Method for comprehensively evaluating energy efficiency of dense low-permeability gas field development gathering and transportation pipe network system

Abstract

The invention discloses an energy efficiency comprehensive evaluation method for a dense low-permeability gas field development gathering and transportation pipe network system, and the method comprises the following specific steps: S100, constructing an energy efficiency evaluation index system, and selecting an evaluation index; s200, determining a subjective weight by adopting an analytic hierarchy process (AHP); s300, determining an objective weight by adopting an entropy weight method; s400, performing dimensionless processing on the evaluation index value by adopting a fuzzy membership function method; and S500, by adopting a linear weighting method, multiplying the quantized value of the evaluation index by the weight coefficient of each index to obtain an evaluation value of each index, and adding the evaluation values to obtain a final total evaluation value. The gas field gathering and transportation process is systematically, comprehensively and deeply analyzed, the whole system is divided into a plurality of subsystems according to the gas field process, and the gas reservoirs, the shafts and the gathering and transportation pipe network system are classified and calculated by considering the influence conditions of the underground shafts and the gas reservoirs on the ground gathering and transportation system; the reliability of the energy efficiency level of the gas field gathering and transportation system calculated by the method is higher.

Description

Technical Field

[0001] This invention relates to the field of oil and gas gathering and transportation technology, specifically to a comprehensive energy efficiency evaluation method for a gathering and transportation pipeline system for the development of tight, low-permeability gas fields. Background Technology

[0002] The development of gas fields requires the establishment of corresponding surface gathering and transmission pipeline systems to realize the collection, processing, and transportation of natural gas. In the production and operation of a gas field, there are mutual influences and constraints among the gas reservoir, wellbore, and gathering and transmission pipeline system. Changes in development plans will affect the design and adjustment of surface gathering and transmission facilities, while unreasonable surface gathering and transmission pipeline system technology will hinder the stable operation of the gas field, affecting its stable production capacity and recovery rate. Therefore, there is an urgent need to construct an energy efficiency evaluation index system for the development and gathering pipeline system.

[0003] For example, Chinese patent application CN105046040A discloses an energy efficiency evaluation method for a condensate gas field gathering and transportation process system. Based on a black box model, it statistically analyzes the logistics of each link and calculates the energy efficiency of the surface process system and each local process system based on the first law of thermodynamics. In this method, energy efficiency refers to energy utilization efficiency, and the starting point of the natural gas production system mentioned in the index is the wellhead. However, this invention provides the direction for system energy saving optimization from four aspects: capacity, energy consumption, efficiency, and benefits. The starting point of the constructed energy efficiency index system is the gas reservoir.

[0004] The aforementioned patent provides an energy efficiency evaluation method for condensate gas field gathering and transportation systems; however, existing energy efficiency methods have limited applicability and cannot evaluate energy efficiency under various different conditions. In practice, research on energy efficiency evaluation methods for gas storage surface process systems employs the Delphi method to construct an energy efficiency evaluation index system for gas storage surface process systems, proposing a method based on a combination of the analytic hierarchy process (AHP) and fuzzy function method. The constructed energy efficiency evaluation index system mainly includes the energy efficiency of the surface dehydration system and the surface pressurization system, but does not involve underground indicators closely related to surface processes. The AHP is used to determine the weight coefficients of the energy efficiency evaluation indicators, and the calculation results are greatly affected by subjective factors. Summary of the Invention

[0005] This invention proposes a comprehensive energy efficiency evaluation method for the gathering and transportation pipeline system in tight, low-permeability gas fields. This method not only helps gas field managers understand the overall energy efficiency of the gas field's gathering and transportation system at various stages, but also identifies factors affecting the overall energy efficiency of the production system based on the evaluation results, providing technical support for improving energy efficiency and implementing energy-saving and consumption-reducing upgrades in gas fields.

