Method and device for predicting pore scale rock salting-out of porous underground gas storage

Abstract

The invention discloses a porous underground gas storage pore scale rock salting-out prediction method and device, and the method comprises the following steps: obtaining pore structure data of target reservoir rock, and constructing a three-dimensional digital core model based on the pore structure data; establishing a multi-phase multi-component migration mathematical model for describing the salting-out phase change process, wherein the mathematical model is coupled with a salt dissolution-precipitation kinetic equation, a mass conservation equation, a momentum conservation equation and a component transportation equation; importing the three-dimensional digital core model and the mathematical model into a computational fluid dynamics simulation platform, and setting boundary conditions and initial conditions to simulate gas storage injection-production working conditions; running simulation calculation to obtain salt particle precipitation volume fraction distribution, brine flow field distribution and permeability evolution data under the pore scale; predicting the salting-out blockage risk of the reservoir based on the permeability evolution data; according to the method, refined and quantitative simulation and prediction of the salting-out process of the underground gas storage on the pore scale are realized for the first time.

Description

Technical Field

[0001] This application relates to the field of underground gas storage technology, and in particular to a method and apparatus for predicting rock salt precipitation at the pore scale in porous underground gas storage. Background Technology

[0002] Natural gas, as a key bridge in the clean energy transition, occupies an important strategic position in my country's energy structure. Underground gas storage facilities (UGS), due to their advantages such as large storage capacity, low cost, and high safety, have become core infrastructure for balancing peak and off-peak natural gas demand. There are 783 operational underground gas storage facilities globally, with a total working gas volume of 429 billion cubic meters, and another 76 under construction. With the continued growth in natural gas demand, the role of underground gas storage facilities is becoming increasingly important.

[0003] However, with the large-scale application of underground gas storage facilities, salt precipitation and blockage have gradually become key technical challenges restricting their efficient operation. During enhanced injection and production operations of gas storage facilities, high-salinity formation water evaporates and concentrates after entering the wellbore with the produced gas, leading to salt precipitation. As production continues, the formation water salinity reaches a supersaturated state, and salt crystals deposit at the rock pore throats, blocking the flow channels, severely impairing injection and production efficiency and threatening the long-term stability of the storage facility.

[0004] The injection-production cycle of porous reservoir-type gas storage facilities leads to the repeated injection of large amounts of dry gas. After the formation water evaporates, it forms a miscible phase with the natural gas. During subsequent gas production, this miscible fluid is extracted as wet gas, which is more likely to induce reservoir salt precipitation. Currently, freshwater "well washing" operations are often used in engineering to alleviate the salt deposition problem in the wellbore and near-wellbore area. However, this method will further aggravate the accumulation of fluid at the bottom of the well and in the near-wellbore reservoir, thereby affecting the injection-production efficiency and effective storage capacity of porous reservoir-type gas storage facilities.

[0005] Rock pores are the primary site of salt precipitation and the main storage space in gas storage facilities. Studying the multiphase, multi-component transport mechanisms considering salt precipitation phase transitions under multi-cycle intensive injection and production conditions in porous reservoir-type gas storage facilities, starting from the pore scale, is more in line with practical engineering needs. However, current technologies lack effective pore-scale modeling and prediction methods, making it impossible to quantitatively assess the risk of salt precipitation blockage. Macroscopic empirical models struggle to capture the complex physicochemical processes within pores, while traditional experimental methods are time-consuming, labor-intensive, and unable to provide real-time observation of salt precipitation dynamics within pores.

