CN112528453A - Defeated system energy consumption computing system of collection based on data wash - Google Patents

Defeated system energy consumption computing system of collection based on data wash Download PDF

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CN112528453A
CN112528453A CN201910809683.8A CN201910809683A CN112528453A CN 112528453 A CN112528453 A CN 112528453A CN 201910809683 A CN201910809683 A CN 201910809683A CN 112528453 A CN112528453 A CN 112528453A
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temperature
energy consumption
heating furnace
pressure
data
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李振泉
郑炜博
王强
师祥洪
孙东
范路
齐光峰
黄珊
刘聪
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China Petroleum and Chemical Corp
Technology Inspection Center of Sinopec Shengli Oilfield Co
Shengli Oilfield Testing and Evaluation Research Co Ltd
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China Petroleum and Chemical Corp
Technology Inspection Center of Sinopec Shengli Oilfield Co
Shengli Oilfield Testing and Evaluation Research Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention provides a data cleaning-based energy consumption calculation system for a gathering and transportation system, and belongs to the field of petroleum exploration. The technical scheme is as follows: a gathering system energy consumption calculation system based on data cleansing, comprising: collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning; analyzing the coordination data to determine main control factors; obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment; analyzing the relation model to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode; and coupling the energy consumption evaluation models and establishing a cost optimization model. The invention has the beneficial effects that: on the basis of cleaning the operation data, a relational model of production parameters of main energy consumption equipment of the gathering and transportation system is established, the relational model is analyzed and fitted to obtain a cost optimization model of the gathering and transportation system, and reference is made for determining energy-saving potential spaces of different units.

Description

Defeated system energy consumption computing system of collection based on data wash
Technical Field
The invention relates to the field of oil exploration, in particular to a gathering and transportation system energy consumption calculation system based on data cleaning.
Background
The problems that the oil field gathering and transportation system is large in energy consumption proportion, part of links are unclear in energy consumption, a large amount of collected data is not subjected to quality evaluation and the like cause continuous attention, energy consumption key parameter accounting and potential evaluation method research is developed, energy consumption composition and energy consumption indexes of the oil field gathering and transportation system are further deeply researched, energy consumption weak links are found out, energy saving potential is mined, and the method has important practical significance for energy saving and consumption reduction of the gathering and transportation system.
In order to solve the problems, a relational model of production parameters of main energy consumption equipment of the gathering and transportation system needs to be established on the basis of cleaning operation data, a cost optimization model of the gathering and transportation system is obtained, and reference is made for determining energy-saving potential spaces of different units.
Disclosure of Invention
The invention aims to establish a relational model of production parameters of main energy consumption equipment of a gathering and transportation system on the basis of cleaning operation data, analyze and fit the relational model to obtain a cost optimization model of the gathering and transportation system, and provide a data cleaning-based energy consumption calculation system for determining energy-saving potential spaces of different units.
The invention is realized by the following measures: a gathering system energy consumption calculation system based on data cleansing, comprising: collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning; analyzing the coordination data to determine main control factors; obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment; analyzing the relation model and the energy consumption formula of the main energy consumption equipment to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode; and coupling the energy consumption evaluation models and establishing a cost optimization model.
Cleaning the operation data according to a Rhein criterion to obtain the coordination data, wherein the cleaning is based on the following processing basis:
Figure RE-GDA0002259075940000011
xi is a gross error and should be discarded;
Figure RE-GDA0002259075940000012
xi is normal data and should be reserved;
the coordination data comprises the standard density of crude oil, water content in export, flow rate in the import station, temperature in the import station, pressure in the import station, efficiency of a heating furnace, sedimentation temperature, pump efficiency, temperature in the export station, water content in the export station, flow rate in the export station, pump efficiency, water content in a three-phase outlet, electric dehydration voltage V, electric dehydration current A, temperature in a tower, water content in the import station, ambient temperature and pressure in the import station.
The step of cleaning the operation data by the Rheinda criterion (PanTa) is as follows:
measurement columns for the collected operating data X1, X2, … …, Xn
(1) Calculating the arithmetic mean
Figure RE-GDA0002259075940000021
(2) Calculating a residual error
Figure RE-GDA0002259075940000022
(3) Calculating the root mean square deviation according to the Bessel method
Figure RE-GDA0002259075940000023
The criteria for discrimination are as follows (assuming vi follows a normal distribution, i.e. the measurement columns also follow a normal distribution):
Figure RE-GDA0002259075940000024
xi is a gross error and should be discarded;
Figure RE-GDA0002259075940000025
xi is normal data and should be preserved.
According to probability theory statistics, when the error obeys normal distribution, the probability of the occurrence of the observation data with the error larger than 3 delta is less than 0.003, namely 1 occurrence is possible in more than 300 observations. Therefore, if the leigneta criterion (also called 3 δ criterion) is used for gross rejection, the rejection probability is small, so that unreasonable abnormal values are sometimes retained.
Analyzing the coordination data by a grey correlation analysis method, calculating the correlation degree of the coordination data and the energy consumption, and performing multiple regression analysis on the correlation degree to obtain a calculation formula of the comprehensive energy consumption, wherein the calculation formula comprises the following steps:
y ═ 0.0176 × external flow rate-6.55 × density +0.008 × external water +3.28 station pressure-0.7 × three-phase external water-0.02 × settling temperature +0.04 × furnace efficiency-0.06 × tower temperature +0.067 × external oil temperature + 0.02% power consumption;
the above formula is analyzed to determine that the main control factors are as follows: heating furnace efficiency, temperature of output oil and electric energy consumption.
