Method and system for optimizing 100-kilometer friction resistance of reference flow of long-distance liquid pipeline

Abstract

The invention provides a reference flow 100-kilometer friction optimization method and system for a long-distance liquid conveying pipeline. The reference flow 100-kilometer friction optimization method for the long-distance liquid conveying pipeline comprises the following steps that the concept of reference flow 100-kilometer friction is determined; according to the concept of the reference flow 100-kilometer friction, a calculation formula of the reference flow 100-kilometer friction is determined; according to the calculation formula of the reference flow hundred-kilometer friction resistance, the reference flow hundred-kilometer friction resistance change trend of the long-distance liquid conveying pipeline is calculated; analyzing the pipe cleaning effect of the long-distance liquid pipeline according to the change trend of the reference flow 100-kilometer friction resistance of the long-distance liquid pipeline, making a reference flow 100-kilometer friction resistance research scheme, and optimizing the operation mode of a crude oil pipe network. According to the technical scheme, data support is provided for the pipe cleaning scheme, and the pipe cleaning period and the pipe cleaning time are optimized.

Description

Background Art

[0002] During the pipeline transportation process, crude oil will be waxed and scaled on the pipe wall, which will deteriorate the fluidity of the pipeline, increase the transportation energy consumption, and even affect the safety of the pipeline. By studying the friction resistance, it can be determined whether the crude oil flows smoothly in the pipeline and whether the current operation mode is optimal. The study of friction resistance has always been an important part of the optimization operation analysis of crude oil pipelines. In the past, the friction resistance per 100 kilometers between stations was used as the research object, and the impact of the change in output on friction resistance was not considered. When the output changes, the friction resistance per 100 kilometers also changes dramatically. In daily operation, the output changes frequently, so it is difficult to use the law of change of friction resistance per 100 kilometers to explain the problem or guide operation. Therefore, it is of great significance to eliminate the influence of flow on friction resistance, use the reference flow friction resistance data to quantitatively study the influence of wax and scale on the inner wall of the pipeline on the flow characteristics of the pipeline, and scientifically formulate the cleaning cycle. Summary of the invention

[0003] The present application provides a method and system for optimizing the friction resistance per 100 kilometers of a long-distance liquid pipeline benchmark flow rate, which are used to optimize the operation mode of a crude oil pipeline network.

[0004] In a first aspect, a method for optimizing the friction resistance of a long-distance liquid pipeline at a benchmark flow rate per 100 kilometers is provided, comprising the following steps:

[0005] Determine the concept of friction resistance per 100 kilometers of reference flow;

[0006] According to the concept of friction resistance per 100 km of base flow rate, the calculation formula of friction resistance per 100 km of base flow rate is determined;

[0007] According to the calculation formula of friction resistance per 100 km of base flow rate, the friction resistance change trend of base flow rate per 100 km of long-distance liquid pipeline is calculated;

[0008] The cleaning effect of long-distance liquid pipelines is analyzed according to the changing trend of friction resistance per 100 km at the benchmark flow rate of long-distance liquid pipelines, and a research plan on friction resistance per 100 km at the benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network.

[0009] In the above technical scheme, the concept of frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the concept of frictional resistance per 100 kilometers at a benchmark flow rate, a calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate, a change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline is calculated; based on the change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline, the cleaning effect of the long-distance liquid pipeline is analyzed, and a research plan for the frictional resistance per 100 kilometers at a benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network; data support is provided for the cleaning plan to optimize the cleaning cycle and cleaning time.

[0010] In a specific implementation scheme, the concept of the reference flow rate per 100 km friction resistance is: converting the inter-station per 100 km friction resistance of stations A and B to a value under a certain flow rate;

[0011] Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B.

[0012] In a specific implementation scheme, according to the concept of friction resistance per 100 kilometers of reference flow rate, the steps of determining the calculation formula of friction resistance per 100 kilometers of reference flow rate are specifically as follows:

[0013] According to the concept of frictional resistance per 100 km / h for benchmark flow, historical operating experience, theoretical calculations and data after process adjustment are combined with statistical analysis methods to sort out and screen the data of long-distance liquid pipelines, and determine the calculation formula for frictional resistance per 100 km / h for benchmark flow based on steady-state energy equation and turbulent head loss.

[0014] In a specific implementation scheme, the benchmark flow rate 100 kilometers friction resistance research scheme is a graded scheme.

