Method for determining injection parameters of carbon dioxide flooding displacement agent in low permeability oil reservoir

Abstract

The application discloses a method for determining injection parameters of a carbon dioxide flooding displacement agent in a low-permeability oil reservoir, and specifically comprises the following steps: step 1, establishing a reservoir geological model of a research area and determining a dominant channel; step 2, selecting cores that can represent the heterogeneity of a reservoir in the research area, injecting displacement agents with different concentrations, and comparing adsorption degrees; step 3, selecting cores with different permeabilities, comparing the influence degrees of the displacement agent on the cores with different permeabilities; step 4, summarizing experimental results to obtain a solid-phase adsorption model and a permeability time-varying model; step 5, forming a carbon dioxide flooding gas channeling prevention and treatment simulation system; and step 6, obtaining optimal injection parameters of the displacement agent by using the carbon dioxide flooding gas channeling prevention and treatment simulation system. The application provides a method for determining reasonable injection parameters of a carbon dioxide flooding gas channeling prevention and treatment displacement agent, and provides certain theoretical basis and guidance for improving the development effect of a carbon dioxide flooding field.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology and provides a method for determining the injection parameters of carbon dioxide flooding and regulation agent in low-permeability reservoirs. Background Technology

[0002] Carbon dioxide flooding can significantly improve the recovery rate of low-permeability reservoirs and effectively solve the problem of large amounts of low-permeability, difficult-to-access reserves.

[0003] Most low-permeability oilfields require fracturing development and are highly heterogeneous, with significant differences in reservoir properties such as interlayer and planar permeability and effective thickness. Therefore, during carbon dioxide injection development, due to reservoir heterogeneity and the presence of dominant channels, indiscriminate gas injection can easily lead to gas channeling. Gas channeling causes the injected carbon dioxide to circulate ineffectively, significantly reducing the oil displacement efficiency and the extent of improved oil recovery. Furthermore, the channeled carbon dioxide will cause severe corrosion problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for determining the injection parameters of the modifier for carbon dioxide flooding in low-permeability oil reservoirs. By determining the parameters, the problem that the improvement effect of the modifier in the process of preventing gas channeling during carbon dioxide flooding in low-permeability oilfields needs to be further improved is solved.

[0005] The technical solution adopted in this invention is,

[0006] The method for determining the injection parameters of the carbon dioxide flooding modifier in low-permeability reservoirs is as follows:

[0007] Step 1: Establish a reservoir geological model for the study area and perform historical fitting based on dynamic data to determine the dominant channels;

[0008] Step 2: Using an experimental model, select cores that can represent the heterogeneity of the reservoir in the study area, inject different concentrations of modulators, and compare the effects of different concentrations of modulators on the core permeability and the degree of core adsorption of modulators.

[0009] Step 3: Using an experimental model, select cores with different permeabilities, inject the same concentration of modulator into the cores, and compare the degree of influence of the modulator on cores with different permeabilities.

[0010] Step 4: Obtain the adsorption degree of different concentrations of modulators on the core and the influence of modulators on the permeability of cores with different permeabilities, and obtain solid phase adsorption dataset and permeability time-varying dataset;

[0011] Step 5: Input the obtained solid-phase adsorption dataset and permeability time-varying data into the reservoir numerical simulation model to form a carbon dioxide drive channeling prevention and control simulation system;

[0012] Step 6: Using the carbon dioxide flooding and channeling prevention simulation system, calculate different injection volumes, injection rates, and injection slugs to predict the changes in the sweep range of carbon dioxide flooding and the final recovery rate, and obtain the optimal flooding agent injection parameters.

[0013] Step 2 specifically includes;

[0014] Step 2.1: Fully saturate the artificial core with simulated formation water for the experiment. After saturation for 48 hours, displace the core at a constant flow rate of 0.05 ml / min until the outflow reaches 2-3 PV. Then measure the permeability of the core.

[0015] Step 2.2: Conduct sample displacement flow experiments with displacement samples at concentrations of 0.1%, 0.2%, 0.3%, and 0.4%, and measure the liquid permeability after the experiment.

[0016] Step 3 specifically involves:

[0017] Step 3.1: Fully saturate the artificial core with simulated formation water for the experiment. After saturation for 48 hours, displace the core at a constant flow rate of 0.05 ml / min until the outflow reaches 2-3 PV. Measure the permeability of the core using the liquid.

[0018] Step 3.2: Using a 0.2% concentration of the displacement sample, conduct displacement flow experiments on the displacement agent sample under controlled permeability levels of 0.5mD, 2mD, 10mD, and 30mD, and injection volumes of 0.5PV, 1PV, 2PV, and 3PV. After the experiment, measure the permeability of the liquid.

[0019] In step 4, the plugging efficiency of the modulator is positively correlated with the injection volume.

[0020] In step 4, the plugging effect of the modulator is negatively correlated with the permeability.

