Numerical simulation method for nanofluid oil displacement and related products thereof

Abstract

The invention discloses a numerical simulation method for nanofluid oil displacement and a related product thereof. The method comprises the following steps: constructing an oil displacement model; obtaining a correlation between the average particle size and / or viscosity of the nanofluid at the formation temperature and the retention time; the oil displacement model is updated according to the correlation formula, so that the updated oil displacement model can describe the dynamic behavior of oil displacement of the nanofluid with different average particle sizes and / or viscosities at the formation temperature; and performing numerical simulation on the oil displacement behavior of the nanofluid according to the updated oil displacement model. Through the method, the average particle size and / or viscosity of the nanofluid in the oil displacement model can change along with the increase of the retention time, so that the migration behavior of the nanoparticles in the nanofluid can be simulated more accurately, and the oil displacement model is closer to an actual oil displacement scene; and the dynamic behavior of nanofluid oil displacement can be described more accurately.

Description

Technical Field

[0001] The present application generally relates to the technical field of numerical simulation of oil reservoirs, and more specifically, to a numerical simulation method for nanofluid oil recovery. Background Art

[0002] Numerical reservoir simulation is a technology that uses mathematical models and computer technology to simulate the flow of underground oil and gas. The mathematical model it simulates is based on basic laws such as conservation of mass, conservation of momentum and conservation of energy. Through appropriate numerical solutions, complex partial differential equations can be converted into a form that can be solved by computer.

[0003] Numerical simulation of reservoirs plays an important role in reservoir engineering. Engineers can use numerical simulation of reservoirs to better understand the dynamic characteristics of reservoirs, optimize development plans, and predict production indicators. For example, in the oil recovery scenario where nanofluids are used as oil recovery media, the process of nanofluid oil recovery can be simulated through numerical simulation, and then the injection parameters of nanofluids can be optimized to improve the recovery rate.

[0004] However, the numerical simulation methods for nanofluid oil recovery currently available in the market all simplify the assumption that the particle size of nanoparticles remains unchanged during the oil recovery process, and are unable to accurately describe the migration behavior of nanoparticles during nanofluid oil recovery, resulting in an inability to provide a reliable basis for accurate prediction of recovery rates.

[0005] In view of this, the present application discloses a numerical simulation scheme for nanofluid oil flooding, which can accurately describe the migration behavior of nanoparticles during nanofluid oil flooding, thereby improving the accuracy of nanofluid oil recovery prediction. Summary of the invention

[0006] In order to at least solve one or more of the technical problems mentioned above, the present application proposes a numerical simulation solution for nanofluid oil displacement.

[0007] In a first aspect, the present application provides a numerical simulation method for nanofluid oil recovery, comprising: constructing an oil recovery model, the oil recovery model is used to describe the dynamic behavior of nanofluid oil recovery in an oil reservoir while maintaining an initial particle size and / or viscosity; obtaining a correlation equation between the average particle size and / or viscosity of the nanofluid at formation temperature and the residence time, the correlation equation is used to describe the change pattern of the average particle size and / or viscosity of the nanofluid at formation temperature with the residence time; updating the oil recovery model according to the correlation equation, so that the updated oil recovery model is used to describe the dynamic behavior of nanofluid oil recovery with different average particle sizes and / or viscosities at formation temperature; and numerically simulating the oil recovery behavior of the nanofluid according to the updated oil recovery model.

[0008] In some embodiments, the nanofluid includes nanoparticles and formation water, the nanoparticles are selected from one or more of SiO2, Fe3O4, and carbon nanomaterials; the mass fraction of the nanoparticles in the nanofluid is 0.1%-5%.

[0009] In some other embodiments, constructing the oil displacement model includes: establishing a geological model according to the geological structure of the oil reservoir; gridding the geological model; and numerically converting the gridded geological model to obtain an oil displacement model of the nanofluid that maintains the initial particle size and / or viscosity unchanged.

