Effect prediction method and device of bio-nano oil recovery technology

Abstract

The present invention discloses a method and device for predicting the effect of bio-nano oil recovery technology. The method includes: obtaining basic data of the target area and constructing a corresponding three-dimensional geological model; using reservoir numerical simulation software to construct a numerical model that characterizes the bio-nano oil recovery technology; using the numerical model to simulate and optimize process parameters and construct a process parameter optimization chart; predicting the effect of the bio-nano oil recovery technology and characterizing its mechanism. The method and device of the present invention can quantitatively and accurately describe the remaining oil distribution and technical mechanism of low-permeability reservoirs after using bio-nano oil recovery technology.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration, and in particular to a method and device for predicting the effect of bio-nano oil displacement technology. Background Art

[0002] After primary, secondary, and even tertiary oil recovery, the recovery rate of medium- and high-permeability old oil fields has reached 40%-60%, but more than 40% of crude oil is still unused. New oil resources are mainly low-permeability reservoirs, and about 30% of reservoirs cannot be injected with water. Conventional EOR technology is to solve the problems of water drive crossflow and oil washing efficiency in the affected area of ​​water drive, which is basically ineffective for low-permeability reservoirs that "cannot be injected or produced". Due to the problems of traditional EOR technology, some researchers in the field of oil extraction have turned their attention to nanomaterials. Nanoparticles provide a new way to solve unresolved challenges. Compared with gas drive, water drive and chemical drive used in traditional EOR processes, nanoparticles are injected into the ground as a medium, showing some unusual advantages.

[0003] Nanoparticles have a small particle size. One of the problems with polymer chemical flooding is that injecting polymers into the reservoir can easily lead to formation pore blockage, which reduces formation permeability and increases water injection and injection costs. Commonly used nanoparticles, such as SiO 2 , TiO 2 and Al 2 O 3 , with a diameter of approximately 1nm-100nm, which is much smaller than the reservoir pore size. Therefore, nanoparticles can easily flow through porous media and will not cause a decrease in formation permeability, which increases the effectiveness of nanofluid flooding to enhance oil recovery. Also due to the ultra-small size of nanoparticles, they can reach some tiny gaps that traditional displacement media cannot enter. Therefore, nanoparticles can affect more areas and increase macroscopic sweep efficiency. However, there is currently no effective method to characterize the distribution of remaining oil and the technical mechanism after using bio-nano flooding technology in low-permeability reservoirs. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a method and device for predicting the effect of bio-nano oil recovery technology that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for predicting the effect of bionano oil displacement technology is provided, comprising:

[0006] Step S1: Obtain basic data of the target area and construct a corresponding three-dimensional geological model;

[0007] Step S2: constructing a numerical model to characterize the bio-nano oil recovery technology using reservoir numerical simulation software;

[0008] Step S3: Utilizing the numerical model to simulate and optimize process parameters, and constructing a process parameter optimization chart;

[0009] Step S4: predict the effect of bio-nano oil recovery technology and characterize its mechanism.

[0010] Further, step S2 includes:

[0011] Step S21: Obtain basic data using indoor physical model experiments to obtain corresponding keywords;

[0012] Step S22: writing keywords ENDSCALE, ENPTPC, SOWCR, TRMMULTC and TRMMULTT into the fluid parameter file, and writing historical production data, perforation data and completion data of the target area and keywords WTRACER, ACTIONW, ARITHMETIC, ENDACTIO and WBHZONE into the production parameter file;

[0013] Step S23: constructing the numerical model according to the fluid parameter file, the production parameter file, the biological nanoparticle diffusion reaction formula and the biological nanoparticle adsorption reaction formula.