The grey correlation analysis can be analyzed by adopting matlab software programming, and the specific steps are as follows: (1) and taking the comprehensive energy consumption as a reference array and the coordination data as a comparison array. (2) And carrying out non-dimensionalization processing on the reference number sequence and the comparison number sequence. (3) And solving a gray correlation coefficient Xi (Xi) of the reference sequence and the comparison sequence. Wherein rho is a resolution coefficient, generally ranges from 0 to 1, and is usually 0.5; Δ min is the second order minimum difference; Δ max is the two-step maximum difference; the absolute difference between each point on the curve of the comparison series Xi and each point on the curve of the reference series X0 is denoted as Δ 0i (k). The correlation coefficient ξ (Xi) can be simplified as follows:
Figure 462150DEST_PATH_BDA0002184730930000081
(4) and solving the association degree of the coordination data:
Figure RE-RE-GDA0002259075940000027
ri is the gray degree of correlation of the comparison sequence Xi to the reference sequence X0, or is called sequence degree of correlation, average degree of correlation, line degree of correlation. The closer the ri value is to 1, the better the correlation is illustrated.
(5) And sorting the relevance. And finally analyzing according to the arrangement sequence of the association degrees to obtain a conclusion.
And solving the relevance of the coordination data and performing multivariate regression analysis, wherein the multivariate regression analysis can be performed by matlab software programming, and the expression of the multivariate regression analysis is that y is b0+b1x1+b2x2+...+bnxnIn the formula, xtRepresents the value of the independent variable in the t period; y istA value representing a dependent variable at t; a. b represents parameters of a unary linear regression equation, and the a and b parameters are obtained by the following formula:
Figure RE-GDA0002259075940000031
through the steps, comprehensive energy consumption is taken as a main column in matlab software, data crude oil standard density, output water content, station entering flow, station entering temperature, station entering pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, output temperature, output water content, output flow, pump efficiency, three-phase outlet water content, electric separation voltage V, electric separation current A, tower entering temperature, station entering water content, environment temperature and station entering pressure are coordinated as sub columns, 653 working conditions in a source database are subjected to standardization processing, and the association degree is calculated through grey association analysis; performing multiple regression analysis on the correlation degree to obtain a calculation formula of the comprehensive energy consumption as follows:
y ═ 0.0176 × external flow rate-6.55 × density +0.008 × external water +3.28 station pressure-0.7 × three-phase external water-0.02 × settling temperature +0.04 × furnace efficiency-0.06 × tower temperature +0.067 × external oil temperature + 0.02% power consumption;
analyzing a calculation formula of the comprehensive energy consumption to determine that the main control factors are as follows: heating furnace efficiency, temperature of output oil and electric energy consumption. Obtaining the main energy consumption equipment according to the calculation formula of the comprehensive energy consumption comprises the following steps: the system comprises an electric dehydrator, a heating furnace, a stabilizing tower and a settling tank, and a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizing tower and a relation model of the settling tank are established.
The relation model of the electric dehydrator is specifically that field data of the concentration of the demulsifier, the demulsification temperature and the dehydration rate are collected, Matlab software is adopted to fit the concentration of the demulsifier, the dehydration rate and the demulsification temperature, Matlab software can be adopted to fit, and the relation between the dehydration rate of the electric dehydrator, the demulsification temperature and the concentration of the demulsifier is obtained:
s＝f(t,c)＝-1.938e4+2.461t+1312c-0.1332t2+0.1377tc-33.12c2+0.004544t2c
-0.006442tc2+0.3698c3-3.849e-5t2c2+5.925e-5tc3-0.00154c4。
the relation model of the heating furnace is specifically as follows: measuring the fuel parameter and the heat balance parameter of the heating furnace on site, carrying out reverse thermodynamic calculation on the heating furnace to obtain the fouling thermal resistance of the heating furnace, and then carrying out optimization calculation based on the calculated fouling thermal resistance to obtain the optimal fuel quantity and the optimal excess air coefficient.
Further, the establishment of the relation model of the heating furnace comprises two parts, wherein the first part is check calculation and comprises the following steps: according to given fuel and components, measured smoke components and temperature and a heating furnace structure, performing combustion and heat balance calculation to obtain parameters such as crude oil flow, heating load, heating furnace heat efficiency, heat resistance of a fire tube smoke tube coil and the like; and (4) carrying out fire tube, tight tube and coil checking thermal calculation to obtain the scale resistance of the hot furnace and the scale resistance of the coil.
The second part is optimization calculation, namely fuel combustion calculation is carried out according to fuel components and a given excess air coefficient, and the optimization calculation comprises the following steps: (1) firstly, assuming the exhaust gas temperature, and carrying out heat balance calculation;
(2) then, assuming the outlet temperature of the fire tube, calculating the average temperature of the flame and the heat balance Q of the fire tubef；
(3) Calculating the blackness of the tube, the blackness of flame, the blackness of a system and the average temperature of flame;
(4) according to the calculated dirt thermal resistance, calculating the tube wall temperature of the fire tube and the heat transfer quantity Q of the fire tubef1；
(5) Substituting the right formula to calculate:
Figure RE-GDA0002259075940000041
(6) if (5) does not work, replacing (2) to recalculate;
(7) if (5) is true, calculating heat balance heat Q of the smoke pipe according to the enthalpy value of the fire tube and the smoke exhaustS(ii) a (8) Calculating heat transfer coefficient, heat transfer temperature difference and heat transfer quantity Q of smoke tube according to calculated fouling thermal resistanceS1；
(9) Substituting the right formula to calculate:
Figure RE-GDA0002259075940000042
(10) if (9) is not true, replacing (1) for recalculation;
(11) and if (9) is true, finishing the optimization simulation calculation.
