Method for predicting dynamic evolution of porosity-permeability of carbon storage layer of abandoned oil reservoir

Abstract

The application discloses a kind of waste oil reservoir carbon storage layer porosity-permeability dynamic evolution prediction method, belong to the technical field of waste oil and gas reservoir carbon seal, comprising: carry out the visualization microfluidic experiment of CaCO3 heterogeneous precipitation under the condition of residual oil;According to the visualization microfluidic experiment result, carry out precipitation characteristic parameter extraction;Based on precipitation characteristic parameter, calculate effective flow porosity, reactive curvature factor and interface reaction correction factor, and then carry out multi-physical field coupling correction to Kozeny-Carman model, obtain the permeability dynamic evolution model;Reservoir permeability evolution prediction is carried out using permeability dynamic evolution model.The application realizes the high-precision prediction of waste oil and gas reservoir carbon storage layer porosity-permeability dynamic evolution, and the multiphase flow and solute transport provide flow velocity and concentration field for geochemical reaction, and the reaction is feedback to affect physical property parameters by changing mineral volume and pore structure, and the physical property parameters are in turn updated flow field, and then dynamic evolution is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of carbon storage of abandoned oil and gas reservoirs, and particularly relates to a method for predicting dynamic evolution of porosity-permeability of carbon storage reservoirs of abandoned oil reservoirs. BACKGROUND

[0002] Carbon dioxide geological storage is a key technical path for addressing global climate change. Among them, carbon storage using abandoned oil and gas reservoirs has unique advantages: clear geological structure, good sealing property, and perfect wellbore and surface facilities, which can greatly reduce the storage cost.

[0003] During the CO2 injection and long-term storage process, a series of complex water-rock-gas phase geochemical reactions will be triggered between the injected CO2, in-situ formation water and reservoir minerals (such as calcite, dolomite, feldspar, clay minerals, etc.), mainly manifested as mineral dissolution and secondary mineral precipitation. This dynamic reaction process will continuously modify the micro-pore structure of the reservoir, causing the evolution of its macro-porosity and permeability, which is the "dynamic evolution of reservoir properties". Accurate prediction of this spatio-temporal evolution process is a core scientific problem and a key technical prerequisite for evaluating the long-term sealing safety of the storage library, the CO2 injection capacity, the effective storage capacity and the overall engineering feasibility. It needs to be particularly emphasized that the main medium of the abandoned oil and gas reservoir carbon storage system is the fractured rock with complex fracture network structure after long-term exploitation disturbance. Its fluid transport capacity, i.e. permeability, is mainly controlled by the fracture permeability, rather than the pore permeability of the matrix rock. In the CO2-salt water-mineral reaction system, the dissolution of minerals on the fracture wall surface may lead to the increase of fracture opening and connectivity, producing the so-called "channeling effect"; on the contrary, the precipitation of secondary minerals may plug or narrow the fractures, forming the "self-sealing" effect or leading to the decrease of injection capacity.

[0004] In addition, a fundamental difference between depleted reservoirs and conventional saline aquifers is that a certain degree of residual hydrocarbons (residual oil) is generally present in the pore and fracture space of depleted reservoirs. The presence of residual oil phase fundamentally changes the physicochemical properties and spatial configuration of liquid-solid multiphase interfaces in the reservoir, and has a profound impact on the reaction-transport processes at the pore scale: First, residual oil will occupy part of the pore space and mineral reaction surface, directly reducing the effective contact area between CO2-brine-minerals, thereby inhibiting the reaction rate and changing the spatial distribution pattern of mineral dissolution / precipitation. Second, as a hydrophobic organic phase, residual oil will change the wettability of the reservoir, affect the capillary force and relative permeability, and thus reshape the micro-migration path and macro-areal sweep efficiency of CO2 and brine. During the dynamic reaction process, the extraction and swelling of CO2 on residual oil may change the oil phase distribution, and the three-phase interface reaction at the oil-water-gas interface may also induce special mineral nucleation and growth behavior. These complex multiphase interface phenomena and reaction space competition mechanisms introduced by residual oil have a profound impact on the mineral reaction kinetics and pore structure evolution trajectory in this particular storage environment.

