An oilfield layered water injection wellbore hydraulic control valve gear intelligent optimization system and method

Abstract

This invention relates to an intelligent optimization system and method for the hydraulic control valve position of a layered water injection wellbore in an oilfield. The method includes constructing a hydraulic control valve opening optimization model; inputting the injection volume and wellhead pressure; inputting model parameters into the hydraulic control valve opening optimization model to output the solution result using a preset model algorithm; determining whether the solution result meets preset target conditions, wherein if the solution result meets the preset target conditions, the current solution result is output; if the solution result does not meet the preset target conditions, the initial input step is returned until a solution result that satisfies the cumulative difference error constraint between the actual injection volume and the injection volume of each layer is obtained. The solution result includes outputting one or more of the following: the minimum error value between the actual injection volume and the injection volume, the optimal hydraulic control valve position, and the actual injection volume of each layer, and the solution result takes the minimum cumulative difference between the actual injection volume and the injection volume of each layer as the objective.

Description

Technical Field

[0001] This invention relates to the field of layered intelligent water injection technology, and in particular to an intelligent optimization system and method for the position selection of hydraulic control valves in oilfield layered water injection wellbores. Background Technology

[0002] Oilfield reservoirs are highly heterogeneous, with significant differences in permeability across different layers. Therefore, stratified water injection is commonly employed for development. To improve the effectiveness of water injection wells, stratified injection must be implemented based on the water absorption characteristics of each layer. This ensures uniform water distribution across all layers, limiting water absorption in high-permeability layers, enhancing the injection effect in medium- and low-permeability layers, and ultimately improving oilfield recovery.

[0003] Currently, the allocation of stratified water injection volume in oilfield water injection wells mainly relies on manual measurement and adjustment of the hydraulic control valve position of the downhole water distributor. Since the impact of multi-layer water distribution on the overall well hydraulic system is not considered, problems such as long measurement and adjustment time and inaccurate position selection are caused, making it difficult to meet the requirements of fine water injection.

[0004] Currently, scholars both domestically and internationally have conducted extensive research on intelligent measurement and adjustment technology for stratified water injection processes. Liu Yigang et al. (Liu Yigang, Chen Zheng, Meng Xianghai et al. Key technologies for intelligent measurement and adjustment of permanent cables in stratified water injection wells in Bohai Oilfield [J]. Oil and Gas Field Surface Engineering, 2006, 25(2):17-18) proposed intelligent measurement and adjustment technology for permanent cables in stratified water injection wells. This technology integrates temperature, pressure, and flow rate testing units into an intelligent measurement and adjustment working cylinder, using cables as the medium for transmitting electrical energy and data. It enables continuous switching of multiple wells and multiple layers of water nozzles under surface control, real-time monitoring of downhole data, significantly improving measurement and adjustment efficiency, shortening operation time, reducing operating space, and solving the measurement and adjustment problems of highly deviated and horizontal wells at sea.

[0005] Yang Lingzhi et al. (Yang Lingzhi, Yu Jiuzheng, Wang Zijian et al. Intelligent water injection monitoring technology for ultra-low permeability reservoirs in Ordos [J]. Petroleum Drilling and Production Technology, 2017, 39(6):756-759) proposed an intelligent water injection full-process monitoring technology, which realizes automatic testing and adjustment of downhole layered flow, long-term testing and storage of dynamic data, remote wireless data transmission between the surface and downhole, and real-time monitoring of the oilfield digital system. It effectively controls single-layer surge, reduces ineffective water circulation, and truly grasps the dynamic process of reservoir development, achieving the purpose of precise water allocation.

[0006] De Li Jia et al. (De LiJia, Feng ShanWang et al. A Novel Multi-Layer Intelligent Test and Adjustment Technology for Water Injection Well[J]. Advanced Materials Research, 2012, 1611) proposed and developed an intelligent layered water injection technology suitable for ultra-high water cut periods based on the concept of synchronous dynamic testing and adjustment. The entire process, through digital clock calibration, achieves synchronous dynamic flow adjustment for each layer, thus avoiding inter-layer interference. This improves the water injection effect and reduces on-site workload.

[0007] Zhao Guozhong et al. (Zhao Guozhong, Sun Wei, He Xin et al. Numerical simulation of reservoir based on layered water injection mathematical model [J]. Journal of Northeast Petroleum University, 2012, 36(6): 82-87) Based on the layered water injection data of the oilfield, the nozzle loss characteristics of the nozzle were analyzed, and a layered water injection mathematical model considering the nozzle loss characteristics such as start-up pressure difference and nozzle diameter was established. The flow pressure of the hydraulic control valve in each layer of the layered water injection well was calculated, and the nozzle loss equation and the well-grid pressure equation were coupled.