[0015] In a specific embodiment, the hierarchical scheme includes a primary scheme, a secondary scheme and a tertiary scheme, wherein:

[0016] The first-level solution includes calculating friction data and turbulent head loss;

[0017] The secondary solution includes establishing a friction calculation model for benchmark flow per 100 kilometers; and performing parameter history fitting;

[0018] The three-level solution includes establishing a friction prediction model; establishing a friction monitoring alarm; and conducting pipeline startup heat flow analysis and pipeline cleaning analysis.

[0019] In a specific implementation scheme, the friction prediction model is used to optimize the pump allocation method, pipeline cleaning and furnace start-up hot washing operations in the operation mode of the crude oil pipeline network by predicting the reference flow rate per 100 kilometers during the operating conditions.

[0020] In a second aspect, a system for optimizing the friction resistance of a long-distance liquid pipeline at a benchmark flow rate per 100 kilometers is provided, including: a concept module for determining the concept of the friction resistance of a benchmark flow rate per 100 kilometers;

[0021] A formula module is used to determine the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate according to the concept of friction resistance per 100 kilometers of the benchmark flow rate;

[0022] A calculation module, used to calculate the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline according to the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate;

[0023] The solution module is used to analyze the cleaning effect of long-distance liquid pipelines according to the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline, and to formulate a research plan for the friction resistance per 100 kilometers of the benchmark flow rate to optimize the operation mode of the crude oil pipeline network.

[0024] In the above technical scheme, the concept of frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the concept of frictional resistance per 100 kilometers at a benchmark flow rate, a calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate, a change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline is calculated; based on the change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline, the cleaning effect of the long-distance liquid pipeline is analyzed, and a research plan for the frictional resistance per 100 kilometers at a benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network; data support is provided for the cleaning plan to optimize the cleaning cycle and cleaning time.

[0025] In a specific implementation scheme, the scheme module includes a primary scheme unit, a secondary scheme unit and a tertiary scheme unit, wherein:

[0026] The first-level solution unit is used to calculate friction data and turbulent head loss;

[0027] The secondary scheme unit is used to establish a friction calculation model for a reference flow rate per 100 kilometers and to perform parameter history fitting;

[0028] The three-level solution unit is used to establish a friction prediction model; establish a friction monitoring alarm; and perform pipeline startup heat flow analysis and pipeline cleaning analysis.

[0029] In a specific implementation scheme, the friction prediction model is used to optimize the pump allocation method, pipeline cleaning and furnace start-up hot washing operations in the operation mode of the crude oil pipeline network by predicting the reference flow rate per 100 kilometers during the operating conditions.

[0030] In a specific implementation scheme, the concept of the reference flow rate per 100 km friction resistance is: converting the inter-station per 100 km friction resistance of stations A and B to a value under a certain flow rate;

[0031] Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers provided in an embodiment of the present application;

[0033] Figure 2A structural block diagram of a long-distance liquid pipeline benchmark flow rate per 100 kilometers friction optimization system provided in an embodiment of the present application;

[0034] Figure 3 A structural block diagram of a friction study scheme for a benchmark flow rate of 100 km provided in an embodiment of the present application;

[0035] Figure 4 The friction trend chart of 100 kilometers between stations along the Shilan Line in 2019 provided in the embodiment of the present application;

[0036] Figure 5 The friction trend chart of the entire Shilan Line per 100 kilometers in 2019 provided in the embodiment of the present application;

[0037] Figure 6 A schematic diagram of a formula for predicting the friction change per 100 kilometers of the Shilan Line provided in an embodiment of the present application;

[0038] Figure 7 A schematic diagram of the friction change of the Shilan Line benchmark flow rate per 100 kilometers provided in an embodiment of the present application;

[0039] Figure 8 A schematic diagram of the Shilan line baseline flow alarm value setting provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The present application is further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present application will become clearer and more specific.