[0005] However, existing models for predicting the evolution of reservoir properties for carbon storage mostly directly follow or simply modify the models for saline aquifers. These models are usually based on the simplified water-rock two-phase reaction assumption and do not fully consider the presence of residual oil, a key third phase in depleted oil and gas reservoirs, and its multiphase interface effects, spatial occupation effects, and regulatory effects on fluid transport. This simplification of the model leads to distortion in the description of the real physical and chemical processes within the depleted oil and gas reservoir, a special storage carrier, resulting in significant deviations in the predicted results of porosity-permeability evolution, especially the changes in key fracture permeability, making it difficult to meet the prediction accuracy requirements for safety assessment and optimization design of storage.

[0006] The existing technology is based on the porosity-permeability relationship model (permeability prediction is usually a small part of carbon storage capacity prediction and safety assessment) of the modified Kozeny-Carman (K-C) equation or power law model; its technical core is: first, based on the assumption of uniform dissolution / precipitation, calculate the change in porosity Δφ caused by mineral dissolution / precipitation through geochemical simulation (such as using TOUGHREACT, PHREEQC, etc. software), and then update the absolute permeability k through a fixed empirical relationship.

[0007] For water-filled sandstone reservoirs or carbonate reservoirs:

[0008] (1) Permeability-porosity model

[0009] The Kozeny-Carman model can be used to calculate the permeability based on the current porosity, and the formula is:

[0010]

[0011] where, is the current permeability, is the initial permeability, is the current porosity, is the initial porosity.

[0012] The power law model can also be used to calculate the permeability based on the current porosity, with the formula:

[0013]

[0014] where, is the current permeability, is the initial permeability, is the current porosity, is the initial porosity, and n is an empirical exponent (usually taken as 2-5).

[0015] (2) Key influencing factors and their correction

[0016] ① Effective stress

[0017] The Barton-Bandis model is used to calculate the change in caprock fracture permeability, which considers that the fracture permeability is a function of the effective stress of the fracture:

[0018]

[0019] where, is the fracture permeability, is the fracture closure permeability, is the current fracture aperture, is the initial fracture aperture, is the stress-to-fracture stiffness ratio, is the normal fracture effective stress, is the initial normal fracture stiffness, is the initial fracture permeability, is the minimum fracture aperture. During CO2 injection, as the injection pressure increases, the fracture aperture decreases, and the permeability decreases accordingly.

[0020] ② Chemical reaction

[0021] The change in porosity is mainly caused by mineral-fluid reactions (such as CO2-mineral dissolution and precipitation reactions):

[0022]

[0023] where, is the change in porosity caused by the reaction.

[0024] ③Aquifer pressure

[0025] During CO2 injection, the aquifer pressure increases, leading to an increase in permeability with pressure:

[0026]

[0027] where, is the permeability modulus (reflecting the sensitivity of permeability to pressure changes), denotes the initial aquifer pressure, denotes the current aquifer pressure (pressure value during evolution). This formula shows that the permeability changes exponentially with pressure, and pressure changes affect the rock pore structure, leading to changes in permeability.

[0028] ④Other factors

[0029] The adsorption of CO2 in the reservoir matrix can cause the matrix to deform, thereby reducing the permeability of the porous medium. Supercritical CO2 can cause the reservoir matrix to expand to a greater extent, which can significantly reduce the permeability of the reservoir compared to subcritical states, further limiting the migration of carbon dioxide.

[0030] The disadvantages of this prior art are:

[0031] 1. The multiphase interface effect of residual oil is not considered: Most existing models are based on the assumption of water-rock two-phase reaction, ignoring the residual oil phase that is ubiquitous in abandoned oil and gas reservoirs. Residual oil occupies pore space and covers mineral reaction surfaces, significantly changing the contact area and reaction path between CO2-brine-minerals, leading to systematic bias in predicting reaction rates and spatial distribution of mineral precipitation.

[0032] 2. Lack of detailed description of dynamic evolution of fracture-pore structure: The traditional K-C model assumes that mineral dissolution / precipitation is uniform, while in reality mineral reactions have strong spatial heterogeneity. The existing model fails to effectively couple the nonlinear effect of reaction location on permeability evolution, resulting in insufficient prediction accuracy of fracture permeability changes.

[0033] 3. No interface reaction kinetics modification: The special physicochemical environment at the oil-water interface (such as interfacial tension changes, presence of organic acids) can induce non-classical nucleation and crystal growth behavior. The existing model does not quantitatively modify such interface reaction mechanisms, making it difficult to accurately reflect the mineral precipitation pattern in actual reservoirs. SUMMARY

[0034] The present application aims at the above-mentioned deficiencies in the prior art, and provides a waste oil reservoir carbon sequestration reservoir porosity-permeability dynamic evolution prediction method to solve the problems that the prior art does not consider the multiphase interface effect of residual oil, lacks fine description of the dynamic evolution of fracture-pore structure, and does not introduce interface reaction kinetics correction.