[0006] Therefore, in order to address the need for quantitative assessment of the risk of salt precipitation blockage in underground gas storage facilities, it is urgent to develop a new method that can simulate and predict the salt precipitation process at the pore scale, so as to provide technical support for the safe and efficient operation of gas storage facilities. Summary of the Invention

[0007] To address the aforementioned issues, this application provides a method and apparatus for predicting rock salt precipitation at the pore scale in porous underground gas storage facilities. By combining digital core reconstruction, multiphase and multicomponent transport theory, and computational fluid dynamics simulation, it achieves for the first time a refined and quantitative simulation and prediction of the salt precipitation process in underground gas storage facilities at the pore scale. The technical solution is as follows: The first aspect of this application provides a method for predicting salt precipitation at the pore scale in porous underground gas storage, comprising the following steps: acquiring pore structure data of the target reservoir rock; constructing a three-dimensional digital core model based on the pore structure data; establishing a multiphase, multi-component transport mathematical model describing the salt precipitation phase transition process, wherein the mathematical model couples salt dissolution-precipitation kinetic equations, mass conservation equations, momentum conservation equations, and component transport equations; importing the three-dimensional digital core model and the mathematical model into a computational fluid dynamics simulation platform, setting boundary conditions and initial conditions to simulate the gas storage injection and production conditions; running simulation calculations to obtain data on the salt particle precipitation volume fraction distribution, brine flow field distribution, and permeability evolution at the pore scale; and predicting the reservoir salt precipitation blockage risk based on the permeability evolution data.

[0008] For example, in the porous underground gas storage pore-scale rock salt precipitation prediction method provided in one embodiment, the salt dissolution-precipitation kinetic equation is constructed based on the Arrhenius formula and used to calculate the salt dissolution-precipitation rate.

[0009] For example, in the method for predicting rock salt precipitation at the pore scale of the porous underground gas storage provided in one embodiment, virtual porous media technology is used to simulate the influence of precipitated salt particles on pore space and fluid transport. The precipitated solid salt particles are regarded as the solid skeleton of the porous media, and the brine is regarded as the pore space.

[0010] For example, in the porous underground gas storage pore-scale rock salt precipitation prediction method provided in one embodiment, the porosity and effective permeability of the virtual porous medium region are dynamically updated by calculating the salt grain volume fraction, and the effective permeability is converted into the source term of the momentum equation.

[0011] For example, in the porous underground gas storage pore-scale rock salt precipitation prediction method provided in one embodiment, the component transport equation uses the Maxwell-Stefan equation to calculate the multi-component diffusion coefficient.

[0012] For example, in one embodiment of the porous underground gas storage pore-scale rock salt precipitation prediction method, the boundary conditions include the injection-production flow rate and pressure of the injection well and the initial salinity of the formation water.

[0013] For example, in the porous underground gas storage pore-scale rock salt precipitation prediction method provided in one embodiment, the mathematical model is embedded into the computational fluid dynamics simulation platform through a user-defined function.

[0014] A second aspect of this application provides an apparatus for performing the above-described method, comprising a data processing unit, a model building unit, a simulation calculation unit, and a result analysis unit. The data processing unit is configured to acquire and process rock pore structure data to construct a three-dimensional digital core model. The model building unit is configured to establish a multiphase, multi-component transport mathematical model coupled with salt precipitation phase transformation. The simulation calculation unit is configured to import the digital core model and the mathematical model into CFD simulation software, set parameters, and run the simulation. The result analysis unit is configured to extract and analyze the simulation results and output a salt precipitation blockage risk prediction.

[0015] For example, in one embodiment of the apparatus, the simulation calculation unit employs computational fluid dynamics software based on the finite volume method.

[0016] For example, in the apparatus provided in one embodiment, the result analysis unit quantifies the degree of salting-out blockage by calculating a curve of normalized permeability over time.

[0017] The beneficial effects of the method and apparatus for predicting rock salt precipitation at the pore scale in porous underground gas storage provided in some embodiments of this application are as follows: By combining digital core reconstruction, multiphase and multicomponent transport theory, and computational fluid dynamics simulation, this application achieves for the first time a refined and quantitative simulation and prediction of the salt precipitation process in underground gas storage at the pore scale. This method can dynamically display the entire process of salt precipitation, growth, and pore blockage, and accurately quantify the permeability damage caused by salt precipitation. It provides a powerful theoretical tool and technical support for assessing the risk of salt precipitation blockage in gas storage and optimizing injection-production regimes to mitigate salt precipitation, and has the advantages of high prediction accuracy, clear mechanism, and strong applicability. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method for predicting rock salt precipitation at the pore scale in porous underground gas storage in this application; Figure 2 This is a schematic diagram of a three-dimensional porous media digital core mesh model provided in one embodiment; Figure 3The volume fraction cloud map of salt particles in the pores at a certain time step obtained from the simulation; Figure 4 The velocity field contour map of brine in the pores at a certain time step obtained from the simulation; Figure 5 The evolution curve of salt precipitation mass in the pores over time was obtained through simulation. Detailed Implementation

[0020] 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 some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure 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” 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, and these relative positional relationships may change accordingly when the absolute position of the described objects changes.