The relation model of the stabilizing tower is specifically as follows: modeling and simulating an on-site production process in Hysys, then calling a Balance module to calculate and obtain saturated vapor pressure before and after stabilization, changing flash pressure and temperature in a stabilization tower, simulating the saturated vapor pressure under different temperatures and flash pressures, and analyzing to obtain the relation between the pressure in the stabilization tower and the incoming oil temperature: pm1.3524t-25.058, the relationship between the gas output of the stabilizing tower and the temperature of the incoming oil is as follows: vys＝1.163t-12.685。
The relation model of the settling tank is specifically as follows: sampling according to the on-site proportion, carrying out a sedimentation experiment by using a residence time method, and fitting an experiment result to obtain the relation between the sedimentation temperature and the water content as follows:
Figure RE-GDA0002259075940000043
further processing the experimental result, picking the corresponding water content of the oil product under different medicament concentrations and different sedimentation temperatures to draw a relation curve, wherein the sedimentation time is 5h, and the relation of the temperature and the medicament concentration obtained by fitting is tcj＝1.29× 1011cy-5.094+51.72。
The energy consumption evaluation model is as follows:
Figure RE-GDA0002259075940000044
Figure RE-GDA0002259075940000051
By＝c·Q·10-6
in the formula, BrlAmount of fuel required for the furnace, m3D; m is the flow of the heated medium, kg/s; c. CpmkJ/(kg. DEG C) which is the average specific heat capacity of crude oil in the dehydration furnace; t is tci、triThe temperature of oil flowing out of the ith heating furnace and oil flowing into the ith heating furnace is measured at DEG C; qydwThe lower calorific value of the fuel used by the heating furnace, kJ/kg; etajrliThe working efficiency of the ith heating furnace is percent. Sigma BdfThe total required electricity charge; qi is the volume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriThe pressure of the pump inlet (namely the pressure of the incoming liquid of the ith pipeline), MPa; η bi is the efficiency of the ith pump; vdtiIs the electric dehydrator working voltage (i.e. the ith electric dehydrator working voltage), V; dtsiIs the working current of the electric dehydrator (i.e. the working current of the ith electric dehydrator), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit; b isyThe amount of chemical demulsifier added, kg/d; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d。
Aiming at the lowest total operating cost, a cost optimization model established by coupling 3 consumptions is as follows:
minF＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fswherein, tgwOptimizing the temperature (namely the outlet temperature of the pipe network heating furnace) for the pipe network, and measuring the temperature in DEG C; pgwOptimizing the pressure (namely the outlet pressure of a pipe network pump) for the pipe network, wherein the pressure is MPa; t is tcjThe optimum temperature for the settling tank (i.e. settling tank temperature), deg.c; t is twdOptimizing the temperature (namely stabilizing the outlet temperature of the heating furnace) for the stabilizing tower at the temperature of DEG C; c. CyjThe concentration of the medicament (namely the concentration of the added medicament in a settling tank) is mg/L; t is twsOptimizing the temperature (namely the outlet temperature of the outward heating furnace) for the pipe network, wherein the temperature is DEG C; pwsOptimizing the pressure (namely the outlet pressure of the purifying oil pump) for output, namely, MPa; frIs the cost of the fuel; fdIs the cost of electricity; fyIs the cost of the medicament.
The fuel cost is as follows:
Figure RE-GDA0002259075940000052
wherein R isrIs the price of fuel, Yuan/m3；QrkJ/m as fuel calorific value3；ηliHeating furnace thermal efficiency (derived from the furnace optimization model); giThe flow of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external transportation heating furnace is t/h; c. Co、cwThe specific heat of oil and water, kJ/(kg DEG C); t is tci、triTemperatures at the furnace outlet and inlet, respectively (when calculating the fuel consumption of the grid, t)ci、triRespectively indicating the optimum temperature t of the pipe networkgwAnd the inlet temperature of the pipe network heating furnace; when calculating furnace fuel cost before settling tank, tci、triRespectively represents the optimum temperature t of the settling tankcjAnd the inlet temperature t of the heating furnacecj(ii) a When calculating furnace fuel cost before settling tank, tci、triRespectively showing the optimized temperature of the stabilizer and the inlet temperature t of the heating furnacewd(ii) a When calculating the fuel cost of the outgoing heating furnace, tci、triRespectively representing the output optimized temperature and the inlet temperature t of the heating furnacews)；
The electric power cost is as follows:
Figure RE-GDA0002259075940000061
wherein R isdThe power charge is yuan/(kW h); qiVolume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriFor pump inlet pressure (i.e. incoming fluid pressure in ith line), MPa；ηbiThe efficiency of the ith pump; vdtiIs the electric dehydrator working voltage (i.e. the ith electric dehydrator working voltage), V; dtsiIs the working current of the electric dehydrator (i.e. the working current of the ith electric dehydrator), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit;
the cost of the medicament is Fy＝c·Q·Ry·10-6In the formula, FyCost of chemical demulsifier added, yuan/day; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d；RyPrice of demulsifier, yuan/ton.
The constraint conditions of the cost optimization model are as follows:
(1) the crude oil temperature is obtained by mutual coupling of the temperatures in energy consumption equipment in a gathering and transportation system, the inlet temperature of a heating furnace in front of a primary settling tank is determined by subtracting the temperature drop in front of the inlet of the heating furnace from the weighted average temperature of incoming flow of a pipe network, the outlet temperature of the heating furnace is taken as the inlet temperature of the settling tank, the outlet temperature of the primary settling tank is subtracted by the on-way temperature drop after passing through the primary settling tank to be taken as the inlet temperature of a stable heating furnace, and the outlet temperature of the stable heating furnace is taken as the inlet temperature of the settling tank, so that;
(2) the water content psi of the outer pipe network is less than or equal to 0.3%;
(3) the pressure of crude oil is 0.24-0.32 MPa, and no pressurizing equipment is arranged in the united station, so that the inlet of the united station needs to ensure that the pressure of the incoming crude oil is 0.32-0.24 MPa to ensure the smooth operation of each flow of the united station, the pressure of the incoming crude oil of a pipe network is a constraint condition of the pressure, the pressure loss is mainly related to the flowing length of the crude oil and the viscosity of the crude oil, and the pressure loss and the viscosity of the crude oil can be coupled with the temperature. On the one hand, the crude oil temperature is related to the heating of the heating furnace, thereby affecting the fuel cost; on the other hand, pressure losses require a pump to replenish, which in turn affects power costs. Therefore, the optimal value of the fuel consumption and the power consumption of the whole pipe network is obtained according to the secondary relation.
