CN112528453B - Gathering and transmitting system energy consumption computing system based on data cleaning - Google Patents
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Abstract
The invention provides a data cleaning-based gathering and transmission system energy consumption computing system, and belongs to the field of petroleum exploration. The technical proposal is as follows: a data cleaning-based gathering and delivery system energy consumption computing system, comprising: collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment; analyzing the coordination data to determine a main control factor; obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set; and coupling the energy consumption evaluation model and establishing a cost optimization model. The beneficial effects of the invention are as follows: on the basis of cleaning operation data, a relation model of production parameters of main energy consumption equipment of the gathering and transportation system is established, and analysis and fitting are carried out on the relation model to obtain a cost optimization model of the gathering and transportation system, so that energy-saving potential spaces of different units are determined to be used as references.
Description
Technical Field
The invention relates to the field of petroleum exploration, in particular to a data cleaning-based energy consumption computing system of a gathering and transportation system.
Background
The problems of large energy consumption proportion, unclear energy consumption of part of the ring, no quality evaluation of a large amount of collected data and the like of the oilfield gathering and conveying system cause continuous attention, the research of energy consumption key parameter accounting and potential evaluation methods is developed, the energy consumption composition and the energy consumption index of the oilfield gathering and conveying system are further deeply researched, the energy consumption weak links are found out, the energy saving potential is excavated, and the method has important practical significance for energy saving and consumption reduction of the gathering and conveying system.
In order to solve the problems, a relation model of production parameters of main energy consumption equipment of the gathering and transportation system is established on the basis of cleaning operation data, a cost optimization model of the gathering and transportation system is obtained, and references are made for determining energy-saving potential spaces of different units.
Disclosure of Invention
The invention aims to establish a relation model of production parameters of main energy consumption equipment of a gathering and transportation system on the basis of cleaning operation data, and analyze and fit the relation model to obtain a cost optimization model of the gathering and transportation system, and the energy consumption calculation system of the gathering and transportation system based on data cleaning is used for determining energy saving potential spaces of different units as references.
The invention is realized by the following measures: a data cleaning-based gathering and delivery system energy consumption computing system, comprising: collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment; analyzing the coordination data to determine a main control factor; obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set; and coupling the energy consumption evaluation model and establishing a cost optimization model.
And cleaning the operation data according to the Rhin reaching criterion to obtain the coordination data, wherein the cleaning is based on the following steps: xi is a coarse error and should be discarded;
xi is normal data and should be reserved;
The coordination data comprise crude oil standard density, external water content, incoming flow, incoming temperature, incoming pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, external temperature, external water content, external flow, pump efficiency, three-phase outlet water content, electric dehydration voltage V, electric dehydration current A, incoming temperature ℃, incoming water content, environment temperature and incoming pressure.
The step of cleaning the operation data by the Rhin reaching criterion (PanTa) is as follows:
measurement columns X1, X2, … …, xn for the acquired operating data
(1) Obtaining arithmetic mean value
(2) Obtaining residual error
(3) Root mean square deviation according to Bessel methodThe discrimination is based on the following (assuming vi conforms to the normal distribution, i.e., the measurement columns also conform to the normal distribution):
xi is a coarse error and should be discarded; Xi is normal data and should be reserved.
According to the probability theory statistics, when the error is subjected to normal distribution, the probability of occurrence of the observation data with the error larger than 3 delta is smaller than 0.003, namely, the probability of occurrence of 1 time in the observation with the error larger than 300 times is higher. Therefore, if the coarse reject is performed by using the leine criterion (also called 3 delta criterion), the reject probability is small, so that unreasonable outliers are also preserved in some cases.
Analyzing the coordination data by a gray 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 is as follows:
y=0.0176 x output-6.55 x density +0.008 x output water content +3.28 inlet pressure-0.7 x three phase output water content-0.02 x sedimentation temperature +0.04 x heating furnace efficiency-0.06 x inlet temperature +0.067 x output water temperature +0.02 x electric energy consumption;
analyzing the formula to determine the main control factors as follows: heating furnace efficiency, temperature of outward oil delivery and electric energy consumption.
The gray correlation analysis can be performed by adopting matlab software programming, and comprises the following specific steps: (1) The comprehensive energy consumption is taken as a reference sequence, and the coordination data is taken as a comparison sequence. (2) And carrying out dimensionless treatment on the reference sequence and the comparison sequence. (3) The gray correlation coefficient ζ (Xi) of the reference number series and the comparison number series is calculated. Wherein ρ is a resolution factor, typically between 0 and 1, typically 0.5; Δmin is the second stage minimum difference; Δmax is the two-stage 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 Δ0i (k). The correlation coefficient ζ (Xi) can be reduced to the following formula:
(4) And solving the association degree of the coordination data: ri is the gray correlation of the comparison sequence Xi to the reference sequence X0, or sequence correlation, average correlation, line correlation. The closer the ri value is to 1, the better the correlation is explained.