[0014] Furthermore, ENDSCALE and ENPTPC represent the phase permeability endpoint calibration;

[0015] SOWCR is expressed as the relationship between the bionanoparticle concentration and the residual oil saturation in the phase permeability curve and is calculated using the following formula (1):

[0016]

[0017] Where δ is the concentration of bio-nanoparticles, in %; V is the volume of bio-nanoparticles, in m 3 ; g is the coefficient affected by the physical properties of the formation, ranging from 0 to 1; r p is the radius of the bio-nanoparticles reaching the formation, in m; h is the formation thickness, in m; is the density of bionanoparticles, in g / cm 3 ; S or is the residual oil saturation of the formation, in %;

[0018] TRMMULTC is expressed as the relationship between the bionanoparticle concentration and the relative permeability of the oil phase and is calculated using the following formula (2):

[0019]

[0020] Among them, K r is the relative permeability of the oil phase, in %; b is a coefficient determined by the lithology of the reservoir rock, ranging from 1 to 10; f is a coefficient related to the diffusion rate of the bio-nanoparticle system, ranging from 0 to 1; δ is the concentration of bio-nanoparticles, in %;

[0021] TRMMULTT is the relationship between the bionanoparticle formation retention time and permeability, which is calculated by the following formula (3):

[0022]

[0023] Where t is the retention time of the bio-nanoparticle formation, in years; c is the coefficient related to the adsorption and desorption rate of the bio-nanoparticle system, ranging from 1 to 100; δ 0 is the initial concentration of bio-nanoparticles, in %; a is the bio-nanoparticle injection capacity increase coefficient, Among them, K D is the dimensionless average permeability from the wellbore to the injection front, r e is the supply radius of the injection well, in m, r w is the borehole radius of the injection well, in m; r p is the radius of the bio-nanoparticles reaching the formation, in m.

[0024] Furthermore, WTRACER was used to characterize the initial concentration of bio-nanoparticles and the concentration of bio-nanoparticles after injection; WBHZONE was used to characterize the average reservoir porosity and average permeability of the target area; ACTIONW represented the start activity, ARITHMETIC represented the mathematical operation, and ENDACTIO represented the end activity.

[0025] Furthermore, the biological nanoparticle diffusion reaction equation is

[0026] nanoFH→0.5nanoSH+0.5nanoXF

[0027] Among them, nanoFH is a biological nanoparticle, nanoSH is a nanoparticle with a particle size range of 10 to 20 nm, and nanoXF is a nanoparticle with a particle size range of 30 to 50 nm.

[0028] Further, the bionanoparticle adsorption reaction equation is

[0029]

[0030]

[0031] Where k is the reaction rate constant, ranging from 1 to 10.

[0032] Furthermore, the step S3 comprises:

[0033] Step S31: perform orthogonal design to obtain different solutions;

[0034] Step S32: using the numerical model to predict the oil increase results of different schemes;

[0035] Step S33: Compare the oil increase results of different schemes to obtain the optimal scheme.

[0036] According to another aspect of the present invention, there is provided a device for predicting the effect of bio-nano oil displacement technology, comprising:

[0037] An acquisition module is used to obtain basic data of the target area;

[0038] Model building module, used to build corresponding three-dimensional geological model according to basic data of target area; and to build numerical model representing bio-nano oil recovery technology by using reservoir numerical simulation software;

[0039] An optimization module, used to use the numerical model to simulate and optimize process parameters and construct a process parameter optimization chart;

[0040] The effect prediction module is used to predict the effect of bio-nano oil recovery technology and characterize its mechanism.

[0041] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0042] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned method for predicting the effect of bio-nano oil displacement technology.

[0043] According to another aspect of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for predicting the effect of bio-nano oil recovery technology.

[0044] Compared with the prior art, the method and device for predicting the effect of bionano oil displacement technology of the present invention have at least the following beneficial effects:

[0045] (1) The present invention provides a comprehensive method to enable effective characterization of the residual oil distribution and technical mechanism after using bionano flooding technology in low permeability reservoirs;

[0046] (2) The present invention uses commercial numerical simulation software and indoor physical modeling experiments to provide a detailed description of the remaining oil distribution and technical mechanism after using bionano oil recovery technology in low permeability reservoirs;

[0047] (3) The present invention provides quantitative and operational technical methods and implementation steps;

[0048] (4) The method proposed in this invention provides an extremely valuable reference for other innovative technologies in the petroleum field. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0050] Figure 1 It is a schematic flow chart of a method for predicting the effect of a bio-nano oil displacement technology of the present invention.

