A lattice boltzmann-based gas-liquid two-phase nozzle flow simulation method

Abstract

The present application belongs to the technical field of oil and gas field development, and particularly relates to a gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann, which comprises the following steps: S1, constructing a gas-liquid two-phase nozzle flow model, wherein the gas-liquid two-phase nozzle flow model adopts a single-component two-phase pseudo-potential model; S2, simulating the real-time fluid dynamics of the gas phase and the liquid phase in the space before and after the oil nozzle through the gas production rate, the liquid production rate, the production pressure difference and the oil nozzle size, and outputting the pressure, the density and the velocity data at any position in the space. The lattice Boltzmann method has excellent mobility and wide applicability, and by constructing the model in equal proportions and introducing the multiphase lattice Boltzmann method and the boundary processing mode, the real-time dynamic changes of the gas-liquid two-phase before and after the oil nozzle can be simulated.

Description

Technical Field

[0001] This invention belongs to the field of shale gas development technology, and in particular relates to a gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann. Background Technology

[0002] Shale gas production is primarily achieved through fracturing, and the proppant in the fractures is crucial for maintaining flow channels and ensuring stable production in the later stages of shale gas well production. When using nozzles, excessively large nozzles can lead to sand production in the well. This sand erosion affects the safety of the gathering and transportation pipeline network, and proppant backflow impacts the well's ability to maintain stable production in the later stages. Controlling well production can suppress sand production, and the nozzle flow formula can be used to predict well production under different nozzle sizes.

[0003] In the 1960s, ROS derived the nozzle flow formula for downhole throttling based on the energy balance equation. Subsequently, Poettman and Beck made certain modifications to the formula. In 1954, Gilbert first proposed a throttling mechanism model for two-phase flow. He believed that oil pressure and fluid production were directly proportional and proposed a three-parameter relationship applicable to the critical flow state. Later, ROS, Baxendell, Secen, Osman, and others successively used test data to re-merge and correct the empirical coefficients in the relationship. Towailib & Marhounl et al. derived from field data that the total fluid volume and the upstream pressure of the nozzle had a non-linear relationship, thus developing a four-parameter relationship. Ashford established a gas-liquid two-phase nozzle flow mechanism model based on the isentropic expansion of gas. He believed that this model had two major advantages: it considered fluid properties; the model defined the conditions for the transition of gas-liquid two-phase nozzle flow from subcritical to critical flow from an energy perspective, applicable to noncritical states, and could be used to determine the flow regime. Subsequently, Sachdeva and Perkins established a similar model based on the assumption that the gas undergoes polytropic expansion.

[0004] During on-site production, fracturing fluid accompanies shale gas production, forming a two-phase flow of gas and liquid. Using a single-phase flow formula for prediction will result in a significant difference between the predicted and actual production, thus affecting the rational control of production systems and the accurate prediction of gas well production. Using a multi-phase flow formula is more complex, requires more parameters, and some parameters cannot be easily obtained on-site. Summary of the Invention

[0005] To address the shortcomings of existing single-phase and multi-phase nozzle flow prediction formulas, this invention employs the lattice Boltzmann method and introduces a two-phase pseudo-potential model to simulate and quantitatively describe the dynamics of gas-liquid two-phase nozzle flow under different nozzle sizes, inlet and outlet pressure differences, and liquid production rates. A gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann is proposed.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] A gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann includes the following steps:

[0008] S1, Construct a gas-liquid two-phase nozzle flow model, wherein the gas-liquid two-phase nozzle flow model adopts a single-component two-phase pseudo-potential model;

[0009] S2 simulates the real-time fluid dynamics of the gas and liquid phases in the space before and after the nozzle by measuring gas production, liquid production, production pressure difference, and nozzle size, and outputs pressure, density, and velocity data at any location in the space.

[0010] As a preferred embodiment, the gas-liquid two-phase flow model in step S1 includes a fluid model, which includes a single-phase lattice Boltzmann model and a multiphase lattice Boltzmann model.