[0010] In some other embodiments, numerically converting the geological model after grid division includes: numerically converting the geological model after grid division through mathematical model, boundary condition setting and model initialization, wherein the mathematical model includes one or more of porous medium model, Euler-Euler multiphase flow model, and fluid volume method; the boundary condition setting includes setting one or more of injection pressure, temperature, flow rate, and concentration of the nanofluid; and the model initialization includes specifying one or more of initial fluid distribution, initial temperature, and initial pressure in the reservoir.

[0011] In some other embodiments, obtaining the correlation formula between the average particle size and / or viscosity of the nanofluid at the formation temperature and the residence time includes: after heating the nanofluid to the formation temperature, measuring the average particle size and / or viscosity of the nanofluid once every n hours, for a total of m times, where 2≤n≤24, 5≤m≤50; determining the residence times corresponding to the average particle sizes and / or viscosities of the m nanofluids at the formation temperature; performing function fitting on the average particle sizes and / or viscosities of the m nanofluids at the formation temperature and the m residence times corresponding thereto, to obtain the correlation formula.

[0012] In some other embodiments, updating the oil displacement model according to the correlation equation includes: obtaining the residence time of the nanofluid in the oil displacement model; determining the average particle size and / or viscosity of the nanofluid corresponding to the residence time according to the correlation equation and the residence time, so as to update the oil displacement model.

[0013] In some other embodiments, the nanofluid includes a first component and / or a second component, and obtaining the residence time of the nanofluid in the oil displacement model includes: determining the maximum reaction time and reaction rate of the first component converted into the second component according to the correlation equation, so that the oil displacement model simulates the conversion process of the first component into the second component through a component transport model; wherein the first component is used to describe the initial state of the nanofluid at the formation temperature, and the second component is used to describe the state when the average particle size and / or viscosity of the nanofluid reaches a maximum value at the formation temperature; obtaining the mass fraction of the second component in the oil displacement model; and determining the residence time according to the mass fraction of the second component.

[0014] In some other embodiments, determining the residence time according to the mass fraction of the second component includes: when the mass fraction of the second component is equal to 1, the residence time is the maximum reaction time; when the mass fraction of the second component is less than 1, determining the residence time according to the mass fraction of the second component and the maximum reaction time.

[0015] In a second aspect, the present application provides an electronic device comprising: a processor; a memory on which program instructions for numerical simulation of nanofluid oil displacement are stored, and when the program instructions are executed by the processor, the method described in the first aspect of the present application and its multiple embodiments is implemented.

[0016] In a third aspect, the present application provides a computer-readable storage medium having stored thereon program instructions for numerically simulating nanofluid oil displacement. When the program instructions are executed by one or more processors, the operations of the method described in the first aspect of the present application and its multiple embodiments are implemented.

[0017] Through the numerical simulation scheme of nanofluid oil displacement provided above, the embodiment of the present application can accurately simulate the migration behavior of nanoparticles when the nanofluid is used to drive oil in the reservoir, and then more accurately describe the dynamic behavior of the nanofluid oil displacement, providing a good basis for behaviors such as improving the recovery rate. Furthermore, the embodiment of the present application can also simulate the conversion process of the nanofluid between two different components in the oil displacement model through the component transport model, so that the residence time of the nanofluid in the oil reservoir can be characterized by the process of conversion, and then the change law of the average particle size and / or viscosity of the nanofluid at the formation temperature can be coupled with the oil displacement model according to the residence time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easy to understand. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0019] Figure 1 A schematic diagram showing a numerical simulation method for nanofluid oil displacement according to some embodiments of the present application;

[0020] Figure 2 A schematic diagram showing another numerical simulation method for nanofluid oil displacement according to some embodiments of the present application;

[0021] Figure 3 A schematic diagram showing another numerical simulation method for nanofluid oil displacement according to some embodiments of the present application;

[0022] Figure 4 A schematic diagram showing another numerical simulation method for nanofluid oil displacement according to some embodiments of the present application;

[0023] Figure 5 A schematic block diagram of an electronic device according to some embodiments of the present application is shown. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0025] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present application indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this application specification and claims, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms. It should also be further understood that the term "and / or" used in this application specification and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0027] As used in this specification and claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0028] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0029] Exemplary application scenarios

[0030] Nanofluid is a dispersed system composed of nanosized materials (such as nanoparticles, nanofibers, nanotubes) dispersed in a base fluid (such as water, oil, etc.). Due to the unique interaction between nanofluid and the oil-water interface, it is also widely used in the field of oil recovery.