[0021] In step 5, the adsorption model is shown in the following formulas (1), (2), (3) and (4):

[0022] C a =C a (C s (1),

[0023] C a =(1-f s )·W s / WR (2),

[0024] W R =ρ R ·(1-θ) / θ (3),

[0025] C s =f s ·W s(4),

[0026] C a C represents the mass of solid particles adsorbed per unit mass of rock; s Mass of solid particles per unit volume concentration; f s Solid particle concentration; W s Mass of solid particles per unit pore volume; W R Rock mass per unit pore volume; ρ R Rock density; f s Solid particle concentration; W s Mass of solid particles per unit pore volume.

[0027] In step 5, the time-varying permeability module is shown in formulas (5) and (6) below:

[0028]

[0029] k s =k sc (C a (6),

[0030] Among them, M p c x represents the mobility of component c in the p phase; p c k is the molar percentage of component c in the p phase; s k is the coefficient of permeability change due to adsorption of solid particles. rp S represents the relative permeability of the p phase; p b represents the saturation of the p phase; p μ is the molar density of the p phase; p Let be the viscosity of the p phase.

[0031] The beneficial effects of this invention patent are as follows: A method for determining the injection parameters of the modifier in low-permeability reservoirs using carbon dioxide flooding; obtaining the dominant channel based on a numerical simulation model of the carbon dioxide flooding reservoir; using indoor experimental methods to obtain the adsorption degree of different concentrations of modifier on the core and the influence of the modifier on the permeability of cores with different permeabilities; solving the mathematical model using a nodal system analysis method and embedding the model into the reservoir numerical simulation model; and using a numerical iteration method to solve the problem, determining the reasonable gas injection volume, gas injection rate, and injection slugs based on the reservoir numerical simulation.

[0032] The study considered the variation characteristics of carbon dioxide physical parameters with temperature and pressure; in the reservoir seepage model, the non-Darcy flow law of fluids and the adsorption and release of solid particles in rock pores were considered; the permeability time-varying due to the influence of the modifier on reservoir permeability was considered; and the carbon dioxide sweep range and final recovery rate were predicted by simulation calculation, which can determine the reasonable gas injection volume, gas injection rate and injection slug for single wells and reservoirs.

[0033] This study provides a method for determining reasonable injection parameters for the carbon dioxide flooding and control agent injection process, and offers a certain theoretical basis and guidance for improving the on-site carbon dioxide flooding development effect. Attached Figure Description

[0034] Figure 1 In an example of a method for determining the injection parameters of a carbon dioxide flooding modifier in low-permeability reservoirs, the average variation of the plugging rate under different concentrations of modifiers is shown.

[0035] Figure 2 ; In an example of a method for determining the injection parameters of a carbon dioxide flooding modifier in low-permeability reservoirs, the plugging rate varies under different displacement volume ratios.

[0036] Figure 3 An experimental model diagram comparing the effects of different concentrations of the modifier on core permeability and the degree of core adsorption of the modifier is presented in an example of a method for determining the injection parameters of the modifier for carbon dioxide flooding in low-permeability reservoirs.

[0037] In the diagram, 1. ISCO pump, 2. six-way valve, 3. formation water injection port, 4. regulating agent injection port, 5. core placement tube, 6. constant temperature chamber, 7. flow meter manual pump, 8. liquid receiving device, 9. flow meter, 10. information acquisition system. Detailed Implementation

[0038] The method for determining the injection parameters of the carbon dioxide flooding agent in low-permeability reservoirs according to the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] This invention discloses a method for determining the injection parameters of a carbon dioxide flooding modifier in low-permeability oil reservoirs. The reservoir has an average porosity of 5%, an average permeability of 0.5 mD, a formation temperature of 85℃, an initial formation pressure of 19.74 MPa, a saturation pressure of 10.27 MPa, and a surface crude oil viscosity of 1.8 mPa·s. Carbon dioxide is injected to enhance oil production through flooding. The method for determining the injection parameters of the carbon dioxide flooding modifier specifically includes the following steps:

[0040] Step 1: Establish a reservoir geological model for the study area and perform historical fitting based on dynamic data to determine the dominant channels;

[0041] Step 2: Using an experimental model, select core samples representative of the reservoir heterogeneity in the study area, inject different concentrations of modulators, and compare the effects of different concentrations of modulators on core permeability and the degree of core adsorption of the modulators. For example, Figure 1As shown, the experimental model includes an ISCO pump 1, a six-way valve 2, a formation water injection port 3, a regulating agent (gel particle) injection port 4, a constant temperature chamber 6, and an information acquisition system 10 connected in sequence. The constant temperature chamber 6 contains a core placement cylinder 5, and the constant temperature chamber 6 is equipped with a flow meter manual pump 7, a liquid receiving device 8, and a flow meter 9 corresponding to the core placement cylinder 5.

