Volcanic gas reservoir productivity prediction method and system based on digital core model
The method for predicting the production capacity of volcanic gas reservoirs based on digital core models solves the problem of large prediction errors and achieves high-precision production capacity evaluation.
Patent Information
- Application Number
- CN202410906332.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-08
- Publication Date
- 2026-01-09
AI Technical Summary
Existing technologies lack mathematical models and production capacity prediction methods for complex multi-medium seepage in volcanic gas reservoirs, resulting in large and inaccurate production capacity prediction errors.
Based on the digital core model, a productivity evaluation model for volcanic gas reservoirs is established by acquiring three-dimensional images, phase segmentation, microscopic two-phase gas-water flow simulation, absolute permeability measurement, and stress sensitivity effect analysis. This model includes image processing, two-phase flow simulation, absolute permeability acquisition, and effective permeability calculation.
It improves the accuracy of production capacity prediction for volcanic gas reservoirs, with a theoretical production error of less than 3%, effectively guiding the development of complex gas reservoirs.
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Figure CN121302944A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of exploration technology, specifically to a method and system for predicting the production capacity of volcanic gas reservoirs based on digital core models. Background Technology
[0002] Compared to conventional gas reservoirs, volcanic gas reservoirs exhibit more complex geological conditions, diverse reservoir rock types, and a greater number of controlling factors. Their reservoir space characteristics and microscopic pore-throat matching relationships are complex, resulting in significant variations in physical properties, strong heterogeneity, deep burial, and high concealment, making them unique and uncertain. The complex internal pore structure and inherent strong heterogeneity contribute to the complexity of their seepage processes, impacting production. Currently, there is a lack of mathematical models for seepage and production prediction methods applicable to the complex multi-medium nature of volcanic gas reservoirs. To further analyze the influencing factors in the production process of volcanic reservoirs, in-depth research is needed on microscopic pore-throat characteristics and seepage mechanisms, combined with necessary production capacity assessments. This will allow for a combination of microscopic and macroscopic understanding, enabling a synergistic analysis of the development prospects of volcanic reservoirs.
[0003] In the prior art, patent CN111624147A discloses a method and apparatus for determining the relative permeability of rock cores. The method includes: obtaining image data of a rock core sample; constructing a three-dimensional pore network structure model of the rock core sample based on the image data, the three-dimensional pore network structure model including multiple pore networks of the rock core sample; constructing a three-dimensional digital rock core model based on the three-dimensional pore network structure model of the rock core sample, the three-dimensional digital rock core model including pore structure data of the rock core sample at different scales; establishing multiphase LBM models at different scales based on the three-dimensional digital rock core model; and simulating the two-phase fluid in the rock core displacement process using the multiphase LBM models at different scales to obtain the relative permeability of the two-phase fluid at different scales of the rock core. This invention can achieve high accuracy in determining the relative permeability of rock cores, especially ultra-low permeability rock cores.
[0004] The aforementioned patents used digital core technology to determine the relative permeability of cores, but there is still a gap in the application of digital core technology to evaluate the production capacity of complex volcanic gas reservoirs. Summary of the Invention
[0005] The purpose of this invention is to overcome the problems of large errors and inaccurate prediction of oil and gas reservoir production capacity in the prior art. It proposes a method and system for predicting the production capacity of volcanic rock gas reservoirs based on digital core models. The invention uses digital core technology to predict the production capacity of volcanic rock gas reservoirs, thereby improving the accuracy of production capacity prediction.
[0006] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:
[0007] A method for predicting the production capacity of volcanic gas reservoirs based on digital core models includes the following steps:
[0008] A three-dimensional image of the corresponding digital core is obtained, and the porosity phase is determined by phase segmentation of the three-dimensional image.
[0009] Microscopic two-phase gas-water flow simulation was carried out on the determined pore phase digital core to obtain the relative permeability curves of gas and water, and the absolute permeability of the core in different directions was determined based on the lattice Boltzmann method.
[0010] The relative permeability of gas and water is determined by the water saturation obtained from the well logging data in the relative permeability curve, and the effective permeability of gas and water is obtained by multiplying the corresponding relative permeability of gas and water by the absolute permeability.