(5) And performing association degree sequencing. And finally analyzing according to the arrangement sequence of the association degree to obtain a conclusion.
Calculating the association degree of the coordination data and performing multiple regression analysis, wherein the multiple regression analysis can be performed by matlab software programming, and the expression of the multiple regression analysis is y=b 0+b1x1+b2x2+...+bnxn, wherein x t represents the value of the t-phase independent variable; y t represents the value of the t-phase dependent variable; a. b represents the parameters of the unitary linear regression equation, and the a and b parameters are calculated by the following formula:
Through the steps, comprehensive energy consumption is taken as a parent column in matlab software, standard density, external water content, incoming flow, incoming temperature, incoming pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, external water content, external flow, pump efficiency, three-phase outlet water content, electric stripping voltage V, electric stripping current A, incoming temperature, incoming water content, environmental temperature and incoming pressure of data are coordinated, standard treatment is carried out on 653 times of working conditions in a source database, and the association degree is calculated through grey association analysis; multiple regression analysis is carried out on the association degree, and a calculation formula of comprehensive energy consumption is obtained:
y=0.0176 x output-6.55 x density +0.008 x output water content +3.28 inlet pressure-0.7 x three phase output water content-0.02 x sedimentation temperature +0.04 x heating furnace efficiency-0.06 x inlet temperature +0.067 x output water temperature +0.02 x electric energy 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 outward oil delivery and electric energy consumption. The obtaining the main energy consumption equipment according to the calculation formula of the comprehensive energy consumption comprises the following steps: the method comprises the steps of an electric dehydrator, a heating furnace, a stabilizer and a settling tank, and establishing a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizer and a relation model of the settling tank.
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 Matlab software can be adopted to fit, so that the relation between the dehydration rate of the electric dehydrator, the demulsification temperature and the demulsifier concentration is obtained:
s＝f(t,c)＝-1.938e4+2.461t+1312c-0.1332t2+0.1377tc-33.12c2+0.004544t22c-0.006442tc2+0.3698c3-3.849e-5t2c2+5.925e-5tc3-0.00154c4.
The relation model of the heating furnace specifically comprises the following steps: and (3) measuring fuel parameters and heat balance parameters of the heating furnace on site, performing reverse thermal calculation on the heating furnace to obtain the dirt thermal resistance of the heating furnace, and performing optimization calculation based on the calculated dirt thermal resistance to obtain the optimal fuel quantity and the optimal excess air coefficient.
Further, the building of the relation model of the heating furnace comprises two parts, wherein the first part is a checking calculation, and the building comprises the following steps: according to given fuel and components, measured smoke components and temperature, and heating furnace structure, performing combustion and heat balance calculation to obtain parameters such as crude oil flow and heating load, heating furnace heat efficiency, fire tube smoke tube coil thermal resistance and the like; and performing heat checking calculation on the fire tube, the severe tube and the coil pipe to obtain the scale resistance of the hot furnace and the scale resistance of the coil pipe.
The second part is optimization calculation, namely, fuel combustion calculation is carried out according to the fuel composition and the given excess air coefficient, and the method comprises the following steps: firstly, assuming the exhaust gas temperature, and performing heat balance calculation;
(2) Then, assuming the temperature of the outlet of the fire tube, calculating the average flame temperature and the heat balance Q f of the fire tube;
(3) Calculating the cylinder blackness, the flame blackness, the system blackness and the average flame temperature;
(4) According to the calculated dirt thermal resistance, calculating the wall temperature of the fire cylinder and the heat transfer quantity Q f1 of the fire cylinder;
(5) Substituting the formula to calculate:
(6) If (5) is not established, replacing the step (2) to recalculate;
(7) If (5) is established, calculating the heat balance heat Q S of the smoke tube according to the enthalpy values of the fire tube and the smoke exhaust; (8) According to the calculated dirt thermal resistance, calculating a heat transfer coefficient, a heat transfer temperature difference and a heat transfer quantity Q S1 of the smoke tube;
(9) Substituting the formula to calculate:
(10) If (9) is not established, replacing the step (1) to recalculate;
(11) If (9) is true, the optimization simulation calculation is ended.
The relation model of the stabilizer is specifically as follows: modeling and simulating the field production process in Hysys, then calling a Balance module to calculate saturated vapor pressure before and after stabilization, changing flash evaporation pressure and temperature in the stabilizer, simulating saturated vapor pressure at different temperatures and flash evaporation pressure, and analyzing to obtain the relationship between pressure in the stabilizer and the temperature of the oil: p m = 1.3524t-25.058, the relationship between the stabilizer exit and the incoming oil temperature is: v ys = 1.163t-12.685.
