Crude oil exploitation well station integrated production optimization method giving consideration to new energy consumption

Abstract

The invention discloses a crude oil exploitation well station integrated production optimization method giving consideration to new energy consumption, which brings new energy consumption benefits into an optimization decision of a crude oil exploitation process, carries out refined division on an oil production well regulation and control object at the same time, and constructs an oil production and infusion integrated well station collaborative production energy consumption constraint to improve the oil production efficiency. And a periodic mixed integer programming solving method is adopted to obtain a dynamic well-station integrated production scheduling scheme capable of adapting to a new energy fluctuating power generation output process. The scheduling scheme generated by the method meets the self-adaptive adjustment requirement of the new energy capacity fluctuation on the power utilization side load, the new energy consumption capacity of an oil extraction plant in the aspect of production energy consumption is effectively improved, 'the load moves along with the source 'in the crude oil production process is promoted, and the production power utilization cost is saved. The method has an important value for realizing production energy consumption minimization, green power consumption maximization and source-load collaborative optimization of oil extraction enterprises under the new energy layout.

Description

Technical Field

[0001] The present invention relates to the field of crude oil production, and specifically to a method for optimizing well-station integrated collaborative production in the crude oil production process, which can take into account the new energy consumption demand of an oil production plant and aim to minimize production electricity costs. Background Art

[0002] The process of green and low-carbon transformation of my country's economy and society is constantly advancing. Domestic crude oil extraction companies are actively deploying new energy for oil fields, and the scale of new energy construction in the oil extraction industry is becoming increasingly large. With the continuous expansion of new energy power generation capacity, the insufficient absorption capacity of new energy power, the high rate of green power abandonment, and the adverse oscillation caused by green power access to the grid are becoming increasingly prominent in the oil extraction industry. Therefore, how to match the volatile power generation process of new energy, reasonably regulate the crude oil extraction process on the production power consumption side, realize "load follows source" and local consumption of green electricity, thereby reducing the production electricity cost of oil production plants, achieving energy conservation and consumption reduction in the oil extraction industry, and promoting the further replacement of traditional fossil energy with new energy has become a key issue that crude oil extraction companies need to solve under the current new energy layout.

[0003] At the same time, with the continuous improvement of the automation and information construction level of oil fields, and the vigorous implementation of the digital transformation of oil wells and joint stations, this also provides a basis for realizing flexible production control of well-station integration. However, in the process of crude oil extraction, the currently commonly used extensive manual production scheduling methods and static optimization methods are still difficult to meet the adaptive adjustment requirements of the power consumption side load caused by the fluctuation of new energy production capacity. The dynamic and refined degree of control of oil wells and joint stations is also in urgent need of improvement. Summary of the invention

[0004] In view of the above problems, the present invention discloses a method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy. The method incorporates the benefits of new energy consumption into the well station integrated collaborative optimization decision of the crude oil production process. Based on the prediction data of new energy power generation, the production process of the oil production plant under the layout of new energy is scheduled by differentiating the oil well control mode, constructing the integrated production energy consumption constraints of production and injection, and periodic mixed integer programming. The method specifically includes the following steps:

[0005] 1) According to the crude oil production, pump efficiency, and normalized pumping times of the oil field production wells, the oil production wells are divided into adjustable wells: var With non-adjustable well O con The adjustable wells are further divided into intermittent pumping wells s and flushing adjustable wells c. The specific classification criteria are:

[0006] (1-1) If the daily crude oil production of an oil well is stable and greater than 1.5 tons, or the pump efficiency of its oil production machine is greater than 70% and can maintain a normalized pumping frequency of 2.5 times / minute or more, then the oil well is a main oil production well and will not be regulated during the optimization process. It should be classified as an unregulated oil well. con ;

[0007] (1-2) If the daily crude oil production of an oil well is less than 1.5 tons, and the pump efficiency of the oil well is extremely low (less than 30%), or the pump efficiency is between 30% and 50% but the normalized pumping frequency is less than 2.0 times / minute, then the oil well has a serious lack of oil production and fluid supply capacity and is classified as a controllable oil well. var The intermediate pumping well S SN , the control should be carried out by non-stop pumping;

[0008] (1-3) If the daily crude oil production of an oil well is less than 1.5 tons, and the pump efficiency of the oil well is moderate (between 50% and 70%), or the pump efficiency is low (between 30% and 50%) but the normalized pumping frequency is high (higher than 2.0 times / minute), or the pump efficiency is high (greater than 70%) but the normalized pumping frequency is low (lower than 2.5 times / minute), then the oil well has room for optimization in terms of pump efficiency and pumping frequency, and is classified as a controllable oil well. var The stroke frequency can be adjusted in the well S CN , the pulse frequency regulation method should be adopted for regulation, among which:

[0009] s∈S SN ={1,2,...,SN},c∈S CN ={1,2,...,CN}

[0010] S SN is the set of all intermittent pumping wells in the oil field, SN is the number of intermittent pumping wells, S CN is the set of all wells with adjustable stroke times in the oil field, and CN is the number of wells with adjustable stroke times;

[0011] 2) Divide the total scheduling duration into R optimization periods T r Each optimization cycle is further divided to determine N+1 decision time points with a time interval of Δt. The N decision time periods obtained by dividing the decision time points constitute a set S Tr ={1,…,N}, indicating that in T r All decision time periods within the optimization cycle, r = 1, 2, ..., R;

[0012] 3) Define all state variables of the mixed integer programming model and determine the directly controllable decision variables as follows:

[0013] (3-1) Well side state variables, including the start and stop states SW of all pumping well production machiness , pump efficiency η s , Liquid production per unit time Q s , and the stroke frequency of all well production machines with adjustable stroke frequency f c , power P c , pump efficiency η c , Liquid production per unit time Q c , where s belongs to S SN , c belongs to S CN , the pump efficiency η of the oil production machine is specifically defined as:

[0014]

[0015] Where Q act is the actual liquid production of the oil well, Q the is the theoretical liquid production of the oil well, f is the stroke, d is the stroke of the rod, D is the pump diameter, and the unit time liquid production Q of the intermittent pumping well s and the stroke-adjustable well c in the decision time period n s,n With Q c,n The specific calculation method is:

[0016]

[0017] Among them SW s,n is the start / stop state of the intermittent pumping well s in the decision time period n, and its value is 0 or 1. c,n is the flushing state of the flushing-adjustable well in the decision time period n;

[0018] (3-2) State variables at the joint station side, including the input flow IN of all oil, gas and water treatment devices k , output flow OUT k , Processing efficiency PR k , Energy efficiency P k , the input flow IN of all oil, gas and water storage tanks l , output flow OUT l 、Reserves V l , and the flow load L of all oil, gas and water delivery pumps p , pump power P p , where k belongs to S K , S K is the set of all oil, gas and water treatment devices in the joint station {1,2,…,K}, which specifically includes: one or more three-phase separators, metering separators, deoilers, filters, coalescing deoilers, compressors, and dryers in the actual crude oil production site. K is the number of oil, gas and water treatment devices in the joint station; l belongs to S L , S Lis the set of all oil, gas and water storage tanks in the joint station {1,2,…,L}, which specifically include: good / bad oil tanks, bottom water tanks, oil unloading pools, buffer tanks, dirty oil recovery tanks, sewage tanks, sewage pools, oil separators, regulating tanks, air flotation tanks, water filter tanks, water injection tanks, fire tanks, hot water tanks, air storage tanks, fuel tanks, and dosing barrels in the actual crude oil production site. L is the number of oil, gas and water storage tanks in the joint station; p belongs to S P , S P is the set of all oil, gas and water delivery pumps in the joint station {1,2,…,P}, which specifically include: mixed delivery pump, sewage pump, submerged oil pump, hot water pump, oil recovery pump, lifting pump, backwash pump, water transfer pump, external delivery pump, water feeding pump, water injection pump, water supply pump, water mixing pump, mud discharge pump, fuel pump, medicine replenishment pump, water injection booster pump, mixed delivery booster pump in the actual crude oil production site. P is the number of oil, gas and water delivery pumps in the joint station;

[0019] (3-3) State variables on the renewable energy power generation side, i.e., the available power generation power P of the renewable energy power station device g ;

[0020] Among the above state variables, the decision variables of the mixed integer programming model are the start and stop states SW of all the oil production units in the intermittent wells. s 、The number of strokes for all wells with adjustable strokes f c 、The treatment efficiency PR of all oil, gas and water treatment devices in the joint station k 、Flow load L of all oil, gas and water delivery pumps p , all decision variables in the mixed integer programming model together constitute the set S VN = {1, 2, …, VN}, VN is the number of decision variables, and all state variables in the mixed integer programming model together constitute the set S YN ={1,2,…,YN}, YN is the number of state variables, and S YN Contains S VN ;

[0021] 4) Determine the objective function min(Cost) of the mixed integer programming model, as follows:

[0022]

[0023] Cost is the total electricity cost of the oil production plant under the new energy layout in an optimization cycle. are the average power consumption of the intermittent pumping well s, the adjustable flushing well c, the oil, gas and water treatment device k of the joint station, and the oil, gas and water delivery pump device p of the joint station in the decision time period n, is the average available power generation of the new energy power generation devices deployed in the oil production plant in the decision time period n, and price(n) is the power price of the power grid in the area where the oil production plant is located in the decision time period n;

[0024] 5) Determine the constraint group COTs of the mixed integer programming model, including:

[0025] (5-1) Time series state transfer rule: All state variables on the oil well side and the joint station side at the end of any decision time period and all state variables at the beginning of the corresponding decision time period should satisfy the state transfer rule:

[0026]

[0027] SAT(n)=[y 1,n ,y 2,n ,...,y YN,n ] T

[0028] SAT(n+1)=[y 1,n+1 ,y 2,n+1 ,...,y YN,n+1 ] T

[0029]

[0030] y i,n ∈S YN,n ,y i,n+1 ∈S YN,n+1 ,v j,n ∈S VN,n ,i=1,2,...,YN,j=1,2,...,VN

[0031] Where S YN,n With S YN,n+1 is the set of all state variables of the mixed integer programming model at the initial time of decision time period n and n+1, y i,n With y i,n+1 are the elements thereof, namely the i-th state variable, S VN,n is the set of decision variables given by the mixed integer programming model at the initial moment of the decision time period n, v j,n is the element in it, i.e. the jth decision variable, f i,j (v 1,n ,v 2,n ,…,v VN,n ,Δt) represents the jth state variable after passing through the decision variable v 1,n ,v 2,n ,…,v VN,n After the interaction time Δt, the state transfer function of the i-th state variable, fi,j (i, j = 1, 2, ..., YN) is a mathematical expression of the oil well production model, oil well energy consumption model, joint station production model, joint station energy consumption model, production electricity pricing model, new energy power generation model and new energy consumption model, which is determined by function fitting of historical production data or mechanism modeling;