[0011] As a preferred embodiment, the gas-liquid two-phase flow model in step S1 further includes a boundary model.

[0012] The boundary model includes the construction of the nozzle mesh boundary space and the boundary processing format.

[0013] The boundary processing formats include collision boundary processing format, entrance / exit boundary processing format, and interface adhesion processing format.

[0014] As a preferred embodiment, the construction of the nozzle mesh boundary space includes the following steps:

[0015] S1, Actual production design; S2, simplification of flow space; S3, spatial matrixing; S4, establishment of the LBM flow space model.

[0016] As a preferred embodiment, the collision boundary processing format includes a combined boundary and an unbalanced extrapolation boundary, wherein the combined boundary is a boundary format formed by mixing a non-slip bounce format with a free-slip mirror bounce format;

[0017] The non-equilibrium extrapolation boundary is defined by splitting the distribution function at the boundary node into an equilibrium distribution function and a non-equilibrium distribution function.

[0018] As a preferred embodiment, the inlet / outlet boundary processing format is a pressure boundary; the interface adhesion processing format is non-adhesive, and the wetting angle is 120°.

[0019] As a preferred embodiment, the nozzle size is 3-7mm; the production pressure differential range is 5MPa~50MPa.

[0020] A gas-liquid two-phase flow simulation device based on lattice Boltzmann includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform any of the methods described above.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This paper constructs a nozzle flow model equivalent to the actual flow and introduces the Lattice Boltzmann Method (LBM) to simulate gas-liquid two-phase flow, generating a gas-liquid two-phase nozzle flow chart. This improves the accuracy of production prediction and planning and provides data support for shale gas production allocation and management. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of collision and migration in Embodiment 1 of the present invention;

[0024] Figure 2 This is a diagram showing the composition of the gas-liquid two-phase nozzle flow model in Embodiment 1 of the present invention;

[0025] Figure 3 This is a flowchart of the mouth flow space model construction in Embodiment 1 of the present invention;

[0026] Figure 4 This is a typical boundary diagram in Embodiment 1 of the present invention;

[0027] Figure 5 This is a schematic diagram of the boundary nodes and distribution function in Embodiment 1 of the present invention;

[0028] Figure 6 This is a schematic diagram of the non-equilibrium extrapolation boundary processing format in Embodiment 1 of the present invention;

[0029] Figure 7 This is a flowchart of the model solution process in Embodiment 1 of the present invention;

[0030] Figure 8 This is a schematic diagram of the real-time pressure field of the two-phase nozzle flow model in Embodiment 1 of the present invention;

[0031] Figure 9 This is a schematic diagram of the real-time velocity field of the two-phase nozzle flow model in Embodiment 1 of the present invention;

[0032] Figures 10(a) to 10(e) This is the gas-liquid two-phase nozzle flow pressure difference-daily production curve (3-7mm) in Embodiment 1 of the present invention;

[0033] Figure 11 This is the gas-liquid two-phase nozzle flow pressure difference-daily production comparison curve in Embodiment 1 of the present invention;

[0034] Figure 12 This is the dimensionless pressure difference-daily production comparison curve of the gas-liquid two-phase nozzle flow in Embodiment 1 of the present invention;

[0035] Figure 13(a) is a comparison curve of gas-liquid two-phase nozzle flow production volume and daily production in Example 1 of the present invention;

[0036] Figure 13(b) Comparison curve of water-gas ratio and daily gas production in gas-liquid two-phase nozzle flow;

[0037] Figure 14 This is a comparison curve of the size of the gas-liquid two-phase nozzle and the daily production in Embodiment 1 of the present invention;

[0038] Figures 15(a) to 15(c) This is a two-dimensional / three-dimensional drawing (3-7mm) of the gas-liquid two-phase nozzle flow interpolation in Embodiment 1 of the present invention;

[0039] Figure 16 This is a schematic diagram of the gas-liquid two-phase nozzle flow interpolation chart verification in Embodiment 1 of the present invention;