[0006] This invention is implemented as follows:

[0007] A comprehensive energy efficiency evaluation method for gathering and transmission pipeline systems in tight, low-permeability gas field development is provided, and the specific steps of this evaluation method are as follows:

[0008] S100. Construct an energy efficiency evaluation index system and select evaluation indicators;

[0009] S200. Subjective weights are determined using the Analytic Hierarchy Process (AHP).

[0010] S300. The objective weights are determined using the entropy weight method.

[0011] S400. The evaluation index values ​​are dimensionless by using the fuzzy membership function method.

[0012] S500. Using the linear weighting method, the quantitative value of the evaluation index is multiplied by the weight coefficient of each index to obtain the evaluation value of each index. Then, these evaluation values ​​are added together to obtain the final total evaluation value.

[0013] Preferably, in step S100, an energy efficiency evaluation index system is constructed and evaluation indicators are selected; from both technical and economic dimensions, the comprehensive energy efficiency evaluation index of the natural gas development and transmission pipeline network system is evaluated, resulting in 10 technical indicators and 4 economic indicators. The evaluation index system is shown in Table 1.

[0014] Table 1. Comprehensive Energy Efficiency Evaluation Index System for Natural Gas Development and Transmission Pipeline Network Systems

[0015]

[0016] Preferably, in step S200, questionnaires are distributed to oil and gas gathering and transportation technology experts to determine the degree of influence of different indicators on the development of the gathering and transportation pipeline network system; based on the hierarchical structure and the constructed judgment matrix, an expert scoring method is used to score each indicator according to a scale of "1 to 9"; the judgment matrix is ​​as follows:

[0017]

[0018] Among them, a ij (i,j=1,2,3…n) represents the importance of indicator i relative to indicator j, then a ji It represents the importance of indicator j relative to i, and a ij =1 / a ji ,a ij >0,a ii =1;

[0019] Using the expert scoring method, each indicator was scored according to the scale of "1-9" in Table 2.

[0020] Table 2. Scales “1-9” and their meanings

[0021]

[0022] Preferably, in step S200, the indicator weights are determined as follows:

[0023]

[0024] right Normalization is performed, and the normalization result is the weight of each indicator.

[0025] Reliability of test results;

[0026] First, find the largest eigenvalue: Then calculate the value of CI:

[0027] Then, by looking up the table, the value corresponding to RI is obtained, and the consistency ratio is calculated: If CR is less than 0.1, it means that the results obtained by using the AHP method are reliable and can be adopted; otherwise, it needs to be readjusted.

[0028] Preferably, in step S300, a membership matrix is ​​established, dividing each evaluation index into m evaluation intervals, and a level boundary value a is set for each evaluation index's evaluation interval. j For each evaluation indicator (j = 1, 2, ..., m), calculate the membership degree of the evaluation interval relative to the evaluation set, and then determine the membership degree. Membership degree of the constructed matrix The resulting matrix is ​​as follows:

[0029]

[0030] make Then, normalization is performed to obtain t. ij ;

[0031] Positive indicators:

[0032]

[0033] Negative indicators:

[0034]

[0035] Then r' ij or r” ij Let t be the value of the j-th evaluation interval of the i-th influencing factor (i = 1, 2, ..., n; j = 1, 2, ..., m), and the normalized data be denoted as t. ij

[0036] Preferably, in step S300, the i-th influencing factor t under the j-th evaluation interval is calculated.ij The weight of each evaluation indicator is calculated based on its proportion within the evaluation interval and the entropy value of the j-th evaluation interval. The weights of each evaluation indicator are then calculated, and the specific calculations are as follows:

[0037] t ij The proportion of this evaluation interval

[0038] The entropy value of the evaluation interval for the j-th item:

[0039] Calculate the information entropy redundancy d j =1-e j Then calculate the weights of each indicator. The weights are calculated and determined as follows: W = (w1, w2, ..., w n The weights in the formula are summed to 1.

[0040] Preferably, in step S400, the evaluation indexes are dimensionless by using the fuzzy membership function method; first, the lower limit value a and the upper limit value b of each evaluation index are determined, and then the ridge fuzzy membership function is used to perform dimensionless processing on the indexes.