[0032] (5-2) Device load constraints:

[0033]

[0034] where f c,min With f c,max are the upper and lower limits of the pumping frequency of the well c with adjustable pumping frequency, PR k,min With PR k,max are the upper and lower limits of the treatment efficiency of the oil, gas and water treatment device k in the joint station, L p,min With L p,max They are the upper and lower limits of the flow load of the oil, gas and water treatment device p of the joint station respectively;

[0035] (5-3) Material balance constraints:

[0036]

[0037] Where V l,n is the storage capacity of oil, gas and water storage tank device l at the end of decision time period n, V l,0 is the storage capacity of the oil, gas and water storage tank device l of the joint station at the initial moment of the current optimization cycle, IN l,i and OUT l,i is the input flow and output flow of the oil, gas and water storage tank device l of the joint station in the i-th decision time period of the current optimization cycle;

[0038] (5-4) Pipeline flow constraints. During the crude oil extraction and processing process, the oil, gas and water pipelines at each stage are subject to upper and lower flow limits:

[0039]

[0040] Where IN PI,k,min with IN PI,k,max They are the upper and lower limits of the input flow rate for the oil, gas and water treatment device k of the joint station due to pipeline flow restrictions, OUT PI,k,min and OUT PI,k,max are the upper and lower limits of the output flow rate for the oil, gas and water treatment device k of the joint station due to pipeline flow restrictions, IN PI,l,min with IN PI,l,maxThey are the upper and lower limits of the input flow rate for the oil, gas and water storage tank device l of the joint station due to pipeline flow restrictions, OUT PI,l,min and OUT PI,l,max are the upper and lower limits of the output flow rate for the oil, gas and water storage tank device l of the joint station due to pipeline flow restrictions, L PI,p,min With L PI,p,max They are the upper and lower limits of flow load for the oil, gas and water delivery pump device p of the joint station caused by pipeline flow restriction;

[0041] (5-5) Reserve constraints. The storage capacity of oil, gas and water storage tanks in any joint station is limited:

[0042] V l,min ≤V l ≤V l,max ,l∈S L

[0043] Where V l,min With V l,max They respectively represent the upper and lower limits of the storage capacity of the oil, gas and water storage tank device l of the joint station due to the volume limitation;

[0044] (5-6) Adjustment time constraint: the controllable decision variables cannot be adjusted at any time during the actual crude oil production process. To ensure the safety and stability of the production process, there is a lower limit on the time interval between two adjustments of the controllable decision variables:

[0045]

[0046] The auxiliary variable TH v,Tr Characterizes the decision variable v in the optimization period T r The shortest time interval between two adjustments, TH v,min It represents the lower limit of the adjustment time interval allowed for the decision variable v in any optimization cycle;

[0047] (5-7) Production constraint: the crude oil production in any optimization period must exceed a specific lower limit, that is, the crude oil production target:

[0048]

[0049] Where D Tr is an optimization period T r Crude oil production in the internal oil fields, Q s,n With Q c,n are the unit time production of the intermittent pumping well s and the well with adjustable flushing times c in the decision time period n, respectively, s and ρ c are the water content of the intermittent pumping well s and the adjustable flushing well c, respectively, Tr,min is an optimization period Tr minimum crude oil production targets for internal oil fields;

[0050] 6) Obtain the starting time of the total scheduling duration, align the initial time of the first decision time period of the first optimization cycle with it, monitor the working conditions of the oil well side and the joint station side at this time, create the constraint condition group COTs(r) at this time, where r = 1, and collect the initial values ​​of all variables of the mixed integer programming model at this time;

[0051] 7) Get the current optimization cycle T r The available power generation power P of new energy in the future N decision time periods g,n , n belongs to S Tr ;

[0052] 8) The optimizer solves the current optimization cycle T r The decision variables at N+1 decision time points within the optimization period T r The scheduling plan within the system is executed;

[0053] 9) Determine whether the optimization calculation of R optimization cycles is completed. If not, set the current optimization cycle T r The scheduling scheme in the optimization cycle is combined with the scheduling scheme obtained in all previous optimization cycles, and then the step 10) is performed. If it is completed, the step 12) is performed.

[0054] 10) Monitoring and optimization cycle T r The working conditions of the oil well side and the joint station side at the end time, update the constraint condition group COTs(r), where r = r + 1;

[0055] 11) The mixed integer programming model variable solution result at the end of the previous optimization cycle is used as the current optimization cycle T r The initial values ​​of the mixed integer programming model variables are collected, the actual values ​​of the current variables are corrected and then go to step 7);

[0056] 12) Combine all the scheduling plans obtained in R optimization cycles, output the total scheduling plan, and the production optimization is completed.

[0057] Beneficial effects:

[0058] The present invention incorporates the benefits of new energy consumption into the optimization decision-making of the crude oil extraction process, and at the same time, refines the control objects of oil wells. By constructing the integrated production and injection well station collaborative production energy constraints, and adopting the solution method of periodic mixed integer programming, a dynamic well station integrated production scheduling scheme that can adapt to the fluctuating power generation output process of new energy is obtained. This method has high optimization and solution efficiency, and the generated scheduling scheme has strong dynamic adaptive capabilities. It can effectively improve the oil production plant's ability to absorb new energy in terms of production energy, solve the green electricity consumption needs of oil production enterprises, promote the "load follows source" in the crude oil production process, save production electricity costs, and has important application value for oil production enterprises under the new energy layout to minimize production energy, maximize green electricity consumption, and optimize source-load coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a crude oil production well station integrated production optimization method taking into account new energy consumption of the present invention;

[0060] Figure 2 The photovoltaic power generation curve in a single optimization cycle and the production power consumption curve before and after optimization in the embodiment of the present invention;

[0061] Figure 3 It is a bar chart of electricity cost and green electricity consumption benefit within the total dispatching time in an embodiment of the present invention.