[0041] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0042] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0043] To facilitate understanding of the method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate of 100 kilometers provided in the embodiment of the present application, its application scenario is first explained. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate of 100 kilometers provided in the embodiment of the present application is used to optimize the operation mode of the crude oil pipeline network. During pipeline transportation, crude oil will be waxed and scaled on the pipe wall, which will deteriorate the fluidity of the pipeline, increase the transportation energy consumption, and even affect the safety of the pipeline. By studying the friction resistance, it can be determined whether the crude oil flows smoothly in the pipeline and whether the current operation mode is optimal. The study of friction resistance has always been an important part of the optimization operation analysis of the crude oil pipeline network. In the past, the friction resistance of 100 kilometers between stations was used as the research object, and the impact of the change in output on the friction resistance was not considered. When the output changes, the friction resistance of 100 kilometers also changes dramatically. In daily operation, the output changes frequently, so it is difficult to use the law of the change of friction resistance of 100 kilometers to explain the problem or guide the operation. Therefore, it is of great significance to eliminate the influence of flow on friction resistance, use the reference flow friction resistance data to quantitatively study the influence of wax and scaling on the inner wall of the pipeline on the flow characteristics of the pipeline, and scientifically formulate the cleaning cycle. To this end, the present application provides a method for optimizing the friction resistance of a long-distance liquid pipeline benchmark flow rate per 100 kilometers to optimize the operation mode of the crude oil pipeline network. The following is a detailed description of the method with reference to the specific drawings and embodiments.

[0044] refer to Figures 1 to 8 , Figure 1 A flowchart of a method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers provided in an embodiment of the present application; Figure 2 A structural block diagram of a long-distance liquid pipeline benchmark flow rate per 100 kilometers friction optimization system provided in an embodiment of the present application; Figure 3 A structural block diagram of a friction study scheme for a benchmark flow rate of 100 km provided in an embodiment of the present application; Figure 4 The friction trend chart of 100 kilometers between stations along the Shilan Line in 2019 provided in the embodiment of the present application; Figure 5 The friction trend chart of the entire Shilan Line per 100 kilometers in 2019 provided in the embodiment of the present application; Figure 6 A schematic diagram of a formula for predicting the friction change per 100 kilometers of the Shilan Line provided in an embodiment of the present application; Figure 7 A schematic diagram of the friction change of the Shilan Line benchmark flow rate per 100 kilometers provided in an embodiment of the present application; Figure 8 A schematic diagram of the Shilan line baseline flow alarm value setting provided in an embodiment of the present application.

[0045] exist Figure 1 In the embodiment of the present application, a method for optimizing the friction resistance of a long-distance liquid pipeline benchmark flow rate per 100 kilometers is provided, comprising the following steps:

[0046] Determine the concept of friction resistance per 100 kilometers of reference flow;

[0047] According to the concept of friction resistance per 100 km of base flow rate, the calculation formula of friction resistance per 100 km of base flow rate is determined;

[0048] According to the calculation formula of friction resistance per 100 km of the benchmark flow rate, the friction resistance per 100 km of the long-distance liquid pipeline is calculated; the friction resistance per 100 km change trend is to calculate the friction resistance difference between two stations within a time period using the calculation formula of friction resistance per 100 km of the benchmark flow rate, so as to judge the friction resistance change during this period;

[0049] The cleaning effect of long-distance liquid pipelines is analyzed according to the changing trend of friction resistance per 100 km at the benchmark flow rate of long-distance liquid pipelines, and a research plan on friction resistance per 100 km at the benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network.

[0050] In the above technical scheme, the concept of frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the concept of frictional resistance per 100 kilometers at a benchmark flow rate, the calculation formula of frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the calculation formula of frictional resistance per 100 kilometers at a benchmark flow rate, the trend of frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline is calculated; based on the trend of frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline, the cleaning effect of the long-distance liquid pipeline is analyzed, and a research plan on frictional resistance per 100 kilometers at a benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network; data support is provided for the cleaning plan to optimize the cleaning cycle and cleaning time. In a specific feasible implementation plan, the concept of frictional resistance per 100 kilometers at a benchmark flow rate is: converting the frictional resistance per 100 kilometers between stations AB to a value at a certain flow rate;

[0051] Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B.

[0052] In a specific implementation scheme, according to the concept of friction resistance per 100 kilometers of reference flow rate, the steps of determining the calculation formula of friction resistance per 100 kilometers of reference flow rate are specifically as follows:

[0053] According to the concept of frictional resistance per 100 km / h for benchmark flow, historical operating experience, theoretical calculations and data after process adjustment are combined with statistical analysis methods to sort out and screen the data of long-distance liquid pipelines, and determine the calculation formula for frictional resistance per 100 km / h for benchmark flow based on steady-state energy equation and turbulent head loss.

[0054] Specifically, the theoretical formula for converting the friction resistance per 100 kilometers of different outputs to the benchmark output is as follows:

[0055] The formula for calculating the friction resistance per 100 kilometers between two stations AB is:

[0056]

[0057] Among them, P f — Friction resistance per 100 km between stations AB, MPa;

[0058] P c —Outlet pressure of station A, MPa;

[0059] P R —Entry pressure at station B, MPa;

[0060] △P H —Elevation pressure difference between stations AB, MPa;

[0061] △L—the distance between stations AB, km.