[0041] Positive indices are calculated using fuzzy membership functions based on ascending ridge distribution:

[0042]

[0043] Inverse indices are calculated using ridge-shaped fuzzy membership functions:

[0044]

[0045] The evaluation index value x i Substituting the values ​​into the fuzzy membership function calculation, we can obtain the membership degree value f(x) of the index. i After removing the influence of dimensions, the standardized value F(x) of the index is obtained. i )=f(x i )×100.

[0046] Preferably, in step S500, a linear weighting method is used to multiply the quantitative value of the evaluation index by the weight coefficient of each index to obtain the evaluation value of each index, and then these evaluation values ​​are added together to obtain the final total evaluation value.

[0047] Calculate the evaluation value of the criteria layer indicators:

[0048] In the formula U i U is the evaluation value of the i-th criterion layer indicator. ij The evaluation value of the j-th indicator in the i-th criterion layer, ω ijThe weight of the j-th indicator in the i-th criterion layer indicator;

[0049] Calculate the evaluation value of the target layer index:

[0050] In the formula, U is the comprehensive evaluation value of the target layer index. i It is the evaluation value of the i-th criterion layer in the target layer index, ω i is the weight of the i-th criterion layer in the target layer index.

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

[0052] 1. This invention provides a systematic and comprehensive in-depth analysis of gas field gathering and transportation technology. Based on the gas field technology, the entire system is divided into several subsystems. Considering the impact of underground wellbores and gas reservoirs on the surface gathering and transportation system, the gas reservoir, wellbore, and gathering and transportation pipeline systems are classified and calculated. This method yields more reliable energy efficiency levels for the gas field gathering and transportation system. This invention can not only be applied to the comprehensive energy efficiency evaluation of gathering and transportation systems in tight, low-permeability gas fields, but also, with targeted adjustments to the indicators, can be used in the comprehensive energy efficiency evaluation of development and gathering and transportation systems in other shale gas fields, coalbed methane fields, and conventional gas fields.

[0053] 2. This evaluation method can be used not only during the operation of the gathering and transportation system, but also for selecting optimal gathering and transportation process schemes. The algorithm proposed in this invention can be applied to scenarios such as gas production and drilling that require comprehensive energy efficiency evaluation based on multiple indicators, and has strong promotional value. This invention can systematically and comprehensively evaluate the overall energy efficiency level of tight, low-permeability gas fields, helping gas fields identify energy efficiency bottlenecks and providing important support for achieving sustainable development of gas fields.

[0054] 3. The energy efficiency comprehensive evaluation method of this invention is selected considering that the development and transportation system index system has many types of indicators, a wide range of aspects, and multiple dimensions. Using a single evaluation method can easily lead to poor rationality and accuracy of the evaluation results. Therefore, the Analytic Hierarchy Process (AHP) and the entropy weight method are used to determine the weights of the indicators. The subjectivity of the AHP and the objectivity of the entropy weight method are combined to form the comprehensive energy efficiency evaluation method for the development and transportation system.

[0055] 4. Based on operational data from typical gas field gathering and transportation systems, this invention selects energy efficiency indicators for developing gathering and transportation pipeline systems and constructs an energy efficiency evaluation index system for these systems using the analytic hierarchy process (AHP) and entropy weight method. The evaluation indicators are dimensionless using the fuzzy membership function method, and the evaluation values ​​are calculated using a linear weighting method. Attached Figure Description

[0056] Figure 1 Flowchart of a comprehensive energy efficiency evaluation method for developing and transmitting pipeline systems based on typical tight, low-permeability gas fields;

[0057] Figure 2 Chart showing the trend of capacity changes before and after the adjustment of Block A system;

[0058] Figure 3 A graph showing the change in production rate of a certain well after adjusting the foaming and defoaming agent;

[0059] Figure 4 A graph showing the change in production output before and after the change in the second-row extraction process of a certain well. Detailed Implementation

[0060] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0061] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details:

[0062] Example 1

[0063] like Figure 1 As shown, a comprehensive energy efficiency evaluation method for a gathering and transportation pipeline system in the development of tight, low-permeability gas fields is presented. The specific steps of this evaluation method are as follows:

[0064] S100. Construct an energy efficiency evaluation index system and select evaluation indicators;

[0065] S200. Subjective weights are determined using the Analytic Hierarchy Process (AHP).