[0031] In actual oil recovery scenarios, nanoparticles in nanofluids tend to aggregate in oil layers due to their high surface energy and surface charge, which in turn affects their stability and oil recovery performance. Agglomeration of nanofluids refers to the phenomenon that nanoparticles in nanofluids aggregate into larger particles or aggregates in liquid media due to various forces that attract each other.

[0032] The agglomeration of nanofluids in oil layers continues to increase with their residence time in the reservoir. As the diameter of nanoparticles in the nanofluid changes, the migration behavior of the nanoparticles also changes, thereby changing the properties of the nanofluid and ultimately affecting the oil recovery effect.

[0033] However, the current numerical simulation methods of nanofluid oil recovery all simplify and assume that the particle size of nanoparticles remains unchanged during the oil recovery process. The actual process of nanofluid oil recovery cannot be accurately described, and it is impossible to make accurate predictions on the recovery rate or make reasonable plans for the mining project.

[0034] In view of this, the present application discloses a numerical simulation scheme for nanofluid oil recovery, which can accurately describe the change law of the particle size of nanoparticles in the nanofluid due to the gradual increase in agglomeration, thereby accurately describing the migration behavior of the nanoparticles, and providing an important basis for the dynamic behavior of nanofluid oil recovery and the accurate prediction of the recovery rate.

[0035] Figure 1 A schematic diagram of a numerical simulation method 100 for nanofluid oil displacement according to some embodiments of the present application is shown.

[0036] like Figure 1As shown, in step S110, an oil recovery model is constructed, and the oil recovery model is used to describe the dynamic behavior of nanofluids in oil recovery in oil reservoirs while keeping the initial particle size and / or viscosity unchanged. Oil reservoirs are geological structures that can store and / or produce oil. Those skilled in the art can select different oil reservoirs for simulation according to actual application conditions. In some embodiments, the oil recovery model can be, for example, a model constructed by computer simulation technology to describe the flow and oil recovery process of nanofluids in oil reservoirs; the initial particle size and / or viscosity of the nanofluid is kept unchanged, that is, the average particle size and / or viscosity of the nanofluid is assumed to be a fixed value during the simulation. In some embodiments, the oil recovery model can include a three-dimensional model and a mathematical model. The three-dimensional model can be, for example, a digital representation of the oil reservoir structure simulated by a computer; the mathematical model can be, for example, a mathematical equation and formula used by a computer to describe the flow and oil recovery process of nanofluids in oil reservoirs through a mathematical model.

[0037] As mentioned above, nanofluid is a dispersion system of nano-sized materials in a base fluid. In one implementation scenario, the nanofluid can be prepared by, for example, adding nanoparticles to formation water (taken from the formation where the reservoir to be simulated is located) and ultrasonic dispersion. In some embodiments, the nanofluid includes nanoparticles and formation water, and the nanoparticles can be, for example, one or more selected from SiO2, Fe3O4, and carbon nanomaterials; the mass fraction of nanoparticles in the nanofluid can be, for example, 0.1%-5%.

[0038] Next, the process enters step S120, where the correlation between the average particle size and / or viscosity of the nanofluid at the formation temperature and the residence time is obtained. Wherein, the formation temperature can be, for example, the temperature of the formation where the reservoir to be simulated is located, the average particle size of the nanofluid at the formation temperature can be, for example, the average particle size of the nanoparticles in the nanofluid at the formation temperature, and the residence time can be, for example, the time the nanofluid stays in the reservoir. As mentioned above, the nanofluid will agglomerate in the oil layer, and the average particle size of the nanoparticles in the nanofluid will gradually increase with the residence time of the nanofluid in the formation. It is understandable that the change in the average particle size of the nanoparticles in the nanofluid will also change the viscosity of the nanofluid, and the change in viscosity will also affect the stability and oil displacement performance of the nanofluid. In view of this, the relationship between the residence time of the nanofluid at the formation temperature and the average particle size and / or viscosity can be used to describe the change law of the average particle size and / or viscosity of the nanofluid in the formation, that is, to obtain a correlation formula for describing the change law of the average particle size and / or viscosity of the nanofluid at the formation temperature.