[0062] Specific implementation process

[0063] The following is a specific operation flow in conjunction with the accompanying drawings and specific implementation examples to further illustrate the present invention. This implementation example is implemented based on the technical solution of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0064] The present invention takes the new energy construction test area of ​​an oil field of an oil production enterprise as an example. The test area has a total of 10 oil production wells and is equipped with corresponding joint station process flows. The available new energy power generation facilities are: photovoltaic power generation components with a total installed capacity of 820kW. When the optimization method described in the present invention is not adopted, the production electricity of the test area is all taken from the power grid, and green electricity consumption has not been introduced yet. Now, taking the well-station integrated production optimization that takes into account new energy consumption in the crude oil production test area under the new energy layout as an example, the effectiveness of this method in improving the green electricity consumption capacity in the oil production process, saving production energy, and reducing production electricity costs is analyzed.

[0065] The implementation process of this method is as follows Figure 1 As shown, the specific process is as follows:

[0066] 1) According to the crude oil production, pump efficiency, and normalized extraction and flushing frequency of the oil wells, a total of 10 oil wells in the test area were divided into adjustability and control methods. The production, pump efficiency, and normalized extraction and flushing frequency parameters of the oil wells in the test area are shown in Table 1:

[0067] Table 1 Daily production, pump efficiency, number of normalized extraction strokes, and oil well classification in the test area

[0068]

[0069] Since the daily oil production of all oil wells in the test area is less than 1.5t, all oil wells are adjustable wells. The regulation method of adjustable wells is further subdivided: the pump efficiency of oil wells #1 and #2 is greater than 70%, but the stroke frequency is less than 2.5 times / minute, so they are classified as stroke adjustable wells; the pump efficiency of oil well #3 is between 50% and 70%, so it is classified as stroke adjustable well; the pump efficiency of oil wells #4, #6, #7, #9, and #10 is less than 30%, and they are preferentially classified as intermittent pumping wells; the pump efficiency of oil wells #5 and #8 is between 30% and 50%, but the stroke frequency is less than 2.0 times / minute, so they are also classified as intermittent pumping wells. The final classification results of all oil wells in the test area are shown in Table 1.

[0070] 2) Divide the total scheduling duration into R optimization periods T r , and further divide each optimization cycle to determine N+1 decision time points with a time interval of Δt. In this embodiment, since the minimum sampling period of the dynamometer sampling of the oil well production machine in the test area and the process monitoring sampling of the joint station is 1 hour, Δt=1h is taken; and the shortest time period of the production target in the test area is 24h, so an optimization cycle of 24h is selected, and the total scheduling time is 30 days. Therefore, each optimization cycle is divided into 24 decision time periods, with a total of 25 decision time points with an interval of 1h.

[0071] 3) Define all state variables of the mixed integer programming model and determine the directly controllable decision variables as follows:

[0072] (3-1) State variables on the oil production well side. Taking oil wells #1 and #6 as examples, the method of the present invention defines the stroke frequency f of the oil production machine of the adjustable stroke well #1. #1 , power P #1 , pump efficiency η #1 And the liquid production per unit time Q #1 As its corresponding state variable, define the start and stop state SW of the intermittent pumping well #6 #6 , pump efficiency η #6 , Liquid production per unit time Q #6 is its corresponding state variable. Among them, the decision variable for oil well #1 is the pumping machine stroke f #1 , while the decision variable for oil well #6 is the start / stop state SW#6 .

[0073] (3-2) State variables at the joint station side. In this embodiment, the coalescing deoiler in the test area joint station is used as a definition example of an oil, gas and water treatment device, the water injection tank #1 in the test area joint station is used as a definition example of an oil, gas and water storage tank device, and the water injection pump #1 in the test area joint station is used as a definition example of an oil, gas and water delivery pump device. The method of the present invention defines the relevant state variables of the coalescing deoiler #1 as its input flow IN 聚#1 , output flow OUT 聚#1 , Processing efficiency PR 聚#1 , Energy efficiency P 聚#1 , and its relevant decision variable is processing efficiency PR 聚#1 ; Define the relevant state variables of water injection tank #1 as its input flow IN 注#1 , output flow OUT 注#1 、Reserves V 注#1 The method of the present invention does not define the decision variables of the oil, gas and water storage tanks of the joint station. Its related state variables only appear as constraints of the decision variables in the constraint condition group; the related state variables of the injection pump #1 are defined as its flow load L 注#1 , pump power P 注#1 In this embodiment, since the power frequency of all oil, gas and water delivery pump devices cannot be adjusted, that is, their flow load cannot be continuously adjusted, their relevant decision variables are transformed and defined as their start and stop states SW 注#1 .

[0074] (3-3) State variables on the new energy generation side, that is, the method described in the present invention defines the available power generation power of the photovoltaic power generation assembly with a total installed capacity of 820kW in the test area as its corresponding state variable P g For the renewable energy power generation side, the method of the present invention does not define its corresponding decision variables, but only optimizes and solves by obtaining the predicted values ​​of its state variables.

