Adaptive Prediction Methods for CO2 Displacement in Shale Oil Reservoirs

Abstract

This invention discloses a method for predicting the adaptability of CO2 injection to shale oil reservoirs, comprising: obtaining the influence law of formation dip angle on reservoir development based on online nuclear magnetic resonance CO2 displacement experiments of long core samples with varying dip angles; correcting the binary interaction coefficients, injection pressure, injection rate, model dip angle, and relative permeability curves in a numerical simulation model based on the influence law of formation dip angle on reservoir development; establishing a three-dimensional geological model based on the corrected parameters in the corrected numerical simulation model and simulating the dynamics of CO2 displacement under different reservoir physical property conditions to obtain the quantitative relationship between multiple parameters and recovery rate; constructing a multi-factor comprehensive evaluation index including permeability, formation dip angle, crude oil viscosity, and formation pressure based on the quantitative relationship between multiple parameters and recovery rate; determining the geological adaptability level of the target shale oil reservoir for CO2 injection based on the multi-factor comprehensive evaluation index; and predicting the adaptability of CO2 displacement based on the geological adaptability level.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas resource development technology, and in particular relates to a method for predicting the adaptability of CO2 displacement in shale oil reservoirs. Background Technology

[0002] Shale oil reservoirs are characterized by low porosity, low permeability, and insufficient natural energy. Conventional water injection development faces problems such as high seepage resistance, high water injection pressure, and low displacement efficiency, resulting in crude oil recovery rates generally below 20%. As the proportion of proven shale oil reserves in my country increases year by year (accounting for more than 50% of the total proven reserves), how to efficiently develop such reservoirs has become an urgent need for the oil and gas industry.

[0003] CO2 flooding technology, with its advantages of viscosity reduction, crude oil expansion, and miscibility displacement, is considered a core means of enhancing oil recovery in shale reservoirs. However, the effectiveness of CO2 displacement technology is highly dependent on reservoir geological conditions (such as pore-throat structure, heterogeneity, and burial depth). Existing technologies lack systematic geological adaptability evaluation methods for shale reservoirs, leading to blind application in the field. In some blocks, poor geological compatibility has resulted in problems such as gas channeling and insufficient energy replenishment, ultimately limiting the improvement in oil recovery.

[0004] However, the geological adaptability assessment of shale oil reservoirs faces the following challenges:

[0005] High complexity: Shale oil reservoirs have complex geological structures and physical characteristics, such as ultra-low permeability, high viscosity oil, and complex porous media, making it difficult to predict the dynamic behavior of shale oil reservoirs after CO2 injection.

[0006] High computational complexity: Traditional numerical simulation methods require a lot of computational resources and time, making it difficult to complete the prediction of large-scale reservoir models in a short period of time.

[0007] Insufficient prediction accuracy: Existing geological adaptability assessment methods (such as pressure testing and permeability analysis) have significant errors in predicting the long-term dynamics and production of reservoirs after CO2 injection.

[0008] Lack of long-term effect assessment: Existing methods are difficult to comprehensively assess the long-term geological adaptability of reservoirs after CO2 injection and easily overlook the impact of dynamic changes on reservoir development.

[0009] The above problems mean that existing CO2 displacement adaptability evaluation methods for shale oil reservoirs do not consider the effect evaluation during reservoir development. They have errors in feasibility, prediction range, and prediction accuracy, which is not conducive to the effect evaluation during the actual CO2 displacement development of shale oil reservoirs and brings difficulties to the selection of development methods to improve oil recovery in shale oil reservoirs. Summary of the Invention

[0010] To address the aforementioned technical problems, this invention proposes a method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs, which solves the problems of low prediction accuracy, complex process, and limitations of single-factor evaluation in existing technologies.

[0011] To achieve the above objectives, this invention provides a method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs, comprising:

[0012] Based on online nuclear magnetic resonance CO2 displacement experiments of long cores with varying dip angles, the influence of formation dip angle on reservoir development was obtained.