[0022] This application provides a method for predicting rock salt precipitation at the pore scale in porous underground gas storage, such as... Figure 1 As shown, it includes the following steps: Acquire pore structure data of the target reservoir rock, and construct a three-dimensional digital core model based on the pore structure data; A multiphase, multi-component transport mathematical model describing the salting-out phase transition process is established. The mathematical model is coupled with the salt dissolution-precipitation kinetic equation, the mass conservation equation, the momentum conservation equation, and the component transport equation. The three-dimensional digital core model and the mathematical model were imported into a computational fluid dynamics simulation platform, and boundary conditions and initial conditions were set to simulate the gas storage injection and production conditions. Simulation calculations were performed to obtain data on the volume fraction distribution of salt precipitation, brine flow field distribution, and permeability evolution at the pore scale. Predict reservoir salt precipitation blockage risk based on the aforementioned permeability evolution data.

[0023] Specifically: Step 1: Digital Core Reconstruction First, high-resolution two-dimensional image sequences of the target reservoir rock samples were acquired using micro-CT scanning technology. Image processing algorithms (such as thresholding and median filtering) were then used to extract the rock pore structure information. Finally, three-dimensional reconstruction algorithms (such as the marching cubes algorithm) were employed to generate a three-dimensional digital core model that accurately reflects the pore structure of the rock samples. Figure 2 The diagram shown is a schematic of a three-dimensional porous media digital core mesh model provided in one embodiment. This model accurately characterizes the spatial distribution and connectivity of the pore space, providing a geometric basis for subsequent simulations.

[0024] Step 2: Mathematical Model Construction A multiphase, multi-component transport mathematical model describing the salting-out phase transition process is established, mainly including the following equations: 1. Salt precipitation reaction equation: 2. Calculation of salt volume fraction: Where, Φ sa V represents the volume fraction of precipitated salt particles. salt V is the volume of the precipitated salt grains, and V is the volume of the grid cell.

[0025] 3. Mass balance equation: In the formula, ρ s Let q be the density of the salt grains. s Let represent the amount of salt precipitation. The above equation is numerically solved using a user-defined scalar (UDS) in Fluent software at each time step iteration.

[0026] 4. Salt dissolution-precipitation kinetics equation: In the formula, r is the salt dissolution-precipitation rate, k is the pre-exponential factor, and E a Let T be the activation energy, T be the temperature, R be the universal gas constant, and M be the activation energy. NaCl It is C sa C is the mole fraction of NaCl in water. so This represents the solubility of NaCl.

[0027] 5. Virtual porous media technology: The precipitated solid salt particles are considered as the solid framework of the porous medium, and the brine is considered as the pore space. The porosity of the porous region is expressed as: The source term of the momentum equation for the virtual porous medium region is: In the formula, K e The effective permeability of the virtual porous media region; A sa For the Kozeny-Carman equation constants; ε is a constant introduced to avoid divisibility by 0. In this study, A sa =1.0×10 7 ε=0.001. The virtual porous media technology treats newly precipitated salt particles as a dynamically changing pore structure, enabling real-time simulation of pore structure changes and flow channel blockage caused by salting-out—a crucial step for accurate prediction. By dynamically updating porosity and effective permeability and using them as momentum sources, the macroscopic flow resistance changes caused by salting-out are fed back into the fluid dynamics calculations. This achieves fully coupled simulation of pore structure changes and fluid flow, ensuring the model's computational accuracy.

