CO2 oil displacement and storage numerical simulation method considering storage mechanism

Abstract

The invention relates to a CO2 oil displacement and storage numerical simulation method considering a storage mechanism, which comprises the following steps of: determining action parameters of the CO2 storage mechanism by utilizing a CO2 storage action characterization mechanism, acquiring the action parameters of the CO2 storage mechanism through various aspects such as an experiment or a theoretical model, and then adding the action parameters into a CO2 oil displacement and storage numerical simulation model. And the numerical simulation method for CO2 oil displacement and storage considering the storage mechanism is formed. According to the method, the CO2 storage mechanism and the CO2 oil displacement and storage model are combined, the blank that the CO2 storage mechanism is not considered in the oil reservoir in the CO2 oil displacement and storage process is filled up, and compared with a common CO2 oil displacement and storage model, the model has higher authenticity and reliability.

Description

Technical Field

[0001] The present invention belongs to the field of CO2 enhanced oil recovery technology and CO2 storage numerical simulation technology, and specifically relates to a CO2 oil displacement and storage numerical simulation method taking the storage mechanism into consideration. Background Art

[0002] To ensure energy security, technologies are needed to improve oil recovery. CO2 flooding, as a relatively mature technology, can both improve oil recovery and achieve geological storage of CO2, and is widely used in oil reservoirs.

[0003] To clarify the relationship between injection and production parameters and oil recovery and storage, many researchers have conducted extensive research on optimizing injection parameters based on numerical simulations. Malik and Islam et al. were the first to identify optimal CO2 recovery and storage parameters by comparing different CO2 concentrations, injection methods, and reservoir conditions. Guo Chaobin et al. considered the CO2 hysteresis effect and explored the impact of injection and production parameters on storage in saline aquifers. Kalra et al. evaluated the effectiveness of CO2 in improving oil shale recovery and studied the mechanism of CO2 storage capacity in shale reservoirs. Chen Xiulin et al. used nuclear magnetic resonance and microscopic numerical simulation techniques to study the microscopic storage morphology of CO2 during oil recovery. Luo Yu used three-dimensional geological models to study the feasibility of CO2 storage in depleted gas reservoirs. In summary, in the research on CO2 flooding, most scholars have focused on the optimization of parameters such as CO2 flooding methods and reservoir conditions, and research on the impact of CO2 storage mechanism on oil flooding has not yet been carried out; and in the research on CO2 storage, although some scholars have studied the impact of CO2 storage mechanism on storage results, these studies have all focused on geological bodies such as coal seams, saline water layers and gas reservoirs, and the research on CO2 storage in oil reservoirs has not considered the CO2 storage mechanism. Summary of the Invention

[0004] To overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a CO2 flooding and storage numerical simulation method that takes the storage mechanism into account. This method addresses the problem that the CO2 storage mechanism is not fully considered in current CO2 flooding and storage simulations in oil reservoirs. Based on the CO2 storage mechanism, the characterization parameters of the CO2 storage mechanism are clarified, and the storage capacity of the structural storage mechanism, the bound storage mechanism, and the dissolution storage mechanism are quantitatively characterized in the model. Ultimately, a numerical simulation model for low-permeability oil reservoirs that takes the CO2 storage mechanism into account is established. This method fills the gap in the fact that the CO2 storage mechanism is not considered in the CO2 flooding and storage processes of oil reservoirs, and can quantify the storage capacity of each part of the CO2 storage mechanism in the model.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A numerical simulation method for CO2 flooding and storage considering the storage mechanism, characterized by comprising the following steps:

[0007] Step 1: Establish a three-dimensional three-phase black oil numerical simulation model for CO2 flooding and storage;

[0008] A three-dimensional, three-phase black oil numerical simulation model was established by obtaining specific reservoir parameters (structural maps, formation thickness, porosity, permeability, etc.), fluid parameters (PVT data for crude oil, natural gas, and water, including density, viscosity, compressibility, and relative permeability), production data (historical pressure, production, water cut, injection rate, etc.), and well data (well location, completion data, well type, well pattern, etc.). The core formulas of this black oil model are the mass conservation equation and Darcy's law.