[0066] S300. The objective weights are determined using the entropy weight method.

[0067] S400. The evaluation index values ​​are dimensionless by using the fuzzy membership function method.

[0068] S500. Using the linear weighting method, the quantitative value of the evaluation index is multiplied by the weight coefficient of each index to obtain the evaluation value of each index. Then, these evaluation values ​​are added together to obtain the final total evaluation value.

[0069] Example 2

[0070] like Figure 1 As shown, a comprehensive energy efficiency evaluation method for a gathering and transportation pipeline system in the development of tight, low-permeability gas fields is presented. The specific steps of this evaluation method are as follows:

[0071] S100. Construct an energy efficiency evaluation index system and select evaluation indicators;

[0072] Based on the principles of indicator system establishment, the comprehensive evaluation indicators of energy efficiency of natural gas development and transmission pipeline network system were evaluated from both technical and economic dimensions, resulting in 10 technical indicators and 4 economic indicators. The evaluation indicator system is shown in Table 1.

[0073] Table 1. Comprehensive Energy Efficiency Evaluation Index System for Natural Gas Development and Transmission Pipeline Network Systems

[0074]

[0075]

[0076] S200. Subjective weights are determined using the Analytic Hierarchy Process (AHP).

[0077] Questionnaires were distributed to oil and gas gathering and transportation technology experts to determine the impact of different indicators on the development of gathering and transportation pipeline systems. Based on the hierarchical structure and constructed judgment matrix, expert scoring was conducted, assigning scores to each indicator on a scale of 1 to 9. The judgment matrix is ​​as follows:

[0078]

[0079] Among them, a ij (i,j=1,2,3…n) represents the importance of indicator i relative to indicator j, then a ji It represents the importance of indicator j relative to i, and a ij =1 / a ji ,a ij >0,a ii =1;

[0080] Using the expert scoring method, each indicator was scored according to the scale of "1-9" in Table 2.

[0081] Table 2. Scales “1-9” and their meanings

[0082]

[0083] The weights are calculated using the analytic hierarchy process (AHP), and the weight criteria are determined by verifying the consistency of the judgment matrix.

[0084] The indicator weights are determined as follows:

[0085]

[0086] right Normalization is performed, and the normalization result is the weight of each indicator.

[0087] Use the AHP method and implement the consistency check of the judgment matrix;

[0088] First, find the largest eigenvalue: Then calculate the value of CI: Then, by looking up the table, the value corresponding to RI is obtained, and the consistency ratio is calculated: If CR is less than 0.1, it means that the results obtained by using the AHP method are reliable and can be adopted; otherwise, it needs to be readjusted.

[0089] Table 3. Values ​​of RI

[0090] Matrix order 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0091] S300. The objective weights are determined using the entropy weight method.

[0092] Establish a membership matrix, divide each evaluation indicator into m evaluation intervals, and set the level boundary value 'a' corresponding to the evaluation interval of each evaluation indicator. j For each evaluation indicator (j = 1, 2, ..., m), calculate the membership degree of the evaluation interval relative to the evaluation set, and then determine the membership degree. Membership degree of the constructed matrix The matrix formed;

[0093] Then calculate Convert to relative values, let Then, normalization is performed to obtain t. ij ;

[0094] Finally, calculate the t of the i-th influencing factor under the j-th evaluation interval. ij The weight of each evaluation indicator is calculated based on the proportion of the evaluation interval and the entropy value of the j-th evaluation interval. Then, the weight of each evaluation indicator is calculated, and the weight of each evaluation indicator is obtained.