[0028] 6. Component transport equations: The convective diffusion of NaCl in brine follows the following rules: In the formula, Y i Let S be the mass fraction of the i-th substance. i For substances dissolved in water gas, Let i be the diffusion flux vector of the i-th substance with respect to concentration and temperature gradient: In the formula, D ij Let D be the binary mass diffusion coefficient of substance i in substance j. T is the thermal diffusivity.

[0029] The diffusion coefficient of multiple components is calculated using the Maxwell-Stefan equation and can be expressed as: In the formula, X is the mole fraction of the i-th substance. This refers to the diffusion rate. The Maxwell-Stefan equation is used to calculate multi-component diffusion, which accurately describes the diffusion behavior of ions in brine under multi-component conditions, avoiding errors that may arise from using Fick's law. This method is particularly suitable for high-salinity environments.

[0030] R i The net formation rate of the i-th substance in the reaction is calculated as the N-th substance participating in the reaction. R The sum of the Arrhenius sources of the reaction: In the formula, M w,i Let i be the molecular weight of substance i. Let represent the molar rate of formation / destruction of substance i in reaction r. A kinetic equation based on the Arrhenius formula is employed, which effectively considers the significant influence of temperature on the salting-out reaction rate. This allows the model to more realistically simulate salting-out behavior under varying underground reservoir temperature fields, thus improving prediction accuracy.

[0031] Step 3: Simulation Calculation Settings The above mathematical model was imported into ANSYS Fluent software using a user-defined function (UDF). Boundary conditions were set: injection wells used flow inlet boundaries, corresponding to the gas storage injection-production conditions; production wells used pressure outlet boundaries. Initial conditions were set: the initial salinity distribution was set based on formation water chemical analysis data.

[0032] Setting boundary and initial conditions that match actual injection and production conditions ensures that the simulation environment closely approximates the actual engineering situation, guaranteeing that the predicted results have direct guiding value for field operations. Embedding the model with user-defined functions fully utilizes the powerful flow field calculation capabilities of mature commercial CFD software, while also endowing it with the ability to handle specific salting-out phase transition problems, balancing computational efficiency and model flexibility.

[0033] Step 4: Simulation Execution and Result Analysis Transient simulations were run, solving the coupled flow, mass transfer, and reaction equations at each time step. Post-processing yielded: pore-scale salt particle volume fraction distribution contour maps, brine flow field velocity distribution contour maps, and salting-out mass evolution curves over time.

[0034] Figure 3 To simulate the volume fraction cloud map of salt particles in the pores at a certain time step, Figure 4 To simulate the velocity field contour map of brine in the pores at a certain time step, Figure 5 The curve showing the evolution of salt precipitation mass in the pores over time, obtained from simulation.

[0035] Step 5: Permeability Damage Assessment According to Darcy's law, the evolution of permeability is calculated, and the permeability K is: In the formula, L is the length of the porous medium, and A is the cross-sectional area.

[0036] Normalized permeability is defined as the permeability (K) at time t. t Divide by the absolute permeability (K0) of the porous medium at t=0: By analyzing the trend of normalized permeability over time, the degree of reservoir damage caused by salting out can be quantitatively assessed, and the risk of salting out blockage can be predicted.

[0037] This application presents a method for predicting rock salt precipitation at the pore scale in porous underground gas storage. By constructing a three-dimensional digital core model, it accurately reflects the complex pore structure of underground reservoirs, providing a precise geometric basis for subsequent simulations. Through the establishment of a multiphase, multi-component transport model coupled with salt precipitation kinetics, it precisely describes the precipitation, transport, and deposition processes of salts from a mechanistic perspective. Simulation on a CFD platform quantifies the salt precipitation dynamics and permeability changes at the pore scale, enabling accurate and quantitative prediction of salt precipitation blockage risk and providing a decision-making basis for the safe production and optimized operation of gas storage facilities. This method accurately simulates the dynamic salt precipitation process within pores under different injection and production conditions, and the prediction results show good agreement with experimental data. This method provides an effective technical means for early warning of salt precipitation risks and optimization of injection and production regimes in underground gas storage facilities, which is of great significance for ensuring the safe and efficient operation of gas storage facilities.

