Carbon dioxide multi-layer unified injection dynamic regulation and control method and device

Abstract

The invention provides a carbon dioxide multi-layer integration injection dynamic regulation and control method and device, and relates to the field of carbon dioxide geological sequestration, and the method comprises the steps: determining the optimal layer number and layer spacing distance of multi-layer integration injection based on obtained geological parameters, and obtaining a plurality of injection layers; according to the geological parameters and the length of the gas injection well, an interlayer allowable pressure difference is calculated in combination with a seepage mechanical formula, and spatial distribution parameters of all injection layers are determined; determining an inlet angle of an injection port corresponding to each injection layer; analyzing the porosity change rate between the injection layers and the coupling influence coefficient of the stress field change; and dynamically adjusting the opening degree of the injection port of each layer by adopting a model predictive control algorithm, and determining a delayed injection strategy according to a configured formation permeability grading standard. According to the method, the injection flow uniformity and the sealing efficiency can be improved, the interlayer fluid channeling risk is reduced, and accurate dynamic regulation and control of multi-layer unified injection are achieved.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide geological storage, and more specifically, to a method and apparatus for dynamic control of multi-layer carbon dioxide injection. Background Technology

[0002] In existing multilayer carbon dioxide injection technologies, the design of injection layer parameters largely relies on empirical judgment, making it difficult to accurately determine the optimal number of layers and spacing based on geological characteristics. The optimization of injection port angles lacks quantitative methods, easily leading to uneven flow distribution. Insufficient analysis of the coupled effects of porosity evolution and stress field changes caused by carbon dioxide dissolution between layers often results in interlayer crossflow and pressure imbalance. Furthermore, the control strategies are mostly static, unable to dynamically adapt to changes in formation parameters, leading to low storage efficiency and high engineering risks, thus limiting the stability and economic viability of large-scale carbon sequestration. Summary of the Invention

[0003] The purpose of this application is to provide a method and apparatus for dynamic control of multilayer carbon dioxide injection, which solves the above-mentioned problems existing in the prior art, can accurately control multilayer injection parameters, improve storage efficiency and reduce interlayer risks.

[0004] Firstly, a method for dynamic control of multi-level carbon dioxide injection is provided, which may include: Based on the acquired geological parameters, the optimal number of layers and the layer spacing for multi-layer injection are determined, resulting in at least two injection layers. Based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. The inlet angle of the injection port corresponding to each injection layer is determined by a genetic algorithm. Based on the configured multi-field coupling model, the coupling influence coefficient of porosity change rate and stress field change between each injection layer is analyzed; Based on the porosity change rate and the coupling influence coefficient, a model predictive control algorithm is used to dynamically adjust the injection port opening of each layer, and a delayed injection strategy is determined according to the configured formation permeability classification standard.

[0005] In one possible implementation, the geological parameters include formation permeability, porosity, and geostress field distribution.

[0006] In one possible implementation, determining the optimal number of layers and the layer spacing for multi-level annotation includes: Based on the vertical permeability, porosity, and stress field distribution of the formation, a support vector machine classification algorithm is used to perform hierarchical clustering of the formation to obtain clustering results; the clustering results include the permeability difference of each potential injection layer; When the difference in penetration rate exceeds the preset threshold, the target number of multi-layer injection is determined. The interlayer spacing is determined based on the porosity and geostress field distribution of each layer.

[0007] In one possible implementation, the spatial distribution parameters of each injection layer are determined, including: Based on the geological parameters, including formation permeability, fluid viscosity, and injection well length, the allowable interlayer pressure difference is determined. Using the interlayer allowable pressure difference as a constraint, and combining the lateral extension range and longitudinal depth of each injection layer, the spatial distribution parameters of each injection layer are determined; the spatial distribution parameters include the lateral coverage radius and longitudinal depth range of each injection layer.

[0008] In one possible implementation, a genetic algorithm is used to determine the entry angle of the injection port corresponding to each injection layer, including: Based on the spatial distribution parameters and geological permeability of each injection layer, the range of angle variation is determined. A genetic algorithm is used to iteratively optimize the range of angle variables to obtain the optimal inlet angle of the injection port for each injection layer.