[0013] Based on the influence of the formation dip angle on reservoir development, the binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve in the numerical simulation model were corrected.

[0014] Based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model was established and the CO2 displacement dynamics under different reservoir physical property conditions were simulated to obtain the quantitative relationship between multiple parameters and recovery rate.

[0015] Based on the quantitative relationship between the multiple parameters and the recovery rate, a multi-factor comprehensive evaluation index including permeability, formation dip angle, crude oil viscosity and formation pressure is constructed.

[0016] Based on the multi-factor comprehensive evaluation index, the geological adaptability level of CO2 injection displacement of the target shale oil reservoir is determined;

[0017] CO2 displacement adaptability is predicted based on the geological adaptability level.

[0018] Optionally, based on online nuclear magnetic resonance CO2 displacement experiments of long cores with varying dip angles, the influence of formation dip angle on reservoir development can be obtained, including:

[0019] A long core sample saturated with gas-bearing active oil was placed on a high-temperature and high-pressure nuclear magnetic resonance clamping device, the clamping device was adjusted to a preset tilt angle, and CO2 displacement fluid was injected into the core.

[0020] During the displacement process, the nuclear magnetic resonance T2 signal in vertical scanning mode was continuously monitored along the core axis;

[0021] Based on the spatial coordinates of the T2 signal difference between adjacent monitoring locations exceeding the threshold, the arrival location of the CO2 displacement front is determined, and the recovery rate and gas-oil ratio data at the corresponding injection multiple are recorded.

[0022] Based on the recovery rate and gasoline ratio data, the influence of formation dip angle on reservoir development was obtained.

[0023] Optionally, the calibration numerical simulation model includes:

[0024] A numerical simulation of CO2 displacement was performed using a numerical simulation model to obtain initial simulation results.

[0025] A fluid composition model was established based on high-temperature and high-pressure PVT experimental data, and the component interaction coefficient and C were adjusted. 30+ The molar mass and crude oil viscosity parameters are used to ensure that the fitting accuracy between the initial simulation results and the PVT experimental data reaches a predetermined value;

[0026] A numerical model of the same size as the long core experiment was established, and the same temperature, pressure, porosity, permeability and oil saturation parameters were set. The numerical model was then used for numerical simulation.

[0027] The CO2 saturation distribution, recovery rate, and gas-oil ratio obtained from numerical simulation are compared with the experimental results from the long core. The binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve are iteratively adjusted until the error between the numerical simulation results and the experimental results is less than the allowable deviation.

[0028] Optionally, based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model is established and the CO2 displacement dynamics under different reservoir physical property conditions are simulated to obtain the quantitative relationship between multiple parameters and recovery rate, including:

[0029] Input the corrected binary interaction coefficients, injection pressure, injection velocity, model tilt angle, and relative permeability curve into the numerical simulation model;

[0030] Set up one injection-production well pair based on the actual well network location of the target reservoir, and set parameters such as reservoir depth, temperature, pressure, porosity, permeability and oil saturation.

[0031] By changing parameters such as permeability, formation pressure, and crude oil viscosity, numerical simulations were run to obtain recovery rate data under different parameter combinations, and the quantitative relationship between the multiple parameters and the recovery rate was obtained.

[0032] Optionally, based on the quantitative relationship between the multiple parameters and the recovery rate, a multi-factor comprehensive evaluation index including permeability, formation dip angle, crude oil viscosity, and formation pressure is constructed, including:

[0033] ;

[0034] Among them, S i R represents the comprehensive evaluation score of the parameters for the i-th reservoir, where j is the parameter number, and R is the parameter index. ij R represents the single influence of the j-th parameter on the recovery rate of the i-th reservoir. jmin R is the minimum value of the single influence of the j-th parameter on the recovery rate in a set of reservoirs. jmax Let $j$ be the maximum value of the single influence of the $j$ parameter on the oil recovery rate in a set of reservoirs. The weight coefficient for the j-th parameter.