[0038] A second aspect of this application provides an apparatus for performing the above-described method, comprising: The data processing unit is configured to acquire and process rock pore structure data and construct a three-dimensional digital core model. The model building unit is configured to establish a multiphase, multicomponent transport mathematical model coupled with salting-out phase transition; The simulation calculation unit is configured to import digital core models and mathematical models into CFD simulation software, set parameters, and run simulations. The results analysis unit is configured to extract and analyze simulation results and output a prediction of salt precipitation blockage risk.

[0039] This device integrates a complete functional chain from data processing to risk analysis, and its effect is to provide an integrated salt precipitation prediction tool, enabling the analysis method to be applied in an engineered and tool-like manner, thus improving its practicality.

[0040] For example, in one embodiment of the apparatus, the simulation calculation unit employs computational fluid dynamics software based on the finite volume method. Using CFD software based on the finite volume method as the core calculation unit ensures the convergence and stability of complex pore flow field calculations and enables efficient handling of large-scale computational problems.

[0041] For example, in the apparatus provided in one embodiment, the result analysis unit quantifies the degree of salting-out blockage by calculating the normalized permeability over time. Quantifying blockage risk by analyzing the normalized permeability evolution curve provides an intuitive and reliable evaluation indicator that is directly related to the gas storage facility's injection and production capacity, facilitating understanding and application of the prediction results by field engineers.

[0042] Although the embodiments of this application have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for this application. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, this application is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for predicting rock salt precipitation at the pore scale in porous underground gas storage, characterized in that, Includes the following steps: Acquire pore structure data of the target reservoir rock, and construct a three-dimensional digital core model based on the pore structure data; A multiphase, multi-component transport mathematical model describing the salting-out phase transition process is established. The mathematical model couples the salt dissolution-precipitation kinetic equation, the mass conservation equation, the momentum conservation equation, and the component transport equation. The three-dimensional digital core model and the mathematical model were imported into a computational fluid dynamics simulation platform, and boundary conditions and initial conditions were set to simulate the gas storage injection and production conditions. Simulation calculations were performed to obtain data on the volume fraction distribution of salt precipitation, brine flow field distribution, and permeability evolution at the pore scale. Predict reservoir salt precipitation blockage risk based on the permeability evolution data.

2. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 1, characterized in that, The salt dissolution-precipitation kinetic equation is constructed based on the Arrhenius formula and is used to calculate the salt dissolution-precipitation rate.

3. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 1, characterized in that, The effect of precipitated salt particles on pore space and fluid transport was simulated using virtual porous media technology. The precipitated solid salt particles were regarded as the solid skeleton of the porous media, and the brine was regarded as the pore space.

4. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 3, characterized in that, The porosity and effective permeability of the virtual porous medium region are dynamically updated by calculating the salt volume fraction, and the effective permeability is transformed into the source term of the momentum equation.

5. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 1, characterized in that, The component transport equations are calculated using the Maxwell-Stefan equations to determine the diffusion coefficients of the multicomponents.

6. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 1, characterized in that, The boundary conditions include the injection and production flow rate and pressure of the injection well, as well as the initial salinity of the formation water.

7. The method for predicting rock salt precipitation at the pore scale in porous underground gas storage according to claim 1, characterized in that, The mathematical model is embedded into the computational fluid dynamics simulation platform through user-defined functions.

8. An apparatus for performing the method according to any one of claims 1-7, characterized in that, include: The data processing unit is configured to acquire and process rock pore structure data and construct a three-dimensional digital core model. The model building unit is configured to establish a multiphase, multicomponent transport mathematical model coupled with salting-out phase transition; The simulation calculation unit is configured to import digital core models and mathematical models into CFD simulation software, set parameters, and run simulations. The results analysis unit is configured to extract and analyze simulation results and output a prediction of salt precipitation blockage risk.

9. The apparatus according to claim 8, characterized in that, The simulation calculation unit uses computational fluid dynamics software based on the finite volume method.

10. The apparatus according to claim 8, characterized in that, The results analysis unit quantifies the degree of salting-out blockage by calculating the normalized permeability change curve over time.

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