[0009] The mass conservation equation:

[0010] Oil phase:

[0011]

[0012] Gas phase (considering free gas and dissolved gas):

[0013]

[0014] Aqueous phase:

[0015]

[0016] Where: φ is porosity, S α is the phase saturation, B α is the volume coefficient, k rα is the relative permeability, μ α is the viscosity, ρ α is the density, R s is the dissolved gas-oil ratio (the amount of gas dissolved in unit volume of oil), q α is the source-sink term.

[0017] Darcy's Law:

[0018]

[0019] Where: K is the absolute permeability tensor, g is the acceleration of gravity, and z is the vertical depth.

[0020] Step 2: Characterization of CO2 storage mechanism in numerical simulation model;

[0021] Step 2.1, construct the storage mechanism characterization;

[0022] The capacity of structural storage mechanisms is generally evaluated through three aspects: trap sealing (caprock), reservoir capacity (reservoir), and the gravity difference between gas and water. For oil reservoirs, trap sealing and reservoir capacity do not require much exploration. In order to characterize the CO2 structural storage mechanism, it is necessary to transform the three-dimensional three-phase black oil numerical simulation model into a three-dimensional three-phase full-component numerical simulation model. The characterization method is to use the relevant parameters of the state equations of different gas components to describe the changes in the physical properties of injected CO2 and other crude oil components with pressure and temperature. The Peng Robinson equation (PR equation) is currently the most widely used and most accurate cubic PVT state equation.

[0023]

[0024] a 0.5 =1+F(1-T t 0.5 ) (8)

[0025] Where P is pressure; V is volume, T is temperature; RT is a constant; a(T t ) is the contrast temperature T t function; F can be obtained by correlating the saturated vapor pressure of pure substances with the density data of liquids.

[0026] Step 2.2, characterization of the confinement storage mechanism;

[0027] Binding sequestration is caused by the Jamin effect, so the mechanism of binding sequestration in numerical simulation models is phase permeability hysteresis and capillary force hysteresis. Based on the construction of a storage mechanism representation (a three-dimensional, three-phase, full-component model), this paper uses the Killough model to obtain the relevant hysteresis curves to characterize the CO2 binding sequestration mechanism.

[0028] The phase permeability hysteresis model is as follows:

[0029] ① Calculation of non-wetting phase (gas phase) saturation:

[0030]

[0031] Where: S N,trap is the bound gas saturation endpoint of the imbibition curve; S N Hyst is the gas saturation value when displacement turns to imbibition (in the “scanning curve” (maximum imbibition curve) it is the maximum gas saturation value); C is the gas trapping coefficient; S N,trap max is the maximum bound gas saturation value of the gas phase; S N max is the maximum saturation value of the gas phase.

[0032] ②Calculation of non-wetting phase imbibition curve:

[0033] Under the condition of determining the phase permeability curve of the displacement process, the imbibition curve at any time is calculated as follows:

[0034]

[0035] Among them: K rg Im (S N ) is the relative permeability at gas saturation on the displacement curve; K rg Dr (S N Hyst ) is the relative permeability at the corresponding gas saturation on the imbibition curve and the displacement curve; λ is the fitting coefficient of the model, which takes a value between 0 and 1 according to the actual situation.