[0051] Figure 2 It is a structural schematic diagram of a device for predicting the effect of bio-nano oil displacement technology of the present invention.

[0052] Figure 3 It is a structural schematic diagram of a computing device of the present invention.

[0053] Figure 4 It is the oil-water relative permeability curve after the bio-nanoparticles are injected in Example 1 of the present invention, wherein Kro represents the oil phase relative permeability, Krw represents the water phase relative permeability, and Sw represents the water saturation.

[0054] Figure 5 This is a graph showing the oil washing efficiency of the bio-nanoparticles in Example 1 of the present invention.

[0055] Figure 6 This is a rock type diagram of the sampled core in Example 1 of the present invention.

[0056] Figure 7 This is a graph showing the experimental results of the core / injected water contact angle after different treatments in Example 1 of the present invention.

[0057] Figure 8 It is the numerical model for characterizing the bio-nano oil recovery technology constructed in Example 1 of the present invention.

[0058] Fig. 9 The effect of injection rounds of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown.

[0059] Fig.10The effect of the injection concentration of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown.

[0060] Fig.11 The effect of the injection amount of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown.

[0061] Fig.12 The effect of the injection rate of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown.

[0062] Fig.13 The effect of the injection time of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown.

[0063] Fig.14 This is a comparison of the remaining oil distribution before and after bionano flooding in the low permeability reservoir in Example 1 of the present invention.

[0064] Fig.15 This is a comparison of the remaining oil distribution before and after bionano flooding in the medium permeability reservoir in Example 1 of the present invention.

[0065] Fig.16 This is a comparison of the remaining oil distribution before and after bionano flooding in the high permeability reservoir in Example 1 of the present invention.

[0066] Fig.17 It is a schematic diagram of the principle of the bio-nano oil displacement technology in Example 1 of the present invention. DETAILED DESCRIPTION

[0067] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0068] First, it should be noted that "bio-nanoparticles", "bio-nanofluids" and "bio-nano oil-displacing agents" have the same meaning in the present invention and have the meanings commonly understood in the art and can be used interchangeably in the present invention.

[0069] Bio-nano oil recovery technology is a new type of oil recovery technology that has emerged in recent years. Its characteristics can be summarized as "biological profile control and nano oil recovery". Specifically, bio-nano oil recovery technology has the following advantages:

[0070] Environmentally friendly: good biodegradability, microbial COD and ammonia nitrogen degradation rates are both around 95%;

[0071] Improve sweep efficiency: Improve the seepage law of low-permeability reservoir fluids, improve self-dispersion performance, and make water drive sweep to medium and low permeability layers with low production degree;

[0072] Improve oil washing efficiency: the oil washing efficiency can reach more than 70%, the viscosity reduction rate can reach more than 90%, and it has a good oil-rock stripping effect;

[0073] Neutral wetting: Wetting reversal makes it difficult for oil droplets to be adsorbed by rocks, which is conducive to oil-rock stripping and reduces the flow resistance of crude oil;

[0074] Reduce interfacial tension: effectively miscible with crude oil, the contact angle can reach as low as 14.3°, and the interfacial tension can be reduced to 10 -3 level;

[0075] Establish an effective displacement relationship: reduce the capillary resistance caused by nano / micron pore throats, reduce the water injection start-up pressure gradient, and the nanosystem generates microscopic osmotic pressure to establish an effective displacement relationship.

[0076] In view of the above advantages of bio-nano oil recovery technology, the industry urgently needs a prediction method that can effectively characterize the effect of bio-nano oil recovery technology.

[0077] In a first aspect, the present invention provides a method for predicting the effect of bio-nano oil recovery technology, which can effectively predict the effect of bio-nano oil recovery technology in reservoirs, and is particularly suitable for effectively predicting the effect of bio-nano oil recovery technology in low-permeability reservoirs.