[0008] Yang Lingzhi et al. (Yang Lingzhi, Ju Yafeng, Shen Xiaoli et al. Research and analysis of flow characteristics of digital stratified water injection [J]. Petroleum Machinery, 2014, 42(10): 52-55) addressed the problems of difficult real-time monitoring and complex measurement and adjustment processes in conventional stratified water injection technology for low-permeability reservoirs. They proposed a digital stratified water injection technology, and established a mechanical model for the flow characteristics of water injection in digital stratified water injection wells by analyzing the flow resistance along the tubing, the resistance loss of the nozzle and flow meter. Using this model, the relationship between the water injection pressure and the nozzle opening in each layer of the well can be calculated, thereby optimizing the water injection parameters and improving the stratified water injection effect.

[0009] CN114718548A discloses a method for intelligent optimization control of water injection in cable-controlled sub-injection wells. Under the same wellbore water injection pressure system, based on the monitoring of real-time flow rates at each layer, the method predicts the daily / monthly water injection compliance status of each layer using a judgment formula and a preset water injection qualification rate. Based on the prediction results, it intelligently optimizes and adjusts the water injection volume of each single layer. For the optimized adjustment and control of the water injection layers, a fluid dynamics numerical simulation (CFD) calculation is performed using an intelligent calculation system to obtain the key parameter flow coefficient. Then, using fluid dynamics formulas, with the goal of maximizing stable water injection, water injection compliance, and overall wellbore water injection efficiency, and minimizing the pressure loss of the water nozzles, the optimal water injection volume and the optimal water nozzle opening value for each layer are determined. The water nozzle opening is automatically adjusted by an intelligent distributor to meet the optimized water injection volume requirements of each injection layer.

[0010] However, while the above-mentioned technologies have played a practical role in achieving stratified water injection, they have not established a stratified water injection testing and adjustment optimization model. The selection of the size of each layer of water nozzles is mainly achieved through repeated manual adjustments, which results in problems such as long testing time and inaccurate gear selection, making it difficult to meet the requirements of fine water injection.

[0011] Zhang Xin et al. (Zhang Xin, Zhou Xingyuan, Zhang Haoran et al. Mixed integer nonlinear programming model for optimizing the layout of water injection pipeline network in oilfield [J]. Oil and Gas Field Surface Engineering, 2020, 39(11):37-43) To study the rational layout of water injection pipeline network, a mixed integer nonlinear programming model was established for the optimization of water injection well area division and water distribution station location. The optimal location of the water distribution station was obtained by two-stage solution using simulated annealing genetic algorithm.

[0012] Fu Qiang (Fu Qiang, Yu Jian'en, Yan Qiang, et al. Optimization of water supply network based on different valve openings in Flowmaster[J]. Journal of Irrigation and Drainage Machinery Engineering, 2020, 38(03):266-270+291) took the fluid pipeline system as the research object, modeled the system as a whole, and optimized the resistance loss law of gate valves and pipelines. Finally, the influence of valve opening degree on pipeline resistance loss was explored for different water supply conditions, and the optimal valve opening degree was obtained.

[0013] Vassileios D et al. (Vassileios D. Kosmidis, John D. Perkins et al. A mixed integer optimization formulation for the well scheduling problem on petroleum fields[J]. Computers and Chemical Engineering, 2004, 29(7)) proposed a mixed integer nonlinear (MINLP) model for well production scheduling in oilfields, which simultaneously considers reservoir nonlinear constraints, multiphase flow in the well, and surface facility constraints. Discrete variables include the well's operating state (on or off), and continuous variables include oil production. Using the proposed model can increase oil production by up to 10%.

[0014] To address the shortcomings of existing oilfield stratified water injection optimization control methods, this invention aims to establish a mixed integer linear programming model by using mathematical programming modeling. This model considers the constraints of injection volume and pressure in each layer of the injection well and aims to minimize the error between the actual inflow and outflow volume and the allocated injection volume. It then optimizes the settings of the stratified water injection hydraulic control valves to provide a practical and feasible optimization scheme for the refined stratified water injection of injection wells.

[0015] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0016] To address the shortcomings of existing technologies, this invention provides an intelligent optimization system and method for the gear position of hydraulic control valves in oilfield stratified water injection wells, aiming to solve at least one or more technical problems existing in the prior art.

[0017] To achieve the above objectives, the present invention provides a method for intelligent selection of valve positions in oilfield stratified water injection wellbore hydraulic control valves, comprising:

[0018] Construct an optimization model for the opening degree of a hydraulic control valve;

[0019] Enter the injection volume and wellhead pressure;

[0020] Input the model parameters into the hydraulic control valve opening optimization model to output the solution results using the preset model algorithm;

[0021] Determine whether the solution result meets the preset target conditions, whereby...

[0022] If the solution meets the preset target conditions, output the current solution result;

[0023] If the solution does not meet the preset target conditions, return to the initial input step until the solution that satisfies the cumulative difference error constraint of the actual injection volume and the amount of injection in each layer is obtained;

[0024] The solution results include the minimum error between the actual injection volume and the prepared volume, the optimal setting of the hydraulic control valve, and one or more of the actual injection volumes of each layer. The objective of the solution is to minimize the cumulative difference between the actual injection volume and the prepared volume of each layer.