[0009] In one possible implementation, based on a configured multi-field coupling model, the coupling influence coefficients of porosity change rate and stress field change between each injection layer are analyzed, including: Based on the geological parameters and injection parameters of each injection layer, the coupling parameters of the multi-field coupling model are configured; The coupling parameters of the multi-field coupling model are processed using a numerical iterative method to obtain the porosity change rate, stress disturbance range, and coupling influence coefficient between each injection layer.

[0010] In one possible implementation, the multi-field coupling model includes a chemical field module, a porosity evolution module, a seepage field module, and a stress field module.

[0011] Secondly, a dynamic control device for multi-layer carbon dioxide injection is provided, which may include: The determination unit is used to determine the optimal number of layers and the layer spacing distance for multi-layer injection based on the acquired geological parameters, so as to obtain at least two injection layers; Furthermore, based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. Furthermore, the inlet angle of the injection port corresponding to each injection layer is determined using a genetic algorithm; The analysis unit is used to analyze the porosity change rate and the coupling influence coefficient of stress field changes between each injection layer based on the configured multi-field coupling model. The determining unit is further configured to dynamically adjust the opening of the injection port of each layer based on the porosity change rate and the coupling influence coefficient using a model predictive control algorithm, and to determine the delayed injection strategy according to the configured formation permeability classification standard.

[0012] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0013] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0014] This application provides a method and apparatus for dynamic control of multi-layer carbon dioxide injection. The method includes: determining the optimal number of layers and the layer spacing based on acquired geological parameters to obtain multiple injection layers; calculating the allowable pressure difference between layers according to geological parameters and injection well length, combined with seepage mechanics formulas, and determining the spatial distribution parameters of each injection layer; determining the inlet angle of the injection port corresponding to each injection layer; analyzing the coupling influence coefficient of porosity change rate and stress field change between each injection layer; dynamically adjusting the opening of the injection port of each layer using a model predictive control algorithm, and determining a delayed injection strategy according to a configured formation permeability classification standard. This application can improve the uniformity of injection flow and storage efficiency, reduce the risk of inter-layer crossflow, and achieve precise dynamic control of multi-layer injection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A system architecture diagram for a dynamic control method for multi-level carbon dioxide injection provided in this application embodiment; Figure 2 A flowchart illustrating a method for dynamic control of multilayer carbon dioxide injection provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a multi-layer dynamic control device for carbon dioxide injection provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] The carbon dioxide multi-level injection dynamic control method provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include a server and a terminal. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be connected directly or indirectly through wired or wireless communication methods; this application does not impose any limitations on this.

[0019] The terminal is used to acquire geological parameters and send them to the server.

[0020] The server is used to receive geological parameters in order to execute the dynamic control method for multi-level carbon dioxide injection provided in this application.

[0021] In existing multilayer carbon dioxide injection technologies, the design of injection layer parameters largely relies on empirical judgment, making it difficult to accurately determine the optimal number of layers and spacing based on geological characteristics. The optimization of injection port angles lacks quantitative methods, easily leading to uneven flow distribution. Insufficient analysis of the coupled effects of porosity evolution and stress field changes caused by carbon dioxide dissolution between layers often results in interlayer crossflow and pressure imbalance. Furthermore, the control strategies are mostly static, unable to dynamically adapt to changes in formation parameters, leading to low storage efficiency and high engineering risks, thus limiting the stability and economic viability of large-scale carbon sequestration.

[0022] Therefore, this application provides a dynamic control method for multilayer carbon dioxide injection, which solves the above-mentioned problems in the prior art, can accurately control multilayer injection parameters, improve storage efficiency and reduce interlayer risks.

[0023] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0024] Figure 2 This is a schematic flowchart illustrating a multi-layered dynamic control method for carbon dioxide injection provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Based on the acquired geological parameters, determine the optimal number of layers and the layer spacing for multi-layer injection, and obtain at least 2 injection layers.

[0025] Among them, geological parameters include parameters such as formation permeability, porosity, and distribution of geostress field.

[0026] Specifically, three-dimensional geological parameters of the target formation are obtained through 3D seismic exploration and well logging technologies, including vertical permeability distribution, porosity, and geostress field distribution. For example, array induction logging tools are used to collect formation permeability data with a resolution of 0.1m; dipole acoustic logging is used to obtain the principal direction and magnitude of the geostress field with an error range of ≤5%. The raw data is standardized to eliminate the influence of dimensions, forming an input matrix suitable for machine learning.