[0062] The elevation pressure difference between stations AB can be expressed by the measured pressure difference when the pipeline is shut down, or determined by the following formula:

[0063] △P H =ρg(H A -H B )×10 -6 (2)

[0064] Where, ρ is the density of the oil being transported, which is 850 kg / cm 3 ;

[0065] g—gravitational acceleration constant, taken as 9.80m / s 2 ;

[0066] H A —the elevation of station A, m;

[0067] H B —The elevation of station B, m;

[0068] The calculation formula of head loss along the turbulent hydraulic smooth zone is:

[0069]

[0070] According to formulas (1), (2), and (3), the friction resistance per 100 km between stations A and B is converted into a certain reference flow rate Q s Frictional resistance per 100 km:

[0071]

[0072] Where: —Friction resistance of benchmark flow per 100 km between stations AB;

[0073] Q—is the instantaneous flow between two stations, m 3 / h;

[0074] Q s — is the pipeline base flow rate, m 3 / h.

[0075] In a specific implementation scheme, the benchmark flow rate 100 kilometers friction resistance research scheme is a graded scheme.

[0076] In a specific embodiment, the hierarchical scheme includes a primary scheme, a secondary scheme and a tertiary scheme, wherein:

[0077] The first-level solution includes calculating friction data and turbulent head loss;

[0078] The secondary solution includes establishing a friction calculation model for benchmark flow per 100 kilometers; and performing parameter history fitting;

[0079] The three-level solution includes establishing a friction prediction model; establishing a friction monitoring alarm; and conducting pipeline startup heat flow analysis and pipeline cleaning analysis.

[0080] In a specific implementation scheme, the friction prediction model is used to optimize the pump allocation method, pipeline cleaning and furnace start-up hot washing operations in the operation mode of the crude oil pipeline network by predicting the reference flow rate per 100 kilometers during the operating conditions.

[0081] Specifically, in the above technical scheme, the big data analysis method is used to optimize the pump allocation mode, pipeline cleaning and boiler hot washing operations in the operation mode of the crude oil pipeline network: the relevant data from previous operation experience, theoretical calculations and process adjustments are combined with statistical analysis methods to sort out and screen the relevant data, and a calculation formula for the friction resistance per 100 kilometers of the benchmark flow rate based on the steady-state energy equation and turbulent head loss is proposed. The cleaning effect is analyzed by analyzing the changing trend of the friction resistance per 100 kilometers of the benchmark flow rate, and a reasonable cleaning cycle is formulated to optimize the operation mode of the crude oil pipeline network.

[0082] Preferably, based on the process operation experience combined with the big data analysis method, the real-time data of SCADA (Supervisory Control And Data Acquisition) is combined with the friction calculation model of the benchmark flow rate per 100 kilometers, and the friction data of the benchmark flow rate per 100 kilometers is calculated online, and the data change trend is displayed. Through the actual production data history in recent years, the impact of various production activities on the friction of the benchmark flow rate per 100 kilometers is analyzed, and by setting high and low thresholds, early warning and monitoring of pipeline operation status, the pipeline startup hot washing and pigging work are guided. Specifically,

[0083] 1. Calculation model of friction resistance at benchmark flow rate per 100 km

[0084] By studying friction, we can determine whether the crude oil flows smoothly in the pipeline and whether the current operation mode is optimal. In the past, we used the friction per 100 km between stations as the research object, and did not consider the impact of the change in output on friction. When the output changes, the friction per 100 km also changes dramatically. In daily operation, the output changes frequently, so it is difficult to use the law of friction per 100 km to explain the problem or guide operation. Therefore, the impact of output changes on friction should be eliminated in the study. By establishing a friction calculation model for 100 km at a benchmark flow rate, the changes before and after pipeline cleaning are compared, the cleaning effect is evaluated, and the pipeline cleaning and furnace hot washing operations are guided.

[0085] 2. Friction prediction and determination of cleaning cycle

[0086] The friction resistance data can reflect the current flow characteristics of the pipeline. Through discussion and analysis, it is found that the baseline flow rate per 100 kilometers of each section of the pipeline tends to a fixed value within a period of time after pipeline cleaning. It is proposed to establish a pipeline baseline flow rate per 100 kilometers friction resistance prediction model. In actual application, the baseline flow rate per 100 kilometers friction resistance under operating conditions is predicted to guide the pump allocation method, pipeline cleaning and furnace hot washing operations.