[0036] ③ Calculation of the imbibition curve of the wetting phase (water phase):

[0037] In the Killough model, the imbibition curve of the water phase can be obtained by calculating the endpoint value of the relative permeability of the water phase at the maximum bound gas saturation during the imbibition process. The model is as follows:

[0038]

[0039] ΔK rw =K rw *Im (S N,trap max )-K rw Dr (S N,trap max ) (13)

[0040] Among them: K rw Im (S N,trap ) is the endpoint relative permeability value of the water phase imbibition curve at the bound gas saturation of the gas phase; K rw Dr (S N,trap ) is the endpoint relative permeability value of the water phase displacement curve at the gas phase bound gas saturation; K rw *Im (S N,trap max ) is the endpoint relative permeability value of the water phase imbibition curve at the maximum bound gas saturation; K rw Dr (S N,trap max ) is the endpoint relative permeability value of the water phase displacement curve at the maximum gas phase bound gas saturation; β is the fitting coefficient.

[0041] After calculating the water phase saturation endpoint value of the imbibition curve, the calculation formula for the imbibition scanning curve of the remaining water saturations under this endpoint value is as follows:

[0042]

[0043] Capillary force hysteresis model:

[0044] P c =P cd +F(P ci -P cd ) (16)

[0045] ① Water phase capillary force hysteresis model:

[0046]

[0047] ②Gas phase capillary force hysteresis model:

[0048]

[0049] Where: P ci Indicates the capillary force value on the displacement curve; P cd represents the capillary force value on the imbibition curve; E is a curvature parameter with a value of 0.1; S w is the current water saturation, S w Hyst Indicates the endpoint where the displacement curve turns into the imbibition curve (here, the minimum water saturation endpoint); S w max Indicates the maximum water saturation; S g Hyst Indicates the endpoint where the displacement curve turns into the imbibition curve (here, the endpoint of the maximum bound gas saturation); S g max Indicates the maximum gas saturation value.

[0050] Step 2.3, characterization of dissolution and sealing mechanism;

[0051] Based on the CO2 dissolution and storage mechanism, CO2 dissolution and storage needs to be quantitatively characterized by the solubility of CO2 in the reservoir and the diffusion rate of CO2 in the reservoir. The diffusion rate of CO2 in the reservoir is determined by experiments on the relationship between CO2 diffusion rate and permeability.

[0052] Characterizing solubility in CO2 flooding and storage models requires considering both CO2 solubility in water and CO2 solubility in oil. The solubility of CO2 in oil can be simply characterized using a three-dimensional, three-phase black oil model. Furthermore, the three-dimensional, three-phase, full-component model established when characterizing the structural storage mechanism provides a more refined representation of CO2 variations in oil. Therefore, here, the solubility prediction model is sufficient to characterize CO2 solubility in water.

[0053] The solubility model formula is as follows:

[0054]

[0055] According to phase equilibrium It can be concluded that:

[0056]

[0057]

[0058] The CO2 molar content of the gas phase in the above mathematical model is calculated as follows:

[0059]

[0060] Where, is the standard chemical formula of CO2 liquid phase; is the standard chemical formula of CO2 gas phase; Calculated according to the state equation of pure CO2; is the pressure of pure water, C i The value of is the coefficient in the Duan model. The diffusion coefficient is determined by experimentally studying the relationship between CO2 diffusion rate and permeability.

[0061] Based on the CO2 storage mechanism, the clear CO2 storage mechanism characterization parameters are added to the CO2 flooding and storage model, and finally a CO2 flooding and storage model considering the storage mechanism is formed.

[0062] The beneficial effects of the present invention are:

[0063] The model proposed in this invention fills the gap in the fact that the CO2 storage mechanism is not considered in the process of CO2 flooding and CO2 storage in oil reservoirs. It can quantify the storage capacity of each part of the CO2 storage mechanism in the model, making the simulation of CO2 flooding and storage more realistic and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Three-dimensional permeability distribution diagram of the black oil model in the embodiment of the present invention;

[0065] Figure 2The gas-water two-phase permeability hysteresis and capillary force hysteresis curves in the embodiment of the invention;

[0066] Figure 3 The solubility chart of CO2 in water in the invention examples;

[0067] Figure 4 The relationship between CO2 diffusion rate and permeability in the embodiment of the invention;