[0011] Considering stress sensitivity and high-speed non-Darcy effect, a productivity evaluation model for volcanic gas reservoirs under different well types and completion methods is established. The obtained gas and water effective permeability is substituted into the productivity evaluation model for productivity evaluation.
[0012] Furthermore, the acquisition of the corresponding three-dimensional image of the digital core and the determination of the pore phase after phase segmentation of the three-dimensional image specifically includes: acquiring the three-dimensional image data of the digital core, extracting the characterization unit that reflects the overall pore property distribution of the core by analyzing the three-dimensional image data, filtering the obtained characterization unit image to obtain a clearer image, binarizing the image to extract the pore phase, and finally reconstructing the three-dimensional image to obtain a digital core containing only the pore phase.
[0013] Furthermore, the method of obtaining the relative permeability curves of gas and water by conducting microscopic two-phase gas-water flow simulation on the determined porous phase digital core includes: obtaining gas and water physical property parameters under high temperature and high pressure, simulating microscopic gas-water two-phase flow using the fluid volume method, tracking the interface by solving the volume conservation equation, solving the flow using the Navier-Stokes equation, obtaining the inlet and outlet pressure difference, cumulative gas production, and cumulative liquid production at different times, calculating the relative permeability of water and gas, and finally obtaining the gas-water relative permeability curve with water saturation as the abscissa and permeability as the ordinate.
[0014] Furthermore, in the determination of absolute permeability of the core in different directions based on the lattice Boltzmann method, the LBM method is used, and the D3Q19 discrete velocity model is employed to simulate the permeability of the three-dimensional digital core, thereby obtaining the absolute permeability of the core.
[0015] Furthermore, the relative permeability k of water rw The relative permeability k of the air rg Calculate according to the following formula:
[0016]
[0017] In the formula, f g (S w ) represents the gas content; V g (t) represents the dimensionless cumulative gas production; V(t) represents the dimensionless liquid production; I represents the relative injection capacity; Q o ΔP represents the initial gas production flow rate at the rock sample outlet face; Q(t) represents the initial liquid production flow rate at the rock sample outlet face; ΔP o The initial driving pressure difference; ΔP(t) is the displacement pressure difference at time t, μ w The viscosity of water, μ g The viscosity of the gas.
[0018] Furthermore, the formula for calculating the production capacity equation considering stress-sensitive effects and high-speed non-Darcy effects is as follows:
[0019]
[0020] In the formula, q sc —Gas flow rate under standard conditions, 10 4 m 3 / d, i.e., production capacity; K0—original permeability of the gas layer, mD, i.e., effective permeability k. g h—reservoir thickness, m; T sc Temperature, 273.15K; p sc —Standard pressure, 0.101 MPa; T—Formation temperature, K; p i — Formation pressure, MPa; —Formation pseudo-pressure, bottom hole pseudo-pressure, MPa 2 / (mPa·s); α—stress sensitivity coefficient, MPa -1 ;r e r w —Discharge radius, wellbore radius, m; M g — Gas molecular weight, kg / mol; R — Gas constant, 8.314 × 10 -6 MPa·m 3 / (mol·K); S—epidermal coefficient.
[0021] This invention also provides a volcanic gas reservoir productivity prediction system based on a digital core model, comprising:
[0022] The image processing module is used to acquire a three-dimensional image of the corresponding digital core and to determine the pore phase after phase segmentation of the three-dimensional image;
[0023] The two-phase flow simulation module is used to simulate the microscopic two-phase gas-water flow in porous digital cores and obtain the relative permeability curves of gas and water.
[0024] The absolute permeability acquisition module is used to determine the absolute permeability in different (discrete) directions based on the lattice Boltzmann method.
[0025] The effective permeability acquisition module is used to determine the relative permeability of gas and water in the relative permeability curve based on the water saturation obtained from the well logging data, and to obtain the effective permeability of gas and water by multiplying the relative permeability and the absolute permeability.
[0026] The production capacity establishment module is used to establish production capacity evaluation models for volcanic gas reservoirs under different well types and completion methods, taking into account stress sensitivity and high-speed non-Darcy effect.