[0027] Based on the vertical permeability, porosity, and stress field distribution of the formations, a support vector machine (SVM) classification algorithm is used to perform hierarchical clustering of the formations, yielding clustering results. The clustering results include the permeability difference between potential injection layers. Specifically, an SVM classification model is constructed using a radial basis function (RBF) kernel, with vertical formation parameters (permeability, porosity, and stress) as input features. The penalty parameter C and kernel function parameter γ are optimized through grid search. During the clustering process, the model automatically identifies formation units with similar geological characteristics and outputs the permeability difference index for each potential injection layer. The permeability difference is defined as the ratio of the difference in the mean permeability of adjacent potential layers to the average permeability.

[0028] When the permeability difference exceeds the preset threshold, the target number of layers for multi-layer injection is determined. This can be understood as the geological conditions suitable for multi-layer injection being determined when the permeability difference exceeds the preset threshold (e.g., 0.6), thus avoiding the risk of crossflow due to too small permeability differences between layers, or uneven injection efficiency due to too large differences.

[0029] Based on the porosity and geostress field distribution of each layer, the interlayer spacing is determined. Specifically, based on the clustering results, when the permeability difference meets the threshold condition, the optimal number of injection layers is determined with the optimization objectives of minimizing the number of layers and minimizing interlayer interference. For example, after SVM clustering of exploration data from an oilfield, three groups of formations with significant permeability differences were identified (differences of 0.72 and 0.65, respectively), and a three-layer unified injection scheme was finally determined.

[0030] Furthermore, determining the interlayer spacing requires simultaneously satisfying two constraints: A. Geological stability constraints: Based on the distribution of the geostress field, the interlayer spacing must be greater than 1.5 times the thickness of the geostress concentration zone to avoid interlayer shear failure caused by injection pressure; B. Independence of seepage constraint: According to Darcy's law, the interlayer spacing must be greater than the diffusion radius of carbon dioxide in the target formation (calculated by permeability, porosity, and injection pressure) to ensure that there is no significant overlap between the injection areas of each layer. For example, for a medium-permeability layer (permeability 50mD) at an injection pressure of 8MPa, the diffusion radius is calculated to be 20m, and the interlayer spacing is finally determined to be 30m (1.5 times the diffusion radius).

[0031] This application improves the accuracy of layer number determination through data-driven clustering algorithms, enhances the matching degree between layer spacing design and actual formation conditions, and controls the interlayer pressure difference within 0.3 MPa (compared to 1.2 MPa in the traditional scheme) after adopting this scheme, effectively reducing the risk of crossflow and improving the storage efficiency.

[0032] Step S220: Calculate the allowable pressure difference between layers based on geological parameters and the length of the injection well, combined with seepage mechanics formulas, and determine the spatial distribution parameters of each injection layer.

[0033] Specifically, based on geological parameters and the length of the injection well, the allowable interlayer pressure difference is calculated using seepage mechanics formulas, including: The configured differential pressure algorithm is used to calculate the geological parameters and obtain the allowable differential pressure between layers.

[0034] The differential pressure algorithm is as follows:

[0035] Where K is the average formation permeability (mD), obtained through 3D seismic inversion and core experiments; h is the effective thickness of the injection layer (m), determined by well logging curves; The maximum injection flow rate (cubic meters / day) for a single well is determined based on the engineering design. is the viscosity of carbon dioxide fluid (mPa•s), determined by PVT testing with varying temperature and pressure; B is the volume coefficient, dimensionless. The discharge radius (m) is taken as 1 / 2 of the influence range of the injection well; Let be the wellbore radius (m).

[0036] For example: A certain injection well section has a permeability of 80mD, an effective thickness of 15m, an injection flow rate of 5000 cubic meters / day, a carbon dioxide viscosity of 0.08mPa•s, a discharge radius of 100m, and a wellbore radius of 0.1m. The calculated allowable pressure difference between layers is 1.2MPa, meaning that the pressure difference between adjacent injection layers must not exceed this value to avoid crossflow.

[0037] Determine the spatial distribution parameters of each injection layer, including: Based on geological parameters such as formation permeability, fluid viscosity, and injection well length, the allowable inter-layer pressure difference is determined. Using the interlayer allowable pressure difference as a constraint, and combining the lateral extension range and longitudinal depth of each injection layer, the spatial distribution parameters of each injection layer are determined; the spatial distribution parameters include the lateral coverage radius and longitudinal depth range of each injection layer.