[0040] Figure 17 This is the verification diagram of the gas-liquid two-phase nozzle flow interpolation diagram in Embodiment 1 of the present invention;

[0041] Figure 18 This is a graph of the gas-liquid two-phase nozzle flow production process in Embodiment 1 of the present invention;

[0042] Figure 19 This is a verification diagram of different production systems during the liquid discharge stage in Embodiment 1 of the present invention. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0044] Example 1

[0045] 1. Multiphase lattice Boltzmann model

[0046] Multiphase or multicomponent lattice Boltzmann models have always been an important area of ​​LBM research. Simulating multiphase porous media flow based on LBM also has multiple advantages, and various models have been developed, such as color models, free energy models, pseudopotential models, and kinetic models. In shale gas reservoirs, describing the gas-liquid two-phase flow usually refers to the natural gas and water two-phase flow, which should be described by constructing a two-component two-phase pseudopotential model. However, in shale gas reservoir seepage, the liquid phase water usually does not undergo phase change. In this case, a single-component two-phase pseudopotential model is constructed to describe the gas-water flow.

[0047] In this single-component two-phase pseudopotential model, the evolution equation of the distribution function is:

[0048]

[0049] In the formula, f α (r, t) is the distribution function in the α direction at position r at time t, δ t e is the time step. α The velocity in the α direction, passing through δ t Then, the particle moves from position r to position r+e. α δ t f α (r+e α δ t ,t+δ t () is time t+δ t Time position r+e α δ t The distribution function at that location, Let τ be the equilibrium distribution function, representing the distribution function under the current equilibrium conditions, and let τ be the relaxation time, representing the speed at which the equilibrium state is reached.

[0050] In standard LBM, all parameters are typically set to 1, and physical quantities are expressed in lattice units. These can be converted to physical units after calculation. When using LBM for fluid simulation, the evolution of fluid particles within the flow field can be broken down into two steps: collision and migration. The collision step is as follows:

[0051]

[0052] The migration steps are as follows:

[0053]

[0054] Fluid particles located at node r collide with each other. During this process, the distribution functions in different discrete velocity directions will be redistributed. After the collision, the particles in different discrete velocity directions will migrate along their respective directions.

[0055] Figure 1 A schematic diagram of collisions and migrations is provided. For example... Figure 1 As shown, within a certain time step, the fluid particles at node r collide with each other and determine the target node for migration, i.e., they move to r+e. α δ t Node r. Simultaneously, other nodes will perform the same operation, and some particles will migrate to node r. After all nodes have completed collisions and migrations, they can proceed to the next time step for evolution. Since the standard Boltzmann method satisfies e αx =δ x / δ t and e αy =δ y / δ tTherefore, r+e α δ t It can strictly guarantee that it is on the grid nodes.

[0056] Because a single-component model is constructed, there are no interactions between components. Different phases of the same component are distinguished by density. Forces are primarily exerted by influencing equilibrium velocities and the equilibrium distribution function; therefore, the evolution equation of this distribution function does not contain external force terms. Using the D2Q9 model, the corresponding equilibrium distribution function is:

[0057]

[0058] In the formula—ω α As the weighting coefficient, we take D2Q9, that is...

[0059] u eq The equilibrium velocity is the distribution function of the equilibrium velocity. In single-phase models, the equilibrium velocity is usually the flow velocity under the corresponding mesh. In this multiphase model, the equilibrium velocity is additionally affected by the force F; e α c represents the lattice discrete velocity. s The speed of sound.

[0060] ρ is the lattice density, and its calculation formula is:

[0061]

[0062] f α It is f α The simplified form of (r, t) is α, which represents the direction. In this paper, the D2Q9 model is used, which is a 2D model with 9 directions.

[0063] The pseudopotential model reflects the interactions between fluids and between fluid and solid structures by influencing the equilibrium velocity; its mechanism of action can be written as follows:

[0064] ρu eq =ρu′+τFδ t (4)

[0065] Where F is the interaction force between particles; δ t τ is the time step, ρ is the relaxation time, and u is the lattice density. eq This is the equilibrium velocity.