[0027] The production capacity evaluation module is used to calculate the predicted production capacity of volcanic gas reservoirs by substituting the effective permeability into the production capacity evaluation model.
[0028] Furthermore, the image processing module is specifically used to scan the core using Micro-CT to obtain image data. By analyzing the image data, it extracts characterizing units that can reflect the overall pore property distribution of the core. The obtained characterizing unit images are filtered to obtain images with higher clarity. The images are then binarized to extract the pore phase. Finally, a three-dimensional reconstruction is performed to obtain a digital core containing only the pore phase.
[0029] Furthermore, the absolute permeability acquisition module is specifically used to acquire the gas-water physical property parameters under high temperature and high pressure. It uses the fluid volume method to simulate the microscopic gas-water two-phase flow, and performs interface tracking by solving the volume conservation equation. The flow is solved using the Navier-Stokes equation to obtain the inlet and outlet pressure difference, cumulative gas volume, and cumulative liquid volume at different times. The relative permeability of water and gas is obtained by calculation, and finally, a gas-water relative permeability curve is obtained with water saturation as the abscissa and permeability as the ordinate.
[0030] Furthermore, the two-phase flow simulation module is specifically used to simulate the permeability of a three-dimensional digital core using the D3Q19 discrete velocity model, thereby obtaining the absolute permeability of the core.
[0031] In summary, the present invention has the following advantages:
[0032] 1. The volcanic gas reservoir production prediction method based on digital core model described in this invention verifies the production capacity of gas wells by using the permeability obtained by the microscopic digital core method. By comparing the well test interpretation data and the calculated data of digital core, the theoretical production error obtained is less than 3%, which has a good guiding effect on the production capacity evaluation of complex volcanic gas reservoirs.
[0033] 2. The volcanic gas reservoir production prediction method based on digital core model described in this invention obtains the relative permeability curves of gas and water through two-phase flow simulation. The simulation process considers the gas and water physical properties under high temperature and high pressure to obtain dynamic relative permeability curves. In the face of the complex volcanic gas reservoir, the stress-sensitive effect is also taken into account under high pressure conditions when establishing the production capacity evaluation model, which improves the accuracy of production capacity prediction. Attached Figure Description
[0034] Figure 1 This is a schematic diagram illustrating the implementation steps of the present invention;
[0035] Figure 2 This is a technical roadmap for obtaining core pore phase using CT in this invention;
[0036] Figure 3 This invention provides a technical roadmap for obtaining effective penetration rates based on the VOF and LBM methods.
[0037] Figure 4 This is a gas well productivity evaluation diagram under different completion methods according to the present invention;
[0038] Figure 5 This is a comparison chart of the digital core calculation productivity results of this invention. Detailed Implementation
[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0040] Example 1
[0041] This embodiment discloses a method for predicting the production capacity of volcanic gas reservoirs based on digital core models, including the following steps:
[0042] Step 1: Obtain three-dimensional images of the corresponding digital core through computed tomography (CT) scanning, and determine the pore phase after phase segmentation of the three-dimensional images.
[0043] like Figure 2 As shown, this step utilizes Micro-CT to scan the core and obtain image data. By analyzing the image data, characterizing units reflecting the overall porosity distribution of the core are extracted. The obtained characterizing unit images are filtered to obtain clearer images, then binarized to extract the porosity phase. Finally, a 3D reconstruction is performed to obtain a digital core containing only the porosity phase. Since CT images exhibit differences in grayscale values under different density conditions, the porosity phase, being the region with the lowest density, appears darkest in color when imaged. Based on this rule, different thresholds are set, and the phase with the lowest threshold is the porosity phase.
[0044] Step 2, as follows Figure 3 As shown, the relative permeability curve was obtained by performing microscopic gas-water two-phase flow simulation on the pore phase digital core using the VOF method, and the permeability of the core in different directions was determined based on the lattice Boltzmann method to obtain the effective permeability of the core.