[0068] Figure 5 The impact of CO2 storage mechanism on oil displacement before and after characterization in the embodiment of the invention;

[0069] Figure 6 The impact of CO2 storage mechanism characterization on storage before and after inventive examples; DETAILED DESCRIPTION

[0070] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0071] The test reservoir is based on the typical reservoir and fluid physical parameters of a low permeability reservoir. A 41×31×10 inverse seven-point horizontal well mechanism model was established using numerical simulation technology. The grid step size on the model plane is 15×15m, and the vertical grid size is 3.08m. The model is divided into four small layers. The mechanism model uses the black oil model. The specific three-dimensional distribution of reservoir permeability is shown in the attached figure. Figure 1 Show.

[0072] Example:

[0073] As described in the patent claim method, a three-dimensional three-phase black oil model of CO2 flooding and storage is established based on the actual reservoir physical properties, well pattern development mode, etc. in the study area, as follows Figure 1 shown.

[0074] As described in the patent claim method, the mechanism of action and characterization parameters of each storage mechanism are shown in Table 1. Based on the structural storage mechanism, the PR equation was used to perform PVTi fitting on the crude oil components, resulting in the crude oil pseudo-components shown in Tables 2 to 4, as well as tables of related pseudo-component state parameters and binary interaction coefficients, converting the black oil model into a full-component model.

[0075] As described in the patent claim method, based on the mechanism of bound sealing, we obtained the original phase permeability curve and capillary displacement curve of the study area, combined with the above hysteresis model, and finally obtained the phase permeability and capillary imbibition curve model of the wetting phase (water phase) and non-wetting phase (gas phase) in the study area, as shown in the following figure: Figure 2 shown.

[0076] As described in the patent claim method, based on the CO2 dissolution storage mechanism, we use the CO2 solubility prediction model and diffusion coefficient and permeability experiments to clarify the specific CO2 dissolution storage characterization parameters, such as Figure 3-4shown.

[0077] Finally, the model that considers the CO2 structural storage mechanism, CO2 dissolution storage mechanism, and CO2 binding storage mechanism is compared with the model that does not consider the CO2 storage mechanism ( Figure 5-6 ) found that after accounting for CO2 structural storage mechanisms, CO2 bound storage mechanisms, and CO2 dissolution storage mechanisms, the cumulative oil production, cumulative water production, cumulative gas production, and water cut per well of CO2 flooding all decreased to varying degrees. This is because after accounting for the CO2 storage mechanism, some CO2 dissolves in water, resulting in a slower increase in the water flooding front and gas sweep range of the CO2 flooding, delayed gas breakthrough in production wells, and reduced cumulative gas production. As a wetting phase, water increases its hysteresis curve permeability and mobility after accounting for the CO2 storage mechanism. Therefore, compared to not considering the CO2 storage mechanism, the water production capacity of production wells increases, the cumulative water production increases, the cumulative oil production decreases, and the water cut increases more rapidly. The amount of CO2 dissolved in water increases from zero to a certain amount, and the amount of free CO2, bound CO2, and CO2 dissolved in oil all decrease. Considering the CO2 storage mechanism can make the storage capacity of each storage mechanism in the reservoir more consistent with the actual geological storage of CO2 in the reservoir. When conducting numerical simulations of CO2 flooding and storage in the reservoir, the CO2 storage mechanism should be considered.

