact index values ​​when the heavy oil gathering and transportation system to be evaluated adopts each preset gathering and transportation process flow and is within each key energy consumption impact index value range; The comparable comprehensive energy consumption corresponding to each oilfield block is obtained according to the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated in all oilfield blocks and the correction coefficients of the multiple key energy consumption influencing index values ​​obtained.

2. The method according to claim 1, characterized in that The step of obtaining a normalized matrix of index values ​​corresponding to the heavy oil gathering and transportation system to be evaluated according to the index values ​​corresponding to multiple preset energy consumption impact indexes of the heavy oil gathering and transportation system to be evaluated in multiple statistical time periods of multiple oilfield blocks includes: Using the range method, normalizing the index values ​​corresponding to the plurality of preset energy consumption influencing indexes of the heavy oil gathering and transportation system to be evaluated in a plurality of statistical time periods of a plurality of oilfield blocks, to obtain normalized index values; The indicator value normalization matrix is ​​obtained based on the normalized indicator values.

3. The method according to claim 1, characterized in that The method of using the index value normalization matrix to determine the pre-energy consumption influencing index having the greatest impact on the heavy oil gathering and transportation system to be evaluated from the plurality of pre-energy consumption influencing indexes as the key energy consumption influencing index includes: The subjective weights corresponding to the plurality of preset energy consumption influencing indicators are calculated by using a sequential relationship analysis method; Based on the index value normalization matrix, a principal component analysis method is used to calculate the first objective weight corresponding to each of the plurality of preset energy consumption influencing indicators; Calculate the second objective weight corresponding to each of the plurality of preset energy consumption impact indicators by using the CRITIC weight method; Calculate the objective combined weights corresponding to each of the plurality of preset energy consumption impact indicators based on the first objective weights and the second objective weights of the plurality of preset energy consumption impact indicators, and calculate the subjective and objective comprehensive weights corresponding to each of the plurality of preset energy consumption impact indicators based on the subjective weights and the objective combined weights of the plurality of preset energy consumption impact indicators; The preset energy consumption influencing index having the greatest impact on the heavy oil gathering and transportation system to be evaluated is determined according to the subjective and objective comprehensive weights of the plurality of preset energy consumption influencing indexes, and the determined preset energy consumption influencing index is used as the key energy consumption influencing index.

4. The method according to claim 3, characterized in that The method of using the sequence relationship analysis method to calculate the subjective weights corresponding to the plurality of preset energy consumption impact indicators includes: For multiple preset important indicator selection methods, obtain the weight corresponding to each of the preset important indicator selection methods, and the preset energy consumption impact indicator selected by each of the preset important indicator selection methods from multiple preset energy consumption impact indicators; Using a preset function, the weights corresponding to the multiple preset important indicator selection methods and the selected preset energy consumption impact indicators are integrated and calculated to obtain the calculation result values ​​corresponding to the multiple preset important indicator selection methods, and the obtained multiple calculation result values ​​are processed in descending order, and the multiple preset energy consumption impact indicators are sorted according to the multiple calculation result values ​​processed in descending order; According to each of the preset important indicator selection methods, a relative importance interval value is assigned to each of the two adjacent preset energy consumption impact indicators, and an adjacent indicator comparison matrix is ​​established with the sorted plurality of the preset energy consumption impact indicators, and the relative importance point value of each of the two adjacent preset energy consumption impact indicators is obtained based on the adjacent indicator comparison matrix; The subjective weights corresponding to the plurality of preset energy consumption impact indicators are obtained according to the plurality of relative importance point values ​​obtained.

5. The method according to claim 3, characterized in that: The method of calculating the first objective weights corresponding to the plurality of preset energy consumption influencing indicators based on the indicator value normalization matrix and using the principal component analysis method includes: Obtaining all eigenvalues ​​of the index value normalization matrix, and calculating the variance contribution rate and cumulative contribution rate of each of the preset energy consumption impact indicators based on all eigenvalues; Obtaining a preset energy consumption impact index corresponding to a cumulative contribution rate greater than a preset contribution rate, and using the obtained preset energy consumption impact index and all preset energy consumption impact indicators arranged before the obtained preset energy consumption impact index as target preset energy consumption impact indicators; Establishing a principal component analysis model corresponding to all target preset energy consumption influencing indicators based on the normalized matrix of the indicator value and the eigenvector corresponding to the eigenvalue, and solving the linear combination coefficient in the principal component analysis model; For each of the preset energy consumption impact indicators, based on the linear combination coefficient in the principal component analysis model and the variance explanation rate and cumulative explanation rate of the preset energy consumption impact indicator, a comprehensive score coefficient of the preset energy consumption impact indicator is obtained; For each of the preset energy consumption impact indicators, a first objective weight of the preset energy consumption impact indicator is obtained based on the comprehensive score coefficients of all the preset energy consumption impact indicators and the comprehensive score coefficient of the preset energy consumption impact indicator.