[0045] (1) Obtaining absolute penetration rate
[0046] Absolute permeability is obtained by simulating the permeability of a three-dimensional digital core using the LBM method and the D3Q19 discrete velocity model. The basic evolution equation is shown below:
[0047]
[0048] In the formula, f α It is the velocity distribution function along the discrete direction α, where α = 0, 1, 2, ..., 18. r represents the spatial position of the node, e α The vector expression representing the discrete direction α, δ t Let t represent the time step and τ represent the relaxation time. It is the equilibrium distribution function.
[0049] In the D3Q19 model, the vectors in the 19 discrete directions can be represented as:
[0050]
[0051] The relaxation time τ represents the average time interval between two collisions, and its value is related to the fluid viscosity υ, which can be expressed as:
[0052]
[0053] The equilibrium distribution function is the distribution function of fluid particles when the system reaches equilibrium. For an incompressible fluid driven by pressure, its equilibrium distribution function is expressed as:
[0054]
[0055] In the formula, ρ0 is the fluid density, ρ = ρ0 + δρ, where δρ represents the local fluctuation of fluid density. u is the macroscopic velocity, and c s It is the speed of sound in a lattice, with a value of w α It is the weight coefficient in the α direction, and its expression is:
[0056]
[0057] To ensure the model satisfies the Navier-Stokes equations for incompressible fluids, pressure is treated as an independent variable in the calculation, and an equilibrium distribution function for pressure is introduced:
[0058]
[0059] In the formula, The evolution equation for pressure is as follows:
[0060]
[0061] The macroscopic pressure and macroscopic velocity at each grid point in the model can be expressed as:
[0062]
[0063]
[0064] When simulating the inherent permeability of digital cores, the wall surface is typically treated as a no-slip boundary condition. The bounce scheme is a commonly used scheme for handling no-slip boundaries. When fluid particles reach the solid boundary from the pore space, they will return to the pore space along the original path. Thus, the normal velocity of the fluid at the solid boundary can be zero. The evolution equation after the collision between the fluid and the solid boundary is:
[0065] f α (r b ,t)=f α (r p ,t)(0.4)
[0066] In the formula, r b Represents a solid wall node, r p α' represents a pore space node near a solid wall, with α' and α' being two directions opposite to each other.
[0067] Since fluid flow is driven by pressure, periodic pressure boundary conditions are set at the left and right boundaries, with a fixed pressure gradient.
[0068] In this embodiment, all the above variables are dimensionless lattice units. During flow simulation, if the difference in macroscopic fluid velocity between two consecutive time steps is less than 10... -6 This indicates that the system has reached equilibrium, and the simulation terminates. At this point, the macroscopic flow rate can be calculated using the area integral of the velocity at each node on the outlet face:
[0069]
[0070] In the formula, R e The resolution of the image is represented in μm. Finally, the permeability of the shale digital core is calculated using the Cadbury law:
[0071]
[0072] In the formula, U is the flow rate at the outlet, A represents the cross-sectional area at the outlet, and k is the absolute permeability (μm). 2 .
[0073] (2) Obtaining relative penetration rate
[0074] The gas-water physical properties under high temperature and high pressure were obtained, and the flow of the microscopic gas-water two-phase flow was simulated using the Volume of Fluid (VOF) method. The interface was tracked by solving the volume conservation equation, and the flow was solved using the Navier-Stokes equation. The inlet and outlet pressure difference, cumulative gas production, and cumulative liquid production at different times were obtained. The relative permeability of water and gas was then calculated using the following method. Finally, the gas-water relative permeability curve was obtained with water saturation as the abscissa and relative permeability as the ordinate.
[0075] The relative permeability of water, k rw The relative permeability k of the air rg Calculate using the following formula:
[0076]
[0077] In the formula, f g (S w ) represents the gas content; V g (t) represents the dimensionless cumulative gas production; V(t) represents the dimensionless liquid production; I represents the relative injection capacity; Q o ΔP represents the initial gas production flow rate at the rock sample outlet face; Q(t) represents the initial liquid production flow rate at the rock sample outlet face; ΔP o The initial driving pressure difference; ΔP(t) is the displacement pressure difference at time t, μ w The viscosity of water, μ g The viscosity of the gas.
[0078] (3) Calculation of effective penetration rate
[0079] The relative permeability of gas and water is determined by the water saturation obtained from well logging data in the relative permeability curve, and the effective permeability is calculated based on the product relationship between relative permeability and absolute permeability.