[0025] Preferably, in this invention, the step of constructing the hydraulic control valve opening optimization model includes establishing an objective function to represent the cumulative difference between the actual injection volume and the dispensing volume at each layer. This objective function can be expressed as:

[0026]

[0027] In the formula: i represents the i-th dispensing layer, N represents the total number of dispensing layers, k represents the k-th position of the hydraulic control valve, K represents the total number of positions for each hydraulic control valve, and Q i,k m represents the actual injection volume when the k-th gear of the i-th injection layer is opened. 3 U i,kQ represents the on / off state of the k-th position of the i-th dispensing layer. i,k U i,k m represents the actual injection amount in the i-th layer. 3 S i m represents the injection volume of the i-th layer. 3 .

[0028] Preferably, the model parameters include functions and / or parameter values ​​corresponding to one or more constraints, wherein the functions corresponding to one or more constraints include the gear switch constraint function of each hydraulic control valve, the nozzle loss function in the nozzle loss-flow function constraint of the hydraulic control valve, the differential pressure function in the pressure constraint of the hydraulic control valve, and the error value function in the error constraint between the injection volume and the distribution volume of each layer.

[0029] Preferably, in this invention, the position switching constraint function of each hydraulic control valve can be expressed as:

[0030]

[0031] Preferably, in this invention, the nozzle loss function in the nozzle loss-flow function constraint of the hydraulic valve can be expressed as:

[0032] Q i,k =f(ΔP) i A i,k )

[0033] In the formula: Q i,k This indicates the injection flow rate at each gear level, m 3 A i,k This indicates the cross-sectional area of ​​the valve opening corresponding to each gear position, in meters. 3 ;ΔP i The pressure difference across the water tap, in MPa, is represented by f(ΔP). i A i,k ) represents the flow rate function of the hydraulic control valve nozzle loss, where the injection flow rate Q at each setting is... i,k The corresponding pressure function is related to the gear level of each hydraulic control valve.

[0034] Preferably, in this invention, the pressure difference function in the pressure constraint of the hydraulic control valve can be expressed as:

[0035] ΔP=P1-P2≥0

[0036] In the formula: P1 represents the pressure before the nozzle of the hydraulic control valve, and P2 represents the pressure after the nozzle of the hydraulic control valve. Wherein, P1 = wellhead pressure + injection pressure P w - Frictional resistance P f - Local loss; P2 = Formation pressure P r +Injection volume / Average water absorption index J 吸 .

[0037] Preferably, the injection pressure P wIt can be calculated using the liquid density and the tubing height, specifically:

[0038] P w =ρ 液 gh

[0039] Preferably, the frictional resistance p f It can be calculated using the following formula, specifically:

[0040]

[0041] In the formula: λ is the friction coefficient, which is a function of the Reynolds number Re and the relative roughness of the pipe wall Δ / d, i.e.:

[0042] λ=f(Re,Δ / d)

[0043] Preferably, the Reynolds number Re can be calculated using the following formula:

[0044]

[0045] Preferably, in this invention, the error value function in the error constraint between the water injection volume and the distribution volume of each layer is expressed as:

[0046]

[0047] Wherein, the actual injection volume Q when the k-th gear of the i-th injection layer is opened. i,k The amount of fluid S supplied to the i-th layer i The difference depends on the pre-set requirements of the project.

[0048] Preferably, in this invention, the average water absorption index J 吸 It can be represented as:

[0049]

[0050] In the formula: Q 注2 -Q 注1 P represents the difference in daily water injection volume under the two working systems. wf2 -P wf1 This indicates the difference in bottom hole flowing pressure under two different operating conditions.

[0051] Preferably, in this invention, the formation pressure P r It can be represented as:

[0052]

[0053] In the formula: P0 represents the pressure in front of the hydraulic control valve during well testing and commissioning, and Q represents the water absorption of the corresponding injection layer.

[0054] Preferably, the present invention also provides a system for the aforementioned intelligent selection method of hydraulic control valve position in oilfield stratified water injection wellbore, the system comprising:

[0055] Modeling unit, used to build an optimization model for the opening degree of hydraulic control valves;

[0056] The input unit is used to obtain the input injection volume and wellhead pressure;

[0057] The acquisition unit is used to acquire model parameters and input them into the hydraulic control valve opening optimization model.

[0058] The processing unit is used to output the solution result based on the model parameters using a preset model algorithm, and to determine the degree of conformity between the current solution result and the preset target conditions.