[0078] like Figure 1 As shown, the effect prediction method of the bionano oil displacement technology of the present invention comprises the following steps S1 to S4:

[0079] Step S1: Obtain basic data of the target area and construct a corresponding three-dimensional geological model;

[0080] Step S2: constructing a numerical model to characterize the bio-nano oil recovery technology using reservoir numerical simulation software;

[0081] Step S3: Utilizing the numerical model to simulate and optimize process parameters, and constructing a process parameter optimization chart;

[0082] Step S4: predict the effect of bio-nano oil recovery technology and characterize its mechanism.

[0083] In an optional embodiment, in step S1, the basic data of the target area include the original density, crude oil viscosity, volume coefficient, dissolved gas-oil ratio, formation pressure, saturation pressure, current formation residual oil saturation, reservoir average porosity, average permeability and other data of the target area. Of course, those skilled in the art can also take other data as needed to complete the construction of the three-dimensional geological model of the target area, which will not be elaborated here.

[0084] The present invention does not limit the specific method of constructing the three-dimensional geological model. For example, tNavigator can be used to construct the three-dimensional geological model. The specific construction method can refer to the relevant solutions in the prior art and will not be described in detail here.

[0085] In another optional implementation, step S2 further includes:

[0086] Step S21: Obtain basic data using indoor physical model experiments to obtain corresponding keywords.

[0087] Indoor physical model experiments include core displacement experiments, interfacial tension tests and wetting angle reversal evaluations, scanning electron microscopy, and imbibition oil displacement evaluations.

[0088] According to the results of indoor physical model experiments, the keywords ENDSCALE, ENPTPC, SOWCR, TRMMULTC, TRMMULTT, WTRACER, ACTIONW, ARITHMETIC, ENDACTIO and WBHZONE can be obtained.

[0089] ENDSCALE and ENPTPC represent the end point calibration of phase permeability.

[0090] SOWCR represents the relationship between the concentration of bio-nanoparticles and the residual oil saturation in the phase permeability curve. It is calculated by formula (1) that the concentration of nanoparticles dissolved in the injected water in the pore space of the formation at time dt is:

[0091]

[0092] Where δ is the concentration of bio-nanoparticles, in %; V is the volume of bio-nanoparticles, in m 3 ; g is the coefficient affected by the physical properties of the formation, ranging from 0 to 1; r p is the radius of the bio-nanoparticles reaching the formation, in m; h is the formation thickness, in m; is the density of bionanoparticles, in g / cm 3 ; S or is the residual oil saturation of the formation, in %.

[0093] TRMMULTC represents the relationship between the bionanoparticle concentration and the relative permeability of the oil phase, which is calculated by formula (2):

[0094]

[0095] Among them, K r is the relative permeability of the oil phase, in %; b is a coefficient determined by the lithology of the reservoir rock, ranging from 1 to 10; f is a coefficient related to the diffusion rate of the bio-nanoparticle system, ranging from 0 to 1; δ is the concentration of bio-nanoparticles, in %.

[0096] TRMMULTT represents the relationship between the bionanoparticle formation retention time and permeability, which is calculated by formula (3):

[0097]

[0098] Where t is the retention time of the bio-nanoparticle formation, in years; c is the coefficient related to the adsorption and desorption rate of the bio-nanoparticle system, ranging from 1 to 100; δ 0 is the initial concentration of bio-nanoparticles, in %; a is the bio-nanoparticle injection capacity increase coefficient, Among them, K D is the dimensionless average permeability from the wellbore to the injection front, r e is the supply radius of the injection well, in m, r w is the borehole radius of the injection well, in m; r p is the radius of the bio-nanoparticles reaching the formation, in m.

[0099] WTRACER is used to characterize the initial concentration of bio-nanoparticles and the concentration of bio-nanoparticles after injection; WBHZONE is used to characterize the average reservoir porosity and average permeability of the target area; ACTIONW represents the start activity, ARITHMETIC represents the mathematical operation, and ENDACTIO represents the end activity.

[0100] Step S22: Write the keywords ENDSCALE, ENPTPC, SOWCR, TRMMULTC and TRMMULTT into the fluid parameter file (.props file), and write the historical production data, perforation data and completion data of the target area and the keywords WTRACER, ACTIONW, ARITHMETIC, ENDACTIO and WBHZONE into the production parameter file (.schedule file).