6. The method according to claim 4, characterized in that The method of using the CRITIC weight method to calculate the second objective weights corresponding to the plurality of preset energy consumption impact indicators includes: Calculate the variability index values ​​corresponding to each of the plurality of preset energy consumption impact indicators, and for each of the preset energy consumption impact indicators, calculate the conflict index value between the preset energy consumption impact indicator and an adjacent preset energy consumption impact indicator as the conflict index value corresponding to the preset energy consumption impact indicator; wherein the adjacent preset energy consumption impact indicator is a preset energy consumption impact indicator adjacent to the preset energy consumption impact indicator among the plurality of preset energy consumption impact indicators that have been sorted; Calculate the amount of information corresponding to each of the preset energy consumption impact indicators based on the variability index value and the conflict index value corresponding to each of the preset energy consumption impact indicators; For each of the preset energy consumption impact indicators, a second objective weight of the preset energy consumption impact indicator is obtained according to the information amount of all the preset energy consumption impact indicators and the information amount of the preset energy consumption impact indicator.

7. The method according to claim 3, characterized in that The objective combined weights corresponding to the plurality of preset energy consumption influencing indicators are calculated based on the first objective weights and the second objective weights of the plurality of preset energy consumption influencing indicators; Obtaining objective weight preprocessing results corresponding to each of the preset energy consumption impact indicators based on the first objective weight and the second objective weight corresponding to each of the preset energy consumption impact indicators; For each of the preset energy consumption impact indicators, the objective combined weight of the preset energy consumption impact indicator is obtained according to the objective weight preprocessing results of all the preset energy consumption impact indicators and the objective weight preprocessing result of the preset energy consumption impact indicator.

8. The method according to claim 3, characterized in that The step of calculating the subjective and objective comprehensive weights corresponding to the plurality of preset energy consumption impact indicators based on the subjective weights and the objective combined weights of the plurality of preset energy consumption impact indicators comprises: Based on the minimum discrimination principle, corresponding boundary conditions are set according to the subjective weights and objective combined weights of the plurality of preset energy consumption influencing indicators to establish a target optimization function; Solve the target optimization function to obtain the subjective and objective comprehensive weight of each preset energy consumption influencing indicator.

9. The method according to claim 1, characterized in that: The method of determining the key energy consumption impact index value correction coefficient corresponding to each preset gathering and transportation process flow and each key energy consumption impact index value range interval when the heavy oil gathering and transportation system to be evaluated adopts each preset gathering and transportation process flow according to the obtained multiple benchmark energy consumptions includes: Selecting a benchmark energy consumption from the obtained multiple benchmark energy consumptions as the target benchmark energy consumption, and using a preset benchmark coefficient as a correction coefficient of a key energy consumption impact index value that the target benchmark can correspond to; In addition to the preset gathering and transportation process flow and the key energy consumption influencing indicator value range interval corresponding to the target benchmark energy consumption, for each preset gathering and transportation process flow and in each key energy consumption influencing indicator value range interval, based on the target benchmark energy consumption, the preset benchmark coefficient, and the benchmark energy consumption of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flow is adopted and in the key energy consumption influencing indicator value range interval, the key energy consumption influencing indicator value correction coefficient of the heavy oil gathering and transportation system to be evaluated when the preset gathering and transportation process flow is adopted and in the key energy consumption influencing indicator value range interval is obtained.

10. The method according to claim 9, characterized in that The comparable comprehensive energy consumption includes: the comparable comprehensive energy consumption per unit of liquid gathering and transportation and the comparable comprehensive energy consumption per unit of crude oil gathering and transportation. The comparable comprehensive energy consumption corresponding to each oil field block is obtained based on the production data and energy consumption of the heavy oil gathering and transportation system to be evaluated under all oil field blocks, and the correction coefficients of the multiple key energy consumption influencing index values ​​obtained, including: An oilfield block that adopts a preset gathering and transportation process corresponding to the target benchmark energy consumption and is within the index value range corresponding to the target benchmark energy consumption is used as the target oilfield block; the unit liquid volume gathering and transportation comprehensive energy consumption of the target oilfield block is obtained according to the unit liquid volume gathering and transportation gas consumption and unit liquid volume gathering and transportation electricity consumption of the heavy oil gathering and transportation system to be evaluated in the target oil block; the unit crude oil gathering and transportation comprehensive energy consumption of the target oilfield block is obtained according to the unit crude oil gathering and transportation electricity consumption and unit crude oil gathering and transportation gas consumption of the heavy oil gathering and transportation system to be evaluated in the target oil block; For each oil field block, perform the following operations: Determine the correction coefficient of the key energy consumption impact index value corresponding to the oil block in the oil field according to the preset gathering and transportation process flow of the oil block in the oil field and the key energy consumption impact index value range interval in which the key energy consumption impact index value is located; Obtain the comparable comprehensive energy consumption per unit liquid volume gathering and transportation of the oil block in the oil field according to the daily liquid production per well of the oil block in the oil field, the correction coefficient of the key energy consumption influencing index value corresponding to the oil block in the oil field, the daily liquid production per well of the target oil block in the oil field and the comprehensive energy consumption per unit liquid volume gathering and transportation of the target oil block in the oil field; According to the daily liquid production of a single well in the oil field block, the correction coefficient of the key energy consumption influencing index value corresponding to the oil field block, the daily liquid production of a single well in the target oil field block and the comprehensive energy consumption for gathering and transporting the crude oil liquid volume of the target oil field block, the comparable comprehensive energy consumption per unit crude oil gathering and transport of the oil field block is obtained.

11. An energy consumption evaluation device for a heavy oil gathering and transportation system, characterized in that: including memory and processor; The memory is used to store executable programs; The processor is used to read and execute the executable program to implement the energy consumption evaluation method for a heavy oil gathering and transportation system according to any one of claims 1 to 10.

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