The relation model of the settling tank is specifically as follows: sampling according to the site proportion, carrying out a sedimentation experiment by using a residence time method, and fitting the experimental result to obtain the relationship between the sedimentation temperature and the water content, wherein the relationship is as follows: And further processing the experimental result, picking up corresponding oil water content drawing relation curves at different sedimentation temperatures under different medicament concentrations, wherein the sedimentation time is 5h, and fitting to obtain the relation between the temperature and the medicament concentration as t cj＝1.29×1011cy -5.094+51.72.
The energy consumption evaluation model is as follows:
By＝c·Q·10-6
Wherein B rl is the fuel quantity needed by the heating furnace, and m 3/d; m is the flow rate of the heated medium, kg/s; c pm is the average specific heat capacity of crude oil in the dehydration heating furnace, kJ/(kg. DEG C); t ci、tri is the temperature of the oil and water exiting from the ith heating furnace and entering the ith heating furnace, and the temperature is lower than the temperature; Q y dw is the low calorific value of the fuel used by the heating furnace, kJ/kg; η jrli is the working efficiency of the ith heating furnace,%. Sigma B df is the total amount of electricity charge required; Q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; V dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit; the amount of chemical demulsifier added in B y, kg/d; c is the concentration of the demulsifier added, ppm; q is the daily liquid supply amount of the electric dehydration system, and m 3/d.
The cost optimization model established by 3 consumption couplings is as follows, with the minimum total operation cost as a target:
min F＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fs, Wherein,
T gw is the pipe network optimization temperature (namely the pipe network heating furnace outlet temperature) and the temperature; p gw is the pipe network optimization pressure (namely the outlet pressure of the pipe network pump) and MPa; t cj is the optimum temperature (i.e. settling temperature) of the settling tank, DEG C; t wd is the optimized temperature of the stabilizer (namely the outlet temperature of the stable heating furnace) and the temperature is lower than the temperature; c yi is the concentration of the medicament (namely the medicament adding concentration of a settling tank) and mg/L; t ws is the pipe network optimization temperature (namely the outlet temperature of the external heating furnace) at the temperature of DEG C; p ws is the optimal pressure of the output (namely the outlet pressure of the purified oil pump) and MPa; f r is fuel cost; f d is the electric power cost; f y is the cost of the medicament.
The fuel cost is as follows:
Wherein R r is the fuel price, yuan/m 3;Qr is the fuel heating value, kJ/m 3;ηil is the heating furnace thermal efficiency (obtained according to a heating furnace optimization model); g i is the flow rate of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external conveying heating furnace, and t/h; c o、cw is the specific heat of oil and water, kJ/(kg. Deg.C); t ci、tri is the temperature of the outlet and inlet of the heating furnace respectively (t ci、tri is the pipe network optimized temperature t gw and pipe network inlet temperature respectively when calculating pipe network fuel cost, t ci、tri is the settling tank optimized temperature t cj and heating furnace inlet temperature t cj respectively when calculating settling tank front heating furnace fuel cost, t ci、tri is the stabilizer optimized temperature and heating furnace inlet temperature t wd respectively when calculating settling tank front heating furnace fuel cost, and t ci、tri is the external optimized temperature and heating furnace inlet temperature t ws respectively when calculating external heating furnace fuel cost);
the electric power cost is as follows:
Wherein R d is electric charge, yuan/(kW.h); q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; v dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit;
The cost of the medicament is F y＝c·Q·Ry·10-6, wherein F y is added into the chemical demulsifier, and the cost is Yuan/day; c is the concentration of the demulsifier added, ppm; q is the liquid input amount of the electric dehydration system per day, and m3/d; price of R y demulsifier, yuan/ton.
Constraint conditions of the cost optimization model are as follows:
(1) The crude oil temperature is obtained by mutually coupling the temperatures in energy equipment in a gathering and conveying system, the weighted average temperature of pipe network inflow of the inlet temperature of a heating furnace in front of a primary sedimentation tank is subtracted by the temperature drop before the inlet of the heating furnace, the outlet temperature of the heating furnace is taken as the inlet temperature of the sedimentation tank, after the primary sedimentation tank is passed, the outlet temperature of the primary sedimentation tank is subtracted by the temperature drop along the path 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 sedimentation tank, so that the coupling of the global temperature is realized;
(2) The water content psi of the pipe network is less than or equal to 0.3%;
(3) The crude oil pressure is 0.24-0.32 MPa, the inlet of the combined station needs to ensure that the incoming crude oil has 0.32-0.24 MPa because of no pressurizing equipment in the combined station, so as to ensure that each flow of the combined station is smoothly carried out, therefore, the crude oil pressure of the pipe network incoming flow is a constraint condition of pressure, the pressure loss is mainly related to the length of the crude oil flowing through and the viscosity of the crude oil, and the two can be coupled with 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, the pressure loss requires a pump to supply, thereby affecting the power cost. Therefore, the optimal value of the fuel consumption and the electric power consumption of the whole pipe network is obtained according to the secondary relation.