[0080]
[0081] k rw k rg These are the relative permeabilities of water and air, respectively; k w k gLet be the effective permeability of water and air, respectively, and k be the absolute permeability of the rock. The final effective permeability can be obtained using Formula 1.13.
[0082] Step 3: Well test interpretation and productivity evaluation based on digital core data
[0083] (1) Considering stress sensitivity and high-speed non-Darcy effect, a productivity evaluation model for volcanic gas reservoirs under different well types and completion methods is established, such as... Figure 4 The paper presents two radial flow models for casing perforation that penetrates the contaminated zone, casing perforation that does not penetrate the contaminated zone, open-hole perforation that penetrates the contaminated zone, and open-hole perforation that does not penetrate the contaminated zone.
[0084] Stress sensitivity effect analysis method:
[0085] As pore fluids are produced in volcanic reservoirs, pore pressure decreases. However, since the pressure from the overlying strata remains constant, the pressure difference between the reservoir and its interior increases, causing elastoplastic deformation of the reservoir rock. This compression of the bedrock and pores leads to pore throat closure, resulting in a decrease in physical properties such as permeability, porosity, and rock compressibility. Numerous mechanistic experiments have confirmed that the permeability of low-permeability gas reservoirs varies significantly with stress, which affects the production capacity and extraction efficiency of such reservoirs. The stress-sensitive effect can be expressed by the following formula:
[0086]
[0087] K(p) — Formation permeability after being affected by stress-sensitive effects, 10 -3 μm 2 K0—Formwork permeability in its original state, 10 -3 μm 2 ;α K —Influence coefficient of rock deformation on permeability, MPa -1 .
[0088] Considering a single-layer, homogeneous, isotropic circular isopaque boundary gas reservoir with a central perforated vertical well. The productivity equation calculation formula considering stress-sensitive effects and high-velocity non-Darcy effects is as follows:
[0089]
[0090] In the formula, q sc —Gas flow rate under standard conditions, 10 4 m 3 / d, which represents production capacity;
[0091] K0—Original permeability of the gas layer, mD, i.e., effective permeability k g ;
[0092] h—Reservoir thickness, m;
[0093] T sc Temperature, 273.15K;
[0094] p sc —Standard pressure, 0.101 MPa;
[0095] T—formation temperature, K;
[0096] p i — Formation pressure, MPa;
[0097] —Formation pseudo-pressure, bottom hole pseudo-pressure, MPa 2 / (mPa·s);
[0098] α — Stress sensitivity coefficient, MPa -1 ;
[0099] r e r w —Discharge radius, wellbore radius, m;
[0100] M g —Gas molecular weight, kg / mol;
[0101] R—gas constant, 8.314 × 10⁻⁶ -6 MPa·m 3 / (mol·K);
[0102] S—epidermal coefficient.
[0103] Substituting the effective permeability obtained from digital core simulation into the productivity evaluation model of volcanic gas reservoirs, that is, into the productivity calculation formula of equation (1.16), the productivity q is... sc To conduct an evaluation; such as Figure 5 As shown, by using well test interpretation data, the production obtained through the production capacity evaluation model was compared with the actual test production. The error was less than 3%, indicating that digital core technology has a good effect on production capacity evaluation and prediction, and verifying the correctness of the production capacity evaluation model.
[0104] Example 2
[0105] This embodiment provides a volcanic gas reservoir productivity prediction system based on a digital core model, including:
[0106] The image processing module is used to acquire a three-dimensional image of the corresponding digital core and to determine the pore phase after phase segmentation of the three-dimensional image;
[0107] More specifically, the image processing module is used to scan the core using Micro-CT to obtain image data. By analyzing the image data, it extracts characterizing units that reflect the overall pore properties distribution of the core. The obtained characterizing unit images are filtered to obtain clearer images. The images are then binarized to extract the pore phase. Finally, a three-dimensional reconstruction is performed to obtain a digital core containing only the pore phase.
[0108] The two-phase flow simulation module is used to simulate the microscopic two-phase gas-water flow in porous digital cores and obtain the relative permeability curves of gas and water.