[0075] 4) Determine the objective function min(Cost) of the mixed integer programming model. In this embodiment, due to the production process in the test area, all production electrical equipment are listed as shown in Table 2:

[0076] Table 2 Electricity-consuming equipment for production in the test area

[0077]

[0078] Therefore, the objective function of the mixed integer programming model for production optimization of the test area in each optimization cycle according to the method of the present invention is specifically:

[0079]

[0080] The power grid electricity price in the test area is calculated after peak-valley conversion as follows:

[0081]

[0082] are the average power consumption of the intermittent pumping well s, the well with adjustable flushing times c, the oil, gas and water treatment device k and the oil, gas and water delivery pump device p in the decision time period n. In this embodiment, the power consumption of all intermittent pumping wells in the open state is constant, that is, In addition, in this embodiment, the power frequency of all oil, gas and water delivery pump devices cannot be adjusted, that is, their flow load cannot be continuously adjusted, so the power consumption of the oil, gas and water delivery pump device in the startup state is constant. It only depends on the start and stop status of the oil, gas and water delivery pump device p. It is the average available power of photovoltaic power generation components in the test area during the decision time period n.

[0083] 5) Determine the constraint group COTs of the mixed integer programming model, including:

[0084] (5-1) Sequential state transfer rules. In this embodiment, the state variable vector composed of all state variables of the well #1 with adjustable stroke frequency and all state variables of the water injection pump #1 is taken as an example to analyze the establishment method of this type of constraint condition group. The state variables of the oil well #1 are the stroke frequency of the oil production machine f, power P, pump efficiency η and liquid production per unit time Q, while all state variables of the water injection pump #1 are the start and stop state S, pump power P 注 , which contains decision variables f and S, so the temporal state transition rule in the current example is:

[0085]

[0086] All transfer functions f i,j ,i,j∈{f,P,η,Q,S,P 注 It is obtained through fitting historical production data or mechanism modeling, and represents the transfer relationship of state variable j to state variable i after decision variables f and S act on one decision time period, i.e., 1h.

[0087] (5-2) Device load constraints:

[0088]

[0089] where f c,min With f c,max are the upper and lower limits of the stroke frequency of the oil production machine of the stroke frequency adjustable well c, respectively. In this embodiment, all f c,min Take 1 time / minute for all f c,max Take 5 times / minute; PRk,min With PR k,max are the upper and lower limits of the processing efficiency of the oil, gas and water treatment device k of the joint station, respectively. In this embodiment, the constraint is taken to be in an ideal state, that is, all PR k,min All PR k,max All are taken as 1, L p,min With L p,max are the upper and lower limits of the flow load of the oil, gas and water treatment device p of the joint station, respectively. Taking the water injection pump #1 of the joint station as an example, its L 注#1,min Take 0m 3 / h, its L 注#1,max Take 1.2m 3 / h.

[0090] (5-3) Material balance constraints:

[0091]

[0092] In this embodiment, the material balance relationship of water injection tank #1 of the test area joint station after two consecutive decision time periods from the initial time is taken as an example, assuming that its inlet and outlet flow rates do not change in the two decision time periods, that is, IN 注#1,1 =IN 注#1,2 =2.5m 3 ,OUT 注#1,1 =OUT 注#1,2 =2.6m 3 , and its initial reserves are 2800m 3 , then the relationship between reserves changes is as follows:

[0093]

[0094] (5-4) Pipeline flow constraints:

[0095]

[0096] In this embodiment, the pipeline flow constraint is strict, so all constraints formed by pipeline flow restriction are unified to take the highest pipeline flow lower limit and the lowest pipeline flow upper limit in the well station integrated system of the test area, that is, the pipeline flow rate of each pipeline should be within 15m 3 Up to 300m 3 between.

[0097] (5-5) Storage constraints: The storage capacity of oil, gas and water storage tanks in any joint station is limited:

[0098] V l,min ≤V l ≤V l,max ,l∈S L

[0099] Take the water injection tank #1 of the joint station in the test area as an example. 注#1,min The value is 2000m 3 , its V 注#1,max The value is 10000m 3 .

[0100] (5-6) Adjustment time constraint. In this embodiment, the actual adjustment frequency of the controllable decision variables can be close to the ideal situation. Under the premise of ensuring the safety and stability of the production process, the time interval between two adjustments of all controllable decision variables is allowed to be as low as 1 decision time period, that is:

[0101]

[0102] (5-7) Production constraint: the crude oil production in any optimization period must reach the crude oil production target in the optimization period. In this embodiment, one optimization period is one day, and the daily crude oil production target of the test area is 10t, that is:

[0103]

[0104] Where D Tr is an optimization period T r Crude oil production in the internal oil fields, Q s,n With Q c,n are the unit time production of the intermittent pumping well s and the well with adjustable flushing times c in the decision time period n, respectively, s and ρ c They are the water content of the intermittent pumping well s and the well with adjustable flushing times c respectively.

[0105] 6) Get the starting time of the total scheduling duration, and align the initial time of the first decision time period of the first optimization cycle with it. In this embodiment, the selected total scheduling time range is from July 1, 2024 to July 30, 2024, a total of 30 days, that is, 30 optimization cycles. Among them, the first optimization cycle time is 0:00 on July 1, 2024-24:00 on July 1, 2024. At the same time, monitor the working conditions of the oil well side and the joint station side at 0:00 on July 1, 2024, create a constraint condition group COTs (r) at this time, let r = 1, and collect the initial values ​​of all variables of the mixed integer programming model at 0:00 on July 1, 2024.