[0087] 3. Friction monitoring and early warning

[0088] The reference flow rate per 100 km friction resistance is introduced into the SCADA production system, and the reference flow rate per 100 km friction resistance of each pipeline section is calculated online and monitored in real time. According to the operation requirements of the hot oil pipeline and the physical properties of Changqing crude oil, the reference flow rate per 100 km friction resistance is correlated with operations such as pigging, boiler start-up and hot washing. Through the actual SCADA monitoring data, the high and low thresholds and change rules of the reference flow rate per 100 km friction resistance of different pipeline sections are summarized. A reference flow rate per 100 km friction resistance database is established, and alarms are set to monitor and detect pipeline anomalies, so as to determine whether the wax and scaling on the pipeline wall are serious, evaluate whether the oil temperature of the pipeline section is safe, and guide the pipeline start-up and hot washing and pigging work. The monitoring and early warning of each pipeline section are realized to ensure the safe operation of the hot oil pipeline.

[0089] In the above technical solution, the advantages of friction resistance at the benchmark flow rate of 100 km / h are:

[0090] 1. Big data statistical analysis provides the baseline value and alarm value (pipe cleaning value) of each pipe section, and early warning of friction changes; the introduction of real-time display and alarm of friction can immediately detect abnormal events such as oil quality deterioration (such as dirty oil, high condensate oil and oil stratification), high point emptying (especially when there are high points between stations, the friction increases abnormally when emptying), gas resistance (especially the tracking and discharge of gas after pipeline ignition), and leakage;

[0091] 2. Through the historical trend of friction resistance, the influence of historical displacement, oil physical properties, pipeline temperature (oil temperature and ground temperature, oil temperature difference) and other factors on it can be accurately judged, and the influencing factors of friction resistance increase or friction resistance displacement can be judged to provide data support for the pigging plan and optimize the pigging cycle and pigging time;

[0092] 3. During the cleaning process, friction resistance is tracked in real time. According to its historical trend and real-time value changes (such as vibration, increase, decrease), combined with pipeline terrain, friction resistance change value, etc., the amount of impurities can be judged, and relevant plans can be made in advance. At the same time, the friction resistance trend is analyzed to determine the specific reasons for the friction resistance change, providing data assistance for subsequent cleaning and the thickness of the condensate layer in the small pipe section.

[0093] In a specific implementation plan, in order to illustrate the effect of the friction optimization method for the benchmark flow rate per 100 km of long-distance liquid pipelines, the Shilan crude oil pipeline is taken as an example to analyze the friction change trend, formulate a pigging plan and a friction limit value for the benchmark flow rate per 100 km, as follows:

[0094] 1. Determination of friction prediction model and cleaning cycle

[0095] A reasonable cleaning cycle is formulated by the change of friction parameters per 100 km of the benchmark flow. Taking the Shilan Line as an example, the Shilan Line carried out 3 cleanings in 2019, as follows:

[0096]

[0097]

[0098] Table 1.1 2019 Pipeline Cleaning Record Table of Shilan Line

[0099] Convert the friction resistance of Shilan Line in 2019 to 600m 3 The friction resistance per 100 km at the standard output of / h is Figure 4 As shown in the figure. It can be seen that the friction resistance data of each pipe section is slightly different, but the change trend is basically the same. The change cycle of friction resistance is highly consistent with the cleaning cycle. After cleaning, the friction resistance per 100 km reaches the minimum, and slowly increases with time until the next cleaning cycle. The friction resistance per 100 km of the benchmark flow can guide the cleaning cycle. Once the friction resistance per 100 km of the benchmark flow of a certain pipe section reaches a certain value, the cleaning operation can be carried out.

[0100] Take the friction resistance of the Shilan Line as a whole and convert the friction resistance of the whole line to 600m 3 The friction resistance per 100 km at the standard output of / h is Figure 5 shown.

[0101] After three cleanings, the minimum friction resistance of the entire line was 2.47, 2.45, and 2.52 respectively. It can be seen that the friction resistance of the entire line after each cleaning tends to be consistent, at about 2.5. The impurities removed from the entire line during the three cleanings were 106kg, 65.9kg, and 70.5kg respectively. It can be considered that the friction resistance of the entire pipeline will tend to 2.5 after each cleaning, and it has no close relationship with the amount of impurities removed.