[0066] In the formula, u′ is the local velocity, which is defined as follows:

[0067]

[0068] The actual macroscopic velocity of the fluid is the average value before and after the collision.

[0069]

[0070] The interaction forces between particles are key to phase changes, and their expression can be written as:

[0071]

[0072] In the formula, f α (r, t) is the distribution function in the α direction at position r at time t, where r is position, t is time, α is direction, and G is the interaction strength coefficient. When G < 0, the particles attract each other; when G > 0, the interaction force between particles exhibits mutual repulsion. In the single-component model, gas-liquid separation is achieved through attraction of the same component, therefore its value is negative. In this formula, ω α It is also a weighting coefficient, but its value differs from the weighting coefficient used in the distribution function calculation. It is only used to calculate the force, and its value is ω. 1-4 =1 / 3,ω 5-8 = 1 / 12. ψ(r,t) is the effective density, a density-related function whose expression is constrained by Maxwell's thermodynamic theory and can only take certain fixed forms. A commonly used form is...

[0073]

[0074] ψ(r,t) represents the effective density, p represents the lattice density corresponding to the concept of effective density, and ρ0 represents the intermediate parameter generated by the conversion between lattice units and physical units using the lattice Boltzmann method. ρ0 is a constant, often taken as 1 in simulations. Rearranging the above equation yields...

[0075]

[0076] The corresponding state equations can be obtained using Chapman-Enskog expansion.

[0077]

[0078] c = δ x / δ t δ x Let δ be the lattice step size. t For time step.

[0079] 2. Gas-liquid two-phase flow model and boundary treatment

[0080] The lattice Boltzmann model typically consists of a fluid component and a boundary component, which respectively describe the fluid motion rules, boundary distribution, and collision rules. A gas-liquid two-phase flow model is composed of, for example... Figure 2 As shown.

[0081] 1) Construction of the boundary space of the nozzle mesh

[0082] For flow problems, the form and treatment of boundaries can significantly impact the accuracy, stability, and efficiency of computational results. The lattice Boltzmann method offers significant advantages in versatility and independence, allowing for the selection of different boundary construction and treatment methods for various application scenarios. Furthermore, the spatial construction and boundary conditions of the boundaries are independent of the flow process and processing.

[0083] Based on the boundary treatment characteristics of the lattice Boltzmann method, we can construct a nozzle flow space model by simplifying the spatial structure and matrixing the space, according to the actual parameters of the nozzle and nozzle sleeve on site. The flowchart of the nozzle flow space model construction is as follows. Figure 3 As shown, the nozzle flow space model is a spatial distribution matrix imported into the flow model. Specifically, it is a numerical matrix filled with 0s and 1s to distinguish between the fluid flow space and the non-flow space (boundary) within a certain spatial range. The nozzle flow space model construction process is as follows: 1. Import the actual design drawing of the nozzle; 2. Remove extra elements from the design drawing, leaving only the flow space and the walls that prevent flow (simplification); 3. Convert the simplified design drawing into a numerical matrix; 4. Binarize the numerical matrix, with a value of 0 representing the flow space and a value of 1 representing the non-flow wall boundary.

[0084] 2) Boundary processing format

[0085] In each time step, we need to receive data from the previous time step and calculate the data for the next time step. For the internal flow field, each node can calculate its distribution function through interactions with surrounding nodes. However, for nodes near the boundary, it is necessary to determine how to obtain collision feedback at the boundary using a reasonable method. Boundary processing is then used to obtain the complete distribution function of the nodes. In this process, the nodes subject to boundary processing are called the boundary processing layer, the methods used are called boundary processing methods, and the calculation rules formulated for different situations are called boundary processing formats. A typical boundary diagram is shown below. Figure 4 As shown, Figure 4 Two typical boundaries are marked: straight boundaries and curved boundaries. The thickness of the boundary treatment layer and the distribution of the boundary layer for curved boundaries will affect the boundary treatment.