[0059] If the solution meets the preset target conditions, output the current solution result;

[0060] If the solution does not meet the preset target conditions, return to the above input and calculation steps until the solution that satisfies the cumulative difference error constraint of the actual injection volume and the amount of injection in each layer is obtained;

[0061] The solution results include the minimum error between the actual injection volume and the prepared volume, the optimal setting of the hydraulic control valve, and one or more of the actual injection volumes of each layer. The objective of the solution is to minimize the cumulative difference between the actual injection volume and the prepared volume of each layer.

[0062] The beneficial technical effects of this invention include: The hydraulic control valve position optimization method provided by this invention aims to minimize the error between the actual injection volume and the allocated injection volume. It considers constraints on the stratified injection volume of the injection well, the hydraulic control valve nozzle loss-flow function constraint, the hydraulic control valve pressure, and the position switch constraint. Combining the pressure difference-flow relationship equation for different hydraulic control valve positions, a mixed integer nonlinear programming model is established. The branch and bound method of the Gurobi solver is applied to select different branch variables and subproblems for branching. Effective constraint bounds control the search process, quickly obtaining the optimal downhole stratified hydraulic control valve position. Results show that under reasonable injection volume and wellhead pressure, this invention can provide an optimal hydraulic control valve position, and this scheme can achieve the lowest error value between the stratified injection volume and the total injection volume, realizing the goal of refined downhole water allocation in the injection well. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a preferred embodiment of the intelligent optimization method for the hydraulic control valve position in an oilfield stratified water injection wellbore provided by the present invention.

[0064] Figure 2 This is a schematic diagram of the structure of a water injection measurement and adjustment pipe column according to a preferred embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of a hydraulic control valve according to a preferred embodiment of the present invention;

[0066] Figure 4 This is a curve showing the relationship between the flow area of ​​the central pipe faucet and the gear position in a preferred embodiment of the present invention.

[0067] List of reference numerals Detailed Implementation

[0068] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0069] According to a preferred embodiment, the flow rate adjustment of stratified water injection in oilfields is achieved through flow throttling using hydraulically controlled nozzles. Specifically, under a certain wellhead injection pressure, the flow area of ​​the nozzles is adjusted by changing the setting of the hydraulic control valve, thereby altering the pressure drop generated when the injected water passes through the nozzles and achieving different injection flow rate control for each stratum. The structure of the water injection measurement and adjustment string (or stratified water injection string) is as follows: Figure 1 As shown.

[0070] Specifically, see Figure 2 The injection measurement and adjustment tubing string contains multiple sets of measurement and adjustment water distributors and cable-passing packers arranged sequentially along its axial direction. Each set of measurement and adjustment water distributors and cable-passing packers corresponds to a specific injection layer. Furthermore, a control system (such as a control box and computer equipment) located outside the wellhead can be connected to each set of measurement and adjustment water distributors and cable-passing packers via communication cables. The control system can be used to control the operating status of the measurement and adjustment water distributors for each injection layer, thereby controlling the injection flow rate of each injection layer. Specifically, this can be achieved by adjusting the hydraulic control valve position through the control system to regulate the flow area of ​​the nozzle, thereby changing the pressure drop generated when the injected water passes through the nozzle.

[0071] According to a preferred embodiment, the water injection measurement and control string may also be equipped with signal acquisition devices (such as flow sensors) for detecting the status parameters of each water injection layer. Each signal acquisition device can communicate with external terminal equipment (such as computer equipment with display and processing functions) to display and output downhole data and / or data in the water injection measurement and control string in a timely manner, so that operators or control terminals can respond promptly based on the data feedback results.

[0072] According to a preferred embodiment, the hydraulic control valve currently in use is typically an eight-position valve. This valve, through a hydraulic control decoder, enables position control of the downhole formations via three hydraulic control lines. The specific structure of this type of hydraulic control valve can be found in [reference needed]. Figure 3Specifically, this hydraulic control valve is bidirectional shifting and unidirectional adjustment. In other words, upward movement is free movement, while downward movement shifts gears and adjusts the opening. The higher the gear, the larger the opening, the larger the flow area, and the smaller the pressure loss. Conversely, the lower the gear, the smaller the opening, the smaller the flow area, and the greater the pressure loss. Furthermore, when the hydraulic control valve is switched to different gears, the flow area of ​​the central water nozzle changes. Preferably, the correspondence between the flow area of ​​the central water nozzle and the hydraulic control valve gear can be referenced... Figure 4 As shown in Table 1.

[0073] Table 1 Mouth loss function table

[0074]

[0075]

[0076] Combination Figure 4 As verified by Table 1, the higher the setting of the hydraulic control valve, the larger the valve opening and the larger the flow area.

[0077] According to a preferred embodiment, when a water injection well has multiple injection layers, there is a certain mutual influence between the pressure and hydraulic systems of each layer. During manual adjustment, the completion of adjustment for one layer can alter the pressure and hydraulic systems of other injection layers, requiring repeated adjustments for each layer. This leads to long adjustment times, inappropriate gear selection, and consequently, errors between the actual injection volume and the allocated injection volume, resulting in a low water injection qualification rate. Therefore, it is necessary to establish an optimization model for the opening of the hydraulic control valve and an optimization algorithm to achieve intelligent control of the hydraulically controlled water injection valves at each layer, meeting the stable injection and adjustment requirements of different injection layers.