[0039] In one implementation scenario, the acquisition of the correlation equation can be, for example, testing the average particle size and / or viscosity of the nanofluid at the formation temperature at equal intervals in units of time, and performing function fitting based on the measured data. Specifically, obtaining the correlation equation between the average particle size and / or viscosity of the nanofluid at the formation temperature and the residence time includes: after heating the nanofluid to the formation temperature, measuring the average particle size and / or viscosity of the nanofluid every n hours, for a total of m times, where 2≤n≤24, 5≤m≤50; determining the residence time corresponding to the average particle size and / or viscosity of the m nanofluids at the formation temperature; performing function fitting on the average particle size and / or viscosity of the m nanofluids at the formation temperature and the m residence times corresponding thereto, to obtain the correlation equation.

[0040] Next, the process enters step S130, where the oil displacement model is updated according to the correlation formula, so that the updated oil displacement model is used to describe the dynamic behavior of nanofluid oil displacement with different average particle sizes and / or viscosities at the formation temperature. Updating the oil displacement model according to the correlation formula can, for example, be based on the variation law of the average particle size and / or viscosity of the nanofluid in the formation described by the correlation formula, and updating the information related to the average particle size and / or viscosity of the nanofluid in the oil displacement model, so that the average particle size and / or viscosity of the nanofluid in the updated oil displacement model is closer to the variation in the actual oil displacement scenario.

[0041] Finally, the process enters step S140, where the oil displacement behavior of the nanofluid is numerically simulated according to the updated oil displacement model. As mentioned above, the average particle size and / or viscosity of the nanofluid in the updated oil displacement model is closer to the changes in the actual oil displacement scenario. Therefore, when the oil displacement behavior of the nanofluid is numerically simulated by the updated oil displacement model, it can be more accurate.

[0042] In one embodiment, numerically simulating the oil displacement behavior of the nanofluid according to the updated oil displacement model may include, for example: determining the recovery factor of the nanofluid oil displacement according to the updated oil displacement model. Specifically, the recovery factor may be determined according to the following formula:

[0043]

[0044] Among them, V 初 is the volume of oil filled in the displacement model at the beginning of the simulation, V is the volume of oil remaining in the displacement model at the end of the simulation, and S is the recovery factor.

[0045] Figure 2 A schematic diagram of another numerical simulation method 200 for nanofluid oil displacement according to some embodiments of the present application is shown.

[0046] like Figure 2As shown, in step S210, a geological model is established based on the characteristics of the oil reservoir and the nanofluid with the initial particle size kept unchanged. The geological model can be, for example, a mathematical and geometric representation constructed based on the characteristics of the oil reservoir to be simulated, and is used to describe the geometric structure characteristics of the oil reservoir. Among them, the characteristics of the oil reservoir can be, for example, the stratigraphic structural characteristics, porosity, permeability, etc. of the oil reservoir, and those skilled in the art can adjust them according to actual conditions, and the present application does not impose any restrictions in this regard.

[0047] Next, the process enters step S220, where the geological model is meshed. Meshing the geological model is to discretize the continuous space in the geological model into a network composed of many small volumes or units, so that the flow of the fluid can be solved by numerical simulation. Furthermore, the type, size, resolution, etc. of the grid involved in the meshing process can be adjusted by those skilled in the art according to the needs of the actual application, and this application does not impose any restrictions in this regard.