[0109] More specifically, the two-phase flow simulation module is used to simulate the permeability of a three-dimensional digital core using the D3Q19 discrete velocity model, thereby obtaining the absolute permeability of the core.
[0110] The absolute permeability acquisition module is used to determine the absolute permeability in different (discrete) directions based on the lattice Boltzmann method.
[0111] More specifically, the absolute permeability acquisition module is used to obtain the gas-water physical property parameters under high temperature and high pressure. It uses the fluid volume method to simulate the microscopic gas-water two-phase flow, and performs interface tracking by solving the volume conservation equation. The flow is solved using the Navier-Stokes equation to obtain the inlet and outlet pressure difference, cumulative gas volume, and cumulative liquid volume at different times. The relative permeability of water and gas is obtained by calculation, and finally, a gas-water relative permeability curve is obtained with water saturation as the abscissa and permeability as the ordinate.
[0112] The effective permeability acquisition module is used to determine the relative permeability of gas and water in the relative permeability curve based on the water saturation obtained from the well logging data, and to obtain the effective permeability of gas and water by multiplying the relative permeability and the absolute permeability.
[0113] The production capacity establishment module is used to establish production capacity evaluation models for volcanic gas reservoirs under different well types and completion methods, taking into account stress sensitivity and high-speed non-Darcy effect.
[0114] The production capacity evaluation module is used to calculate the predicted production capacity of volcanic gas reservoirs by substituting the effective permeability into the production capacity evaluation model.
[0115] The specific functions of each module in this embodiment correspond one-to-one with the methods and steps described in Embodiment 1. Any parts not described in detail will not be repeated.
Claims
1. A method for predicting the productivity of volcanic gas reservoirs based on digital core models, characterized in that, Includes the following steps: A three-dimensional image of the corresponding digital core is obtained, and the porosity phase is determined by phase segmentation of the three-dimensional image. Microscopic two-phase gas-water flow simulation was carried out on the determined pore phase digital core to obtain the relative permeability curves of gas and water, and the absolute permeability of the core in different directions was determined based on the lattice Boltzmann method. The relative permeability of gas and water is determined by the water saturation obtained from the well logging data in the relative permeability curve, and the effective permeability of gas and water is obtained by multiplying the corresponding relative permeability of gas and water by the absolute permeability. Considering stress sensitivity and high-speed non-Darcy effect, a productivity evaluation model for volcanic gas reservoirs under different well types and completion methods is established. The obtained gas and water effective permeability is substituted into the productivity evaluation model for productivity evaluation.
2. The method for predicting the production capacity of volcanic gas reservoirs based on a digital core model according to claim 1, characterized in that, The process of acquiring a three-dimensional image of the corresponding digital core and determining the pore phase after phase segmentation of the three-dimensional image specifically includes: acquiring three-dimensional image data of the digital core; analyzing the three-dimensional image data to extract characterizing units that reflect the overall pore property distribution of the core; filtering the obtained characterizing unit images to obtain images with higher clarity; binarizing the images to extract the pore phase; and finally reconstructing the three-dimensional image to obtain a digital core containing only the pore phase.
3. The method for predicting the production capacity of volcanic gas reservoirs based on a digital core model according to claim 1, characterized in that, The method for obtaining the relative permeability curves of gas and water by conducting microscopic two-phase gas-water flow simulation on a determined porous phase digital core includes: obtaining gas and water physical property parameters under high temperature and high pressure; conducting microscopic gas-water two-phase flow simulation using the fluid volume method; tracking the interface by solving the volume conservation equation; solving the flow using the Navier-Stokes equation; obtaining the inlet and outlet pressure difference, cumulative gas production, and cumulative liquid production at different times; calculating the relative permeability of water and gas; and finally obtaining the gas-water relative permeability curve with water saturation as the abscissa and permeability as the ordinate.
4. The method for predicting the production capacity of volcanic gas reservoirs based on a digital core model according to claim 1, characterized in that, In the determination of absolute permeability of rock cores in different directions based on the lattice Boltzmann method, the LBM method is used to simulate the permeability of three-dimensional digital rock cores using the D3Q19 discrete velocity model, thereby obtaining the absolute permeability of the rock cores.