(4) The stable vapor pressure of crude oil is less than 70kPa;
(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 relation 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 relation model to obtain a cost optimization model of the gathering and transportation system, and taking reference for determining energy-saving potential spaces of different units.
Drawings
Fig. 1 and 2 are both grey correlation analysis mat lab programming interface diagrams.
Fig. 3,4 and 5 are all multiple regression analysis mat lab programming interface diagrams.
FIG. 6 is a graph of operating parameters after fitting of demulsifier concentration, demulsification temperature, and crude oil dehydration rate.
FIG. 7 is a diagram of a heating furnace optimization simulation calculation model.
Fig. 8 is a stabilizer simulation flow chart.
FIG. 9 is a graph showing the water content over time at various temperatures.
FIG. 10 is a plot of sedimentation temperature versus moisture content.
FIG. 11 is a graph showing gray correlation between coordination data and integrated energy consumption.
FIG. 12 is a graph showing the relationship between the dehydration rate of the electric dehydrator and the demulsification temperature and the demulsifier concentration.
FIG. 13 is a table comparing the results of the furnace simulation calculations with the test data.
FIG. 14 is a table comparing current operating costs to optimized costs.
FIG. 15 shows the energy costs for different excess air ratios.
Detailed Description
In order to clearly illustrate the technical characteristics of the scheme, the scheme is explained below through a specific embodiment.
Embodiment one:
A data cleansing based gathering and delivery system energy consumption computing system comprising: collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment; analyzing the coordination data to determine a main control factor; obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set; and coupling the energy consumption evaluation model and establishing a cost optimization model.
Embodiment two:
A data cleansing based gathering and delivery system energy consumption computing system comprising: collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment; analyzing the coordination data to determine a main control factor; obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set; and coupling the energy consumption evaluation model and establishing a cost optimization model.
Constraint conditions of the cost optimization model are as follows: the temperature of the crude oil is obtained by mutually coupling the temperature in energy equipment in a gathering and transporting system, the water content psi of the pipe network is less than or equal to 0.3 percent, the pressure of the crude oil is 0.24-0.32 MPa, the stable vapor 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 percent.
Embodiment III:
A data cleansing based gathering and delivery system energy consumption computing system comprising: collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment; analyzing the coordination data to determine a main control factor; obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set; and coupling the energy consumption evaluation model and establishing a cost optimization model.
And cleaning the operation data according to the Rhin reaching criterion to obtain the coordination data, wherein the cleaning is based on the following steps: xi is a coarse error and should be discarded;
xi is normal data and should be reserved;
The coordination data comprise crude oil standard density, external water content, incoming flow, incoming temperature, incoming pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, external temperature, external water content, external flow, pump efficiency, three-phase outlet water content, electric dehydration voltage V, electric dehydration current A, incoming temperature ℃, incoming water content, environment temperature and incoming pressure.
The step of cleaning the operation data by the Rhin reaching criterion (PanTa) is as follows:
measurement columns X1, X2, … …, xn for the acquired operating data
(1) Obtaining arithmetic mean value
(2) Obtaining residual error
(3) Root mean square deviation according to Bessel methodThe discrimination is based on the following (assuming vi conforms to the normal distribution, i.e., the measurement columns also conform to the normal distribution):
xi is a coarse error and should be discarded; Xi is normal data and should be reserved.
According to the probability theory statistics, when the error is subjected to normal distribution, the probability of occurrence of the observation data with the error larger than 3 delta is smaller than 0.003, namely, the probability of occurrence of 1 time in the observation with the error larger than 300 times is higher. Therefore, if the coarse reject is performed by using the leine criterion (also called 3 delta criterion), the reject probability is small, so that unreasonable outliers are also preserved in some cases.
Analyzing the coordination data by a gray 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 is as follows:
y=0.0176 x output-6.55 x density +0.008 x output water content +3.28 inlet pressure-0.7 x three phase output water content-0.02 x sedimentation temperature +0.04 x heating furnace efficiency-0.06 x inlet temperature +0.067 x output water temperature +0.02 x electric energy consumption;
analyzing the formula to determine the main control factors as follows: heating furnace efficiency, temperature of outward oil delivery and electric energy consumption.
Referring to fig. 1,2 and 11, the gray correlation analysis can be performed by using matlab software programming, and the specific steps are as follows: (1) The comprehensive energy consumption is taken as a reference sequence, and the coordination data is taken as a comparison sequence. (2) And carrying out dimensionless treatment on the reference sequence and the comparison sequence. (3) The gray correlation coefficient ζ (Xi) of the reference number series and the comparison number series is calculated. Wherein ρ is a resolution factor, typically between 0 and 1, typically 0.5; Δmin is the second stage minimum difference; Δmax is the two-stage 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 Δ0i (k). The correlation coefficient ζ (Xi) can be reduced to the following formula:
(4) And solving the association degree of the coordination data: ri is the gray correlation of the comparison sequence Xi to the reference sequence X0, or sequence correlation, average correlation, line correlation. The closer the ri value is to 1, the better the correlation is explained.