[0078] In view of the above objectives, this invention provides an intelligent optimization method for the position of hydraulic control valves in oilfield stratified water injection wells. This method aims to minimize the error between the actual injection volume and the allocated injection volume. It considers constraints on the stratified injection volume of the water injection well, the hydraulic control valve nozzle loss-flow function constraint, and the hydraulic control valve pressure and position switch constraint. Combining the pressure difference-flow relationship equation for different positions of the hydraulic control valve, a mixed-integer nonlinear programming model is established. The branch-and-bound method of the Gurobi solver is applied to select different branch variables and subproblems for branching. Effective constraint bounds control the search process, quickly obtaining the optimal solution for the downhole stratified hydraulic control valve positions.

[0079] According to a preferred embodiment, the intelligent optimization method provided by the present invention mainly covers two parts: model building and model solving. Further, combined with... Figure 1The intelligent optimization method for the valve position of the hydraulic control valve in the oilfield stratified water injection wellbore provided by the present invention may include:

[0080] Construct an optimization model for the opening degree of a hydraulic control valve;

[0081] Obtain model parameters;

[0082] The solution results are output using a preset model algorithm based on the model parameters;

[0083] Determine whether the solution meets the preset requirements, whereby...

[0084] If the solution meets the preset target conditions, output the current solution result;

[0085] If the solution does not meet the preset target conditions, return to the above input and calculation steps until the solution that satisfies the cumulative difference error constraint of the actual injection volume and the amount of injection in each layer is obtained.

[0086] According to a preferred embodiment of the present invention, the solution output using a preset model algorithm requires pre-defined injection volume and wellhead pressure. Specifically, the optimal solution is the one that minimizes the cumulative difference between the actual injection volume and the injection volume at each layer. Alternatively, the hydraulic control valve opening optimization model uses the minimization of the cumulative difference between the actual injection volume and the injection volume at each layer as the control objective.

[0087] According to a preferred embodiment, in this invention, the model parameters may include the functions and / or parameter values ​​corresponding to various constraints involved in the hydraulic control valve opening optimization model. Specifically, the functions and parameter values ​​in the constraints may include the nozzle loss function in the hydraulic control valve nozzle loss-flow function constraint, P1 and P2 in the hydraulic control valve pressure constraint, and the error value E in the error constraint between the water injection volume and the distribution volume of each layer, etc.

[0088] According to a preferred embodiment, the solution result of the present invention includes one or more of the following: the minimum error value between the actual injection volume and the prepared injection volume, the optimal setting of the hydraulic control valve, and the actual injection volume of each layer.

[0089] Specifically, in this invention, model building may include:

[0090] (1) Establish the objective function

[0091]

[0092] Specifically, the objective function Z is represented by the cumulative difference between the actual injection volume and the dispensing volume at each layer. In the formula: i represents the i-th dispensing layer, N represents the total number of dispensing layers, k represents the k-th position of the hydraulic control valve, K represents the total number of positions for each hydraulic control valve, and Q... i,k m represents the actual injection volume when the k-th gear of the i-th injection layer is opened. 3U i,k Q represents the on / off state of the k-th position of the i-th dispensing layer. i,k U i, k represents the actual injection amount in the i-th layer, m 3 S i m represents the injection volume of the i-th layer. 3 Equation (1) indicates that an objective function is set to minimize the error between the actual injection volume and the allocated injection volume.

[0093] (2) Inserting constraints

[0094] ① Position switching constraints for each hydraulic control valve. Specifically, each valve can only be opened to one position or closed.

[0095]

[0096] ② Control valve nozzle loss - flow function constraint. Specifically, the volumetric flow rate Q of fluid passing through the downhole control valve. i,k With pressure difference ΔP i The valve opening area A i,k The function can be expressed as:

[0097] Q i,k =f(ΔP) i A i,k (3)

[0098] In the formula: Q i,k This indicates the injection flow rate at each gear level, m 3 A i,k This indicates the cross-sectional area of ​​the valve opening corresponding to each gear position, in meters. 3 ;ΔP i f(ΔP) represents the pressure difference across the water tap, in MPa. i A i,k Q is the flow rate function of the hydraulic valve nozzle loss; see Table 1 for details. Select Q according to different gear positions. i,k For different pressure functions, such as when the gear is selected as 1st gear, Q i,k The corresponding mouth damage function is ΔP i =0.0019Q 2.1869 .

[0099] ③ Pressure constraint of hydraulic control valve. Specifically, taking the water nozzle of the hydraulic control valve as a node, there is a pressure difference before and after the hydraulic control valve, namely ΔP = P1 - P2.