[0078] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

[0079] Table 1 Characterization of CO2 storage mechanism in the model

[0080]

[0081] Table 2 Properties of pseudo-components after crude oil division in the study area

[0082] Components Mole fraction (%) Mass fraction (%) Molar mass (g / mol) Relative severity <![CDATA[CO2]]> 0.8167 0.21051 44.01 0.777 <![CDATA[C1]]> 19.897 1.8696 16.043 0.425 <![CDATA[C2+]]> 39.648 15.721 67.701 0.632 <![CDATA[C 11 +]]> 20.686 25.377 209.46 0.78823 <![CDATA[C 23 +]]> 18.952 56.822 511.92 0.91998

[0083] Table 3 Parameters of pseudo-component state equation

[0084]

[0085] Table 4 Parameters of binary interaction coefficients of pseudo-components

[0086] Component / Component <![CDATA[CO2]]> <![CDATA[C1]]> <![CDATA[C2+]]> <![CDATA[C 11 +]]> <![CDATA[C 23 +]]> <![CDATA[CO2]]> 0 0.1 0.1 0.1 0.1 <![CDATA[C1]]> 0.1 0 0.012477417 0.05626 0.0562628 <![CDATA[C2+]]> 0.1 0.0124774 0 0.00493 0.004927418 <![CDATA[C 11 +]]> 0.1 0.0562628 0.004927418 0 0 <![CDATA[C 23 +]]> 0.1 0.0562628 0.004927418 0 0