[0101] Step S23: According to the fluid parameter file (.props file), the production parameter file (.schedule file), and in combination with the bio-nanoparticle diffusion reaction equation and the bio-nanoparticle adsorption reaction equation, the effect of bio-nano on improving oil phase permeability is simulated, thereby constructing a numerical model for characterizing the bio-nano oil recovery technology.

[0102] The diffusion reaction of biological nanoparticles is:

[0103] nanoFH→0.5nanoSH+0.5nanoXF

[0104] Among them, nanoFH is a biological nanoparticle, nanoSH is a nanoparticle with a particle size range of 10 to 20 nm, and nanoXF is a nanoparticle with a particle size range of 30 to 50 nm.

[0105] The adsorption reaction formula of biological nanoparticles is:

[0106]

[0107]

[0108] Wherein, k is the reaction rate constant, ranging from 1 to 10.

[0109] The above-mentioned biological nanoparticle diffusion reaction formula and biological nanoparticle adsorption reaction formula can be directly written into the production parameter file.

[0110] The main mechanism of the bio-nano oil displacement technology characterization numerical model of the present invention is:

[0111] (1) Diffusion of bio-nanoparticles: injected bio-nanoparticles react to generate particles of different sizes that diffuse into the pores of the rock, that is, the diffusion reaction formula of bio-nanoparticles is used to characterize the diffusion.

[0112] (2) Bio-nanoparticle adsorption: Assuming there are rock A and rock B, the adsorption on the pore wall is described by the Langmuir isotherm adsorption option, that is, the above-mentioned bio-nanoparticle adsorption reaction equation is used to characterize it.

[0113] The present invention can use existing known software to construct a numerical model, for example, tNavigator can be used. In addition to the above content, other contents involved in the construction process can refer to the relevant solutions of the prior art and will not be described in detail here.

[0114] In another optional implementation, step S3 further includes:

[0115] Step S31: Perform orthogonal design to obtain different implementation plans of bio-nano oil recovery technology. When performing orthogonal design, injection rounds, injection concentration, injection volume, injection speed, and injection time are mainly considered.

[0116] Step S32: For different implementation plans designed in step S31, the numerical model constructed in step S2 is used to predict the oil increase results of different implementation plans.

[0117] Step S33: Compare the oil increase results of different implementation plans to obtain the optimal solution.

[0118] In another optional embodiment, in step S4, the effect of the bio-nano oil recovery technology is predicted and its mechanism is characterized. Specifically, the numerical model constructed by the present invention is used to predict the oil saturation, and the residual oil distribution diagram before and after the simulated bio-nano oil recovery is obtained, and the mechanism of the bio-nano oil recovery technology is finely characterized.

[0119] It should be noted that in the present invention, the purpose of numbering each step is only to clearly indicate each step, rather than to limit the order of each step. Those skilled in the art can determine the order of implementation of each step based on the common technical knowledge in the field and the description of this specification.

[0120] In a second aspect, the present invention provides a schematic diagram of a device for predicting the effect of bio-nano oil displacement technology. Figure 2 As shown, the device 200 includes: an acquisition module 210, a model building module 220, an optimization module 230 and an effect prediction module 240.

[0121] An acquisition module 210 is used to acquire basic data of a target area;

[0122] The model building module 220 is used to build a corresponding three-dimensional geological model according to the basic data of the target area; and to build a numerical model for characterizing the bio-nano oil recovery technology using oil reservoir numerical simulation software;

[0123] An optimization module 230 is used to use the bio-nano oil recovery technology characterization numerical model to perform process parameter simulation optimization and construct a process parameter optimization chart;

[0124] The effect prediction module 240 is used to predict the effect of bio-nano oil recovery by using the bio-nano oil recovery technology characterization numerical model.

[0125] The specific execution process of each module in the device 200 can refer to the steps in the corresponding method embodiment, which will not be described in detail here.