[0038] Furthermore, the lateral coverage radius R of a single well is determined using the steady-state radial seepage formula:

[0039] This radius characterizes the effective diffusion range of carbon dioxide under the target pressure differential. For example, in the above embodiment, the calculated lateral coverage radius is 85m, meaning the lateral distribution parameters of the injection layer are set as a circular area with a radius of 85m centered on the wellbore.

[0040] Based on the distribution of the geostress field and the layer spacing d, the following must be satisfied in the longitudinal depth range: Upper boundary: ≥d distance from the bottom of the previous injection layer; Lower boundary: ≥d distance from the top of the next injection layer; At the same time, avoid areas of concentrated ground stress (such as within 20m above and below fault zones).

[0041] For example, in a three-layer unified injection scheme, the depth of the middle layer is 1200m and the layer interval is 30m. Then its longitudinal depth range is determined to be 1185m~1215m (extending 15m both vertically and horizontally).

[0042] In some embodiments, the constraints further include: Engineering constraints: The length L of the gas injection well must meet the following requirements. Where n is the number of layers. Let the effective thickness of the i-th injection layer be such that the wellbore can cover all injection layers; Flow constraint: Lateral coverage radius of each injection layer Ri The requirement is that (Ri≤0.8×well spacing / 2) must be met to avoid overlap of injection areas between adjacent wells; Dynamic correction: If the measured injection pressure exceeds... 80% of the lateral coverage radius is corrected in real time through a digital twin model, for example, by reducing R by 10% to 15%.

[0043] Step S230: Determine the inlet angle of the injection port corresponding to each injection layer using a genetic algorithm.

[0044] The inlet angle of the injection port includes the dip angle (the angle with the horizontal plane) and the azimuth angle (the angle with the main permeability direction of the formation). The range of variation needs to be limited in conjunction with the spatial distribution parameters of the injection layer and the geological permeability characteristics. Specifically, step S231: Determine the range of angle variation based on the spatial distribution parameters and geological permeability of each injection layer; Based on the longitudinal depth range of the injection layer, the dip angle must ensure that the carbon dioxide injection trajectory falls within the longitudinal depth range of the target layer. For example, if the longitudinal depth range of a certain injection layer is 1185m~1215m (well depth reference level 1000m), then the dip angle θ satisfies: ,in, θ is the length of the horizontal section of the gas injection well (unit: m), and θ is the inclination angle of the injection port (unit: °). The calculated inclination angle range is 20.5°~23.3°.

[0045] Based on the anisotropy of geological permeability (the direction of the main permeability is determined by well logging data), the azimuth angle φ needs to deviate from the direction of the main permeability by ≤30° (to improve the diffusion efficiency of carbon dioxide along the high permeability direction). For example, if the main permeability direction of a certain formation is 45° east of north, then the azimuth angle range is limited to 15°~75° (east of north).

[0046] Step S232: Use a genetic algorithm to iteratively optimize the range of angle variables to obtain the optimal inlet angle of the injection port corresponding to each injection layer.

[0047] Specifically, a real-number encoding method is used to encode the tilt angle of each injection layer ( ) and azimuth ( The chromosomes consist of 2n dimensions (n ​​being the number of injection layers). The initial population size is set to 50, and chromosomes that satisfy the above angle range are randomly generated to ensure population diversity.

[0048] First, with the uniformity of carbon dioxide injection flow rate in each injection layer as the optimization objective, the fitness function F is defined as:

[0049] in, The injection flow rate of the i-th layer is calculated through simulation using a digital twin model, with input parameters including angle, permeability, and porosity. The average flow rate is F. The closer F is to 1, the better the flow uniformity.

[0050] Then, a tournament selection method is used to randomly select 5 individuals to compete, and the one with the highest fitness enters the next generation to preserve superior genes; Arithmetic crossover is used to generate offspring from selected parent chromosomes with a crossover probability of 0.8. For example, for the tilt gene:

[0051] in, The angle of inclination (in degrees) of the offspring after crossover, where α is a random number between 0 and 1 (controlling the proportion of parental gene contribution). , These are the tilt angles of the two parent generations.

[0052] The gene is perturbed with a mutation probability of 0.05. The angle after mutation must still be within the preset range (if the tilt angle exceeds the range, it is truncated to the boundary value).