[0106] 7) Get the current optimization cycle T r The available power generation power P of new energy in the next 24 decision time periods g,n .

[0107] 8) The optimizer solves the current optimization cycle T r The decision variables at the 25 decision time points within the optimization period Tr The scheduling plan within is executed.

[0108] 9) Determine whether the optimization calculation of 30 optimization cycles is completed. If not, set the current optimization cycle T r The scheduling scheme within is merged with the scheduling schemes obtained in all previous optimization cycles, and then go to step 10). If it is completed, go to step 12).

[0109] 10) Monitoring and optimization cycle T r The operating conditions of the oil well side and the joint station side at the end time are updated, and the constraint condition group COTs(r) is set, and r=r+1.

[0110] 11) The mixed integer programming model variable solution result at the end of the previous optimization cycle is used as the current optimization cycle T r The initial values ​​of the mixed integer programming model variables are collected, the actual values ​​of the current variables are corrected, and then go to step 7).

[0111] 12) The scheduling schemes obtained in all 30 optimization cycles are merged, and the overall scheduling scheme is output, and the production optimization is completed. In this embodiment, taking the single optimization cycle on July 12, 2024 as an example, after optimizing the integrated production of the well station in the test area using the method of the present invention, the photovoltaic power generation curve of the test area and the electricity consumption curve of crude oil production before and after optimization are as follows: Figure 2 As shown. Figure 2 It can be seen that the well-station integrated production optimization scheduling scheme given by the method of the present invention, which takes into account the consumption of new energy, can enable the total production power load of the test area to achieve a very ideal following effect on the photovoltaic power generation power in the peak power time zone, and can also timely use the low price of the power grid to increase the production ratio in the valley power time zone, so as to make up for the missing crude oil production in the peak power time zone due to the fluctuation of the output of new energy, and ensure that the crude oil production target is achieved within a single optimization cycle. The production electricity cost and green electricity consumption on July 12, 2024 are shown in Table 3.

[0112] Table 3 Production electricity cost, green electricity consumption benefit and green electricity utilization rate in the test area before and after the single-day regulation on July 12, 2024

[0113]

[0114] For the total dispatching time from July 1, 2024 to July 30, 2024, the production electricity cost and green electricity consumption benefit of the test area after the crude oil production optimization and regulation by the method of the present invention are as follows: Figure 3 As shown. Figure 3It can be seen that within the total scheduling time range, after the test area implements the production optimization scheduling scheme given by the method of the present invention, the production electricity cost of crude oil extraction has been greatly saved. On the one hand, it comes from the green electricity benefit brought by the improvement of green electricity consumption capacity in the production process, and on the other hand, it comes from the energy-saving benefit brought by the decrease in total production electricity consumption after optimized scheduling. The production electricity cost and green electricity consumption data of the test area within the total scheduling time range are shown in Table 4.

[0115] Table 4 Production electricity cost, green electricity consumption benefit and green electricity utilization rate in the test area before and after regulation within the total dispatching time range

[0116]

[0117] comprehensive Figure 3 As shown in Table 4, the well station integrated production optimization scheduling scheme that takes into account the consumption of new energy obtained by the method of the present invention can provide great green electricity consumption economic benefits and energy-saving economic benefits for the test area, thereby greatly reducing the production cost from power grid electricity consumption. It can be seen that this embodiment fully illustrates that the method of the present invention can improve the green electricity consumption capacity in the oil production process, save production energy, and reduce production electricity costs.