[0048] Next, the process enters step S230, where the meshed geological model is numerically converted to obtain an oil displacement model of the nanofluid that keeps the initial particle size and / or viscosity unchanged. In some embodiments, the numerical conversion of the meshed geological model can be performed, for example, through a mathematical model, boundary condition setting, and model initialization, so that the characteristics of the oil reservoir in the geological model and the nanofluid that keeps the initial particle size unchanged can be represented and processed by mathematical equations and numerical methods. Specifically, the mathematical model includes one or more of a porous medium model, an Euler-Euler multiphase flow model, and a fluid volume method, which are used to describe the interaction between the three phases of water, nanoparticles, and oil in the oil reservoir. Among them, the porous medium model can be used to establish mathematical equations for fluid flow and transmission processes; the Euler-Euler multiphase flow model can be used to describe the dynamic behavior of multiphase flow, such as the volume fraction of each phase, relative permeability, capillary pressure, etc.; the fluid volume method can be used to track the interface of different fluid phases, and multiple mathematical models can also be coupled to enhance the description of fluid flow and phase behavior. The boundary condition setting includes setting one or more of the injection pressure, temperature, flow rate, and concentration of the nanofluid. The model initialization includes specifying one or more of the initial fluid distribution, initial temperature, and initial pressure in the reservoir.

[0049] It is understandable that the above mathematical model is illustrative rather than restrictive, and those skilled in the art may select it according to actual application conditions.

[0050] Next, the process enters step S240, where the correlation between the average particle size and / or viscosity of the nanofluid at the formation temperature and time is obtained. Next, the process enters step S250, where the oil displacement model is updated according to the correlation. Finally, the process enters step S260, where the oil displacement behavior of the nanofluid is numerically simulated according to the updated oil displacement model. It should be noted that Figure 2 The operations performed in steps S240-S260 shown in FIG. Figure 1 The S120-S140 shown in FIG. 1 are the same, so the same contents are not repeated here.

[0051] As mentioned above, as the residence time of the nanofluid in the reservoir increases, its average particle size and / or viscosity will also change. In order to enable the oil recovery model to more accurately describe the dynamic behavior of the nanofluid in the reservoir, the average particle size and / or viscosity of the nanofluid at different residence times in the oil recovery model can be updated to make the average particle size and / or viscosity of the nanofluid in the oil recovery model closer to the changes in the actual oil recovery scenario.

[0052] Figure 3 A schematic diagram of yet another numerical simulation method 300 for nanofluid oil displacement according to some embodiments of the present application is shown.

[0053] like Figure 3 As shown, in step S310, an oil displacement model is constructed. Then, the process enters step S320, where a correlation equation between the average particle size and / or viscosity of the nanofluid at the formation temperature and the residence time is obtained. It should be noted that Figure 3 The operations performed in steps S310-S320 shown in FIG. Figure 1 S110-S120 shown in FIG. 1 are the same, so the same contents are not repeated here.

[0054] Next, the process enters step S330, where the residence time of the nanofluid in the oil displacement model is obtained. In the oil displacement model, the residence time of the nanofluid can be the same as the actual time, or can be adjusted by acceleration according to the needs of the simulation. Those skilled in the art can make adjustments based on the needs of the simulation in the actual application scenario, and this application does not impose any restrictions in this regard.

[0055] Then the process enters step S340, at which the average particle size and / or viscosity of the nanofluid corresponding to the residence time is determined based on the correlation formula and the residence time to update the oil displacement model. As mentioned above, the correlation formula can be used to describe the change pattern of the average particle size and / or viscosity of the nanofluid at the formation temperature as the residence time. According to the correlation formula, the average particle size and / or viscosity corresponding to the nanofluid at different residence times in the reservoir can be determined. Further, the correlation formula is coupled with the oil displacement model according to the residence time obtained in step S330, so that the average particle size and / or viscosity of the nanofluid in the oil displacement model can be changed accordingly with the different residence times.