(4) The stable vapor pressure of the crude oil is less than 70 kPa;
(5) the water content psi at the outlet of the electric dehydrator is less than or equal to 0.5 percent.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of cleaning parameters, improving the accuracy of data, establishing a relational model of production parameters of main energy consumption equipment of the gathering and transportation system on the basis of data cleaning, analyzing and fitting the relational model to obtain a cost optimization model of the gathering and transportation system, and making reference for determining energy-saving potential spaces of different units.
Drawings
Both fig. 1 and fig. 2 are grey correlation analysis matlab programming interface diagrams.
Fig. 3, fig. 4 and fig. 5 are all multiple regression analysis matlab programming interface diagrams.
FIG. 6 is a plot of the run parameters after fitting of the demulsifier concentration, demulsification temperature, and crude oil dehydration rate.
FIG. 7 is a diagram of a model for optimization simulation calculation of a heating furnace.
Fig. 8 is a stabilizer simulation flow chart.
FIG. 9 is a graph showing the change of water content with time at different temperatures.
FIG. 10 is a graph showing the change of sedimentation temperature and water content.
FIG. 11 is a gray scale correlation of coordination data with integrated energy consumption.
FIG. 12 is a graph of electric dehydrator dehydration rate versus demulsification temperature and demulsifier concentration.
FIG. 13 is a table comparing the results of the heating furnace simulation calculations with the test data.
FIG. 14 is a table comparing current operating costs to optimization costs.
FIG. 15 shows energy costs for different excess air ratios.
Detailed Description
In order to clearly illustrate the technical features of the present solution, the present solution is explained below by way of specific embodiments.
The first embodiment is as follows:
a gathering system energy consumption calculation system based on data cleansing, comprising: collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning; analyzing the coordination data to determine main control factors; obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment; analyzing the relation model to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode; and coupling the energy consumption evaluation models and establishing a cost optimization model.
Example two:
a gathering system energy consumption calculation system based on data cleansing, comprising: collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning; analyzing the coordination data to determine main control factors; obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment; analyzing the relation model to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode; and coupling the energy consumption evaluation models and establishing a cost optimization model.
The constraint conditions of the cost optimization model are as follows: the temperature of the crude oil is obtained by mutual coupling of the temperature in energy consumption equipment in a gathering and transportation system, the water content psi of the external output of a pipe network is less than or equal to 0.3% ", the pressure of the crude oil is 0.24-0.32 MPa, the stable steam pressure of the crude oil is less than 70kPa, and the water content psi of an outlet of an electric dehydrator is less than or equal to 0.5%.
Example three:
a gathering system energy consumption calculation system based on data cleansing, comprising: collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning; analyzing the coordination data to determine main control factors; obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment; analyzing the relation model to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode; and coupling the energy consumption evaluation models and establishing a cost optimization model.
Cleaning the operation data according to a Rhein criterion to obtain the coordination data, wherein the cleaning is based on the following processing basis:
Figure RE-GDA0002259075940000081
xi is a gross error and should be discarded;
Figure RE-GDA0002259075940000082
xi is normal data and should be reserved;
the coordination data comprises the standard density of crude oil, water content in export, flow rate in the import station, temperature in the import station, pressure in the import station, efficiency of a heating furnace, sedimentation temperature, pump efficiency, temperature in the export station, water content in the export station, flow rate in the export station, pump efficiency, water content in a three-phase outlet, electric dehydration voltage V, electric dehydration current A, temperature in a tower, water content in the import station, ambient temperature and pressure in the import station.
The step of cleaning the operation data by the Rheinda criterion (PanTa) is as follows:
measurement columns for the collected operating data X1, X2, … …, Xn
(1) Calculating the arithmetic mean
Figure RE-GDA0002259075940000083
(2) Calculating a residual error
Figure RE-GDA0002259075940000084
(3) Calculating the root mean square deviation according to the Bessel method
Figure RE-GDA0002259075940000085
The criteria for discrimination are as follows (assuming vi follows a normal distribution, i.e. the measurement columns also follow a normal distribution):
Figure RE-GDA0002259075940000086
xi is a gross error and should be discarded;
Figure RE-GDA0002259075940000087
xi is normal data and should be preserved.
According to probability theory statistics, when the error obeys normal distribution, the probability of the occurrence of the observation data with the error larger than 3 delta is less than 0.003, namely 1 occurrence is possible in more than 300 observations. Therefore, if the leigneta criterion (also called 3 δ criterion) is used for gross rejection, the rejection probability is small, so that unreasonable abnormal values are sometimes retained.
Analyzing the coordination data by a grey correlation analysis method, calculating the correlation degree of the coordination data and the energy consumption, and performing multiple regression analysis on the correlation degree to obtain a calculation formula of the comprehensive energy consumption, wherein the calculation formula comprises the following steps:
y ═ 0.0176 × external flow rate-6.55 × density +0.008 × external water +3.28 station pressure-0.7 × three-phase external water-0.02 × settling temperature +0.04 × furnace efficiency-0.06 × tower temperature +0.067 × external oil temperature + 0.02% power consumption;
the above formula is analyzed to determine that the main control factors are as follows: heating furnace efficiency, temperature of output oil and electric energy consumption.