Claims

1. A method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy, characterized in that Incorporate the benefits of new energy consumption into the well-station integrated collaborative optimization decision-making of the crude oil production process. Based on the forecast data of new energy power generation, the production process of the oil production plant under the new energy layout is scheduled by differentiating the oil well control mode, building the integrated production energy constraints of production and injection, and periodic mixed integer programming, including the following steps: 1) According to the crude oil production, pump efficiency, and normalized pumping times of the oil field production wells, the oil production wells are divided into adjustable wells: var With non-adjustable well O con , and the adjustable wells are further divided into intermittent pumping wells s and flushing adjustable wells c, where: s∈S SN ={1,2,...,SN},c∈S CN ={1,2,...,CN} S SN is the set of all intermittent pumping wells in the oil field, SN is the number of intermittent pumping wells, S CN is the set of all wells with adjustable stroke times in the oil field, and CN is the number of wells with adjustable stroke times; 2) Divide the total scheduling duration into R optimization periods T r Each optimization cycle is further divided to determine N+1 decision time points with a time interval of Δt. The N decision time periods obtained by dividing the decision time points constitute a set S Tr ={1,…,N}, indicating that in T r All decision time periods within the optimization cycle, r = 1, 2, ..., R; 3) Define all state variables of the mixed integer programming model and determine the directly controllable decision variables as follows: (3-1) Well side state variables, including the start and stop states SW of all inter-pump well production machines s , pump efficiency ηs, liquid production per unit time Q s , and the stroke frequency of all well production machines with adjustable stroke frequency f c , Power P c , pump efficiency ηc, liquid production per unit time Q c , where s belongs to S SN , c belongs to S CN ; (3-2) State variables at the joint station side, including the input flow IN of all oil, gas and water treatment devices k , output flow OUT k , Processing efficiency PR k , Energy efficiency P k , the input flow IN of all oil, gas and water storage tanks l , output flow OUT l 、Reserves V l , and the flow load L of all oil, gas and water delivery pumps p , pump power P p , where k belongs to S K , S K is the set of all oil, gas and water treatment devices in the joint station {1, 2, ..., K}, K is the number of oil, gas and water treatment devices in the joint station, l belongs to S L , S L is the set of all oil, gas and water storage tanks in the joint station {1, 2, ..., L}, L is the number of oil, gas and water storage tanks in the joint station, and p belongs to S P , S P is the set of all oil, gas and water delivery pump devices in the joint station {1, 2, …, P}, where P is the number of oil, gas and water delivery pump devices in the joint station; (3-3) State variables on the renewable energy power generation side, i.e., the available power generation power P of the renewable energy power station device g ; Among the above state variables, the decision variables of the mixed integer programming model are the start and stop states SW of all the oil production units in the intermittent wells. s 、The number of strokes for all wells with adjustable strokes f c , the treatment efficiency PR of all oil, gas and water treatment devices in the joint station k 、Flow load L of all oil, gas and water delivery pumps p , all decision variables in the mixed integer programming model together constitute the set S VN = {1, 2, …, VN}, VN is the number of decision variables, and all state variables in the mixed integer programming model together constitute the set S YN ={1,2,…,YN}, YN is the number of state variables, and S YN Contains S VN ; 4) Determine the objective function min(Cost) of the mixed integer programming model, as follows: Cost is the total electricity cost of the oil production plant under the new energy layout in an optimization cycle. are the average power consumption of the intermittent pumping well s, the adjustable flushing well c, the oil, gas and water treatment device k of the joint station, and the oil, gas and water delivery pump device p of the joint station in the decision time period n, is the average available power generation of the new energy power generation devices deployed in the oil production plant in the decision time period n, and price(n) is the power price of the power grid in the area where the oil production plant is located in the decision time period n; 5) Determine the constraint group COTs of the mixed integer programming model, including: (5-1) Time series state transition rule: All state variables on the oil well side and the joint station side at the end of any decision time period and all state variables at the beginning of the corresponding decision time period should satisfy the state transition rule: Where SAT(n) represents the column vector composed of all the state variables on the production well side and all the state variables on the joint station side at the initial time of the decision time period n, S VN,n is the set of decision variables given by the mixed integer programming model at the initial moment of the decision time period n, A Δt represents the state transfer matrix when the length of the decision time period is Δt; (5-2) Device load constraints: where f c,min With f c,max are the upper and lower limits of the pumping frequency of the well c with adjustable pumping frequency, PR k,min With PR k,max are the upper and lower limits of the treatment efficiency of the oil, gas and water treatment device k in the joint station, L p,min With L p,max They are the upper and lower limits of the flow load of the oil, gas and water treatment device p of the joint station respectively; (5-3) Material balance constraints: Where V l,n is the storage capacity of oil, gas and water storage tank device l at the end of decision time period n, V l,0 is the storage capacity of the oil, gas and water storage tank device l of the joint station at the initial moment of the current optimization cycle, IN l,i and OUT l,i is the input flow and output flow of the oil, gas and water storage tank device l of the joint station in the i-th decision time period of the current optimization cycle; (5-4) Pipeline flow constraints. During the crude oil extraction and processing process, the oil, gas and water pipelines at each stage are subject to upper and lower flow limits: Where IN PI,k,min with IN PI,k,max They are the upper and lower limits of the input flow rate for the oil, gas and water treatment device k of the joint station due to pipeline flow restrictions, OUT PI,k,min and OUT PI,k,max are the upper and lower limits of the output flow rate for the oil, gas and water treatment device k of the joint station due to pipeline flow restrictions, IN PI,l,min with IN PI,l,max They are the upper and lower limits of the input flow rate for the oil, gas and water storage tank device l of the joint station due to pipeline flow restrictions, OUT PI,l,min and OUT PI,l,max are the upper and lower limits of the output flow rate for the oil, gas and water storage tank device l of the joint station due to pipeline flow restrictions, L PI,p,min With L PI,p,max They are the upper and lower limits of flow load for the oil, gas and water delivery pump device p of the joint station caused by pipeline flow restriction; (5-5) Reserve constraints. The storage capacity of oil, gas and water storage tanks in any joint station is limited: In l,min ≤V l ≤V l,max ,l∈S L Where V l,min With V l,max They respectively represent the upper and lower limits of the storage capacity of the oil, gas and water storage tank device l of the joint station due to the volume limitation; (5-6) Adjustment time constraint: the controllable decision variables cannot be adjusted at any time during the actual crude oil production process. To ensure the safety and stability of the production process, there is a lower limit on the time interval between two adjustments of the controllable decision variables: The auxiliary variable TH v,Tr Characterizes the decision variable v in the optimization period T r The shortest time interval between two adjustments, TH v,min It represents the lower limit of the adjustment time interval allowed for the decision variable v in any optimization cycle; (5-7) Production constraint: the crude oil production in any optimization period must exceed a specific lower limit, that is, the crude oil production target: Where D Tr is an optimization period T r Crude oil production in the internal oil fields, Q s,n With Q c,n are the unit time production of the intermittent pumping well s and the well c with adjustable flushing frequency in the decision time period n, ρs and ρc are the water cuts of the intermittent pumping well s and the well c with adjustable flushing frequency, respectively. Tr,min is an optimization period T r minimum crude oil production targets for internal oil fields; 6) Obtain the starting time of the total scheduling duration, align the initial time of the first decision time period of the first optimization cycle with it, monitor the working conditions of the oil well side and the joint station side at this time, create the constraint condition group COTs(r) at this time, where r = 1, and collect the initial values ​​of all variables of the mixed integer programming model at this time; 7) Get the current optimization cycle T r The available power generation power P of new energy in the future N decision time periods g,n , n belongs to S Tr ; 8) The optimizer solves the current optimization cycle T r The decision variables at N+1 decision time points within the optimization period T r The scheduling plan within the system is executed; 9) Determine whether the optimization calculation of R optimization cycles is completed. If not, set the current optimization cycle T r The scheduling scheme in the optimization cycle is combined with the scheduling scheme obtained in all previous optimization cycles, and then the step 10) is performed. If it is completed, the step 12) is performed. 10) Monitoring and optimization cycle T r The working conditions of the oil well side and the joint station side at the end time, update the constraint condition group COTs(r), where r = r + 1; 11) The mixed integer programming model variable solution result at the end of the previous optimization cycle is used as the current optimization cycle T r The initial values ​​of the mixed integer programming model variables are collected, the actual values ​​of the current variables are corrected and then go to step 7); 12) Combine all the scheduling plans obtained in R optimization cycles, output the total scheduling plan, and the production optimization is completed.