[0086] For this gas-liquid two-phase nozzle flow model, two types of boundaries are mainly used: heuristic boundaries and extrapolated boundaries. Heuristic boundaries mainly adopt the combined boundary form (BSR) based on bounce boundaries and specular reflection boundaries, while extrapolated boundaries mainly use the non-equilibrium extrapolated boundary processing scheme. The combined boundary (BSR) is a boundary scheme that combines the no-slip bounce scheme and the free-slip specular bounce scheme.

[0087] A schematic diagram of boundary nodes and distribution functions is shown below. Figure 5 As shown, Figure 5In the diagram, taking the boundary node (i,1) as an example, for ease of description, node x0 represents the boundary node (i,1), and the remaining nodes are marked in the figure, with nodes x1-x8 representing the eight adjacent nodes surrounding the boundary node (i,1). For (i,1), the distribution function f 2,5,6 Unknown, regarding BSR format

[0088]

[0089] In the formula, r is the ratio coefficient between reflective and specular reflection (range: 0 to 1). The larger r is, the smaller the proportion of specular reflection. When r = 0, the BSR format is converted to the specular reflection format; when r = 1, the BSR format is converted to the reflective format.

[0090] Nonequilibrium extrapolation boundary conditions decompose the distribution function at the boundary nodes into equilibrium and nonequilibrium distribution functions. The equilibrium distribution function is obtained through the definition of the boundary conditions, while the nonequilibrium distribution function is obtained through nonequilibrium extrapolation.

[0091] A schematic diagram of the unbalanced extrapolation boundary processing format is shown below. Figure 6 As shown, Figure 6 In this diagram, taking the boundary node (i,1) as an example, for ease of description, node 0 represents the boundary node (i,1), and the remaining nodes are marked in the figure, with nodes 1-8 representing the eight adjacent nodes surrounding the boundary node (i,1). Nodes 3, 0, and 1 are boundary nodes, while nodes 6, 2, and 5 are located within the flow field. Before the collision step at time step t, we need to know the distribution function f at node 0. α (0,t) is decomposed into equilibrium and non-equilibrium states, i.e.

[0092]

[0093] Equilibrium part The equilibrium distribution function at node 0 is obtained using macroscopic physical quantities at node 0. When the macroscopic physical quantities at node 0 are insufficient, the macroscopic physical quantities at node 2 can be used as an approximation. For example, if the boundary is a velocity boundary and the velocity at boundary node 0 is u(0,t), but the density ρ(0,t) is unknown, then the approximate equilibrium distribution function at node 0 is...

[0094]

[0095] Where Feq is the local equilibrium distribution function, the derivation and expression of which can be found in the previous section.

[0096] For the non-equilibrium part We need to first solve for the non-equilibrium distribution function of node 2. Since the velocity u(2,t) and density ρ(2,t) of node 2 are both known quantities, then

[0097]

[0098] At the same time, due to and The following relationship exists

[0099]

[0100] In the formula—δ x This represents the spatial step size.

[0101] At second-order precision, it can be used replace have to

[0102]

[0103] After the collision, the distribution function at node 0 for

[0104]

[0105] 3) Gas-liquid two-phase nozzle flow model

[0106] The combined fluid simulation and boundary model yields a gas-liquid two-phase nozzle flow model, with the following structure:

[0107] Table 1. Composition of the Gas-Liquid Two-Phase Nozzle Flow Model

[0108] Fluid simulation Multiphase lattice Boltzmann model Spatial boundary Proportional oil nozzle sleeve model Collision boundary Combined boundary, non-equilibrium extrapolation boundary Entry and exit boundaries Pressure boundary Interface adhesion Non-adhesive (wetting angle 120°)

[0109] 3. Gas-Liquid Two-Phase Nozzle Flow Chart and Application

[0110] 1) Solving the gas-liquid two-phase flow model

[0111] The gas-liquid two-phase nozzle flow model is a complex fluid system in which four parameters—gas production rate, liquid production rate, production pressure difference, and nozzle size—interact with each other. By introducing the lattice Boltzmann two-phase pseudo-potential model and combining it with the constructed gas-liquid two-phase nozzle flow model, a MATLAB program is used to solve the problem. The key parameters of the model are shown in the table below:

[0112] Table 2 Main Parameters of the Model

[0113]

[0114] The model solution flowchart is as follows Figure 7As shown, the main process of model solving is: reading the model, initializing parameters, boundary processing, collision migration, and statistical recording. The specific steps are: S101. Import the pre-built proportional nozzle sleeve space model to clarify the fluid flow spatial distribution; S102. Read the space model parameters, initialize the mesh space and fluid parameters, and complete the flow simulation preparation; S103. Process the fluid at the outlet and inlet boundaries to keep the pressure values ​​corresponding to the mesh stable (constant pressure boundary format); S104. Execute the collision and migration steps of the lattice Boltzmann method to obtain the fluid state at the next time step; S105. Statistically record the parameters within the mesh; S106. Iterate to the next time step until the termination condition is met; S107. Visualize and store the data. By specifying the nozzle size, production pressure difference, and production volume, the real-time fluid dynamics of the gas and liquid phases in the space before and after the nozzle under these conditions can be simulated. The principle of simulating real-time fluid dynamics is: each time step obtains the flow field at that time step. By continuously simulating the flow fields at different time steps, the dynamic changes in the flow field can be formed, which is the real-time fluid dynamics. The simulation method outputs pressure, density, and velocity data at different time steps within the simulation space, and then visualizes these data to output a dynamic video. A schematic diagram of the real-time pressure field in the two-phase flow model is shown below. Figure 8 As shown in the figure, the real-time velocity field diagram of the two-phase flow model is as follows: Figure 9 As shown.

[0115] 2) Gas-liquid two-phase flow diagram

[0116] Adjusting the nozzle size, production pressure difference, and liquid production parameters can simulate the gas-liquid two-phase nozzle flow under different conditions. After reaching steady state, data is collected and compiled into gas-liquid two-phase nozzle flow graphs. The pressure difference-daily production curves for 3mm gas-liquid two-phase nozzle flow are shown in Figure 10(a), 4mm gas-liquid two-phase nozzle flow pressure difference-daily production curves are shown in Figure 10(b), 5mm gas-liquid two-phase nozzle flow pressure difference-daily production curves are shown in Figure 10(c), 6mm gas-liquid two-phase nozzle flow pressure difference-daily production curves are shown in Figure 10(d), and 7mm gas-liquid two-phase nozzle flow pressure difference-daily production curves are shown in Figure 10(e).

[0117] Under two-phase flow of gas and liquid, the output is positively correlated with the pressure difference and the size of the nozzle, and negatively correlated with the liquid output. The overall trend of the high-pressure section is similar to that of the pure gas phase, while the low-pressure section shows significant differences.

[0118] A comprehensive comparison chart of gas-liquid two-phase nozzle flow pressure difference and daily production curves is shown below. Figure 11 As shown, Figure 11 The data shows that gas production is nearly linearly related to the production pressure difference, increasing with the increase of the production pressure difference. The dimensionless pressure difference-daily production comparison curve for the gas-liquid two-phase nozzle flow is shown below. Figure 12As shown, further analysis of the dimensionless daily gas production calculated from the pure gas phase daily production shows that the ratio of the daily production of the gas-liquid two-phase nozzle to the daily production of the pure gas phase increases as the nozzle size decreases. That is, under the same production pressure difference and liquid production, the smaller nozzle is less affected by the liquid phase.

[0119] Comparing the changes in liquid production and gas production under different production pressure differentials and nozzles: the gas production shows a non-linear and rapid decreasing trend as the liquid production increases. The comparison curves of liquid production and daily production for the gas-liquid two-phase nozzle flow are shown in Figure 13(a), and the comparison curves of water-gas ratio and daily gas production are shown in Figure 13(b).