5. The method for predicting the productivity of volcanic gas reservoirs based on a digital core model according to claim 3, characterized in that, The relative permeability of water, k rw The relative permeability k of the air rg Calculate according to the following formula: In the formula, f g (S w ) represents the gas content; V g (t) represents the dimensionless cumulative gas production; V(t) represents the dimensionless liquid production; I represents the relative injection capacity; Q o ΔP represents the initial gas production flow rate at the rock sample outlet face; Q(t) represents the initial liquid production flow rate at the rock sample outlet face; ΔP o The initial driving pressure difference; ΔP(t) is the displacement pressure difference at time t, μ w The viscosity of water, μ g The viscosity of the gas.
6. The method for predicting the production capacity of volcanic gas reservoirs based on a digital core model according to claim 1, characterized in that, The formula for calculating the productivity equation considering stress-sensitive effects and high-speed non-Darcy effects is as follows: In the formula, q sc —Gas flow rate under standard conditions, 10 4 m 3 / d, i.e., production capacity; K0—original permeability of the gas layer, mD, i.e., effective permeability k. g h—reservoir thickness, m; T sc Temperature, 273.15 K; p sc —Standard pressure, 0.101 MPa; T—Formation temperature, K; p i — Formation pressure, MPa; —Formation pseudo-pressure, bottom hole pseudo-pressure, MPa 2 / (mPa·s); α—stress sensitivity coefficient, MPa -1 ; r e r w —Discharge radius, wellbore radius, m; M g — Gas molecular weight, kg / mol; R — Gas constant, 8.314 × 10 - 6 MPa·m 3 / (mol·K); S—epidermal coefficient.
7. A volcanic gas reservoir productivity prediction system based on a digital core model, characterized in that, include: The image processing module is used to acquire a three-dimensional image of the corresponding digital core and to determine the pore phase after phase segmentation of the three-dimensional image; The two-phase flow simulation module is used to simulate the microscopic two-phase gas-water flow in porous digital cores and obtain the relative permeability curves of gas and water. The absolute permeability acquisition module is used to determine the absolute permeability in different (discrete) directions based on the lattice Boltzmann method. The effective permeability acquisition module is used to determine the relative permeability of gas and water in the relative permeability curve based on the water saturation obtained from the well logging data, and to obtain the effective permeability of gas and water by multiplying the relative permeability and the absolute permeability. The production capacity establishment module is used to establish production capacity evaluation models for volcanic gas reservoirs under different well types and completion methods, taking into account stress sensitivity and high-speed non-Darcy effect. The production capacity evaluation module is used to calculate the predicted production capacity of volcanic gas reservoirs by substituting the effective permeability into the production capacity evaluation model.
8. The volcanic gas reservoir productivity prediction system based on digital core model according to claim 7, characterized in that, The image processing module is specifically used to scan the core using Micro-CT to obtain image data. By analyzing the image data, it extracts characterizing units that reflect the overall pore properties distribution of the core. The obtained characterizing unit images are filtered to obtain clearer images. The images are then binarized to extract the pore phase. Finally, a three-dimensional reconstruction is performed to obtain a digital core containing only the pore phase.
9. The volcanic gas reservoir productivity prediction system based on digital core model according to claim 7, characterized in that, The absolute permeability acquisition module is specifically used to acquire the gas-water physical property parameters under high temperature and high pressure. It uses the fluid volume method to simulate the microscopic gas-water two-phase flow, and performs interface tracking by solving the volume conservation equation. The flow is solved using the Navier-Stokes equation to obtain the inlet and outlet pressure difference, cumulative gas volume, and cumulative liquid volume at different times. The relative permeability of water and gas is obtained by calculation, and finally, a gas-water relative permeability curve is obtained with water saturation as the abscissa and permeability as the ordinate.
10. The volcanic gas reservoir productivity prediction system based on digital core model according to claim 7, characterized in that, The two-phase flow simulation module is specifically used to simulate the permeability of a three-dimensional digital core using the D3Q19 discrete velocity model, thereby obtaining the absolute permeability of the core.