2. The method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy sources according to claim 1, characterized in that The classification method of oil wells according to crude oil production, pump efficiency and normalized extraction strokes in step 1) is specifically as follows: if the daily crude oil production of an oil well is stable greater than 1.5 tons, or the pump efficiency of its oil production machine is greater than 70% and can maintain a normalized extraction stroke of 2.5 times / minute or more, then the oil well is a main oil production well and is not regulated during the optimization process and is classified as an unregulated oil well. con If the daily crude oil production of an oil well is less than 1.5 tons, and the pump efficiency of the oil well is less than 30%, or the pump efficiency is between 30% and 50% but the normalized pumping frequency is less than 2.0 times / minute, then the oil well has a serious lack of oil production and fluid supply capacity and is classified as an adjustable oil well. var The intermediate pumping well S SN , the oil well is regulated by non-stop pumping; if the daily crude oil production of the oil well is less than 1.5t, and the pump efficiency of the oil production machine is between 50% and 70%, or the pump efficiency is between 30% and 50% but the normalized pumping frequency is higher than 2.0 times / minute, or the pump efficiency is greater than 70% but the normalized pumping frequency is lower than 2.5 times / minute, then the oil well has room for regulation and optimization in terms of pump efficiency and pumping frequency, and is classified as a controllable oil well O var The stroke frequency can be adjusted in the well S CN , and the control is carried out by means of impulse adjustment.

3. The method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy sources according to claim 1, characterized in that The definition of the state variable pump efficiency η of the oil well side described in step 3) is specifically: Where Q act is the actual liquid production of the oil well, Q the is the theoretical liquid production of the oil well, f is the number of strokes, d is the stroke of the rod, and D is the pump diameter; Based on the above definition, the unit time liquid production Q of the intermittent pumping well s and the adjustable stroke well c in the decision time period n s,n With Q c,n The specific calculation method is: Among them SW s,n is the start / stop state of the intermittent pumping well s in the decision time period n, and its value is 0 or 1. c,n is the stroke status of the stroke-adjustable well in the decision time period n.

4. The method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy sources according to claim 1, characterized in that Step 3) The oil, gas and water treatment devices in the joint station specifically include: one or more three-phase separators, metering separators, oil removers, filters, coalescing oil removers, compressors, and dryers; the oil, gas and water storage tank devices in the joint station specifically include: good / bad oil tanks, bottom water tanks, oil unloading pools, buffer tanks, dirty oil recovery tanks, sewage tanks, sewage pools, oil separators, regulating tanks, flotation tanks, water filter tanks, water injection tanks, fire tanks, hot water tanks, air storage tanks, fuel tanks, and dosing barrels; the oil, gas and water delivery pump devices in the joint station specifically include: mixed delivery pumps, sewage pumps, submersible oil pumps, hot water pumps, oil collection pumps, lifting pumps, backwash pumps, water transfer pumps, external delivery pumps, water feeding pumps, water injection pumps, water supply pumps, water mixing pumps, mud discharge pumps, fuel pumps, drug replenishment pumps, water injection booster pumps, and mixed delivery booster pumps.

5. The method for optimizing the integrated production of crude oil production well stations taking into account the consumption of new energy sources according to claim 1, characterized in that The mathematical expression of the timing state transfer in step 5) is specifically: SAT(n)=[y 1,n ,and 2,n ,...,and YN,n ] T SAT(n+1)=[y 1,n+1 ,and 2,n+1 ,...,and YN,n+1 ] T the i,n ∈S YN,n , the i,n+1 ∈S YN,n+1 ,v j,n ∈S VN,n ,i=1,2,...,YN,j=1,2,...,VN Where S YN,n With S YN,n+1 is the set of all state variables of the mixed integer programming model at the initial time of decision time period n and n+1, y i,n With y i,n+1 are the elements thereof, namely the i-th state variable, S VN,n is the set of decision variables given by the mixed integer programming model at the initial moment of the decision time period n, v j,n is the element in it, i.e. the jth decision variable; f i,j (v 1,n ,v 2,n ,…,v VN,n ,Δt) represents the jth state variable after passing through the decision variable v 1,n ,v 2,n ,…,v VN,n After the interaction time Δt, the state transfer function of the i-th state variable; f i,j (i, j = 1, 2, ..., YN) is the mathematical expression of the oil well production model, oil well energy consumption model, joint station production model, joint station energy consumption model, production electricity pricing model, new energy power generation model and new energy consumption model, which is determined by function fitting of historical production data or mechanism modeling.

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