[0120] Gas-liquid two-phase nozzle oil nozzle size - Nissan comparison curve as shown Figure 14 As shown, comparing the changes in nozzle size and daily gas production under different liquid production rates, it can be concluded that: the two-phase gas production and the gas phase gas production gradually approach each other as the liquid production decreases; under small nozzle size, the daily production of the two phase and the daily production of the gas phase have similar trends, but as the nozzle size increases, the trends of the two phases gradually show significant differences, with the daily production of the gas phase increasing rapidly with the increase of the nozzle size, while the daily production of the gas-liquid two phases increases more slowly.

[0121] 3) Verification of the gas-liquid two-phase flow chart

[0122] Interpolation was performed on the gas-liquid two-phase flow curve chart to obtain two-dimensional / three-dimensional gas-liquid two-phase flow charts. Figure 15 shows the interpolated two-dimensional / three-dimensional gas-liquid two-phase flow charts (3–7 mm). Figure 15(a) shows the two-dimensional flow production-wellhead pressure charts for 3 mm and 4 mm nozzles, Figure 15(b) shows the two-dimensional flow production-wellhead pressure charts for 5 mm and 6 mm nozzles, and Figure 15(c) shows the two-dimensional flow production-wellhead pressure chart for 7 mm nozzles, as well as a three-dimensional flow production-wellhead pressure-gas production chart for the gas-liquid two-phase nozzle. Actual gas well production test data was imported for comparison and verification. The verification results show that the production data points are distributed near the surface of the chart (see the schematic diagram of the gas-liquid two-phase flow interpolation chart verification). Figure 16 As shown), the production data and the drawing data have a consistency rate of 87.2%, indicating that the drawing has high accuracy (the gas-liquid two-phase nozzle flow interpolation drawing verification is as follows). Figure 17 (As shown).

[0123] 4) Application of gas-liquid two-phase flow chart

[0124] The reasonable production regime for the gas well transportation and liquid discharge stage can be determined by combining the critical sand-carrying flow rate method with the gas-liquid two-phase flow chart (the recommended production regime is shown in Table 3).

[0125] When the bottom hole pressure is 70 MPa, the critical sand-carrying flow rate is approximately 95,000 cubic meters per day. Taking the initial production of the gas well as 200 cubic meters per day as an example, the gas and fluid production data and pressure data are imported into the three-dimensional gas-liquid two-phase nozzle flow chart. The corresponding nozzle size is 4.5 mm. Therefore, it is recommended that the discharge nozzle size be 3-4 mm in the initial stage of gas well opening. Similarly, after the gas well pressure drops by 10 MPa, the critical sand-carrying flow rate is calculated to be approximately 90,000 cubic meters per day, and the calculated nozzle size is 5.5 mm. It is recommended that the discharge nozzle size be <5 mm.

[0126] Table 3 Production System Recommendations

[0127]

[0128] Based on the proposed production system recommendations for the gas-liquid two-phase flow stage, the production status of gas wells with different nozzles for fluid discharge was tracked. In well WY24-1HF, fluid discharge with a 3-4mm nozzle resulted in no sand production; increasing the nozzle to 5mm resulted in slight sand production; further increasing to a 6mm nozzle led to continuous sand production and sand blockage. In well WY26-1HF, continuous fluid discharge with a 3-4mm nozzle resulted in no sand production. The production system curves for the gas-liquid two-phase flow are shown in the figure below. Figure 18 As shown.

[0129] The production performance under different nozzles during the gas well's gas transmission and fluid discharge stages is consistent with the expected gas well production under the two-phase nozzle flow chart (verification chart of different production regimes during the fluid discharge stage is shown below). Figure 19 (As shown).

[0130] 4. Conclusion

[0131] 1) The lattice Boltzmann method has excellent transferability and wide applicability. By constructing a model on an equal scale and introducing the multiphase lattice Boltzmann method and boundary treatment, the real-time dynamic changes of the gas and liquid phases before and after the nozzle can be simulated.