Referring to fig. 1, fig. 2 and fig. 11, the gray correlation analysis may be performed by using matlab software programming, and the specific steps are as follows: (1) and taking the comprehensive energy consumption as a reference array and the coordination data as a comparison array. (2) And carrying out non-dimensionalization processing on the reference number sequence and the comparison number sequence. (3) And solving a gray correlation coefficient Xi (Xi) of the reference sequence and the comparison sequence. Wherein rho is a resolution coefficient, generally ranges from 0 to 1, and is usually 0.5; Δ min is the second order minimum difference; Δ max is the two-step maximum difference; the absolute difference between each point on the curve of the comparison series Xi and each point on the curve of the reference series X0 is denoted as Δ 0i (k). The correlation coefficient ξ (Xi) can be simplified as follows:
Figure 241887DEST_PATH_BDA0002184730930000081
(4) and solving the association degree of the coordination data:
Figure RE-RE-GDA0002259075940000092
ri is the gray degree of correlation of the comparison sequence Xi to the reference sequence X0, or is called sequence degree of correlation, average degree of correlation, line degree of correlation. The closer the ri value is to 1, the better the correlation is illustrated.
(5) And sorting the relevance. And finally analyzing according to the arrangement sequence of the association degrees to obtain a conclusion.
Referring to fig. 3, 4 and 5, the correlation degree of the coordination data is obtained and multivariate regression analysis is performed, the multivariate regression analysis can be performed by matlab software programming, and the expression of the multivariate regression analysis is that y is b0+b1x1+b2x2+...+bnxnIn the formula, xtRepresents the value of the independent variable in the t period; y istA value representing a dependent variable at t; a. b represents parameters of a unary linear regression equation, and the a and b parameters are obtained by the following formula:
Figure RE-RE-GDA0002259075940000093
through the steps, comprehensive energy consumption is taken as a main column in matlab software, data crude oil standard density, output water content, station entering flow, station entering temperature, station entering pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, output temperature, output water content, output flow, pump efficiency, three-phase outlet water content, electric separation voltage V, electric separation current A, tower entering temperature, station entering water content, environment temperature and station entering pressure are coordinated as sub columns, 653 working conditions in a source database are subjected to standardization processing, and the association degree is calculated through grey association analysis; performing multiple regression analysis on the correlation degree to obtain a calculation formula of the comprehensive energy consumption as follows:
y ═ 0.0176 × external flow rate-6.55 × density +0.008 × external water +3.28 station pressure-0.7 × three-phase external water-0.02 × settling temperature +0.04 × furnace efficiency-0.06 × tower temperature +0.067 × external oil temperature + 0.02% power consumption;
analyzing a calculation formula of the comprehensive energy consumption to determine that the main control factors are as follows: heating furnace efficiency, temperature of output oil and electric energy consumption. Obtaining the main energy consumption equipment according to the calculation formula of the comprehensive energy consumption comprises the following steps: the system comprises an electric dehydrator, a heating furnace, a stabilizing tower and a settling tank, and a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizing tower and a relation model of the settling tank are established.
Referring to fig. 6 and 12, the relation model of the electric dehydrator specifically includes acquiring field data of the concentration of the demulsifier, the demulsification temperature and the dehydration rate, fitting the concentration of the demulsifier, the dehydration rate and the demulsification temperature by using Matlab software, and fitting by using Matlab software to obtain the relation between the dehydration rate of the electric dehydrator, the demulsification temperature and the concentration of the demulsifier:
s＝f(t,c)＝-1.938e4+2.461t+1312c-0.1332t2+0.1377tc-33.12c2+0.004544t2c -0.006442tc2+0.3698c3-3.849e-5t2c2+5.925e-5tc3-0.00154c4。
referring to fig. 7, the relationship model of the heating furnace is specifically: measuring the fuel parameter and the heat balance parameter of the heating furnace on site, carrying out reverse thermodynamic calculation on the heating furnace to obtain the fouling thermal resistance of the heating furnace, and then carrying out optimization calculation based on the calculated fouling thermal resistance to obtain the optimal fuel quantity and the optimal excess air coefficient.
Further, the establishment of the relation model of the heating furnace comprises two parts, wherein the first part is check calculation and comprises the following steps: according to given fuel and components, measured smoke components and temperature and a heating furnace structure, performing combustion and heat balance calculation to obtain parameters such as crude oil flow, heating load, heating furnace heat efficiency, heat resistance of a fire tube smoke tube coil and the like; and (4) carrying out fire tube, tight tube and coil checking thermal calculation to obtain the scale resistance of the hot furnace and the scale resistance of the coil.
The second part is optimization calculation, namely fuel combustion calculation is carried out according to fuel components and a given excess air coefficient, and the optimization calculation comprises the following steps: (1) firstly, assuming the exhaust gas temperature, and carrying out heat balance calculation;
(2) then, assuming the outlet temperature of the fire tube, calculating the flame flatUniform temperature, fire tube heat balance quantity Qf；
(3) Calculating the blackness of the tube, the blackness of flame, the blackness of a system and the average temperature of flame;
(4) according to the calculated dirt thermal resistance, calculating the tube wall temperature of the fire tube and the heat transfer quantity Q of the fire tubef1；
(5) Substituting the right formula to calculate:
Figure RE-GDA0002259075940000101
(6) if (5) does not work, replacing (2) to recalculate;
(7) if (5) is true, calculating heat balance heat Q of the smoke pipe according to the enthalpy value of the fire tube and the smoke exhaustS(ii) a (8) Calculating heat transfer coefficient, heat transfer temperature difference and heat transfer quantity Q of smoke tube according to calculated fouling thermal resistanceS1；
(9) Substituting the right formula to calculate:
Figure RE-GDA0002259075940000111
(10) if (9) is not true, replacing (1) for recalculation;
(11) and if (9) is true, finishing the optimization simulation calculation.
As shown in fig. 13, the simulation model of the heating furnace was verified based on the test data provided in the field, and the result is shown in fig. 13. It can be seen that the goodness of fit between the simulation result and the actually measured data exceeds 97.9%, and the heating furnace simulation model is proved to be accurate.