[0095] Find The matrix formed:

[0096]

[0097] The normalization process is as follows:

[0098] Positive indicators:

[0099]

[0100] Negative indicators:

[0101]

[0102] Where r' ij or r” ij Let t be the value of the j-th evaluation interval of the i-th influencing factor (i = 1, 2, ..., n; j = 1, 2, ..., m), and the normalized data be denoted as t. ij

[0103] Calculate the t of the i-th influencing factor within the j-th evaluation interval. ij The weight of each evaluation indicator is calculated based on its proportion within the evaluation interval and the entropy value of the j-th evaluation interval. The specific calculation is as follows:

[0104] t ij The proportion of this evaluation interval

[0105] The entropy value of the evaluation interval for the j-th item:

[0106] Calculate the information entropy redundancy d j =1-e j Then calculate the weights of each indicator. The weights are calculated and determined as follows: W = (w1, w2, ..., w n The weights in the formula are summed to 1.

[0107] S400. The evaluation index values ​​are dimensionless by using the fuzzy membership function method.

[0108] The evaluation indicators were dimensionless using the fuzzy membership function method. First, the lower limit value 'a' and the upper limit value 'b' of each evaluation indicator were determined. Then, the indicators were dimensionless using the ridge fuzzy membership function. The upper and lower limits of each indicator and the types of fuzzy membership functions are shown in Table 4.

[0109] Table 4. Upper and lower limits of evaluation indicators and types of fuzzy membership functions

[0110]

[0111] Positive indices are calculated using fuzzy membership functions based on ascending ridge distribution:

[0112]

[0113] Inverse indices are calculated using ridge-shaped fuzzy membership functions:

[0114]

[0115] The evaluation index value x i Substituting the values ​​into the fuzzy membership function calculation, we can obtain the membership degree value f(x) of the index. i After removing the influence of dimensions, the standardized value F(x) of the index is obtained. i )=f(x i )×100.

[0116] S500. Using the linear weighting method, the quantitative value of the evaluation index is multiplied by the weight coefficient of each index to obtain the evaluation value of each index. Then, these evaluation values ​​are added together to obtain the final total evaluation value.

[0117] The linear weighting method is used to multiply the quantitative value of the evaluation index by the weight coefficient of each index to obtain the evaluation value of each index. Then, these evaluation values ​​are added together to obtain the final total evaluation value.

[0118] Calculate the evaluation value of the criteria layer indicators:

[0119] In the formula U i U is the evaluation value of the i-th criterion layer indicator. ij The evaluation value of the j-th indicator in the i-th criterion layer, ω ij The weight of the j-th indicator in the i-th criterion layer indicator;

[0120] Calculate the evaluation value of the target layer index:

[0121] In the formula, U is the comprehensive evaluation value of the target layer index. i It is the evaluation value of the i-th criterion layer in the target layer index, ω i is the weight of the i-th criterion layer in the target layer index.

[0122] Example 3

[0123] The method described in Example 2 was used to conduct a comprehensive energy efficiency evaluation of the gathering and transmission pipeline system for the DS tight, low-permeability gas field. This evaluation identified the system's energy efficiency bottlenecks and can provide direction for the optimization and adjustment of older gas field blocks. Taking the operational data of Block A in the DS gas field as an example, Block A has a total of 133 wells, 8 compressors, and 4 main gathering and transmission pipelines. Production data from Block A from its commissioning to June 2022 (30 months) was statistically analyzed. Monthly production data was used as the evaluation dataset for a retrospective evaluation, and the evaluation scores are shown in Table 5-10.

[0124] Table 5 Comprehensive Evaluation Calculation of Gas Reservoir Development

[0125]

[0126] Table 6 Calculation of Wellbore Comprehensive Evaluation Score

[0127]

[0128] Table 7 Calculation of Comprehensive Evaluation Score for Gathering and Transmission Pipeline System

[0129]

[0130]

[0131] Table 8 Calculation of Comprehensive Evaluation Score for Technical Indicators

[0132]

[0133] Table 9 Calculation of Comprehensive Economic Evaluation Score

[0134]

[0135] Table 10 Calculation of Total Overall Evaluation Score

[0136]

[0137] According to the energy efficiency evaluation results, the energy efficiency score for this block is 60.55, indicating a low level of energy efficiency. Comparing both technical and economic dimensions, the technical indicators score is also low. Analysis revealed poor energy efficiency in the wellbore, pipeline network, and booster system.