[0035] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is:

[0036] A waste oil reservoir carbon sequestration reservoir porosity-permeability dynamic evolution prediction method, comprising the following steps:

[0037] S1, visual microfluidic experiment of CaCO3 non-uniform precipitation under residual oil condition is carried out;

[0038] S2, according to the visual microfluidic experiment result, the precipitation characteristic parameter is extracted;

[0039] S3, based on the precipitation characteristic parameter, the effective flow porosity, the reactive tortuosity factor and the interface reaction correction factor are calculated;

[0040] S4, based on the effective flow porosity, the reactive tortuosity factor and the interface reaction correction factor, the Kozeny-Carman model is modified by multi-physical field coupling to obtain a permeability dynamic evolution model;

[0041] S5, the reservoir permeability evolution prediction is carried out by using the permeability dynamic evolution model.

[0042] Further, the S1 specifically comprises the following steps:

[0043] S11, a glass microfluidic chip with geometric characteristics is prepared to simulate different reservoir environments;

[0044] S12, the glass microfluidic chip is pre-saturated with different components of petroleum hydrocarbon, then deionized water is injected to form a residual oil environment, and finally Na2CO3 and CaCl2 solution are injected to mix and induce CaCO3 precipitation in the residual oil environment;

[0045] S13, real-time image acquisition of the CaCO3 precipitation process is carried out;

[0046] S14, a micro-pressure difference sensor is connected at the inlet and outlet of the glass microfluidic chip to monitor and record the pressure difference in the CaCO3 precipitation process in real time, and the intrinsic permeability of the microfluidic chip is calculated in real time.

[0047] Further, the S2 specifically comprises: threshold segmentation and binaryzation processing are carried out on the CaCO3 precipitation process image to distinguish solid phase, aqueous phase and oil phase, and then the precipitation characteristic parameters are extracted, including:

[0048] Oil phase volume, water phase volume, solid phase volume, oil-water interface area, water-solid interface length, average gray scale of amorphous calcium carbonate, and calcium carbonate crystal volume.

[0049] Further, in the S3, the effective flow porosity is calculated and expressed as:

[0050]

[0051] wherein:

[0052]

[0053]

[0054] wherein, is the effective flow porosity; φ is the porosity; is the residual oil saturation; V o is the oil phase volume; V w is the water phase volume; V s is the solid phase volume.

[0055] Further, in the S3, the reactive tortuosity factor is calculated and expressed as:

[0056]

[0057] wherein, τ r represents the reactive tortuosity factor; a is a phase factor; b is a structure evolution factor; n is the number of CaCO3 precipitation particles in the calculation area range; is the volume change of the ith CaCO3 precipitation particle; i is the serial number of the CaCO3 precipitation particle.

[0058] Further, in the S3, the interface reaction correction factor is calculated and expressed as:

[0059]

[0060] wherein, f int represents the interface reaction correction factor; R n is the ACC nucleation rate correction factor, R a is the ACC attachment rate correction factor, R g is the crystal growth rate correction factor; A wo represents the oil-water interface area;A ws represents the water-solid interface length.

[0061] Further, according to the ACC average gray ratio of the interface and the non-interface in the binary graph of the CaCO3 precipitation process image, a crystal growth rate correction factor is determined , which is expressed as:

[0062]

[0063] In the formula, represents the ACC average gray of the interface in the binary graph; represents the ACC average gray of the non-interface;

[0064] When >1, it indicates that the interface promotes ACC nucleation and adhesion; <1 indicates that the interface inhibits nucleation and adhesion, =1 indicates that the interface effect is not significant.

[0065] Further, according to the crystal average growth rate ratio of the interface and the non-interface in the binary graph of the CaCO3 precipitation process image, a crystal growth rate correction factor is determined R g , which is expressed as:

[0066]

[0067] In the formula, represents the number of calcium carbonate crystals at the interface; represents the volume of the i th calcium carbonate crystal on the interface; represents the number of calcium carbonate crystals at the non-interface; represents the volume of the j th calcium carbonate crystal at the non-interface.

[0068] Further, in S4, the Kozeny-Carman model is modified by multi-physical field coupling based on the effective flow porosity, the reactive curvature factor and the interface reaction correction factor, to obtain a permeability dynamic evolution model, which is expressed as:

[0069]

[0070] In the formula, is the current permeability, is the initial permeability, is the initial effective flow porosity.