[0053] When the mean change of the fitness function is ≤0.001 for 10 consecutive generations, or when the number of iterations reaches 100 generations, the optimization is terminated, and the angle parameters corresponding to the current best chromosome are output.

[0054] For example, consider a three-tier unified injection system: The injection layer 1 has a longitudinal depth of 1000~1030m, a main permeability direction of 30° north of east, a dip angle range of 18°~22°, and an azimuth angle range of 0°~60°. The parameters of injection layers 2 and 3 are similar, and the angle ranges are adapted to their depth range and permeability direction, respectively.

[0055] Genetic algorithm optimization parameters: population size 50, crossover probability 0.8, mutation probability 0.05, convergence after 85 generations. After optimization, the flow deviation of each layer decreased from the initial ±25% to ±8%, the fitness function value increased from 0.62 to 0.92, and the optimal injection port angle was: Layer 1: Inclination angle 20.1°, azimuth angle 32.5°; Layer 2: Inclination angle 25.3°, azimuth angle 35.2°; Layer 3: Inclination angle 28.7°, azimuth angle 31.8°.

[0056] Step S240: Based on the configured multi-field coupling model, analyze the coupling influence coefficient of porosity change rate and stress field change between each injection layer.

[0057] Specifically, step S241 involves configuring the coupling parameters of the multi-field coupling model based on the geological parameters and injection parameters of each injection layer. Geological parameters may also include initial porosity. ϕ 0, permeability K0, rock elastic modulus E Poisson's ratio (ν) and mineral content (C) carb (e.g., the proportion of carbonate rocks); Injection parameters may include carbon dioxide injection pressure P inj Injection rate Qinj Solubility of carbon dioxide in formation water (S) CO2 , reaction rate constant k rxn (Determined through indoor core dissolution experiments).

[0058] Coupling parameters include: the porosity increment coefficient due to mineral dissolution. Permeability correction factor and Biot coefficient (Characteristics representing the coupling of seepage and stress) .

[0059] Step S242: Using a numerical iteration method, the coupling parameters of the multi-field coupling model are processed to obtain the porosity change rate, stress disturbance range, and coupling influence coefficient between each injection layer.

[0060] Multi-field coupling solution using finite element software: Based on the modified Langmuir kinetic equation, the dissolution reaction rate of carbon dioxide with carbonate rocks is: ,in, This represents the saturation concentration of carbon dioxide under the current temperature and pressure conditions. This represents the actual dissolution concentration. The mineral dissolution amount in each injection layer is obtained through iterative calculations at a time step Δt. (Unit: kg / cubic meter).

[0061] According to the porosity increment coefficient Determine the porosity change rate , Among them, elastic deformation porosity , This represents the bulk modulus of the rock.

[0062] Permeability after porosity update: Establish the seepage equation: ; Through pressure boundary conditions (injection pressure) Initial formation pressure ), calculate the seepage pressure distribution and flow rate Q of each layer. i Output interlayer pressure difference .

[0063] Based on the effective stress principle, formation stress , of which, total stress Determined from initial data of the geostress field. The formation displacement u and stress components are solved using the equations of elasticity. Determine the range of stress disturbance (with the area where the principal stress change exceeds 5% as the boundary).

[0064] Define the interlayer coupling influence coefficient , This coefficient quantifies the bidirectional coupling strength between interlayer porosity variation and stress disturbance. The larger the value, the more significant the interlayer interaction.

[0065] In some embodiments, the multi-field coupling model may include a chemical field module, a porosity evolution module, a seepage field module, and a stress field module.

[0066] The chemical field module is used to calculate the dissolution reaction rate of carbon dioxide with formation minerals (such as carbonate rocks) based on the modified Langmuir kinetic equation, and to obtain the amount of mineral dissolution in each injection layer, providing basic data for subsequent porosity change calculations. The porosity evolution module combines the mineral dissolution amount output by the chemical field module with the coupling parameter β, superimposed with the porosity change caused by elastic deformation, calculates the porosity change rate of each injection layer, and dynamically updates the porosity parameters. The seepage field module is used to correct the permeability based on the porosity updated by the porosity evolution module, and then establish the seepage equation based on Darcy's law to solve the pressure distribution, flow rate and interlayer pressure difference of each injection layer. The stress field module is used to solve for formation displacement and stress components based on the effective stress principle and combined with the pressure data from the seepage field module, to determine the range of stress disturbance (the area where the principal stress changes by more than 5%), and to quantify the impact of stress field changes on formation stability.