[0042] The results obtained after the experiment are shown in Table 1 and Figure 2 As shown,

[0043] Table 1: Plugging efficiency at different concentrations of modulators

[0044]

[0045]

[0046] Step 3: Continue using the experimental setup, select cores with different permeabilities, inject the same concentration of modulator into the cores, and compare the effect of the modulator on cores with different permeabilities. The results are as follows: Figure 3 As shown in Table 2;

[0047] Table 2: Plugging efficiency at different displacement volume ratios

[0048]

[0049]

[0050] Step 4: Summarize the experimental results, obtain the adsorption degree of different concentrations of modulators on the core and the influence of modulators on the permeability of cores with different permeabilities, and obtain the solid phase adsorption dataset and the time-varying permeability dataset; the solid phase adsorption dataset is shown in Table 3 below;

[0051] Table 3: Solid-phase adsorption dataset

[0052] 0.0 0 0.5 0.000019 1.0 0.000033 1.5 0.000046 2.0 0.000057 2.5 0.000069 3.0 0.000081

[0053] Wherein, Ca represents the mass of rock-adsorbed solid particles per unit mass; and Cs represents the mass of solid particles per unit volume concentration.

[0054] The time-varying data set of penetration rate is shown in Table 4 below;

[0055] Table 4: Time-varying data of penetration rate

[0056] 0 1 0.00002 0.6 0.00004 0.3 0.00008 0.1 0.00020 0.08 0.00050 0.05 0.00100 0.02

[0057] Solid-phase adsorption datasets and time-varying permeability datasets are embedded into reservoir numerical simulation models.

[0058] Step 5: Using the carbon dioxide flooding and channeling prevention simulation system, calculate different injection volumes, injection rates, and injection slugs to predict the changes in the sweep range of carbon dioxide flooding and the final recovery rate, and obtain the optimal flooding agent injection parameters as shown in Table 5 below.

[0059] Table 5: Optimal injection parameters for the modulator

[0060]

[0061] Table 5 shows that: the earlier the injection, the better; injection should be done in four stages; the amount of plug used should be reduced by 50% in each stage; and the injection speed should be 15-20m / s. 3 / d, for those with multiple advantageous channels, the injection speed can be appropriately increased.

[0062] This invention provides a method for determining the injection parameters of CO2 flooding modifiers in low-permeability reservoirs. It utilizes a numerical iteration method to solve the problem, determining reasonable gas injection volume, injection rate, and injection slugs based on reservoir numerical simulation. By predicting the CO2 sweep range and final recovery rate through simulation calculations, it can determine reasonable gas injection volume, injection rate, and injection slugs for individual wells and the reservoir. This provides a method for determining reasonable injection parameters for CO2 flooding modifier injection processes, offering a theoretical basis and guidance for improving the effectiveness of CO2 flooding development in the field.

Claims

1. A method for determining the injection parameters of carbon dioxide flooding modifier in low-permeability reservoirs, characterized in that, The specific steps are as follows: Step 1: Establish a reservoir geological model for the study area and perform historical fitting based on dynamic data to determine the dominant channels; Step 2: Fully saturate the artificial core with simulated formation water for 48 hours, then displace and saturate it again at a constant flow rate of 0.05 ml / min. Stop when the outflow reaches 2-3 PV. Measure the permeability of the core. Conduct sample displacement flow experiments with displacement samples at concentrations of 0.1%, 0.2%, 0.3%, and 0.4%, and measure the permeability after the experiment. Step 3: Fully saturate the artificial core with simulated formation water for 48 hours, then displace and saturate it again at a constant flow rate of 0.05 ml / min. Stop when the outflow reaches 2-3 PV. Measure the core permeability. Using a 0.2% concentration of the displacement sample, conduct displacement flow experiments with adjusted displacement agent samples at permeability levels of 0.5 mD, 2 mD, 10 mD, and 30 mD, and injection volumes of 0.5 PV, 1 PV, 2 PV, and 3 PV. Measure the permeability after the experiment. Step 4: Obtain the adsorption degree of different concentrations of modulators on the core and the influence of modulators on the permeability of cores with different permeabilities. Obtain solid phase adsorption dataset and permeability time-varying dataset. The plugging efficiency of the modulator is positively correlated with the injection amount, and the plugging effect of the modulator is negatively correlated with the permeability. Step 5: The obtained solid-phase adsorption dataset and permeability time-varying data are embedded into the reservoir numerical simulation model to form a carbon dioxide flooding and channeling prevention simulation system; the adsorption model is shown in the following formulas (1), (2), (3) and (4): （1）， （2）， （3）， （4）， C a The mass of solid particles adsorbed per unit mass of rock; C s Mass of solid particles per unit volume concentration; f s Solid particle concentration; W s Mass of solid particles per unit pore volume; W R Rock mass per unit pore volume; ρ R Rock density; f s Solid particle concentration; W s Mass of solid particles per unit pore volume; The time-varying permeability module is shown in the following formulas (5) and (6): （5）， （6）， in, M p c The mobility of component c in the p phase; x p c This represents the molar percentage of component c in the p phase. k s The coefficient representing the change in permeability due to the adsorption of solid particles; k rp The relative permeability of the p phase; S p The saturation of the p phase; b p The molar density of the p phase; μ p The viscosity of the p phase; Step 6: Using the carbon dioxide flooding and channeling prevention simulation system, calculate different injection volumes, injection rates, and injection slugs to predict the changes in the sweep range of carbon dioxide flooding and the final recovery rate, and obtain the optimal flooding agent injection parameters.

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