[0126] In a third aspect, the present invention provides a schematic diagram of a computing device, such as Figure 3 The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0127] like Figure 3 As shown, the computing device may include: a processor (processor) 302 , a communication interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

[0128] The processor 302, the communication interface 304, and the memory 306 communicate with each other via the communication bus 308. The communication interface 304 is used to communicate with other devices such as a client or other server network elements. The processor 302 is used to execute the program 310, which can specifically execute the relevant steps in the embodiment of the effect prediction method of the composite oil-increasing measures for the heavy oil reservoir formation.

[0129] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0130] The processor 302 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0131] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The specific execution process of the program 310 can refer to the steps in the corresponding method embodiment, which will not be repeated here.

[0132] In order to enable those skilled in the art to clearly understand the technical solution of the present invention, the present invention is described in detail below through embodiments.

[0133] Example 1

[0134] This embodiment is implemented using tNavigator software.

[0135] Constructing 3D geological models

[0136] The basic data of the target area are: the crude oil density in the study area is 0.734g / cm 3 ~0.791g / cm3 The crude oil viscosity is 1.63mPa·s~5.69mPa·s, the volume coefficient is 1.172~1.241, and the dissolved gas-oil ratio is 30m 3 / m 3 ~70m 3 / m 3 The formation pressure is 22.670MPa~29.668MPa, the saturation pressure is 9.290MPa~11.100MPa, the current residual oil saturation is 56%, the average reservoir porosity is 21.7%, the average permeability is 220.7mD, the medium-porosity and medium-permeability reservoir, including 1 injection well, 5 production wells, and the distance between injection and production wells is 300m. The water injection well is completed by casing perforation, and the water injection is carried out by intelligent measurement and adjustment of injection pipe strings, with a water injection thickness of 24m. The 3D geological model of the target area is constructed using tNavigator software.

[0137] Building a numerical model

[0138] ① Conduct indoor physical model experiments, including: core displacement experiment (specific conditions: put the core into the core holder, tighten the inlet and outlet, adjust the confining pressure to 2-4MPa and make the confining pressure always greater than the inlet pressure by at least 2MPa), interfacial tension test and wetting angle reversal evaluation (specific conditions: the core needs to be placed in the bio-nano oil displacement fluid (agent-water ratio = 1:9) and soaked for 5 hours), scanning electron microscopy (specific conditions: the selected core is the most developed and most representative pore structure in the region, accounting for >35% of the total pores), and imbibition oil displacement evaluation (specific conditions: by applying a pressure of 2MPa, the permeability of the oil sand system is close to that of the target reservoir).

[0139] According to the results of indoor physical model experiments:

[0140] a) represents the ENDSCALE and ENPTPC results of phase permeability endpoint calibration, such as Figure 4 shown.

[0141] b) SOWCR expressed as the relationship between bionanoparticle concentration and residual oil saturation in the phase permeability curve, calculated using formula (1).

[0142] Table 1 Experimental results of oil-water interfacial tension at different concentrations

[0143]

[0144] Combined with Table 1, it can be seen that the bio-nanoparticles with a concentration of 0.4% can form an ultra-low interfacial tension at the oil-water interface, reaching 10 -3 The ultra-low interfacial tension between oil and water makes the bio-nanoparticle liquid effectively miscible with crude oil. The initial concentration of bio-nanoparticles injected into water is designed to be 0.4%. Figure 5As shown in Table 2, after 30 days of bio-nanoparticle injection, the oil washing efficiency can reach 70%, which can achieve the best oil washing effect. The bio-nanoparticle concentration is 0.15%, the bio-nanoparticle concentration change is 0.25%, and the bio-nanoparticle volume change dV is 23m 3 , the coefficient g is taken as 0.2 (the study area is a medium-porosity medium-permeability reservoir), and the radius r of the bio-nanoparticles reaching the formation p is 3.2m, the formation thickness h is 24m, and the density of bio-nanoparticles 2.9g / cm 3 Finally, the residual oil saturation S of the formation after the injection of bio-nanoparticles can be calculated. or The residual oil saturation of the formation before the injection of bio-nanoparticles was 56%, and the residual saturation S after the injection of bio-nanoparticles for 30 days was 41.1%. or It is 41.1%, the initial concentration of bio-nanoparticles is 0.4%, and the concentration of bio-nanoparticles is 0.15% 30 days after the injection of bio-nanoparticles. It is characterized by the keyword SOWCR and written into the simulator file.