[0067] Step S250: Based on the porosity change rate and coupling influence coefficient, the model predictive control algorithm is used to dynamically adjust the opening of the injection port of each layer, and the delayed injection strategy is determined according to the configured formation permeability classification standard.

[0068] Specifically, the opening degree of the injection port of each injection layer is adjusted. Real-time traffic and pressure Constructed as state-space equations: Equations of state:

[0069] Output equation: , where the state vector Control vector A, B, and C are the model coefficient matrices (identified offline through a multi-field coupling model). , This refers to process noise and measurement noise.

[0070] porosity change rate and coupling influence coefficient Construct the objective function using the weighting factors:

[0071] Where P is the prediction time domain and M is the control time domain; This is the reference value for the flow rate of the i-th layer. The historical average flow rate; , , These are the weighting coefficients.

[0072] Based on the configured opening degree constraints, flow rate constraints, and adjustment rate constraints, a quadratic programming (QP) solver is used for online optimization to obtain the optimal opening degree sequence for the next M steps.

[0073] Based on the configured formation permeability classification criteria, the delayed injection strategy can be determined, which may include: Table 1 shows the formation permeability classification standard. (See Table 1 for details.) Table 1

[0074] Delay time The calculation formula is:

[0075] in, The baseline delay time is 10 minutes. The permeability weighting coefficient for the i-th layer; It is the sum of the coupling influence coefficients between the i-th layer and other layers.

[0076] The delay is initiated when any of the following conditions are met: The pressure of the preferentially injected layer reaches 1.1 times the initial pressure: ; The pressure difference between adjacent floors exceeds 80% of the allowable pressure difference between floors: ; Compared with traditional control methods, this approach can improve flow control accuracy and reduce the number of times interlayer pressure difference exceeds the limit.

[0077] This application provides a dynamic control method for multi-layer carbon dioxide injection. The method includes: determining the optimal number of layers and the interlayer spacing based on acquired geological parameters, resulting in multiple injection layers; calculating the allowable interlayer pressure difference based on geological parameters and injection well length, combined with seepage mechanics formulas, and determining the spatial distribution parameters of each injection layer; determining the inlet angle of the injection port corresponding to each injection layer; analyzing the coupling influence coefficient of porosity change rate and stress field change between injection layers; dynamically adjusting the injection port opening of each layer using a model predictive control algorithm; and determining a delayed injection strategy based on a configured formation permeability classification standard. This application accurately determines the injection layer structure through geological parameter-driven layered design and seepage calculation; optimizes the injection port angle using a genetic algorithm to achieve a reasonable configuration of flow distribution; quantifies the coupling influence of interlayer porosity and stress field using a multi-field coupling model; and dynamically adjusts injection parameters based on model predictive control and permeability classification strategies. This method effectively improves the interlayer synergy of multi-layer carbon dioxide injection, reduces the risk of crossflow, achieves dynamic adaptation and efficient control of the injection process, and significantly improves the stability and efficiency of geological storage.

[0078] Corresponding to the above method, embodiments of this application also provide a dynamic control device for multi-layer carbon dioxide injection, such as... Figure 3 As shown, the device includes: The determination unit 310 is used to determine the optimal number of layers and the layer spacing distance for multi-layer injection based on the acquired geological parameters, so as to obtain at least two injection layers; Furthermore, based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. Furthermore, the inlet angle of the injection port corresponding to each injection layer is determined using a genetic algorithm; Analysis unit 320 is used to analyze the porosity change rate and the coupling influence coefficient of stress field change between each injection layer based on the configured multi-field coupling model; The determining unit is further configured to dynamically adjust the opening of the injection port of each layer based on the porosity change rate and the coupling influence coefficient using a model predictive control algorithm, and to determine the delayed injection strategy according to the configured formation permeability classification standard.

[0079] The functions of each functional unit of the carbon dioxide multilayer injection dynamic control device provided in the above embodiments of this application can be realized through the above-described methods and steps. Therefore, the specific working process and beneficial effects of each unit in the carbon dioxide multilayer injection dynamic control device provided in the embodiments of this application will not be repeated here.