(5) And performing association degree sequencing. And finally analyzing according to the arrangement sequence of the association degree to obtain a conclusion.
Referring to fig. 3, 4 and 5, the coordination data is correlated and a multiple regression analysis is performed, wherein the multiple regression analysis can be performed by matlab software programming, and the expression of the multiple regression analysis is y=b 0+b1x1+b2x2+...+bnxn, wherein x t represents the value of the t-phase independent variable; y t represents the value of the t-phase dependent variable; a. b represents the parameters of the unitary linear regression equation, and the a and b parameters are calculated by the following formula:
Through the steps, comprehensive energy consumption is taken as a parent column in matlab software, standard density, external water content, incoming flow, incoming temperature, incoming pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, external water content, external flow, pump efficiency, three-phase outlet water content, electric stripping voltage V, electric stripping current A, incoming temperature, incoming water content, environmental temperature and incoming pressure of data are coordinated, standard treatment is carried out on 653 times of working conditions in a source database, and the association degree is calculated through grey association analysis; multiple regression analysis is carried out on the association degree, and a calculation formula of comprehensive energy consumption is obtained:
y=0.0176 x output-6.55 x density +0.008 x output water content +3.28 inlet pressure-0.7 x three phase output water content-0.02 x sedimentation temperature +0.04 x heating furnace efficiency-0.06 x inlet temperature +0.067 x output water temperature +0.02 x electric energy 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 outward oil delivery and electric energy consumption. The obtaining the main energy consumption equipment according to the calculation formula of the comprehensive energy consumption comprises the following steps: the method comprises the steps of an electric dehydrator, a heating furnace, a stabilizer and a settling tank, and establishing a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizer and a relation model of the settling tank.
Referring to fig. 6 and 12, the relation model of the electric dehydrator specifically collects field data of the demulsifier concentration, the demulsification temperature and the dehydration rate, adopts Matlab software to fit the demulsifier concentration, the dehydration rate and the demulsification temperature, and can adopt Matlab software to fit, so as to obtain the relation between the dehydration rate of the electric dehydrator, the demulsification temperature and the demulsifier concentration as follows:
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 relation model of the heating furnace specifically includes: and (3) measuring fuel parameters and heat balance parameters of the heating furnace on site, performing reverse thermal calculation on the heating furnace to obtain the dirt thermal resistance of the heating furnace, and performing optimization calculation based on the calculated dirt thermal resistance to obtain the optimal fuel quantity and the optimal excess air coefficient.
Further, the building of the relation model of the heating furnace comprises two parts, wherein the first part is a checking calculation, and the building comprises the following steps: according to given fuel and components, measured smoke components and temperature, and heating furnace structure, performing combustion and heat balance calculation to obtain parameters such as crude oil flow and heating load, heating furnace heat efficiency, fire tube smoke tube coil thermal resistance and the like; and performing heat checking calculation on the fire tube, the severe tube and the coil pipe to obtain the scale resistance of the hot furnace and the scale resistance of the coil pipe.
The second part is optimization calculation, namely, fuel combustion calculation is carried out according to the fuel composition and the given excess air coefficient, and the method comprises the following steps: firstly, assuming the exhaust gas temperature, and performing heat balance calculation;
(2) Then, assuming the temperature of the outlet of the fire tube, calculating the average flame temperature and the heat balance Q f of the fire tube;
(3) Calculating the cylinder blackness, the flame blackness, the system blackness and the average flame temperature;
(4) According to the calculated dirt thermal resistance, calculating the wall temperature of the fire cylinder and the heat transfer quantity Q f1 of the fire cylinder;
(5) Substituting the formula to calculate:
(6) If (5) is not established, replacing the step (2) to recalculate;
(7) If (5) is established, calculating the heat balance heat Q S of the smoke tube according to the enthalpy values of the fire tube and the smoke exhaust; (8) According to the calculated dirt thermal resistance, calculating a heat transfer coefficient, a heat transfer temperature difference and a heat transfer quantity Q S1 of the smoke tube;
(9) Substituting the formula to calculate:
(10) If (9) is not established, replacing the step (1) to recalculate;
(11) If (9) is true, the optimization simulation calculation is ended.
The simulation model of the heating furnace was verified based on the test data provided in the field as shown in fig. 13, and the result is shown in fig. 13. From the results, the matching degree of the simulation result and the measured data exceeds 97.9%, and the simulation model of the heating furnace is proved to be accurate.
Referring to fig. 8, the relationship model of the stabilizer is specifically: modeling and simulating the field production process in Hysys, then calling a Balance module to calculate saturated vapor pressure before and after stabilization, changing flash evaporation pressure and temperature in the stabilizer, simulating saturated vapor pressure at different temperatures and flash evaporation pressure, and analyzing to obtain the relationship between pressure in the stabilizer and the temperature of the oil: p m = 1.3524t-25.058, the relationship between the stabilizer exit and the incoming oil temperature is: v ys = 1.163t-12.685.