[0102] In order to study the law of friction increase, the dates after the three cleanings, March 28, June 25 and October 23, were taken as the starting points, and the change trend of the friction of the whole line after the three cleanings was compared. After the three cleanings, the friction increase trend of the whole line was highly consistent. The friction data per 100 kilometers after the three cleanings were averaged and the regression formula was calculated, as follows: Figure 6 shown.

[0103] After cleaning, the whole Shilan Line can be obtained, 600m 3 The prediction formula of friction resistance per 100 kilometers at / h flow rate is:

[0104] Y=0.0117X+2.56 ①

[0105] Where Y is the friction resistance per 100 kilometers of the entire line, and X is the number of days after cleaning.

[0106] The Shilan Line is 3.243 kilometers long. After X days of cleaning, the flow rate is 600m3. 3 / h increase in the friction resistance of the entire line

[0107] ΔP=[Y(X)-Y(0)]*3.243=0.0378X ②

[0108] The Shilan line small pump has a head of 200m and provides a pressure of 1.7MPa.

[0109] Assuming ΔP=0.0379X=1.7, we can get that when X=45 days, the friction resistance of the entire line increases the head of one small pump compared to the cleaning process.

[0110] From formula ①, we can get the prediction formula of friction per 100 km under any output Q:

[0111] Y'=Y*Q1.75 / 6001.75=(0.0117X+2.56)* Q1.75 / 6001.75 ③

[0112] When the output is Q, the increase in friction resistance of the entire line is

[0113] ΔP=[Y'(X)-Y'(0)]*3.243=0.0378X* Q1.75 / 6001.75 ④

[0114] From formula ④, we can get the output Q as 500m 3 / h

[0115] ΔP=0.0275X, it can be obtained that when X=62 days, the friction resistance of the whole line increases the head of one small pump compared with the cleaning process.

[0116] The same method can be used to calculate the number of days required to increase the head of a small pump compared to pipe cleaning under different flow rates due to the friction resistance of the entire line.

[0117] Output 450 500 550 600 650 700 750 Days required to increase the head of a small pump 75 62 53 45 39 35 31

[0118] Table 2 Relationship between friction increase and time

[0119] As shown in Table 2, a reasonable cleaning cycle should not be fixed, but should be related to the planned monthly output. The larger the output, the shorter the cleaning cycle should be.

[0120] As shown in Table 3, if the head of the small pump is increased by 1.5 times the friction resistance of the entire line as the basis for cleaning, the recommended cleaning cycle under different outputs can be obtained:

[0121] <![CDATA[Volume flow rate m 3 / h]]> 450 500 550 600 650 700 750 Recommended cleaning cycle (days) 113 93 80 68 59 53 47

[0122] Table 3 Recommended cleaning cycle table for Shilan Line

[0123] 2. Friction monitoring and early warning

[0124] From 2018 to 2020, a total of 8 rounds of pigging operations were carried out on the Shilan Line (excluding internal inspection pigging operations). Figure 7 It can be seen that the baseline flow rate per 100 km friction of each pipe section shows periodic changes with the pipe cleaning cycle.

[0125] The friction resistance values ​​per 100 kilometers of the benchmark flow rate of the Shilan Line before and after pipe cleaning from 2018 to 2020 are statistically analyzed, as shown in Table 4:

[0126]

[0127] Table 4 Statistics of friction resistance before and after cleaning of Shilan Line

[0128] It can be seen that the benchmark friction is relatively close after each cleaning. The benchmark friction of Jingtai-Sidun is relatively high, with an average of 4.13 before cleaning and an average of 2.75 after cleaning. The benchmark friction of other pipe sections is relatively close, between 3.21-3.73 before cleaning and between 2.06-2.26 after cleaning. Therefore, it is recommended that the high alarm value of Jingtai-Sidun be set to 4.5, and the high alarm value of other pipe sections be set to 4.0. If this suggestion is used as the high alarm value, the number of alarms triggered from 2018 to the present is as follows: 1 time for Shi-Sha, 1 time for Sha-Hong, 1 time for Hong-Jing, 2 times for Jing-Si, 2 times for Si-Lan, a total of 7 times. In order to further study the relationship between benchmark friction and cleaning, based on the benchmark 100-kilometer friction of the entire line, such as Figure 8 shown.

[0129] After cleaning, the baseline friction resistance per 100 km of the entire line is around 2.5, so it can be considered that the initial value after each cleaning is 2.5. Before cleaning, the baseline friction resistance per 100 km of the entire line is between 3.12-3.97, with an average value of 3.53. The high-high ...