[0071] The waste oil reservoir carbon storage reservoir porosity-permeability dynamic evolution prediction method provided by the present application has the following beneficial effects:

[0072] 1. The application realizes high-precision prediction of porosity-permeability dynamic evolution of abandoned oil and gas reservoir carbon sequestration reservoir by constructing a "multi-phase flow-reaction kinetics coupled modified Kozeny-Carman model (MRF-KC Model)", which provides flow velocity and concentration field for geochemical reaction through multi-phase flow and solute transport, and the reaction feedbacks the physical property parameters by changing the mineral volume and pore structure, and the physical property parameters in turn update the flow field, thereby realizing dynamic evolution.

[0073] 2. The application significantly improves the prediction accuracy: by introducing effective flow porosity, reactivity tortuosity factor and interface reaction correction factor, the influence of residual oil on flow space, reaction path and interface nucleation behavior is quantitatively systematized, so that the permeability prediction accuracy is significantly improved compared with traditional models.

[0074] 3. The application has clear physical mechanism and strong model interpretability: each correction factor has clear physical meaning (such as alpha reflecting metastable phase effect and beta representing structure nonlinear evolution), and model parameters can be obtained through microfluidic experiment inversion, avoiding the blindness of pure empirical fitting.

[0075] 4. The application is coupled with experiment and model, and has engineering applicability: key parameters are obtained through microfluidic experiment, realizing cross-scale prediction from pore scale to reservoir scale, and the model can be directly embedded into existing carbon sequestration simulation platform (such as TOUGHREACT), having good engineering transplantability. BRIEF DESCRIPTION OF DRAWINGS

[0076] Figure 1 The flow chart of the abandoned oil reservoir carbon sequestration reservoir porosity-permeability dynamic evolution prediction method of the embodiments of the application. DETAILED DESCRIPTION

[0077] The specific embodiments of the application are described below to facilitate those skilled in the art to understand the application, but it should be clear that the application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the application defined and determined by the appended claims, and all applications utilizing the concept of the application are within the scope of protection.

[0078] The abandoned oil reservoir carbon sequestration reservoir porosity-permeability dynamic evolution prediction method of the embodiments has complete physical and chemical mechanism and high prediction accuracy, and by modifying the traditional K-C model through multi-physical field coupling, the influence of residual oil on fluid transport and interface reaction is considered explicitly, thereby realizing more accurate dynamic prediction of porosity-permeability, and the reference Figure 1 , specifically includes the following contents:

[0079] S1, carry out the visualization microfluidic experiment of CaCO3 non-uniform precipitation under the condition of residual oil, which specifically includes the following steps:

[0080] S11, microfluidic chip design: prepare a glass microfluidic chip with simple geometric features (such as circular pores and orthogonal fractures) to simulate different reservoir environments;

[0081] S12, experimental process: pre-saturate the glass microfluidic chip with different components of petroleum hydrocarbon, then inject deionized water to form a residual oil environment, and finally inject Na2CO3 and CaCl2 solution to mix and induce CaCO3 precipitation in the residual oil environment; this experiment controls the flow rate and concentration of salt solution to regulate the precipitation advantage and spatial distribution pattern under different multiphase flow-reaction kinetics conditions.

[0082] S13, use a high-speed camera system to capture real-time images of the CaCO3 precipitation process for subsequent analysis of the spatial position and size of the oil phase and the precipitate.

[0083] S14, connect micro pressure difference sensors at the inlet and outlet of the glass microfluidic chip to monitor and record the pressure difference during the CaCO3 precipitation process in real time, and calculate the intrinsic permeability of the microfluidic chip in real time;

[0084] The calculation process is as follows:

[0085] k = (q * L) / (W * H * ΔP)

[0086] In the formula, k is the intrinsic permeability; q is the known flow rate per unit time; the chip geometric size is specifically: length L, width W, and depth H; ΔP is the pressure difference during the CaCO3 precipitation process.

[0087] S2, extract the precipitation characteristic parameters according to the results of the visualization microfluidic experiment;

[0088] Specifically, the CaCO3 precipitation process images are subjected to threshold segmentation and binary processing to distinguish between solid, water and oil phases, and then extract the precipitation characteristic parameters, including:

[0089] Oil phase volume V o Water phase volume V w Solid phase volume V s Oil-water interface area A wo Water-solid interface length A ws Average gray level of amorphous calcium carbonate (ACC) G and calcium carbonate crystal volume V i( i =1~n), where different types of crystals are simplified to regular geometric dimensions, such as calcite is assumed to be rhombohedral, aragonite is assumed to be spherical, and aragonite is assumed to be cylindrical.