[0138] As the gas field is further developed, formation energy gradually decreases. Some older wells suffer from insufficient fluid carrying capacity and intermittent production. Simultaneously, the failure of drainage processes leads to excessively high wellhead back pressure, resulting in a gradual reduction in gas field production capacity. This decrease in underground production capacity leads to a reduction in the efficiency and load rate of the gas gathering station compressor units, thereby lowering the energy efficiency of the surface pipeline network. The following table shows the production capacity trends before and after the system adjustment in Block A during 2021. Figure 2 As shown. In the second half of 2021, a series of adjustments were carried out in this block. Regarding the wellbore, the efficiency improvement plan mainly focused on strengthening the selection of drainage agents, changing the drainage process, and implementing wellbore anomaly management to effectively remove accumulated fluid and increase gas production. For example... Figure 3 , Figure 4 As shown, the production change curve of well 1 after the foaming and drainage agent adjustment and the production change curve of well 2 before and after the drainage and production process change show that the production was significantly improved after the first adjustment.

[0139] Generally, economic indicators are one-off indicators that remain largely unchanged during gas field production and operation, while technical indicators will continuously change with gas field development. Given the determined weights, technical indicators have a greater impact on energy efficiency evaluation results than economic indicators. Therefore, the difference in energy efficiency levels before and after system adjustments is mainly reflected in technical indicators. This study evaluates the energy efficiency levels before and after system adjustments in past gas field production processes. Using the second half of 2021 as the adjusted period, production data for Block A after the adjustment are collected, and then energy efficiency evaluation calculations are performed. The comprehensive evaluation calculation of the gathering and transmission pipeline network system of Block A in the DS gas field after adjustment is as follows:

[0140]

[0141] Tables 11-14 show the following:

[0142] Table 11 Comprehensive Evaluation Calculation of Gas Reservoir Development

[0143]

[0144] Table 12 Calculation of Wellbore Comprehensive Evaluation Score

[0145]

[0146] Table 13 Calculation of Comprehensive Evaluation Score for Gathering and Transmission Pipeline System

[0147]

[0148] Table 14 Calculation of Comprehensive Evaluation Score for Technical Indicators

[0149]

[0150] Based on the energy efficiency evaluation results before and after the adjustment of this block, it can be seen that the evaluation method proposed in this invention can accurately reflect the overall energy efficiency level of the gas field's underground surface and the problems existing at different times. The energy efficiency of Block A before and after the historical adjustment was evaluated, and the results show that the energy efficiency level of this block after the adjustment is higher than before the adjustment. The evaluation results are consistent with the actual production situation on site, thus verifying the rationality of the evaluation method invented in this patent.

[0151] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive energy efficiency evaluation method for a gathering and transportation pipeline system in the development of tight, low-permeability gas fields, characterized in that, The specific steps of this evaluation method are as follows: S100. Construct an energy efficiency evaluation index system and select evaluation indicators; S200. Subjective weights are determined using the Analytic Hierarchy Process (AHP). S300. The objective weights are determined using the entropy weight method. S400. The evaluation index values ​​are dimensionless by using the fuzzy membership function method. S500. Using the linear weighting method, the quantitative value of the evaluation index is multiplied by the weight coefficient of each index to obtain the evaluation value of each index. Then, these evaluation values ​​are added together to obtain the final total evaluation value.

2. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S100, an energy efficiency evaluation index system is constructed and evaluation indicators are selected. From the two dimensions of technology and economy, the comprehensive energy efficiency evaluation index of the natural gas development and transmission pipeline network system is evaluated, resulting in 10 technical indicators and 4 economic indicators. The evaluation index system is shown in Table 1. Table 1. Comprehensive Energy Efficiency Evaluation Index System for Natural Gas Development and Transmission Pipeline Network Systems 3. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S200, questionnaires are distributed to oil and gas gathering and transportation technology experts to determine the degree of influence of different indicators on the development of the gathering and transportation pipeline network system; based on the hierarchical structure and the constructed judgment matrix, an expert scoring method is used to score each indicator according to a scale of "1 to 9"; the judgment matrix is ​​as follows: Among them, a ij (i,j=1,2,3…n) represents the importance of indicator i relative to indicator j, then a ji It represents the importance of indicator j relative to i, and a ij =1 / a ji ,a ij >0,a ii =1; Using the expert scoring method, each indicator was scored according to the scale "1-9" in Table 2. Table 2. Scales "1-9" and their meanings 4. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S200, the indicator weights are determined as follows: right Normalization is performed, and the normalization result is the weight of each indicator. Reliability of test results; First, find the largest eigenvalue: Then calculate the value of CI: Then, by looking up the table, the value corresponding to RI is obtained, and the consistency ratio is calculated: If CR is less than 0.1, it means that the results obtained by using the AHP method are reliable and can be adopted; otherwise, it needs to be readjusted.

5. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S300, a membership matrix is ​​established, dividing each evaluation indicator into m evaluation intervals, and setting the level boundary value a corresponding to the evaluation interval of each evaluation indicator. j For each evaluation indicator (j = 1, 2, ..., m), calculate the membership degree of the evaluation interval relative to the evaluation set, and then determine the membership degree. Membership degree of the constructed matrix The resulting matrix is ​​as follows: make Then, normalization is performed to obtain t. ij ; Positive indicators: Negative indicators: Then r' ij or r” ij Let t be the value of the j-th evaluation interval of the i-th influencing factor (i = 1, 2, ..., n; j = 1, 2, ..., m), and the normalized data be denoted as t. ij 6. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 5, characterized in that, In step S300, the i-th influencing factor t under the j-th evaluation interval is calculated. ij The weight of each evaluation indicator is calculated based on its proportion within the evaluation interval and the entropy value of the j-th evaluation interval. The weights of each evaluation indicator are then calculated, and the specific calculations are as follows: t ij The proportion of this evaluation interval The entropy value of the evaluation interval for the j-th item: Calculate the information entropy redundancy d j =1-e j Then calculate the weights of each indicator. The weights are calculated and determined as follows: W = (w1, w2, ..., w n The weights in the formula are summed to 1.

7. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S400, the evaluation indexes are dimensionless by using the fuzzy membership function method. First, the lower limit value a and the upper limit value b of each evaluation index are determined, and then the ridge fuzzy membership function is used to dimensionless the indexes. Positive indices are calculated using fuzzy membership functions based on ascending ridge distribution: Inverse indices are calculated using ridge-shaped fuzzy membership functions: The evaluation index value x i Substituting the values ​​into the fuzzy membership function calculation, we can obtain the membership degree value f(x) of the index. i After removing the influence of dimensions, the standardized value F(x) of the index is obtained. i )=f(x i )×100.

8. The method for comprehensive energy efficiency evaluation of a tight, low-permeability gas field development gathering and transmission pipeline system according to claim 1, characterized in that, In step S500, a linear weighting method is used to multiply the quantitative value of the evaluation index by the weight coefficient of each index to obtain the evaluation value of each index, and then these evaluation values ​​are added together to obtain the final total evaluation value. Calculate the evaluation value of the criteria layer indicators: In the formula U i U is the evaluation value of the i-th criterion layer indicator. ij The evaluation value of the j-th indicator in the i-th criterion layer, ω ij The weight of the j-th indicator in the i-th criterion layer indicator; Calculate the evaluation value of the target layer index: In the formula, U is the comprehensive evaluation value of the target layer index. i It is the evaluation value of the i-th criterion layer in the target layer index, ω i is the weight of the i-th criterion layer in the target layer index.

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