Referring to fig. 9 and 10, the relationship model of the settling tank is specifically: sampling according to the site proportion, sampling and comprehensive water content of about 0.5%, carrying out sedimentation experiments by a residence time method, wherein the residence time is 20h, and researching the change rule of the water content along with time when the temperature is 30 ℃, 35 ℃, 40 ℃, 45 ℃, 50 ℃, 55 ℃ and 60 ℃ respectively as follows: As shown in fig. 9. Further processing the experimental result, picking up the corresponding water content of the oil products at different sedimentation temperatures under different medicament concentrations, drawing a relation curve, as shown in figure 10, wherein the selected sedimentation time is 5h, and fitting to obtain the relation between the temperature and the medicament concentration is t cj＝1.29×1011cy -5.094+51.72.
The energy consumption evaluation model determined by analysis and integration according to the above is as follows:
By＝c·Q·10-6
Wherein B rl is the fuel quantity needed by the heating furnace, and m 3/d; m is the flow rate of the heated medium, kg/s; c pm is the average specific heat capacity of crude oil in the dehydration heating furnace, kJ/(kg. DEG C); t ci、tri is the temperature of the oil and water exiting from the ith heating furnace and entering the ith heating furnace, and the temperature is lower than the temperature; Q y dw is the low calorific value of the fuel used by the heating furnace, kJ/kg; η jrli is the working efficiency of the ith heating furnace,%; sigma B df is the total amount of electricity charge required; Q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; V dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit; the amount of chemical demulsifier added in B y, kg/d; c is the concentration of the demulsifier added, ppm; q is the daily liquid supply amount of the electric dehydration system, and m 3/d.
The cost optimization model established by 3 consumption couplings is as follows, with the minimum total operation cost as a target:
min F＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fs,
wherein t gw is the pipe network optimization temperature (namely the pipe network heating furnace outlet temperature) and the temperature; p gw is the pipe network optimization pressure (namely the outlet pressure of the pipe network pump) and MPa; t cj is the optimum temperature (i.e. settling temperature) of the settling tank, DEG C; t wd is the optimized temperature of the stabilizer (namely the outlet temperature of the stable heating furnace) and the temperature is lower than the temperature; c yi is the concentration of the medicament (namely the medicament adding concentration of a settling tank) and mg/L; t ws is the pipe network optimization temperature (namely the outlet temperature of the external heating furnace) at the temperature of DEG C; p ws is the optimal pressure of the output (namely the outlet pressure of the purified oil pump) and MPa; f r is fuel cost; f d is the electric power cost; f y is the cost of the medicament.
The fuel cost is as follows:
Wherein R r is the fuel price, yuan/m 3;Qr is the fuel heating value, kJ/m 3;ηil is the heating furnace thermal efficiency (obtained according to a heating furnace optimization model); g i is the flow rate of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external conveying heating furnace, and t/h; c o、cw is the specific heat of oil and water, kJ/(kg. Deg.C); t ci、tri is the temperature of the outlet and inlet of the heating furnace respectively (t ci、tri is the pipe network optimized temperature t gw and pipe network inlet temperature respectively when calculating pipe network fuel cost; t ci、tri is the settling tank optimized temperature t cj and heating furnace inlet temperature t cj respectively when calculating settling tank front heating furnace fuel cost; t ci、tri is the stabilizer optimized temperature and heating furnace inlet temperature t wd respectively when calculating settling tank front heating furnace fuel cost; t ci、tri is the external optimized temperature and heating furnace inlet temperature t ws respectively when calculating external heating furnace fuel cost), The water content is the change rule of the water content along with time;
the electric power cost is as follows:
Wherein R d is electric charge, yuan/(kW.h); q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; v dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit;
The cost of the medicament is F y＝c·Q·Ry·10-6, wherein F y is added into the chemical demulsifier, and the cost is Yuan/day; c is the concentration of the demulsifier added, ppm; q is the liquid input amount of the electric dehydration system per day, and m3/d; price of R y demulsifier, yuan/ton.
Constraint conditions of the cost optimization model are as follows:
(1) The crude oil temperature is obtained by mutually coupling the temperatures in energy equipment in a gathering and conveying system, the weighted average temperature of pipe network inflow of the inlet temperature of a heating furnace in front of a primary sedimentation tank is subtracted by the temperature drop before the inlet of the heating furnace, the outlet temperature of the heating furnace is taken as the inlet temperature of the sedimentation tank, after the primary sedimentation tank is passed, the outlet temperature of the primary sedimentation tank is subtracted by the temperature drop along the path 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 sedimentation tank, so that the coupling of the global temperature is realized;
(2) The water content psi of the pipe network is less than or equal to 0.3%;
(3) The crude oil pressure is 0.24-0.32 MPa, the inlet of the combined station needs to ensure that the incoming crude oil has 0.32-0.24 MPa because of no pressurizing equipment in the combined station, so as to ensure that each flow of the combined station is smoothly carried out, therefore, the crude oil pressure of the pipe network incoming flow is a constraint condition of pressure, the pressure loss is mainly related to the length of the crude oil flowing through and the viscosity of the crude oil, and the two can be coupled with 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, the pressure loss requires a pump to supply, thereby affecting the power cost. Therefore, the optimal value of the fuel consumption and the electric power consumption of the whole pipe network is obtained according to the secondary relation.