Claims

1. A numerical simulation method for CO2 flooding and storage considering the storage mechanism, characterized in that: The following steps are involved: Step 1: Establish a three-dimensional three-phase black oil numerical simulation model for CO2 flooding and storage; By acquiring specific reservoir parameters (structural maps, formation thickness, porosity, permeability, etc.), fluid parameters (PVT data of crude oil, natural gas, and water, including density, viscosity, compressibility, relative permeability, etc.), production data (historical pressure, production, water cut, injection rate, etc.), and well data (well location, completion data, well type, well pattern, etc.), a three-dimensional three-phase black oil numerical simulation model was established. The core formulas of this black oil model are the mass conservation equation and Darcy's law. The mass conservation equation: Oil phase: Gas phase (considering free gas and dissolved gas): Aqueous phase: Where: φ is porosity, S α is the phase saturation, B α is the volume coefficient, k rα is the relative permeability, μ α is the viscosity, ρ α is the density, R s is the dissolved gas-oil ratio (the amount of gas dissolved in unit volume of oil), q α is the source-sink term; Darcy's Law: Where: K is the absolute permeability tensor, g is the acceleration of gravity, and z is the vertical depth; Step 2: Characterization of CO2 storage mechanism in numerical simulation model; Step 2.1, construct the storage mechanism characterization; The capacity of structural storage mechanisms is generally assessed through three aspects: trap sealing (caprock), reservoir accommodation (reservoir), and the gravity difference between gas and water. For oil reservoirs, trap sealing and reservoir accommodation do not require extensive exploration. To characterize the CO2 structural storage mechanism, the three-dimensional three-phase black oil numerical simulation model needs to be transformed into a three-dimensional three-phase full-component numerical simulation model. The characterization method uses the relevant parameters of the state equations for different gas components to describe the changes in the physical properties of injected CO2 and other crude oil components with pressure and temperature. The Peng Robinson equation (PR equation) is currently the most widely used and most accurate cubic PVT state equation. a 0.5 =1+F(1-T t 0.5 ) (8) Where P is pressure; V is volume, T is temperature; RT is a constant; a(T t ) is the contrast temperature T t function; F can be obtained by correlating the saturated vapor pressure of pure substances with the density data of liquids; Step 2.2, characterization of the confinement storage mechanism; Binding sequestration is caused by the Jamin effect, so the mechanism of binding sequestration in numerical simulation models is phase permeability hysteresis and capillary force hysteresis. Based on the construction of a storage mechanism representation (a three-dimensional three-phase full-component model), this paper uses the Killough model to obtain the relevant hysteresis curves to characterize the CO2 binding sequestration mechanism. The phase permeability hysteresis model is as follows: ① Calculation of non-wetting phase (gas phase) saturation: Where: S N,trap is the bound gas saturation endpoint of the imbibition curve; S N Hyst is the gas saturation value when displacement turns to imbibition (in the "scanning curve" (maximum imbibition curve) it is the maximum gas saturation value); C is the gas trapping coefficient; S N,trap max is the maximum bound gas saturation value of the gas phase; S N max is the maximum saturation value of the gas phase; ②Calculation of non-wetting phase imbibition curve: Under the condition of determining the phase permeability curve of the displacement process, the imbibition curve at any time is calculated as follows: Among them: K rg Im (S N ) is the relative permeability at gas saturation on the displacement curve; K rg Dr (S N Hyst ) is the relative permeability at the corresponding gas saturation on the imbibition curve and the displacement curve; λ is the fitting coefficient of the model, which takes a value between 0 and 1 according to the actual situation; ③ Calculation of the imbibition curve of the wetting phase (water phase): In the Killough model, the imbibition curve of the water phase can be obtained by calculating the endpoint value of the relative permeability of the water phase at the maximum bound gas saturation during the imbibition process; the model is as follows: ΔK rw =K rw *Im (S N,trap max )-K rw Dr (S N,trap max ) (13) Among them: K rw Im (S N,trap ) is the endpoint relative permeability value of the water phase imbibition curve at the bound gas saturation of the gas phase; K rw Dr (S N,trap ) is the endpoint relative permeability value of the water phase displacement curve at the gas phase bound gas saturation; K rw *Im (S N,trap max ) is the endpoint relative permeability value of the water phase imbibition curve at the maximum bound gas saturation; K rw Dr (S N,trap max ) is the endpoint relative permeability value of the water phase displacement curve at the maximum gas phase bound gas saturation; β is the fitting coefficient; After calculating the water phase saturation endpoint value of the imbibition curve, the calculation formula for the imbibition scanning curve of the remaining water saturations under this endpoint value is as follows: Capillary force hysteresis model: P c =P cd +F(P ci -P cd ) (16) ① Water phase capillary force hysteresis model: ②Gas phase capillary force hysteresis model: Where: P ci Indicates the capillary force value on the displacement curve; P cd represents the capillary force value on the imbibition curve; E is a curvature parameter with a value of 0.1; S w is the current water saturation, S w Hyst Indicates the endpoint where the displacement curve turns into the imbibition curve (here, the minimum water saturation endpoint); S w max Indicates the maximum water saturation; S g Hyst Indicates the endpoint where the displacement curve turns into the imbibition curve (here, the endpoint of the maximum bound gas saturation); S g max Indicates the maximum gas saturation value; Step 2.3, characterization of dissolution and sealing mechanism; According to the mechanism of CO2 dissolution and storage, it is necessary to quantitatively characterize CO2 dissolution and storage through the solubility of CO2 in the reservoir and the diffusion rate of CO2 in the reservoir. The diffusion rate of CO2 in the reservoir is determined by experiments on the relationship between CO2 diffusion rate and permeability. In order to characterize the solubility of CO2 in the CO2 flooding and storage model, it is necessary to consider the solubility of CO2 in water and in oil. The solubility of CO2 in oil can be simply characterized by a three-dimensional three-phase black oil model, and the three-dimensional three-phase full-component model established when characterizing the structural storage mechanism can more finely characterize the changes in CO2 in oil. Therefore, it is only necessary to characterize the solubility of CO2 in water through a solubility prediction model. The solubility model formula is as follows: According to phase equilibrium It can be concluded that: The CO2 molar content of the gas phase in the above mathematical model is calculated as follows: Where, is the standard chemical formula of CO2 liquid phase; is the standard chemical formula of CO2 gas phase; Calculated according to the state equation of pure CO2; is the pressure of pure water, C i The value of is the coefficient in the Duan model; the diffusion coefficient is determined by experimentally studying the relationship between CO2 diffusion rate and permeability; Based on the CO2 storage mechanism, the clear CO2 storage mechanism characterization parameters are added to the CO2 flooding and storage model, and finally a CO2 flooding and storage model considering the storage mechanism is formed.

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