[0100] ΔP≥0 (4)

[0101] In the formula: P1 = wellhead pressure + injection pressure - frictional resistance - local losses; P2 = formation pressure + injection volume / average water absorption index. Specifically, P1 and P2 can be calculated through test and calibration wells.

[0102] Furthermore, at this point, the inlet pressure P1 is calculated downwards from the wellhead, and the outlet pressure P2 is calculated forwards from the formation.

[0103] ④ Constraints on the error between the injection volume and the distribution volume at each level. Specifically, constrain the actual flow rate Q at each level. i,k With the amount of injection S i The difference must be less than a specified error E, which depends on the specific engineering requirements on site.

[0104]

[0105] According to a preferred embodiment, the model solution process in this invention mainly includes two parts: first, providing the function and parameter values ​​in the constraints; and second, solving the MINLP model. Specifically, the function and parameter values ​​in the constraints may include the nozzle loss function in the hydraulic valve nozzle loss-flow function constraint, P1 and P2 in the hydraulic valve pressure constraint, and the error value E in the error constraint between the injection volume and the distribution volume of each layer. In particular, P2 in the hydraulic valve pressure constraint needs to be obtained by measuring and adjusting the well to obtain the layer water absorption index and formation pressure. The MINLP model solution specifically involves inputting the functions and data involved in the above constraints into the established hydraulic valve position optimization model for solution.

[0106] According to a preferred embodiment, the optimal gear position model for the hydraulic control valve is a mixed-integer nonlinear programming (MINLP) model, which is difficult to solve using traditional methods. Therefore, this model can be solved using Python programming and the commercial solver Gurobi. This solver employs a built-in branch-and-bound method. This method is a widely applicable search and iterative algorithm that can solve not only pure integer programming problems but also mixed-integer programming problems. This method selects different branch variables and subproblems for branching, and controls the search process through effective constraints and bounds, enabling it to advance towards the branch with the optimal solution in the state space tree, so as to find an optimal solution as quickly as possible.

[0107] According to a preferred embodiment, refer to Figure 1 The solution process of the MINLP model can be roughly simplified as follows: ① Give the injection volume and wellhead pressure; ② Obtain the model parameters (functions); ③ Input the parameters and solve the problem by programming; ④ Determine the termination condition; ⑤ Output: the minimum error value between the actual injection volume and the injection volume, the optimal setting of the hydraulic control valve under this layered water injection scheme, and the actual injection volume of each layer.

[0108] According to a preferred embodiment, the values ​​of P1 and P2 in the pressure constraint of the hydraulic control valve need to be calculated using the hydraulic parameters of the injection well.

[0109] Specifically, P1 = wellhead pressure + injection pressure - friction loss - local losses; where injection pressure and friction loss are calculated as follows:

[0110] (1) Liquid injection pressure P w

[0111] Liquid injection pressure P w can be derived from Pascal's law, specifically from the liquid density ρ. 液 The height h of the oil pipe is obtained directly by calculation:

[0112] P w = ρ 液 gh (6)

[0113] (2) Friction along the friction P f

[0114] Water is injected into the target formation through the wellhead. During transport, friction between the fluid in the pipeline and the pipe wall causes a pressure drop along the pipeline, resulting in frictional resistance P. f The flow of liquid in an oil pipe can be considered as one-dimensional circular pipe flow. According to the friction formula for one-dimensional circular pipe flow:

[0115]

[0116] In the formula: λ is the friction coefficient, which is a function of the Reynolds number Re and the relative roughness of the pipe wall Δ / d, i.e.:

[0117] λ=f(Re,Δ / d) (8)

[0118] The Reynolds number Re is given by formula (9):

[0119]

[0120] Table 2 shows the relationship between the Reynolds number Re and the friction coefficient λ under different flow conditions.

[0121] Table 2 Relationship between Re and λ under different flow regimes

[0122] Table 2 The relationship between Re and λunder different flow regimes

[0123]

[0124] in,

[0125]

[0126] Specifically, P2 = formation pressure + injection volume / average water absorption index, where the water absorption index J 吸and formation pressure P r The result needs to be obtained through well testing and commissioning. The calculation method is as follows:

[0127] (3) Water absorption index J 吸

[0128] Water absorption index J 吸 This refers to the daily water injection volume of a water injection well under a unit injection pressure differential. It reflects the water injection capacity of the well and the water absorption capacity of the reservoir. It can also be used to analyze the working condition of the water injection well and changes in the water absorption capacity of the reservoir, as shown in the following formula:

[0129]

[0130] Equation (11) represents the difference Q between the daily water injection volume under the two working systems. 注2 -Q 注1 The difference P between the bottom hole flowing pressure under the corresponding two working conditions wf2 -P wf1 The ratio of .

[0131] (3) Formation pressure P r

[0132] Formation pressure is calculated using equation (12):

[0133]

[0134] In the formula: P0 represents the pressure in front of the hydraulic control valve during well testing and commissioning, and Q represents the water absorption of the layer.