Referring to fig. 8, the relationship model of the stabilizing tower is specifically: modeling and simulating an on-site production process in Hysys, then calling a Balance module to calculate and obtain saturated vapor pressure before and after stabilization, changing flash pressure and temperature in a stabilization tower, simulating the saturated vapor pressure under different temperatures and flash pressures, and analyzing to obtain the relation between the pressure in the stabilization tower and the incoming oil temperature: pm1.3524t-25.058, the relationship between the gas output of the stabilizing tower and the temperature of the incoming oil is as follows: vys＝1.163t-12.685。
Referring to fig. 9 and 10, the relationship model of the settling tank is specifically: sampling according to the on-site proportion and sampling healdAbout 0.5 percent of water is combined, a settling experiment is carried out by a residence time method, the residence time is 20 hours, and the change rule of the water content along with the time at the temperature of 30 ℃, 35 ℃, 40 ℃, 45 ℃, 50 ℃, 55 ℃ and 60 ℃ is researched as follows:
Figure RE-GDA0002259075940000114
as shown in fig. 9. Further processing the experimental result, extracting the water content of the oil product corresponding to different sedimentation temperatures under different medicament concentrations to draw a relation curve, as shown in fig. 10, wherein the selected sedimentation time is 5h, and the relation of the temperature and the medicament concentration obtained by fitting is tcj＝1.29×1011cy-5.094+51.72。
The energy consumption evaluation model determined by analyzing and integrating the above contents is as follows:
Figure RE-GDA0002259075940000112
Figure RE-GDA0002259075940000113
By＝c·Q·10-6
in the formula, BrlAmount of fuel required for the furnace, m3D; m is the flow of the heated medium, kg/s; c. CpmkJ/(kg. DEG C) which is the average specific heat capacity of crude oil in the dehydration furnace; t is tci、triThe temperature of oil flowing out of the ith heating furnace and oil flowing into the ith heating furnace is measured at DEG C; qydwThe lower calorific value of the fuel used by the heating furnace, kJ/kg; etajrliThe working efficiency of the ith heating furnace is percent. Sigma BdfThe total required electricity charge; qi is the volume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriThe pressure of the pump inlet (namely the pressure of the incoming liquid of the ith pipeline), MPa; η bi is the efficiency of the ith pump; vdtiIs the electric dehydrator working voltage (i.e. the ith electric dehydrator working voltage), V; dtsiFor operating electric current of electric dehydrators (i.e.The ith electric dehydrator working current), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit; b isyThe amount of chemical demulsifier added, kg/d; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d。
Aiming at the lowest total operating cost, a cost optimization model established by coupling 3 consumptions is as follows:
minF＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fswherein, tgwOptimizing the temperature (namely the outlet temperature of the pipe network heating furnace) for the pipe network, and measuring the temperature in DEG C; pgwOptimizing the pressure (namely the outlet pressure of a pipe network pump) for the pipe network, wherein the pressure is MPa; t is tcjThe optimum temperature for the settling tank (i.e. settling tank temperature), deg.c; t is twdOptimizing the temperature (namely stabilizing the outlet temperature of the heating furnace) for the stabilizing tower at the temperature of DEG C; c. CyjThe concentration of the medicament (namely the concentration of the added medicament in a settling tank) is mg/L; t is twsOptimizing the temperature (namely the outlet temperature of the outward heating furnace) for the pipe network, wherein the temperature is DEG C; pwsOptimizing the pressure (namely the outlet pressure of the purifying oil pump) for output, namely, MPa; frIs the cost of the fuel; fdIs the cost of electricity; fyIs the cost of the medicament.
The fuel cost is as follows:
Figure RE-GDA0002259075940000121
wherein R isrIs the price of fuel, Yuan/m3；QrkJ/m as fuel calorific value3；ηliHeating furnace thermal efficiency (derived from the furnace optimization model); giThe flow of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external transportation heating furnace is t/h; c. Co、cwThe specific heat of oil and water, kJ/(kg DEG C); t is tci、triTemperatures at the furnace outlet and inlet, respectively (when calculating the fuel consumption of the grid, t)ci、triRespectively indicating the optimum temperature t of the pipe networkgwAnd the inlet temperature of the pipe network heating furnace; when calculating the furnace fuel charge before the settling tank,tci、trirespectively represents the optimum temperature t of the settling tankcjAnd the inlet temperature t of the heating furnacecj(ii) a When calculating furnace fuel cost before settling tank, tci、triRespectively showing the optimized temperature of the stabilizer and the inlet temperature t of the heating furnacewd(ii) a When calculating the fuel cost of the outgoing heating furnace, tci、triRespectively representing the output optimized temperature and the inlet temperature t of the heating furnacews)；
The electric power cost is as follows:
Figure RE-GDA0002259075940000122
wherein R isdThe power charge is yuan/(kW h); qiVolume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriThe pressure of the pump inlet (namely the pressure of the incoming liquid of the ith pipeline), MPa; etabiThe efficiency of the ith pump; vdtiIs the electric dehydrator working voltage (i.e. the ith electric dehydrator working voltage), V; dtsiIs the working current of the electric dehydrator (i.e. the working current of the ith electric dehydrator), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit;
the cost of the medicament is Fy＝c·Q·Ry·10-6In the formula, FyCost of chemical demulsifier added, yuan/day; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d；RyPrice of demulsifier, yuan/ton.