[0080] This application also provides an electronic device, such as... Figure 4As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0081] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Based on the acquired geological parameters, the optimal number of layers and the layer spacing for multi-layer injection are determined, resulting in at least two injection layers. Based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. The inlet angle of the injection port corresponding to each injection layer is determined by a genetic algorithm. Based on the configured multi-field coupling model, the coupling influence coefficient of porosity change rate and stress field change between each injection layer is analyzed; Based on the porosity change rate and the coupling influence coefficient, a model predictive control algorithm is used to dynamically adjust the injection port opening of each layer, and a delayed injection strategy is determined according to the configured formation permeability classification standard.

[0082] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0083] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0084] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0085] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0086] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0087] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the above embodiments of a dynamic control method for multi-layered carbon dioxide injection.

[0088] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above embodiments of a dynamic control method for multi-layered carbon dioxide injection.

[0089] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected," "coupled," or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0094] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the embodiments in this application are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments in this application.

[0095] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the embodiments of this application and their equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for dynamic control of multi-layer carbon dioxide injection, characterized in that, The method includes: Based on the acquired geological parameters, the optimal number of layers and the layer spacing for multi-layer injection are determined, resulting in at least two injection layers. Based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. The inlet angle of the injection port corresponding to each injection layer is determined by a genetic algorithm. Based on the configured multi-field coupling model, the coupling influence coefficient of porosity change rate and stress field change between each injection layer is analyzed; Based on the porosity change rate and the coupling influence coefficient, a model predictive control algorithm is used to dynamically adjust the injection port opening of each layer, and a delayed injection strategy is determined according to the configured formation permeability classification standard.

2. The method as described in claim 1, characterized in that, The geological parameters include formation permeability, porosity, and stress field distribution.

3. The method as described in claim 2, characterized in that, Determining the optimal number of layers and layer spacing for multi-level unified annotation includes: Based on the vertical permeability, porosity, and stress field distribution of the formation, a support vector machine classification algorithm is used to perform hierarchical clustering of the formation to obtain clustering results; the clustering results include the permeability difference of each potential injection layer; When the difference in penetration rate exceeds the preset threshold, the target number of multi-layer injection is determined. The interlayer spacing is determined based on the porosity and geostress field distribution of each layer.

4. The method as described in claim 1, characterized in that, Determine the spatial distribution parameters of each injection layer, including: Based on the geological parameters, including formation permeability, fluid viscosity, and injection well length, the allowable interlayer pressure difference is determined. Using the interlayer allowable pressure difference as a constraint, and combining the lateral extension range and longitudinal depth of each injection layer, the spatial distribution parameters of each injection layer are determined; the spatial distribution parameters include the lateral coverage radius and longitudinal depth range of each injection layer.

5. The method as described in claim 1, characterized in that, The genetic algorithm is used to determine the inlet angle of the injection port corresponding to each injection layer, including: Based on the spatial distribution parameters and geological permeability of each injection layer, the range of angle variation is determined. A genetic algorithm is used to iteratively optimize the range of angle variables to obtain the optimal inlet angle of the injection port for each injection layer.

6. The method as described in claim 1, characterized in that, Based on the configured multi-field coupling model, the coupling influence coefficients of porosity change rate and stress field change between each injection layer are analyzed, including: Based on the geological parameters and injection parameters of each injection layer, the coupling parameters of the multi-field coupling model are configured; The coupling parameters of the multi-field coupling model are processed using a numerical iterative method to obtain the porosity change rate, stress disturbance range, and coupling influence coefficient between each injection layer.

7. The method as described in claim 1, characterized in that, The multi-field coupling model includes a chemical field module, a porosity evolution module, a seepage field module, and a stress field module.

8. A dynamic control device for multi-layer carbon dioxide injection, characterized in that, The device includes: The determination unit is used to determine the optimal number of layers and the layer spacing distance for multi-layer injection based on the acquired geological parameters, so as to obtain at least two injection layers; Furthermore, based on the geological parameters and the length of the injection well, the allowable pressure difference between layers is calculated using seepage mechanics formulas, and the spatial distribution parameters of each injection layer are determined. Furthermore, the inlet angle of the injection port corresponding to each injection layer is determined using a genetic algorithm; The analysis unit is used to analyze the porosity change rate and the coupling influence coefficient of stress field changes between each injection layer based on the configured multi-field coupling model. The determining unit is further configured to dynamically adjust the opening of the injection port of each layer based on the porosity change rate and the coupling influence coefficient using a model predictive control algorithm, and to determine the delayed injection strategy according to the configured formation permeability classification standard.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-7.

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