[0056] As mentioned above, the correlation equation may be obtained by fitting a function based on m data measured every n hours. In some embodiments, the correlation equation may include a first correlation equation and a second correlation equation. The first correlation equation may be used to describe the variation of the average particle size of the nanofluid at the formation temperature with the residence time; the second correlation equation may be used to describe the variation of the viscosity of the nanofluid at the formation temperature with the residence time. Specifically, the first correlation equation may be, for example, the following equation (2):

[0057] d=f(t) (2)

[0058] Wherein, d is the average particle size of the nanofluid, and t is the residence time of the nanofluid. According to the formula shown in formula (2), the average particle size of the nanofluid corresponding to different residence times can be determined.

[0059] The second correlation equation can be, for example, the following equation (3):

[0060] η=f(t) (3)

[0061] Wherein, η is the viscosity of the nanofluid, and t is the residence time of the nanofluid. According to the formula shown in formula (3), the viscosity of the nanofluid at different residence times can be determined.

[0062] Finally, the process enters step S350, where the oil displacement behavior of the nanofluid is numerically simulated according to the updated oil displacement model. Figure 3 The operation performed in step S350 shown in FIG. Figure 1 The S140 shown in FIG. 1 is the same as that in FIG. 1 , so the same contents are not repeated here.

[0063] In one implementation scenario, in order to obtain meaningful simulation results within a reasonable simulation time, the time in the oil displacement model is usually accelerated; and in the oil displacement model, continuous time is discretized into a series of points, making it difficult to intuitively describe or observe the residence time. In view of this, the residence time can be indirectly characterized by other parameters related to the residence time that can be intuitively described or observed, thereby obtaining the residence time of the nanofluid.

[0064] Further, as mentioned above, as the nanofluid stays in the reservoir for a longer time, the average particle size and / or viscosity of the nanofluid will gradually increase until it reaches a maximum value. This process can be equivalent to the process of the nanofluid gradually transforming from the initial state of the initial particle size and / or viscosity to the state when the average particle size and / or viscosity reaches the maximum value (hereinafter replaced by the final state). In other words, the change law of the average particle size and / or viscosity of the nanofluid at the formation temperature can be equivalent to the transformation process of the nanofluid between the initial state and the final state.

[0065] In some embodiments, the nanofluid may include a first component and / or a second component; wherein the first component is used to describe the nanofluid in the initial state, that is, the initial state of the nanofluid at the formation temperature; the second component is used to describe the final state of the nanofluid, that is, the state when the average particle size and / or viscosity of the nanofluid at the formation temperature reaches the maximum value. When the simulation starts, all the nanofluids in the oil displacement model are in the initial state, that is, all are the first component. As the residence time of the nanofluid in the reservoir increases, the first component gradually transforms into the second component, that is, the mass of the second component continues to increase until the first component is completely transformed into the second component, and the mass of the second component reaches the maximum value. Through the above method, the process of transformation of the nanofluid from the initial state to the final state can be determined according to the mass fraction of the second component, and then the residence time of the nanofluid in the reservoir can be determined.

[0066] In some embodiments, the component transport model can be used to set the conversion process of the nanofluid from the first component to the second component at a certain reaction rate in the oil displacement model, so that the residence time of the nanofluid in the reservoir can be determined according to the mass fraction of the second component in the oil displacement model, and then the average particle size and / or viscosity of the nanofluid at the formation temperature corresponding to the residence time can be determined according to the residence time to update the oil displacement model. Among them, the component transport model is a mathematical model that can be used to describe and simulate the changes in the distribution of different components in the oil displacement model over time and space.

[0067] Figure 4 A schematic diagram of yet another numerical simulation method 400 for nanofluid oil displacement according to some embodiments of the present application is shown.

[0068] like Figure 4As shown, in step S410, an oil displacement model is constructed. Then, the process enters step S420, where the correlation between the average particle size and / or viscosity of the nanofluid at the formation temperature and time is obtained. It should be noted that Figure 4 The operations performed in steps S410-S420 shown in FIG. Figure 3 S310-S320 shown in FIG. 1 are the same, so the same contents are not repeated here.