[0135] Specifically, taking the XX water injection well in the oilfield as an example, this well has three injection zones, and the basic data is shown in Table 3. The results of well testing and commissioning data and calculations are shown in Table 4.

[0136] Table 3. String Data

[0137] Table 3 String data

[0138]

[0139] Table 4. Well logging and commissioning data and calculation results for Well XX

[0140] Table 4XX well logging data and calculation results

[0141]

[0142]

[0143] Furthermore, with a total injection volume of 350m 3Using a wellhead pressure of 20 MPa as the base data, simulation optimization analysis was conducted on multiple sets of data by changing the total injection volume, the stratified injection volume, and the wellhead pressure. The simulation optimization results are shown in Table 5.

[0144] Table 5 Simulation optimization results

[0145] Table 5 Simulation optimization results

[0146]

[0147]

[0148] Note: IF indicates that the model has no solution (Infeasible).

[0149] Specifically, the model solution results for the first three groups in Table 5 show that when the wellhead pressure remains constant while the injection volume increases, the control valve position gradually increases, meaning the valve opening gradually increases. As the injection volume increases, the control valve position increases. When the injection volume increases to a certain extent, regardless of the valve position selection, the model cannot obtain a feasible solution within reasonable error constraints. Even with the valve fully open, the injection requirements of one or more layers cannot be met, resulting in no solution (IF) for that layer, indicating under-injection. Conversely, the data for the last three groups in Table 5 show that when the total injection volume remains constant while the wellhead pressure decreases, the optimal control valve position gradually increases. When the wellhead pressure decreases to a certain extent, the model also becomes unsolvable (IF), meaning the injected water cannot reach the formation.

[0150] The above analysis shows that as the water injection conditions change, the hydraulic control valve setting scheme changes, and the injection error changes. The main reason is that the change in the hydraulic system leads to a change in the optimal setting scheme during model solving. For a specific wellbore string and hydraulic control valve, there are certain range limitations on the injection volume and injection pressure. If these limitations are exceeded, the model cannot obtain a feasible solution; that is, no matter how the valve setting is adjusted, the water injection requirements cannot be met.

[0151] Example 2

[0152] Based on the intelligent optimization method for the hydraulic control valve position of the oilfield stratified water injection wellbore described in Example 1, the present invention also relates to an intelligent optimization system for the hydraulic control valve position of the oilfield stratified water injection wellbore, which can be used to execute the intelligent optimization method for the hydraulic control valve position described in the present invention.

[0153] Specifically, in this embodiment, the intelligent selection system for the hydraulic control valve position of the oilfield stratified water injection wellbore may include:

[0154] Modeling unit, used to build an optimization model for the opening degree of hydraulic control valves;

[0155] The input unit is used to obtain the input injection volume and wellhead pressure;

[0156] The acquisition unit is used to acquire model parameters and input them into the hydraulic control valve opening optimization model.

[0157] The processing unit is used to output the solution results based on the model parameters using a preset model algorithm, and to determine the degree of conformity between the current solution results and the preset target conditions.

[0158] According to a preferred embodiment, in this example, the solution output using a preset model algorithm requires pre-defined injection volume and wellhead pressure. Specifically, the optimal solution is the one with the minimum cumulative difference between the actual injection volume and the injection volume for each layer. Alternatively, the hydraulic control valve opening optimization model will use the minimum cumulative difference between the actual injection volume and the injection volume for each layer as the control objective.

[0159] According to a preferred embodiment, in this example, the model parameters may include the functions and / or parameter values ​​corresponding to the various constraints involved in the hydraulic control valve opening optimization model. Specifically, the functions and parameter values ​​in the constraints may include the nozzle loss function in the hydraulic control valve nozzle loss-flow function constraint, P1 and P2 in the hydraulic control valve pressure constraint, and the error value E in the error constraint between the water injection volume and the distribution volume of each layer, etc.

[0160] According to a preferred embodiment, in this embodiment, the solution result includes one or more of the following: the minimum error value between the actual injection volume and the prepared injection volume, the optimal setting of the hydraulic control valve, and the actual injection volume of each layer.

[0161] According to a preferred embodiment, in this embodiment, the modeling unit may include a model function construction subunit. Specifically, the model function construction subunit can be used to construct the target function as described in Embodiment 1:

[0162]

[0163] Furthermore, the acquisition unit can input the functions and data involved in various constraints into the established hydraulic valve position optimization model, enabling the processing unit to output the solution results based on the model parameters and using a preset model algorithm, and to determine the degree of conformity between the current solution results and the preset target conditions. Specifically, the constraints may include hydraulic valve position switching constraints, hydraulic valve nozzle loss-flow function constraints, hydraulic valve pressure constraints, and errors in water injection and distribution at each level. Parameter values ​​may include the hydraulic injection pressure P. w Friction along the path P f Water absorption index J 吸 and formation pressure P r wait.