[0035] Optionally, obtain the R ij include:

[0036] Determine whether the permeability, formation dip angle, crude oil viscosity, and formation pressure are greater than the inflection point value, where the inflection point value is the value corresponding to the point where the slope changes as actually measured;

[0037] Based on the judgment result, obtain the R. ij .

[0038] Compared with the prior art, the present invention has the following advantages and technical effects:

[0039] This invention innovatively integrates indoor physical simulation and numerical simulation technologies. By designing online nuclear magnetic resonance CO2 displacement experiments with long cores at different angles, it obtains the influence of formation dip angle on reservoir development, corrects the accuracy of numerical simulation software results, and constructs a multi-factor weighted evaluation system based on experimental data and numerical simulation results. It uses linear regression to establish a quantitative relationship between parameters and recovery rate, and formulates graded evaluation standards to achieve dimensionless processing of geological parameters and calculation of comprehensive evaluation scores. Compared with traditional methods, this invention significantly improves prediction accuracy and applicability through full-scale experimental calibration, precise numerical simulation technology, and multi-factor discriminant mathematical model calculation. This method only requires input of four basic parameters, including formation dip angle and permeability, to quickly output adaptability evaluation results, providing an efficient and reliable decision-making basis for selecting CO2 development methods in shale oil reservoirs. It effectively guides the field implementation of CO2 displacement to enhance oil recovery in shale oil reservoirs, and is characterized by its ease of operation and wide applicability. Attached Figure Description

[0040] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a schematic diagram of the long core displacement experiment process at different angles according to an embodiment of the present invention;

[0042] Figure 2 This is a graph showing the variation of oil displacement efficiency with formation dip angle according to an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a model according to an embodiment of the present invention;

[0044] Figure 4This is a diagram showing the relationship between various parameters and oil displacement efficiency in an embodiment of the present invention, wherein (a) is a schematic diagram of crude oil displacement efficiency under different pressure conditions, (b) is a schematic diagram of crude oil displacement efficiency under different permeability conditions, and (c) is a schematic diagram of crude oil displacement efficiency under different crude oil viscosity conditions.

[0045] Figure 5 These are the parameters R in the embodiments of the present invention. ij Calculation flowchart;

[0046] Figure 6 This is a flowchart of the shale oil reservoir CO2 displacement adaptive prediction method according to an embodiment of the present invention. Detailed Implementation

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] This embodiment proposes an adaptive prediction method for CO2 displacement in shale oil reservoirs, such as... Figure 6 As shown, the specific steps include:

[0050] Based on online nuclear magnetic resonance CO2 displacement experiments of long cores with varying dip angles, the influence of formation dip angle on reservoir development was obtained.

[0051] Based on the influence of the formation dip angle on reservoir development, the binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve in the numerical simulation model were corrected.

[0052] Based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model was established and the CO2 displacement dynamics under different reservoir physical property conditions were simulated to obtain the quantitative relationship between multiple parameters and recovery rate.

[0053] Based on the quantitative relationship between the multiple parameters and the recovery rate, a multi-factor comprehensive evaluation index including permeability, formation dip angle, crude oil viscosity and formation pressure is constructed.

[0054] Based on the multi-factor comprehensive evaluation index, the geological adaptability level of CO2 injection displacement of the target shale oil reservoir is determined;

[0055] CO2 displacement adaptability is predicted based on the geological adaptability level.

[0056] Specifically, this embodiment provides a reference for the numerical simulation correction results through indoor long core nuclear magnetic resonance experiments, and considers the influence of CO2 arrival location, formation dip angle, permeability, formation pressure, and crude oil viscosity on the recovery effect during CO2 displacement development of shale oil reservoirs. After obtaining the relationship between each factor and the degree of recovery, multi-factor weight calculation is performed to establish a multi-factor comprehensive CO2 displacement geological adaptability prediction method, which has higher accuracy and wider applicability. It can provide certain assistance in the pre-selection of shale oil reservoir development methods and predict the recovery effect based on the basic reservoir conditions.