(4) The stable vapor pressure of crude oil is less than 70kPa;
(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, electricity consumption and medicament cost with the cost calculated by theory under the current operation working condition of the Guangling station of the victory oil field, 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 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, and the comprehensive energy consumption is influenced. As can be seen from fig. 15, as the excess air ratio decreases, the heat efficiency of the heating furnace increases and the overall energy consumption decreases, so that the excess air ratio can be adjusted based on the optimum value according to the current state of operation of the heating furnace, thereby further reducing the energy consumption.
The data are obtained according to the actual working conditions of the Guangling Union of the victory oil fields.
The technical features of the present invention that are not described in the present invention may be implemented by or using the prior art, and are not described in detail herein, but the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, but is also intended to be within the scope of the present invention by those skilled in the art.
Claims (4)
1. A data cleaning-based gathering and delivery system energy consumption computing system, comprising:
collecting operation data of an oilfield gathering and transmission system, classifying the operation data and obtaining coordination data through data cleaning treatment;
analyzing the coordination data to determine a main control factor;
Obtaining main energy consumption equipment of the oilfield gathering and transmitting 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 in a mode of an equation set;
coupling the energy consumption evaluation model and establishing a cost optimization model;
The primary energy consuming device comprises: the method comprises the steps of (1) an electric dehydrator, a heating furnace, a stabilizer and a settling tank, and establishing a relation model of the electric dehydrator, a relation model of the heating furnace, a relation model of the stabilizer and a relation model of the settling tank;
The relation model of the electric dehydrator is specifically that field data of demulsifier concentration, demulsification temperature and demulsification rate are collected, matlab software is adopted to fit the demulsifier concentration, the demulsification rate and the demulsification temperature, and the relation between the demulsification rate of the electric dehydrator, the demulsification temperature and the demulsification rate is obtained as follows:
s＝f(t,c)＝-1.938e4+2.461t+1312c-0.1332t2+0.1377tc-33.12c2+0.004544t2c0.006442tc2+0.3698c3-3.849e-5t2c2+5.925e-5tc3-0.00154c4
The relation model of the heating furnace specifically comprises the following steps: measuring fuel parameters and heat balance parameters of the heating furnace on site, performing reverse thermal calculation on the heating furnace to obtain dirt thermal resistance of the heating furnace, and performing optimization calculation based on the calculated dirt thermal resistance to obtain optimal fuel quantity and optimal excess air coefficient;
The relation model of the stabilizer is specifically as follows: modeling and simulating the field production process in Hysys, then calling a Balance module to calculate saturated vapor pressure before and after stabilization, changing flash evaporation pressure and temperature in the stabilizer, simulating saturated vapor pressure at different temperatures and flash evaporation pressure, and analyzing to obtain the relationship between pressure in the stabilizer and the temperature of the oil: p m = 1.3524t-25.058, the relationship between the stabilizer exit and the incoming oil temperature is: v ys = 1.163t-12.685;
The relation model of the settling tank is specifically as follows: sampling according to the site proportion, carrying out a sedimentation experiment by using a residence time method, and fitting the experimental result to obtain the relationship between the sedimentation temperature and the water content, wherein the relationship is as follows: Further processing the experimental result, picking up corresponding oil water content drawing relation curves under different sedimentation temperatures at different medicament concentrations, and fitting to obtain a temperature-medicament concentration relation of t cj＝1.29×1011cy -5.094+51.72;
the energy consumption evaluation model is as follows:
By＝c·Q·10-6
Wherein B rl is the fuel quantity needed by the heating furnace, and m 3/d; m is the flow rate of the heated medium, kg/s; c pm is the average specific heat capacity of crude oil in the dehydration heating furnace, kJ/(kg. DEG C); t ci、tri is the temperature of the oil and water exiting from the ith heating furnace and entering the ith heating furnace, and the temperature is lower than the temperature; Q y dw is the low calorific value of the fuel used by the heating furnace, kJ/kg; η jrli is the working efficiency of the ith heating furnace,%; sigma B df is the total amount of electricity charge required; Q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; V dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit; the amount of chemical demulsifier added in B y, kg/d; c is the concentration of the demulsifier added, ppm; q is the liquid input amount of the electric dehydration system per day, m 3/d;
the cost optimization model established by 3 consumption couplings is as follows, with the minimum total operation cost as a target:
minF＝f(tgw,Pgw,tcj,twd,cyj,tws,Pws)＝Fr+Fd+Fs,
Wherein t gw is the pipe network optimization temperature (namely the pipe network heating furnace outlet temperature) and the temperature; p gw is the pipe network optimization pressure (namely the outlet pressure of the pipe network pump) and MPa; t cj is the optimum temperature (i.e. settling temperature) of the settling tank, DEG C; t wd is the optimized temperature of the stabilizer (namely the outlet temperature of the stable heating furnace) and the temperature is lower than the temperature; c yi is the concentration of the medicament (namely the medicament adding concentration of a settling tank) and mg/L; t ws is the pipe network optimization temperature (namely the outlet temperature of the external heating furnace) at the temperature of DEG C; p ws is the optimal pressure of the output (namely the outlet pressure of the purified oil pump) and MPa; f r is fuel cost; f d is the electric power cost; f y is the cost of the medicament;
The fuel cost is as follows:
Wherein R r is the fuel price, yuan/m 3;Qr is the fuel heating value, kJ/m 3;ηil is the heating furnace thermal efficiency (obtained according to a heating furnace optimization model); g i is the flow rate of a pipe network heating furnace, a dehydration heating furnace, a stable heating furnace and an external conveying heating furnace, and t/h; c o、cw is the specific heat of oil and water, kJ/(kg. Deg.C); t ci、tri is the temperature of the outlet and inlet of the heating furnace respectively (t ci、tri is the pipe network optimized temperature t gw and pipe network inlet temperature respectively when calculating pipe network fuel cost; t ci、tri is the settling tank optimized temperature t cj and heating furnace inlet temperature t cj respectively when calculating settling tank front heating furnace fuel cost; t ci、tri is the stabilizer optimized temperature and heating furnace inlet temperature t wd respectively when calculating settling tank front heating furnace fuel cost; t ci、tri is the external optimized temperature and heating furnace inlet temperature t ws respectively when calculating external heating furnace fuel cost), The water content is the change rule of the water content along with time;
the electric power cost is as follows:
Wherein R d is electric charge, yuan/(kW.h); q i is the volume flow of the ith pump, m 3/h;Pbci is the outlet pressure of the pump (i.e. the starting point pressure of the ith pipeline) and MPa; p bri is the pump inlet pressure (i.e., the hydraulic pressure from the ith line), MPa; η bi is the efficiency of the ith pump; v dti is the electric dehydrator operating voltage (i.e. the ith electric dehydrator operating voltage), V; i dti is the electric dehydrator working current (i.e. the ith electric dehydrator working current), A; η dti is the electrical dehydrator efficiency; n i is the indicated power, kW; η yi is the efficiency of the compressor unit;
The cost of the medicament is F y＝c·Q·Ry·10-6, wherein F y is added into the chemical demulsifier, and the cost is Yuan/day; c is the concentration of the demulsifier added, ppm; q is the liquid input amount of the electric dehydration system per day, and m3/d; price of R y demulsifier, yuan/ton.
2. The data cleaning-based energy consumption computing system of the gathering and transmitting system according to claim 1, wherein the coordination data is obtained by cleaning the operation data according to the reinhardtia criterion, and the cleaning is based on the following processing basis: xi is a coarse error and should be discarded;
xi is normal data and should be reserved; wherein, Is the arithmetic mean, delta is the root mean square deviation;
The coordination data comprise crude oil standard density, external water content, incoming flow, incoming temperature, incoming pressure, heating furnace efficiency, sedimentation temperature, pump efficiency, external temperature, external water content, external flow, pump efficiency, three-phase outlet water content, electric dehydration voltage, electric dehydration current, incoming temperature, incoming water content, environment temperature and incoming pressure.
3. The data cleaning-based energy consumption computing system of the gathering and transmission system according to claim 1, wherein the coordination data is analyzed by a gray correlation analysis method, the correlation degree between the coordination data and the energy consumption is computed, and a computation formula for obtaining the comprehensive energy consumption by performing multiple regression analysis on the correlation degree is as follows:
y=0.0176 x output-6.55 x density +0.008 x output water content +3.28 inlet pressure-0.7 x three phase output water content-0.02 x sedimentation temperature +0.04 x heating furnace efficiency-0.06 x inlet temperature +0.067 x output water temperature +0.02 x electric energy consumption;
analyzing the formula to determine the main control factors as follows: heating furnace efficiency, temperature of outward oil delivery and electric energy consumption.
4. The data cleansing based gathering and delivery system energy consumption computing system as recited in claim 1, wherein constraints of the cost optimization model are: the temperature of the crude oil is obtained by mutually coupling the temperature in energy equipment in a gathering and transporting system, the water content psi of the pipe network outside is less than or equal to 0.3 percent, the pressure of the crude oil is 0.24-0.32 MPa, the stable vapor 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 percent.
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