[0132] 2) Based on a large number of simulation results, gas-liquid two-phase nozzle flow charts with different pressure differences, different liquid production rates, and different nozzle sizes are generated. These charts can accurately describe the complex linkage between pressure difference, liquid production rate, nozzle size, and gas production rate under two-phase flow. Based on the curve charts, gas-liquid two-phase nozzle flow interpolation charts are generated, and the accuracy of the charts is verified to be 87.2%.

[0133] 3) The typical two-phase nozzle flow characteristics were clarified: the output is positively correlated with the pressure difference and nozzle size, and negatively correlated with the liquid output. The gas output decreases rapidly and nonlinearly as the liquid output increases. Under the same production pressure difference and liquid output, the smaller nozzle is less affected by the liquid phase.

[0134] 4) Based on the critical sand-carrying flow rate method and the three-dimensional nozzle flow chart, we proposed a production system recommendation for the gas well gas transportation and liquid discharge stage, and verified its effectiveness in combination with actual production application.

[0135] The foregoing has shown and described the basic principles and main features of the present invention, as well as its advantages. It will be apparent to those skilled in the art that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description, and all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0136] Furthermore, it should be understood that although this specification describes the embodiments, the embodiments do not necessarily contain only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in the embodiments can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for simulating gas-liquid two-phase nozzle flow based on lattice Boltzmann, characterized in that, Includes the following steps: S1, Construct a gas-liquid two-phase nozzle flow model, wherein the gas-liquid two-phase nozzle flow model adopts a single-component two-phase pseudo-potential model; S2 simulates the real-time fluid dynamics of the gas and liquid phases in the space before and after the nozzle by measuring gas production, liquid production, production pressure difference, and nozzle size, and outputs pressure, density, and velocity data at any position in the space; adjusting the nozzle size, production pressure difference, and liquid production parameters can simulate the gas-liquid two-phase nozzle flow under different conditions, and after reaching steady state, the data is collected and organized to form a gas-liquid two-phase nozzle flow chart. The gas-liquid two-phase flow model described in step S1 also includes a boundary model. The boundary model includes the construction of the nozzle mesh boundary space and the boundary processing format. The boundary processing format includes collision boundary processing format, entrance / exit boundary processing format and interface adhesion processing format; The construction of the nozzle mesh boundary space includes the following steps: S1, Import the actual design drawing of the nozzle; S2, remove extra elements from the design drawing, leaving only the flow space and the walls that prevent the flow, thus forming a simplified design drawing; S3 transforms the simplified design diagram into a numerical matrix; S4. Binarize the numerical matrix, with 0 representing the flow space and 1 representing the non-flowing wall boundary. The collision boundary processing format includes a combined boundary and an unbalanced extrapolation boundary. The combined boundary is a boundary format formed by mixing a non-slip bounce format with a free-slip mirror bounce format. The non-equilibrium extrapolation boundary is defined by splitting the distribution function at the boundary node into an equilibrium distribution function and a non-equilibrium distribution function; The steps for constructing the gas-liquid two-phase flow model include: S101. Import the pre-constructed proportional nozzle sleeve space model to clarify the spatial distribution of fluid flow; S102. Read the spatial model parameters, initialize the mesh space and fluid parameters, and complete the preparation for flow simulation; S103. Process the fluid at the outlet and inlet boundaries to keep the pressure values ​​corresponding to the grid stable; S104. Perform the collision and migration steps of the lattice Boltzmann method to obtain the fluid state at the next time step; S105. Collect and record the parameters within the grid; S106. Determine the termination condition. If the termination condition is not met, return to step S103; otherwise, proceed to the next step. S107. Visualize and store the data.

2. The gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann as described in claim 1, characterized in that, The inlet / outlet boundary processing format is pressure boundary; the interface adhesion processing format is non-adhesive, and the wetting angle is 120°.

3. The gas-liquid two-phase nozzle flow simulation method based on lattice Boltzmann as described in claim 2, characterized in that, The nozzle size is 3-7mm; the production pressure differential range is 5MPa~50MPa.

4. A gas-liquid two-phase flow simulation device based on lattice Boltzmann, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 3.

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