[0057] Furthermore, based on online nuclear magnetic resonance CO2 displacement experiments using long cores with varying dip angles, the influence of formation dip angle on reservoir development was obtained, including:

[0058] A long core sample saturated with gas-bearing active oil was placed on a high-temperature and high-pressure nuclear magnetic resonance clamping device, the clamping device was adjusted to a preset tilt angle, and CO2 displacement fluid was injected into the core.

[0059] During the displacement process, the nuclear magnetic resonance T2 signal in vertical scanning mode was continuously monitored along the core axis;

[0060] Based on the spatial coordinates of the T2 signal difference between adjacent monitoring locations exceeding the threshold, the arrival location of the CO2 displacement front is determined, and the recovery rate and gas-oil ratio data at the corresponding injection multiple are recorded.

[0061] Based on the recovery rate and gasoline ratio data, the influence of formation dip angle on reservoir development was obtained.

[0062] Specific steps are as follows: Figure 1 As shown: (1) The cleaned core is fixed on the clamping device of the high temperature and high pressure online nuclear magnetic resonance analyzer using heat shrink tubing. The system is heated to the bottom temperature, the core is evacuated, and the system pressure is increased to above the saturation pressure by using petroleum ether.

[0063] (2) Adjust the angle of the core holder to the planned angle;

[0064] (3) Slowly inject formation water prepared based on heavy water into the core at a rate of 0.05 mL / min to eliminate the nuclear magnetic T2 signal of the formation water. Then saturate the core with gas-bearing active oil. When the gas-oil ratio of the active oil at the production end is constant at the target reservoir gas-oil ratio, the core saturation fluid is considered complete.

[0065] (4) Set the high temperature and high pressure online nuclear magnetic resonance equipment to vertical scanning mode, and perform nuclear magnetic T2 signal scanning at all positions from the injection end to the production end, thereby obtaining the T2 signal of all end faces along the path from the injection end to the production end under the initial oil saturation condition of the core.

[0066] (5) Open the inlet end of the core holder and inject CO2 of different pore volumes (PV) into the core at a constant rate of 0.10 ml / min; until the end of the experiment, collect gas and fluid components at the outlet end for every 0.05 PV of CO2 injected.

[0067] (6) Set the high temperature and high pressure online nuclear magnetic resonance equipment to vertical scanning mode, perform nuclear magnetic resonance T2 signal scanning at all positions from the injection end to the output end, and record the T2 signal of all end faces along the path from the injection end to the output end under different CO2 injection amounts;

[0068] (7) Compare the T2 signal along the path under different CO2 injection rates with the T2 signal along the path under the initial oil saturation condition of the core. When the difference in T2 signal is greater than 0.1%, record this location as the location where CO2 arrives, and record data such as recovery rate and gas-oil ratio.

[0069] (8) Process the recovery rate results after experiments at different angles (0-15°), such as... Figure 2 As shown.

[0070] Furthermore, the calibration numerical simulation model includes:

[0071] A numerical simulation of CO2 displacement was performed using a numerical simulation model to obtain initial simulation results.

[0072] A fluid composition model was established based on high-temperature and high-pressure PVT experimental data, and the component interaction coefficient and C were adjusted. 30+ The molar mass and crude oil viscosity parameters are used to ensure that the fitting accuracy between the initial simulation results and the PVT experimental data reaches a predetermined value;

[0073] A numerical model of the same size as the long core experiment was established, and the same temperature, pressure, porosity, permeability and oil saturation parameters were set. The numerical model was then used for numerical simulation.