[0090] S3. Based on sedimentation characteristic parameters, calculate the effective flow porosity, reactive tortuosity factor, and interfacial reaction correction factor.

[0091] This embodiment calculates the effective flow porosity and then considers the effective permeability correction based on residual oil saturation. The calculation process is as follows:

[0092]

[0093] in:

[0094]

[0095]

[0096] In the formula, Effective flow porosity represents the proportion of pore space in which CO2-saturated brine can flow. φ Porosity; Residual oil saturation; V o This represents the volume of the oil phase. V w The volume of the aqueous phase; V s The volume is the solid phase volume.

[0097] By replacing the porosity in the traditional KC model with the effective flow porosity, a baseline permeability model considering the space occupied by residual oil is obtained:

[0098]

[0099] This embodiment further introduces a reactive bending factor. τ r Dynamic modification of pore structure through coupled geochemical reactions, reactive tortuosity factor τ r Describes the mineral volume change ΔV i The intensity of the modification to the pore structure. This modification is not uniform, but strongly depends on the location of the reaction and the nature of the reaction products;

[0100] The reactive bending factor is expressed as:

[0101]

[0102] In the formula, n is the number of CaCO3 precipitate particles within the calculation area; iThe subscript i denotes the i-th CaCO3 precipitate particle; τ r represents the reactive curvature factor; a is the phase factor, which is used to describe the effect of oil on the crystallization path of CaCO3. When there is residual oil in the system, it can stabilize the metastable phase such as amorphous CaCO3 (ACC), and a is significantly greater than 1 when ACC exists, because ACC has higher reactivity and volume efficiency, and its subsequent crystallization (such as transformation to calcite) process will cause more severe local volume change and pore blockage, i.e. "metastable phase effect". β is the structure evolution factor, which is used to describe the nonlinear behavior of the change of fracture / pore structure. The preferential precipitation at smaller pore and fracture openings leads to β > 1; otherwise, β = 1.

[0103] The present embodiment further introduces an interfacial reaction correction factor f int to describe the unique nucleation and growth behavior at the oil-water two-phase interface; near the oil-water interface, due to the change of interfacial tension and the presence of organic acid, special CaCO3 nucleation and growth behavior can be induced. For this purpose, an empirical correction function, i.e. the interfacial reaction correction factor, is introduced, which is represented as:

[0104]

[0105] In the formula, f int represents the interfacial reaction correction factor; R n is the ACC nucleation rate correction factor, R a is the ACC attachment rate correction factor, R g is the crystal growth rate correction factor; A wo represents the oil-water interface area; A ws represents the water-solid interface length.

[0106] Specifically, the present embodiment calculates the ACC average gray ratio of the interface and the non-interface in the binary image of the CaCO3 precipitation process image according to which is represented as:

[0107]

[0108] In the formula, represents the average gray of ACC at the interface in the binary image; represents the average gray of ACC at the non-interface;

[0109] wherein, when > 1, it indicates that the interface promotes ACC nucleation and attachment; < 1 indicates that the interface inhibits nucleation and attachment, =1 represents that the interface effect is not significant.

[0110] Specifically, the embodiment determines the crystal growth rate correction factor according to the ratio of the average growth rate of crystals at the interface and the average growth rate of crystals at the non-interface in the binary image of the CaCO3 precipitation process image R g which is expressed as:

[0111]

[0112] In the formula, represents the number of calcium carbonate crystals at the interface; represents the volume of the ith calcium carbonate crystal on the interface; represents the number of calcium carbonate crystals at the non-interface (in the bulk phase); represents the volume of the jth calcium carbonate crystal at the non-interface (in the bulk phase).

[0113] S4, the Kozeny-Carman model is modified based on the effective flow porosity, the reactive curvature factor and the interface reaction correction factor to obtain a permeability dynamic evolution model;

[0114] The permeability dynamic evolution model is expressed as:

[0115]

[0116] In the formula, is the current permeability, is the initial permeability, is the initial effective flow porosity.

[0117] The correction factors (parameters) α, β, Rn*Ra and Rg of the embodiment are obtained by inversion according to the measured permeability coefficient k under the corresponding working conditions.

[0118] S5, the reservoir permeability evolution prediction is performed by using the permeability dynamic evolution model.