[0069] Next, the process enters step S430, where the maximum reaction time and reaction rate of the first component converted into the second component are determined according to the correlation equation, so that the oil displacement model simulates the conversion process of the first component into the second component through the component transport model. Among them, the first component can be used to describe the initial state of the nanofluid at the formation temperature, and the second component can be used to describe the state when the average particle size and / or viscosity of the nanofluid reaches the maximum value at the formation temperature.

[0070] As described above, the process of nanofluid conversion from the first component to the second component can be simulated in the oil displacement model through the component transport model. Specifically, the conversion process can be set according to the rate of conversion from the first component to the second component and the maximum reaction time. Among them, the maximum reaction time can be determined according to the formula (2) and / or formula (3) described above, so as to set a cutoff time limit for the conversion process; the reaction rate can be determined, for example, according to the formula (4) shown below:

[0071]

[0072] Where r is the reaction rate of the nanofluid from the first component to the second component, ρ is the density of nanoparticles in the nanofluid, ε is the volume fraction of the nanoparticle phase, and x is the maximum reaction time.

[0073] Next, the process proceeds to step S440, where the mass fraction of the second component in the oil displacement model is obtained. In some embodiments, the mass fraction of the second component can be directly determined according to a component transport model that has been set in the oil displacement model.

[0074] Next, the process enters step S450, where the residence time is determined according to the mass fraction of the second component. It is understandable that, considering that the average particle size and / or viscosity of the nanofluid vary differently before and after the mass fraction of the second component reaches 1, the average particle size and / or viscosity of the nanofluid can be calculated separately before and after the mass fraction of the second component reaches 1.

[0075] Specifically, determining the residence time according to the mass fraction of the second component may include, for example: when the mass fraction of the second component is equal to 1, determining the residence time as the maximum reaction time; when the mass fraction of the second component is less than 1, determining the residence time according to the mass fraction of the second component and the maximum reaction time. In one embodiment, determining the residence time according to the mass fraction of the second component and the maximum reaction time may be performed, for example, by the following formula (5):

[0076] t=yx(5)

[0077] Where t is the residence time of the nanofluid, y is the mass fraction of the second component, and x is the maximum reaction time.

[0078] Next, the process proceeds to step S460, where the average particle size and / or viscosity of the nanofluid corresponding to the residence time is determined based on the correlation and the residence time to update the oil displacement model. Finally, the process proceeds to step S470, where the oil displacement behavior of the nanofluid is numerically simulated based on the updated oil displacement model. It should also be noted that Figure 4 The operations performed in steps S460-S470 shown in FIG. Figure 3 The S340-S350 shown in FIG. 1 are the same, so the same contents are not repeated here.

[0079] pass Figure 4 According to the method shown in , the present application can characterize the residence time of the nanofluid in the reservoir through the process of transformation of the nanofluid from the first component to the second component, and then couple the oil displacement model with the variation law of the average particle size and / or viscosity of the nanofluid at the formation temperature with the residence time according to the residence time, so as to accurately simulate the migration behavior of nanoparticles and provide a basis for accurately describing the oil displacement behavior of the nanofluid.

[0080] In some possible implementations, various aspects of the present application may also be implemented as an electronic device. Figure 5 A schematic block diagram of an electronic device 500 according to some embodiments of the present application is shown.

[0081] like Figure 5 As shown, the electronic device 500 includes a processor 510 and a memory 520. In one embodiment, the memory 520 stores program instructions for numerically simulating nanofluid oil displacement. When the program instructions are executed by the processor, the above-mentioned Figure 1-Figure 4 The method and steps of its various embodiments are described.