[0074] The CO2 saturation distribution, recovery rate, and gas-oil ratio obtained from numerical simulation are compared with the experimental results from the long core. The binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve are iteratively adjusted until the error between the numerical simulation results and the experimental results is less than the allowable deviation.

[0075] Specifically, a numerical model of the same size core as that used in the CO2 displacement experiment of long core nuclear magnetic resonance of shale oil reservoirs considering dip angle was established based on CMG simulation software, and the CO2 displacement numerical simulation process was carried out. The main steps are as follows:

[0076] (1) Based on high-temperature and high-pressure PVT experiments such as constant mass expansion experiment, multi-stage degassing experiment, and gas injection expansion experiment, the fluid composition of formation crude oil was analyzed and obtained. The fluid composition of formation crude oil was imported into the Winprop module of CMG numerical simulation software, and adjustable variables (such as component interaction coefficient, C) were defined. 30+ (Molar mass and crude oil viscosity, etc.) are used to fit numerical simulation and physical simulation experimental data. When the simulation data fits the PVT experimental results with an accuracy greater than 99%, the fluid model is output and the minimum miscibility pressure of the fluid components is calculated.

[0077] (2) A numerical model of the same size core was established based on CMG simulation software, and parameters such as temperature, pressure, porosity, permeability, oil saturation, pressure coefficient, and saturation pressure were set. Then, production parameters such as relative permeability curve, CO2 injection rate, CO2 injection speed, injection pressure, and production pressure were set during the CO2 displacement process.

[0078] (3) The CO2 displacement process of long core was carried out based on the GEM module of CMG simulation software, and the pressure distribution, crude oil saturation distribution, and gas phase saturation distribution in the operation results were analyzed.

[0079] (4) Based on the pressure field diagram, the mass fraction field diagram of gas phase and the oil-gas interface tension field diagram in the numerical simulation results, the CO2 saturation distribution value from the injection end to the production end under different gas injection conditions is analyzed by the numerical simulation software CMG, which is regarded as the location where CO2 arrives, and the oil recovery rate and gas-oil ratio and other parameters of the numerical simulation results are exported.

[0080] (5) Compare the results of CO2 arrival location, oil recovery rate and gas-oil ratio at the production end, and gas and fluid composition at the outlet end with the CO2 displacement experiment of the core nuclear magnetic resonance of shale oil reservoir considering dip angle.

[0081] (6) Adjust the fitting steps of the numerical simulation software, such as "binary interaction coefficient, injection pressure, injection rate, dip angle of long core model, and relative permeability curve", until the error between all parameters of all numerical simulation results and the CO2 displacement experiment results of long core nuclear magnetic resonance of shale oil reservoir considering dip angle is less than 0.1%, and the numerical simulation results are considered to be accurate.

[0082] More specifically, data such as "binary interaction coefficients, injection pressure, injection rate, dip angle of the long core model, and relative permeability curve," established in the numerical simulation method for CO2 displacement in shale reservoirs considering dip angle, are input into the GEM module of the reservoir numerical simulation software to build a three-dimensional geological model. Reservoir parameters (such as depth, temperature, reservoir pressure, reservoir porosity, reservoir permeability, oil saturation, temperature gradient, pressure coefficient, and saturation pressure) are modified according to preset targets. Based on the established three-dimensional geological model, the actual well network locations for CO2 injection are set. After completing the reservoir setup, numerical simulation is run using the GEM module of CMG. By changing parameters such as permeability, formation pressure, and crude oil viscosity in the initial model, a one-injection-one-production process is implemented. Figure 3 Numerical simulations of CO2 displacement were performed. The recovery rates from different model simulations were statistically analyzed and then post-processed.

[0083] Furthermore, based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model was established and the CO2 displacement dynamics under different reservoir physical property conditions were simulated to obtain the quantitative relationship between multiple parameters and recovery rate, including:

[0084] Input the corrected binary interaction coefficients, injection pressure, injection velocity, model tilt angle, and relative permeability curve into the numerical simulation model;

[0085] Set up one injection-production well pair based on the actual well network location of the target reservoir, and set parameters such as reservoir depth, temperature, pressure, porosity, permeability and oil saturation.