[0130] In the above technical solution, the advantages of friction resistance at the benchmark flow rate of 100 km / h are:

[0131] 1. Big data statistical analysis provides the baseline value and alarm value (pipe cleaning value) of each pipe section, and early warning of friction changes; the introduction of real-time display and alarm of friction can immediately detect abnormal events such as oil quality deterioration (such as dirty oil, high condensate oil and oil stratification), high point emptying (especially when there are high points between stations, the friction increases abnormally when emptying), gas resistance (especially the tracking and discharge of gas after pipeline ignition), and leakage;

[0132] 2. Through the historical trend of friction resistance, the influence of historical displacement, oil physical properties, pipeline temperature (oil temperature and ground temperature, oil temperature difference) and other factors on it can be accurately judged, and the influencing factors of friction resistance increase or friction resistance displacement can be judged to provide data support for the pigging plan and optimize the pigging cycle and pigging time;

[0133] 3. During the cleaning process, friction resistance is tracked in real time. According to its historical trend and real-time value changes (such as vibration, increase, decrease), combined with pipeline terrain, friction resistance change value, etc., the amount of impurities can be judged, and relevant plans can be made in advance. At the same time, the friction resistance trend is analyzed to determine the specific reasons for the friction resistance change, providing data assistance for subsequent cleaning and the thickness of the condensate layer in the small pipe section.

[0134] exist Figure 2 and Figure 3 In the embodiment of the present application, a system for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers is provided, including: a concept module for determining the concept of the friction resistance of the reference flow rate per 100 kilometers;

[0135] A formula module is used to determine the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate according to the concept of friction resistance per 100 kilometers of the benchmark flow rate;

[0136] A calculation module, used to calculate the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline according to the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate;

[0137] The solution module is used to analyze the cleaning effect of long-distance liquid pipelines according to the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline, and to formulate a research plan for the friction resistance per 100 kilometers of the benchmark flow rate to optimize the operation mode of the crude oil pipeline network.

[0138] In the above technical scheme, the concept of frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the concept of frictional resistance per 100 kilometers at a benchmark flow rate, a calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate is determined; based on the calculation formula for the frictional resistance per 100 kilometers at a benchmark flow rate, a change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline is calculated; based on the change trend of the frictional resistance per 100 kilometers at a benchmark flow rate of a long-distance liquid pipeline, the cleaning effect of the long-distance liquid pipeline is analyzed, and a research plan for the frictional resistance per 100 kilometers at a benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network; data support is provided for the cleaning plan to optimize the cleaning cycle and cleaning time.

[0139] In a specific implementation scheme, the scheme module includes a primary scheme unit, a secondary scheme unit and a tertiary scheme unit, wherein:

[0140] The first-level solution unit is used to calculate friction data and turbulent head loss;

[0141] The secondary scheme unit is used to establish a friction calculation model for a reference flow rate per 100 kilometers and to perform parameter history fitting;

[0142] The three-level solution unit is used to establish a friction prediction model; establish a friction monitoring alarm; and perform pipeline startup heat flow analysis and pipeline cleaning analysis.

[0143] In a specific implementation scheme, the friction prediction model is used to optimize the pump allocation method, pipeline cleaning and furnace start-up hot washing operations in the operation mode of the crude oil pipeline network by predicting the reference flow rate per 100 kilometers during the operating conditions.

[0144] In a specific implementation scheme, the concept of the reference flow rate per 100 km friction resistance is: converting the inter-station per 100 km friction resistance of stations A and B to a value under a certain flow rate;

[0145] Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B.

[0146] Those skilled in the art will appreciate that the present application may be implemented as a system, method or computer program product.

[0147] Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be completely hardware, it may be completely software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "circuit", "module" or "system". In addition, in some embodiments, the present application may also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium may contain computer-readable program code.

[0148] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0149] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art can change, modify, replace and modify the above embodiments within the scope of the present application. On this basis, a variety of replacements and improvements can be made to the present application, all of which fall within the scope of protection of the present application.