[0119] For a new reservoir system, in the case that the initial porosity, residual oil saturation, hydrodynamic parameters and water chemical parameters are known, the permeability dynamic evolution model can be called to realize the accurate prediction of the permeability coefficient.

[0120] Although the specific embodiments of the application are described in detail with reference to the accompanying drawings, it should not be understood as limiting the protection scope of the patent. Various modifications and variations made by those skilled in the art within the scope described in the claims are still within the protection scope of the patent.

Claims

1. A method for predicting the dynamic evolution of porosity-permeability of a carbon storage reservoir for waste oil, characterized in that, The method comprises the following steps: S1, developing a visual microfluidic experiment of non-uniform precipitation of CaCO3 under residual oil conditions; S2, extracting precipitation characteristic parameters according to the results of the visual microfluidic experiment; S3, calculating effective flow porosity, reactivity tortuosity and interfacial reaction correction factor based on the precipitation characteristic parameters; In the S3, the effective flow porosity is calculated and expressed as: Wherein: wherein is the effective flow porosity; φ is the porosity; is the residual oil saturation; V o is the oil phase volume; V w is the water phase volume; V s is the solid phase volume; In the S3, the reactivity tortuosity is calculated and expressed as: wherein τ r represents the reactive curvature factor; a is the phase factor; b is the structure evolution factor; n is the number of CaC03precipitate particles in the calculation area range; is the volume change of the i-th CaC03precipitate particle; i is the CaC03precipitate particle serial number; In the S3, the interfacial reaction correction factor is calculated and expressed as: wherein represents an interfacial reaction correction factor; R n is an ACC nucleation rate correction factor, R a is an ACC attachment rate correction factor, R g is a crystal growth rate correction factor; A wo represents an oil-water interface area; A ws represents a water-solid interface length; S4, coupling and modifying the Kozeny-Carman model based on the effective flow porosity, reactivity tortuosity and interfacial reaction correction factor to obtain a permeability dynamic evolution model, which is expressed as: wherein is the current permeability, is the initial permeability, is the initial effective flow porosity; S5, using the permeability dynamic evolution model to predict the evolution of reservoir permeability.

2. The abandoned oil reservoir carbon storage reservoir porosity-permeability dynamic evolution prediction method according to claim 1, characterized in that, The S1 specifically comprises the following steps: S11, preparing a glass microfluidic chip with geometric characteristics to simulate different reservoir environments; S12, pre-saturating the glass microfluidic chip with different components of petroleum hydrocarbons, then injecting deionized water to form a residual oil environment, and finally injecting Na2CO3 and CaCl2 solutions to induce CaCO3 precipitation in the residual oil environment; S13, real-time image acquisition of the CaCO3 precipitation process; S14, connecting micro-pressure difference sensors at the inlet and outlet of the glass microfluidic chip to monitor and record the pressure difference during the CaCO3 precipitation process in real time, and to calculate the intrinsic permeability of the microfluidic chip in real time.

3. The abandoned oil reservoir carbon storage reservoir porosity-permeability dynamic evolution prediction method according to claim 1, characterized in that, The S2 specifically comprises: threshold segmentation and binary processing of the CaCO3 precipitation process images to distinguish solid phase, water phase and oil phase, and then extract the precipitation characteristic parameters, including: Oil phase volume, water phase volume, solid phase volume, oil-water interface area, water-solid interface length, amorphous calcium carbonate average gray scale and calcium carbonate crystal volume.

4. The abandoned oil reservoir carbon storage reservoir porosity-permeability dynamic evolution prediction method according to claim 1, characterized in that, The ACC average gray scale ratio at the interface and non-interface in the binary image according to the CaCO3 precipitation process image is calculated which is expressed as: wherein represents the ACC average gray level at the interface in the binary image; represents the ACC average gray level at the non-interface. wherein when >1, indicates that the interface promotes ACC nucleation and attachment; <1 indicates that the interface inhibits nucleation and attachment, =1 indicates that the interface effect is not significant.

5. The abandoned oil reservoir carbon storage reservoir porosity-permeability dynamic evolution prediction method according to claim 1, characterized in that, A crystal growth rate correction factor is determined from a ratio of average crystal growth rates at an interface and non-interface in a binary image of a CaCO3 precipitation process image R g which is expressed as: wherein represents the number of calcium carbonate crystals at the interface; represents the volume of the i-th calcium carbonate crystal at the interface; represents the number of calcium carbonate crystals at the non-interface; represents the volume of the j-th calcium carbonate crystal at the non-interface.

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