The constraint conditions of the cost optimization model are as follows:
(1) the crude oil temperature is obtained by mutual coupling of the temperatures in energy consumption equipment in a gathering and transportation system, the inlet temperature of a heating furnace in front of a primary settling tank is determined by subtracting the temperature drop in front of the inlet of the heating furnace from the weighted average temperature of incoming flow of a pipe network, the outlet temperature of the heating furnace is taken as the inlet temperature of the settling tank, the outlet temperature of the primary settling tank is subtracted by the on-way temperature drop after passing through the primary settling tank to be taken as the inlet temperature of a stable heating furnace, and the outlet temperature of the stable heating furnace is taken as the inlet temperature of the settling tank, so that;
(2) the water content psi of the outer pipe network is less than or equal to 0.3%;
(3) the pressure of crude oil is 0.24-0.32 MPa, and no pressurizing equipment is arranged in the united station, so that the inlet of the united station needs to ensure that the pressure of the incoming crude oil is 0.32-0.24 MPa to ensure the smooth operation of each flow of the united station, the pressure of the incoming crude oil of a pipe network is a constraint condition of the pressure, the pressure loss is mainly related to the flowing length of the crude oil and the viscosity of the crude oil, and the pressure loss and the viscosity of the crude oil can be coupled with the temperature. On the one hand, the crude oil temperature is related to the heating of the heating furnace, thereby affecting the fuel cost; on the other hand, pressure losses require a pump to replenish, which in turn affects power costs. Therefore, the optimal value of the fuel consumption and the power consumption of the whole pipe network is obtained according to the secondary relation.
(4) The stable vapor pressure of the crude oil is less than 70 kPa;
(5) the water content psi at the outlet of the electric dehydrator is less than or equal to 0.5 percent.
Comparing the fuel cost, the power consumption and the medicament cost under the current operation working condition of the wide-profit united station of the victory oil field with the theoretically calculated cost, as can be seen from fig. 14, the current operation excess air coefficient alpha of the heating furnace is 1.9, and the cost can be saved by about 10% after the optimization.
On the basis, when the excess air coefficient alpha of the heating furnace changes, the efficiency of the heating furnace changes, the fuel consumption changes along with the change, and the comprehensive energy consumption is influenced. As can be seen from fig. 15, as the excess air factor decreases, the thermal efficiency of the heating furnace increases, and the overall energy consumption decreases, so that the excess air factor can be adjusted according to the current operating situation of the heating furnace on the basis of the above optimized value, and the energy consumption is further reduced.
The data are obtained according to the actual working condition of the wide-interest union of the victory oil field.
The technical features of the present invention which are not described in the above embodiments may be implemented by or using the prior art, and are not described herein again, of course, the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and variations, modifications, additions or substitutions which may be made by those skilled in the art within the spirit and scope of the present invention should also fall within the protection scope of the present invention.

Claims (9)

1. A gathering system energy consumption calculation system based on data cleansing, comprising:
collecting operation data of an oil field gathering and transportation system, classifying the operation data and obtaining coordination data through data cleaning;
analyzing the coordination data to determine main control factors;
obtaining main energy consumption equipment of the oil field gathering and transportation system according to the main control factors, and establishing a relation model of production parameters of the main energy consumption equipment;
analyzing the relation model to obtain an energy consumption evaluation model of the system and describing the energy consumption evaluation model by using an equation set mode;
and coupling the energy consumption evaluation models and establishing a cost optimization model.
2. The energy consumption calculation system of the gathering system based on data cleaning as claimed in claim 1, wherein the coordination data is obtained by cleaning the operation data according to the rhineine criterion according to the following processing criteria:
Figure FDA0002184730920000011
xi is a gross error and should be discarded;
Figure FDA0002184730920000012
xi is normal data and should be reserved;
the coordination data comprises the standard density of crude oil, water content in export, flow rate in the import station, temperature in the import station, pressure in the import station, efficiency of a heating furnace, sedimentation temperature, pump efficiency, temperature in the export station, water content in the export station, flow rate in the export station, pump efficiency, water content in a three-phase outlet, electric de-voltage, electric de-current, temperature in a tower, water content in the import station, ambient temperature and pressure in the import station.
3. The energy consumption calculation system of the gathering and transportation system based on data cleaning as claimed in claim 1, wherein the coordination data is analyzed by a grey correlation analysis method, the correlation degree between the coordination data and the energy consumption is calculated, and the calculation formula of the comprehensive energy consumption obtained by performing multiple regression analysis on the correlation degree is as follows:
y ═ 0.0176 × external flow rate-6.55 × density +0.008 × external water +3.28 station pressure-0.7 × three-phase external water-0.02 × settling temperature +0.04 × furnace efficiency-0.06 × tower temperature +0.067 × external oil temperature + 0.02% power consumption;
the above formula is analyzed to determine that the main control factors are as follows: heating furnace efficiency, temperature of output oil and electric energy consumption.
4. The data cleansing-based energy consumption calculation system for a gathering and transportation system as recited in claim 1 wherein the primary energy consuming devices comprise: the system comprises an electric dehydrator, a heating furnace, a stabilizing tower and a settling tank, and a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizing tower and a relation model of the settling tank are established.