[0086] By changing parameters such as permeability, formation pressure, and crude oil viscosity, numerical simulations were run to obtain recovery rate data under different parameter combinations, and the quantitative relationship between the multiple parameters and the recovery rate was obtained.

[0087] Specifically, using statistical numerical simulation methods to analyze the influence curves of various parameters on reservoir recovery (pressure: 0.5-1.5 MPa; permeability: 0-15 mD; crude oil viscosity: 0-10 mPa·s), and combining this with sensitivity analysis, a two-stage parameter influence model was established for the three parameters. The permeability, pressure, and viscosity parameters were divided into two stages based on their influence trends on recovery.

[0088] Phase I (Low Displacement Efficiency Zone): Permeability < 5 mD, Pressure < 0.8 times MMP, Viscosity > 5 mPa·s, Capillary Force Dominance. Phase II (High-Efficiency Displacement Zone): Permeability ≥ 5 mD, Pressure ≥ 0.8 times MMP, Viscosity ≤ 5 mPa·s, Gravity Differentiation Dominance.

[0089] Based on oilfield development practices, critical conditions between stages are defined (e.g., when permeability = 5mD, the dip angle threshold = 3°).

[0090] Establish an evaluation index for CO2 displacement adaptability in shale oil reservoirs:

[0091] ;

[0092] Among them, S i Let $j$ be the comprehensive evaluation score of the parameters for the $i$-th reservoir, $j$ be the parameter number, $1$ be permeability, $2$ be formation dip angle, $3$ be crude oil viscosity, $4$ be formation pressure, and $R$ be the parameter index. ij The single influence of the j-th parameter on the recovery rate of the i-th reservoir is calculated as follows: Figure 5 R jmin The minimum value of the single influence of the j-th parameter on the recovery rate in a set of reservoirs is determined by numerical simulation results. Figure 4 (a)-(c)), R jmax The maximum value of the single influence of the j-th parameter on the recovery rate in a set of reservoirs is determined by numerical simulation results. Figure 4 (a)-(c)), The weight coefficient for the j-th parameter is assigned based on the numerical simulation results.

[0093] When selecting a target reservoir, the reservoir permeability, reservoir dip angle, formation pressure, and crude oil viscosity are recorded as inputs, and a score is output for evaluation after input.

[0094] The calculations for CO2 displacement at the shale reservoir scale are shown in Table 1-2:

[0095] Table 1

[0096]

[0097] Table 2

[0098]

[0099] Based on the numerical simulation results, the evaluation criteria for the adaptability of shale oil reservoirs to CO2 displacement are shown in Table 3.

[0100] Table 3

[0101]

[0102] Further, obtain the R ij include:

[0103] Determine whether the permeability, formation dip angle, crude oil viscosity, and formation pressure are greater than the inflection point value;

[0104] If it is greater than the inflection point value, then substitute it into the equation before the inflection point to obtain the R value. ij ;

[0105] Otherwise, substitute the equation after the inflection point to obtain the R. ij .

[0106] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs, characterized in that, include: Based on online nuclear magnetic resonance CO2 displacement experiments of long cores with varying dip angles, the influence of formation dip angle on reservoir development was obtained. Based on the influence of the formation dip angle on reservoir development, the binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve in the numerical simulation model were corrected. Based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model was established and the CO2 displacement dynamics under different reservoir physical property conditions were simulated to obtain the quantitative relationship between multiple parameters and recovery rate. Based on the quantitative relationship between the multiple parameters and the recovery rate, a multi-factor comprehensive evaluation index including permeability, formation dip angle, crude oil viscosity and formation pressure is constructed. Based on the multi-factor comprehensive evaluation index, the geological adaptability level of CO2 injection displacement of the target shale oil reservoir is determined; CO2 displacement adaptability is predicted based on the geological adaptability level.