[0082] In some other possible implementations, various aspects of the present application can also be implemented as a computer-readable storage medium, on which program instructions for numerical simulation of nanofluid oil displacement are stored. When the program instructions are executed by one or more processors, the above combined Figure 1-Figure 4 The described method and steps of multiple embodiments thereof. The computer readable storage medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0083] Although multiple embodiments of the present application have been shown and described herein, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art can think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be adopted. The attached claims are intended to limit the scope of protection of the present application, and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A numerical simulation method for nanofluid oil displacement, characterized in that: include: Constructing an oil displacement model, wherein the oil displacement model is used to describe the dynamic behavior of nanofluids displacing oil in an oil reservoir while maintaining an initial particle size and / or viscosity unchanged; Obtaining a correlation formula between the average particle size and / or viscosity of the nanofluid at formation temperature and the residence time, wherein the correlation formula is used to describe a change rule of the average particle size and / or viscosity of the nanofluid at formation temperature with the residence time; Updating the oil displacement model according to the correlation formula, so that the updated oil displacement model is used to describe the dynamic behavior of the nanofluid oil displacement with different average particle sizes and / or viscosities at formation temperature; as well as The oil displacement behavior of the nanofluid is numerically simulated according to the updated oil displacement model.

2. The numerical simulation method according to claim 1, characterized in that: The nanofluid comprises nanoparticles and formation water, wherein the nanoparticles are selected from one or more of SiO2, Fe3O4 and carbon nanomaterials; the mass fraction of the nanoparticles in the nanofluid is 0.1%-5%.

3. The numerical simulation method according to claim 1, characterized in that: Building an oil recovery model includes: establishing a geological model based on the geological structure of the reservoir; Gridding the geological model; The meshed geological model is numerically converted to obtain an oil displacement model of the nanofluid with the initial particle size and / or viscosity unchanged.

4. The method according to claim 3, characterized in that The numerical conversion of the meshed geological model includes: The meshed geological model is numerically converted through mathematical models, boundary condition settings and model initialization, wherein the mathematical model includes one or more of a porous medium model, an Euler-Euler multiphase flow model, and a fluid volume method; the boundary condition settings include setting one or more of the injection pressure, temperature, flow rate, and concentration of the nanofluid; and the model initialization includes specifying one or more of an initial fluid distribution, an initial temperature, and an initial pressure in a specified reservoir.

5. The method according to claim 1, characterized in that The correlation formula for obtaining the average particle size and / or viscosity of the nanofluid at the formation temperature and the residence time includes: After the nanofluid is heated to the formation temperature, the average particle size and / or viscosity of the nanofluid is measured once every n hours, for a total of m times, where 2≤n≤24, 5≤m≤50; Determine the residence time corresponding to the average particle size and / or viscosity of m nanofluids at the formation temperature; Function fitting is performed on the average particle sizes and / or viscosities of the m nanofluids at the formation temperature and the m residence times corresponding thereto to obtain the correlation equation.

6. The method according to claim 1, characterized in that Updating the oil displacement model according to the correlation formula includes: Obtaining the residence time of the nanofluid in the oil displacement model; The average particle size and / or viscosity of the nanofluid corresponding to the residence time is determined according to the correlation equation and the residence time to update the oil displacement model.

7. The method according to claim 6, characterized in that The nanofluid includes a first component and / or a second component, and obtaining the residence time of the nanofluid in the oil displacement model includes: Determine the maximum reaction time and reaction rate of the first component converted into the second component according to the correlation formula, so that the oil displacement model simulates the conversion process of the first component into the second component through the component transport model; wherein the first component is used to describe the initial state of the nanofluid at the formation temperature, and the second component is used to describe the state when the average particle size and / or viscosity of the nanofluid reaches a maximum value at the formation temperature; Obtaining the mass fraction of the second component in the oil displacement model; The residence time is determined according to the mass fraction of the second component.

8. The method according to claim 7, characterized in that Determining the residence time according to the mass fraction of the second component comprises: When the mass fraction of the second component is equal to 1, the residence time is the maximum reaction time; When the mass fraction of the second component is less than 1, the residence time is determined according to the mass fraction of the second component and the maximum reaction time.

9. An electronic device, characterized in that: include: processor; A memory having stored thereon program instructions for numerical simulation of nanofluid oil displacement, wherein when the program instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having stored thereon program instructions for numerically simulating nanofluid oil displacement, wherein when the program instructions are executed by one or more processors, the method according to any one of claims 1 to 8 is implemented.

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