5. The data cleansing-based gathering system energy consumption calculating system as recited in claim 4,
the relation model of the electric dehydrator is specifically that field data of the concentration of the demulsifier, the demulsification temperature and the dehydration rate are collected, Matlab software is adopted to fit the concentration of the demulsifier, the dehydration rate and the demulsification temperature, and the relation between the dehydration rate of the electric dehydrator, the demulsification temperature and the concentration of the demulsifier is obtained:
s＝f(t,c)＝-1.938e4+2.461t+1312c-0.1332t2+0.1377tc-33.12c2+0.004544t2c-0.006442tc2+0.3698c3-3.849e-5t2c2+5.925e-5tc3-0.00154c4；
the relation model of the heating furnace is specifically as follows: measuring a fuel parameter and a heat balance parameter of the heating furnace on site, carrying out reverse thermodynamic calculation on the heating furnace to obtain fouling thermal resistance of the heating furnace, and then carrying out optimization calculation based on the calculated fouling thermal resistance to obtain an optimal fuel quantity and an optimal excess air coefficient;
the relation model of the stabilizing tower is specifically as follows: modeling and simulating an on-site production process in Hysys, then calling a Balance module to calculate and obtain saturated vapor pressure before and after stabilization, changing flash pressure and temperature in a stabilization tower, simulating the saturated vapor pressure under different temperatures and flash pressures, and analyzing to obtain the relation between the pressure in the stabilization tower and the incoming oil temperature: pm1.3524t-25.058, the relationship between the gas output of the stabilizing tower and the temperature of the incoming oil is as follows: vys＝1.163t-12.685；
The relation model of the settling tank is specifically as follows: sampling according to the on-site proportion, carrying out a sedimentation experiment by using a residence time method, and fitting an experiment result to obtain the relation between the sedimentation temperature and the water content as follows:
Figure FDA0002184730920000021
further processing the experimental result, picking the corresponding water content of the oil product under different medicament concentrations and different sedimentation temperatures to draw a relation curve, and fitting to obtain a relation t between the temperature and the medicament concentrationcj＝1.29×1011cy-5.094+51.72。
6. The data cleansing-based energy consumption calculation system for a gathering and transportation system as recited in claim 1, wherein the energy consumption evaluation model is:
Figure FDA0002184730920000022
Figure FDA0002184730920000023
By＝c·Q·10-6
in the formula, BrlAmount of fuel required for the furnace, m3D; m is the flow rate of the heated medium，kg/s；cpmkJ/(kg. DEG C) which is the average specific heat capacity of crude oil in the dehydration furnace; t is tci、triThe temperature of oil flowing out of the ith heating furnace and oil flowing into the ith heating furnace is measured at DEG C; qydwThe lower calorific value of the fuel used by the heating furnace, kJ/kg; etajrliThe working efficiency of the ith heating furnace is percent. Sigma BdfThe total required electricity charge; qi is the volume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriThe pressure of the pump inlet (namely the pressure of the incoming liquid of the ith pipeline), MPa; η bi is the efficiency of the ith pump; vdtiIs the electric dehydrator working voltage (i.e. the ith electric dehydrator working voltage), V; dtsiIs the working current of the electric dehydrator (i.e. the working current of the ith electric dehydrator), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit; b isyThe amount of chemical demulsifier added, kg/d; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d。
7. The data cleansing-based energy consumption calculation system for a gathering and transportation system as recited in claim 6, wherein a cost optimization model is established by coupling 3 consumptions with the goal of lowest total operating cost as follows:
min F＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fswherein, tgwOptimizing the temperature (namely the outlet temperature of the pipe network heating furnace) for the pipe network, and measuring the temperature in DEG C; pgwOptimizing the pressure (namely the outlet pressure of a pipe network pump) for the pipe network, wherein the pressure is MPa; t is tcjThe optimum temperature for the settling tank (i.e. settling tank temperature), deg.c; t is twdOptimizing the temperature (namely stabilizing the outlet temperature of the heating furnace) for the stabilizing tower at the temperature of DEG C; c. CyjThe concentration of the medicament (namely the concentration of the added medicament in a settling tank) is mg/L; t is twsOptimizing the temperature (namely the outlet temperature of the outward heating furnace) for the pipe network, wherein the temperature is DEG C; pwsOptimizing the pressure (namely the outlet pressure of the purifying oil pump) for output, namely, MPa; frFor fuel cost；FdIs the cost of electricity; fyIs the cost of the medicament.
8. The data cleansing-based gathering system energy consumption calculating system as recited in claim 7,
the fuel cost is as follows:
Figure FDA0002184730920000031
wherein R isrIs the price of fuel, Yuan/m3；QrkJ/m as fuel calorific value3；ηliHeating furnace thermal efficiency (derived from the furnace optimization model); giThe flow of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external transportation heating furnace is t/h; c. Co、cwThe specific heat of oil and water, kJ/(kg DEG C); t is tci、triTemperatures at the furnace outlet and inlet, respectively (when calculating the fuel consumption of the grid, t)ci、triRespectively indicating the optimum temperature t of the pipe networkgwAnd the inlet temperature of the pipe network heating furnace; when calculating furnace fuel cost before settling tank, tci、triRespectively represents the optimum temperature t of the settling tankcjAnd the inlet temperature t of the heating furnacecj(ii) a When calculating furnace fuel cost before settling tank, tci、triRespectively showing the optimized temperature of the stabilizer and the inlet temperature t of the heating furnacewd(ii) a When calculating the fuel cost of the outgoing heating furnace, tci、triRespectively representing the output optimized temperature and the inlet temperature t of the heating furnacews)；
The electric power cost is as follows:
Figure FDA0002184730920000032
wherein R isdThe power charge is yuan/(kW h); qiVolume flow of the ith pump, m3/h；PbciThe outlet pressure of the pump (namely the pressure of the starting point of the ith pipeline), MPa; pbriThe pressure of the pump inlet (namely the pressure of the incoming liquid of the ith pipeline), MPa; etabiThe efficiency of the ith pump; vdtiFor the operating voltage of the electric dehydrator (i.e. the ith electric dehydration)Machine operating voltage), V; dtsiIs the working current of the electric dehydrator (i.e. the working current of the ith electric dehydrator), A; etadtiEfficiency of the electric dehydrator; n is a radical ofiTo indicate power, kW; etaiyEfficiency of the compressor unit;
the cost of the medicament is Fy＝c·Q·Ry·10-6In the formula, FyCost of chemical demulsifier added, yuan/day; c is the concentration of demulsifier added, ppm; q is the daily liquid amount of the electric dehydration system, m3/d；RyPrice of demulsifier, yuan/ton.
9. The data cleansing-based energy consumption calculation system for a gathering and transportation system as recited in claim 7, wherein the constraints of the cost optimization model are: the temperature of the crude oil is obtained by mutual coupling of the temperature in energy consumption equipment in a gathering and transportation system, the water content psi of the external output of a pipe network is less than or equal to 0.3% ", the pressure of the crude oil is 0.24-0.32 MPa, the stable steam pressure of the crude oil is less than 70kPa, and the water content psi of an outlet of an electric dehydrator is less than or equal to 0.5%.
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