[0145] c) TRMMULTC expressed as the relationship between bionanoparticle concentration and oil phase relative permeability, calculated using formula (2).

[0146] According to formula (1), the bionanoparticle concentration δ is 0.15%, and the coefficient b is 7 (see Figure 6 The reservoir rock lithology in the study area is lithic feldspathic sandstone, the overall sorting of rock debris particles is good and medium, and the debris particle size is mainly coarse and medium). The coefficient f is taken as 0.022 (see Figure 7 After displacement, the bio-nanoparticle liquid can effectively adsorb on the rock surface, change the rock wettability, and transform the oil-wet rock interface into an amphoteric wetting interface with a contact angle of 80.3°. Finally, the oil phase relative permeability K can be calculated. r It is 31.3%, the initial concentration of bio-nanoparticles is 0.4%, and the concentration of bio-nanoparticles is 0.15% after 30 days of bio-nanoparticle injection. The relative permeability of the oil phase before the injection of bio-nanoparticles is 39%, and the relative permeability of the oil phase is 47.7% after 30 days of bio-nanoparticle injection. The keyword TRMMULTC is used to characterize and write it into the simulator file.

[0147] d) TRMMULTT expressed as the relationship between bionanoparticle formation retention time and permeability, calculated using formula (3).

[0148] According to the wetting angle reversal evaluation results, the coefficient c is taken as 20, and the initial concentration of bio-nanoparticles δ 0 is 0.4%, the dimensionless average permeability from the wellbore to the injection front is 0.04, and the supply radius of the injection well re is 500m, the radius of the injection well is r w is 0.1 m, and the radius r of the bio-nanoparticles reaching the formation p It is 3.2m, and the bio-nano injection capacity increase coefficient a can be calculated to be 10.56. Finally, it can be calculated that the bio-nano particle formation retention time is 313 days. The dimensionless average permeability from the wellbore to the injection front is 0.04, and the bio-nano particle formation retention time is 313 days. The keyword TRMMULTT is used to characterize and write it into the simulator file.

[0149] e) The initial concentration of the bio-nanoparticles is 0.4%, and the concentration of the bio-nanoparticles after 30 days of injection is 0.15%, which are characterized by the keyword WTRACER and written into the simulator file.

[0150] ② The average reservoir porosity of 21.7% and the average permeability of 220.7 mD are represented by the keyword WBHZONE and written into the simulator file.

[0151] ③ The main mechanism of the numerical model for characterizing bionano oil recovery technology: (1) Diffusion of bionanoparticles. The injected bionanoparticles react to generate particles of different sizes and diffuse into the rock pores: nanoFH→0.5nanoSH+0.5nanoXF, nanoFH—bionanoparticles, nanoSH—10-20nm nanoparticles, nanoXF—30-50nm nanoparticles; (2) Adsorption of bionanoparticles. Assuming there are rocks A and B, adsorption on the pore wall is described by the Langmuir isotherm adsorption option. The chemical reaction equation can be expressed as: Where k is the reaction rate constant (determined by the formation lithology and reservoir properties, generally 1-10). The above chemical formula can be directly written into the production parameter file.

[0152] ④ All the keywords in steps a) to d) in ① are written into the fluid parameter file (.props file); the keywords in step e) in ①, the historical production data, perforation data and completion data of the six wells, represented by ACTIONW for starting activities, ARITHMETIC for mathematical operations, ENDACTIO for ending activities, and the keywords in step ② are all written into the production parameter file (.schedule file); the fluid parameter file and production parameter file with the corresponding keywords written in the above are combined with The chemical reaction formula in step ③ is used to simulate the effect of bio-nano on improving oil phase permeability, thereby constructing a numerical model for characterizing bio-nano oil recovery technology. The grid step length of the model in the X and Y directions is 20m, and the side lengths in the X and Y directions are both 1200m; the Z direction is divided into three layers of medium, high and low permeability (high permeability layer: 580.5mD, 24%, thickness 4m; medium permeability layer: 68.2mD, 21.8%, thickness 8m; low permeability layer: 13.4mD, 19.4%, thickness 12m), the XYZ model grid number is 60×60×11, with a total of 39600 grid nodes, see Figure 8 .