Claims

1. A method for optimizing the friction resistance of a long-distance liquid pipeline at a benchmark flow rate per 100 kilometers, characterized in that: The following steps are involved: Determine the concept of friction resistance per 100 kilometers of reference flow; According to the concept of friction resistance per 100 km of base flow rate, the calculation formula of friction resistance per 100 km of base flow rate is determined; According to the calculation formula of friction resistance per 100 km of base flow rate, the friction resistance change trend of base flow rate per 100 km of long-distance liquid pipeline is calculated; The cleaning effect of long-distance liquid pipelines is analyzed according to the changing trend of friction resistance per 100 km at the benchmark flow rate of long-distance liquid pipelines, and a research plan on friction resistance per 100 km at the benchmark flow rate is formulated to optimize the operation mode of the crude oil pipeline network.

2. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers according to claim 1 is characterized in that: The concept of the reference flow rate per 100 km friction resistance is: converting the inter-station per 100 km friction resistance of stations A and B to a value under a certain flow rate; Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B.

3. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers according to claim 2 is characterized in that: According to the concept of friction resistance per 100 km of base flow rate, the steps to determine the calculation formula of friction resistance per 100 km of base flow rate are as follows: According to the concept of frictional resistance per 100 km / h for benchmark flow, historical operating experience, theoretical calculations and data after process adjustment are combined with statistical analysis methods to sort out and screen the data of long-distance liquid pipelines, and determine the calculation formula for frictional resistance per 100 km / h for benchmark flow based on steady-state energy equation and turbulent head loss.

4. The method for optimizing the friction resistance of a long-distance liquid pipeline based on a reference flow rate per 100 kilometers according to claim 3 is characterized in that: The benchmark flow rate 100 km / h friction resistance research plan is a graded plan.

5. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers according to claim 4 is characterized in that: The grading scheme includes a primary scheme, a secondary scheme and a tertiary scheme, wherein: The first-level solution includes calculating friction data and turbulent head loss; The secondary solution includes establishing a friction calculation model for benchmark flow per 100 kilometers; and performing parameter history fitting; The three-level solution includes establishing a friction prediction model; establishing a friction monitoring alarm; and conducting pipeline startup heat flow analysis and pipeline cleaning analysis.

6. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers according to claim 5 is characterized in that: The friction prediction model is used to optimize the pump allocation mode, pipeline cleaning and furnace start-up hot washing operations in the crude oil pipeline network operation mode by predicting the reference flow rate per 100 kilometers during the operation condition.

7. A friction optimization system for benchmark flow rate per 100 km of a long-distance liquid pipeline, characterized in that: include: Concept module, used to determine the concept of friction resistance per 100 km of benchmark flow; A formula module is used to determine the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate according to the concept of friction resistance per 100 kilometers of the benchmark flow rate; A calculation module, used to calculate the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline according to the calculation formula of the friction resistance per 100 kilometers of the benchmark flow rate; The solution module is used to analyze the cleaning effect of long-distance liquid pipelines according to the friction resistance change trend of the benchmark flow rate per 100 kilometers of the long-distance liquid pipeline, and to formulate a research plan for the friction resistance per 100 kilometers of the benchmark flow rate to optimize the operation mode of the crude oil pipeline network.

8. The method for optimizing the friction resistance of a long-distance liquid pipeline at a reference flow rate per 100 kilometers according to claim 7 is characterized in that: The scheme module includes a primary scheme unit, a secondary scheme unit and a tertiary scheme unit, wherein: The first-level solution unit is used to calculate friction data and turbulent head loss; The secondary scheme unit is used to establish a friction calculation model for a reference flow rate per 100 kilometers and to perform parameter history fitting; The three-level solution unit is used to establish a friction prediction model; establish a friction monitoring alarm; and perform pipeline startup heat flow analysis and pipeline cleaning analysis.

9. The method for optimizing the friction resistance of a long-distance liquid pipeline based on a reference flow rate per 100 kilometers according to claim 8, characterized in that: The friction prediction model is used to optimize the pump allocation mode, pipeline cleaning and furnace start-up hot washing operations in the crude oil pipeline network operation mode by predicting the reference flow rate per 100 kilometers during the operation condition.

10. The method for optimizing the friction resistance of a long-distance liquid pipeline based on a reference flow rate per 100 kilometers according to claim 9, characterized in that: The concept of the reference flow rate per 100 km friction resistance is: converting the inter-station per 100 km friction resistance of stations A and B to a value under a certain flow rate; Among them, the inter-station friction resistance per 100 kilometers is the inter-station friction resistance of stations AB divided by the inter-station distance between the two stations, multiplied by one hundred; the inter-station friction resistance of stations AB is the exit pressure of station A plus the elevation pressure of station A, minus the entry pressure of station B, minus the elevation pressure of station B.

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