[0164] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. An oilfield zonal injection wellbore fluid control valve gear intelligent optimization method, characterized in that, The method comprises the following steps: S1: constructing a hydraulic control valve opening degree optimization model; S2: inputting the injection allocation and wellhead pressure; S3: inputting model parameters into the hydraulic control valve opening degree optimization model to output a solution result by using a preset model algorithm, wherein the model parameters comprise one or more constraint conditions corresponding functions and / or parameter values, and the one or more constraint conditions corresponding functions comprise gear switch constraint functions of each hydraulic control valve, nozzle loss functions in hydraulic control valve nozzle loss-flow function constraints, differential pressure functions in hydraulic control valve pressure constraints, and error value functions in each layer injection volume and injection allocation error constraints; S4: judging whether the solution result meets a preset target condition, wherein if the solution result meets the preset target condition, outputting the current solution result; if the solution result does not meet the preset target condition, returning to step S2 until a solution result meeting the cumulative difference error constraint of each layer actual injection volume and injection allocation is obtained; wherein the solution result comprises one or more of the minimum error value of the output actual injection volume and injection allocation, the optimal gear of the hydraulic control valve, and each layer actual injection volume, and the solution result takes the cumulative difference of each layer actual injection volume and injection allocation as the target.

2. The method of claim 1, wherein, The step of constructing the hydraulic control valve opening degree optimization model comprises establishing a target function for representing the cumulative difference of each layer actual injection volume and injection allocation, and the target function is represented as: Where: i represents the i th injection layer, N represents the total number of injection layers, k represents the k th gear of the hydraulic control valve, K represents the total number of gears of each hydraulic control valve, Q i,k represents the actual injection volume when the k th gear of the i th injection layer is open, m 3 ; U i,k Q i,k U i,k m 3 ; S i m represents the injection volume of the i-th layer 3 .

3. The method of claim 1, wherein, The gear switch constraint function of each hydraulic control valve is represented as:

4. The method of claim 1, wherein, The nozzle loss function in the hydraulic control valve nozzle loss-flow function constraint is represented as: Q i,k = f(ΔP i ,A i,k ); In the formula: Q i,k represents the injection flow rate of each gear, m 3 ; A i,k represents the valve opening cross-sectional area corresponding to each gear, m 3 ; ΔP i represents the pressure difference before and after the water nozzle, MPa, f(ΔP i ,A i,k ) is the loss-flow function of the hydraulic control valve nozzle, wherein the injection flow rate Q i,k of each gear corresponds to the pressure function related to the gear level of each hydraulic control valve.

5. The method of claim 1, wherein, The differential pressure function in the hydraulic control valve pressure constraint is represented as: ΔP = P1 - P2 ≥ 0; Where: P1 represents the pre-nozzle pressure of the hydraulic control valve, v2 represents the post-nozzle pressure of the hydraulic control valve, wherein P1 = wellhead pressure + hydraulic injection pressure P w - frictional resistance P f - local loss; P2 = formation pressure P r + injection volume / average water absorption index J 吸 .

6. The method of claim 1, wherein, The error value function in the error constraint of each layer injection volume and injection allocation is represented as: Wherein the actual injection volume Q of the i-th injection layer at the k-th gear opening i,k The difference between the injection volume S of the i-th layer and the injection volume S of the i-th layer i depends on the engineering preset requirements.

7. The method of claim 5, wherein, The average water absorption index J 吸 is expressed as: where: Q 注2 Q 注1 represents the difference between the daily injection volumes under the two working systems, P wf2 P wf1 represents the difference between the bottom-hole flow pressures under the two working systems.

8. The method of claim 5, wherein, The formation pressure P r is expressed as: In the formula, P0 represents the pressure before the nozzle of the hydraulic control valve when the well is tested and adjusted, and Q represents the water absorption of the corresponding injection layer.

9. A system for the method of intelligent selection of the range of the fluid control valve of the oilfield separate layer injection wellbore according to any one of claims 1 to 8, characterized in that, The method comprises the following steps: A modeling unit is configured to construct a hydraulic control valve opening degree optimization model; An input unit is configured to obtain input injection allocation and wellhead pressure; An obtaining unit is configured to obtain model parameters and input the model parameters into the hydraulic control valve opening degree optimization model; A processing unit is configured to output a solution result by using a preset model algorithm based on the model parameters, and judge the degree of conformity of the current solution result to a preset target condition, wherein if the solution result meets the preset target condition, outputting the current solution result; if the solution result does not meet the preset target condition, returning to the foregoing input and calculation steps until a solution result meeting the cumulative difference error constraint of each layer actual injection volume and injection allocation is obtained; wherein the solution result comprises one or more of the minimum error value of the output actual injection volume and injection allocation, the optimal gear of the hydraulic control valve, and each layer actual injection volume, and the solution result takes the cumulative difference of each layer actual injection volume and injection allocation as the target.

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