2. The method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs according to claim 1, characterized in that, Based on online nuclear magnetic resonance CO2 displacement experiments using long cores with varying dip angles, the influence of formation dip angle on reservoir development was obtained, including: The long core sample containing saturated gas-bearing active oil was placed on a high-temperature and high-pressure nuclear magnetic resonance clamping device. The clamping device was adjusted to a preset tilt angle, and CO2 displacement fluid was injected into the core. During the displacement process, the nuclear magnetic resonance T2 signal in vertical scanning mode was continuously monitored along the core axis; Based on the spatial coordinates of the T2 signal difference between adjacent monitoring locations exceeding the threshold, the arrival location of the CO2 displacement front is determined, and the recovery rate and gas-oil ratio data at the corresponding injection multiple are recorded. Based on the recovery rate and gasoline ratio data, the influence of formation dip angle on reservoir development was obtained.

3. The method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs according to claim 1, characterized in that, The calibration numerical simulation model includes: A numerical simulation of CO2 displacement was performed using a numerical simulation model to obtain initial simulation results. A fluid composition model was established based on high-temperature and high-pressure PVT experimental data, and the component interaction coefficient and C were adjusted. 30+ The molar mass and crude oil viscosity parameters are used to ensure that the fitting accuracy between the initial simulation results and the PVT experimental data reaches a predetermined value; A numerical model of the same size as the long core experiment was established, and the same temperature, pressure, porosity, permeability and oil saturation parameters were set. The numerical model was then used for numerical simulation. The CO2 saturation distribution, recovery rate, and gas-oil ratio obtained from numerical simulation are compared with the experimental results from the long core. The binary interaction coefficient, injection pressure, injection rate, model dip angle, and relative permeability curve are iteratively adjusted until the error between the numerical simulation results and the experimental results is less than the allowable deviation.

4. The method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs according to claim 1, characterized in that, Based on the corrected parameters in the corrected numerical simulation model, a three-dimensional geological model was established and the CO2 displacement dynamics under different reservoir physical property conditions were simulated to obtain the quantitative relationship between multiple parameters and recovery rate, including: Input the corrected binary interaction coefficients, injection pressure, injection velocity, model tilt angle, and relative permeability curve into the numerical simulation model; Set up one injection-production well pair based on the actual well network location of the target reservoir, and set parameters such as reservoir depth, temperature, pressure, porosity, permeability and oil saturation. By changing parameters such as permeability, formation pressure, and crude oil viscosity, numerical simulations were run to obtain recovery rate data under different parameter combinations, and the quantitative relationship between the multiple parameters and the recovery rate was obtained.

5. The method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs according to claim 1, characterized in that, Based on the quantitative relationship between the aforementioned multiple parameters and the recovery rate, a multi-factor comprehensive evaluation index is constructed, including permeability, formation dip angle, crude oil viscosity, and formation pressure. ； Among them, S i R represents the comprehensive evaluation score of the parameters for the i-th reservoir, where j is the parameter number, and R is the parameter index. ij R represents the single influence of the j-th parameter on the recovery rate of the i-th reservoir. jmin R is the minimum value of the single influence of the j-th parameter on the recovery rate in a set of reservoirs. jmax Let $j$ be the maximum value of the single influence of the $j$ parameter on the oil recovery rate in a set of reservoirs. The weight coefficient for the j-th parameter.

6. The method for predicting the adaptability of CO2 injection displacement in shale oil reservoirs according to claim 5, characterized in that, Obtain the R ij include: Determine whether the permeability, formation dip angle, crude oil viscosity, and formation pressure are greater than the inflection point value, where the inflection point value is the value corresponding to the point where the slope changes as actually measured; Based on the judgment result, obtain the R. ij .

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