[0153] Process parameter simulation optimization and construction of process parameter optimization chart

[0154] On the basis of constructing the numerical model, tNavigator software was run to simulate and optimize the process parameters and establish the process parameter optimization chart. According to the orthogonal design, different schemes were obtained. Combined with the established numerical model, the oil increase under different schemes was simulated and predicted. The results of different process schemes are shown in Table 2. The bio-nanofluid was injected for 4 rounds, with an injection concentration of 0.4%, an injection of 0.9 PV, and an injection speed of 160 m 3 / d, the process plan with injection time of 30 days, simulation time of 2 years, the oil increase of the well group is 31528.9m 3 (i.e. Case 19), compared with other solutions, the input-output ratio is the best and it is the optimal solution, see Figures 9 to 13 .in, Fig. 9 The effect of injection rounds of the bionano oil displacement agent on the cumulative oil production in Example 1 of the present invention is shown. Fig.10 The effect of bionano oil displacement agent injection concentration on cumulative oil production is shown; Fig.11 The effect of bio-nano oil displacement agent injection amount on cumulative oil production is shown; Fig.12 The effect of bio-nano oil displacement agent injection rate on cumulative oil production is shown; Fig.13 The effect of bio-nano oil displacement agent injection time on cumulative oil production is shown.

[0155] Table 2 Orthogonal design table of bio-nano oil recovery process scheme

[0156]

[0157] Effect prediction and mechanism characterization

[0158] Based on the optimal solution, Case 19, tNavigator software was used to predict oil saturation and characterize the mechanism. The remaining oil distribution diagram before and after the simulated bio-nano flooding was obtained. Figure 14 to Figure 16 ,in, Fig.14 This is the comparison of the remaining oil distribution before and after bio-nano flooding in low permeability reservoirs; Fig.15 This is the comparison of the remaining oil distribution before and after bionano flooding in medium permeability reservoirs; Fig.16 This is the distribution diagram of residual oil before and after bio-nano flooding in high permeability reservoirs. Two conclusions can be drawn: 1) The technical means of bio-nano flooding can greatly increase the sweep range of bio-nano fluids to medium and low permeability reservoirs, and give full play to the flooding effect of bio-nano fluids; 2) When the reservoir permeability is low permeability, the sweep coefficient increases by 14.7%, and the average recovery rate increases by 14.5%; when the reservoir permeability is medium permeability, the sweep coefficient increases by 19.3%, and the average recovery rate increases by 13.8%; when the reservoir permeability is high permeability, the sweep coefficient increases by 15.1%, and the average recovery rate increases by 11.9%; bio-nano flooding increases the recovery rate by 13.4% compared with water flooding, and increases the recovery rate by 10% compared with general chemical flooding, showing the high efficiency of the system.

[0159] In addition, the mechanism of bio-nano oil recovery technology is carefully characterized, see Fig.17 It can be seen that: ① The in-situ biological nanoparticles in the formation can be cross-linked to form a temporary plug of a certain strength, so that the subsequent displacement fluid flows to the low permeability / deep formation; ② The dewetting effect of the biological nanoparticles (wedge cone) reduces the oil-rock structure separation pressure and promotes crude oil stripping; the biological nanoparticles form a stable structure at the oil-water interface, and the oil and water are self-emulsified and dispersed in the formation in the state of Pickering emulsion, which improves the fluidity of crude oil.

[0160] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other equipment. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description made to specific languages ​​above is for disclosing the best mode of the present invention.

[0161] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0162] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0163] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0164] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0165] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0166] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for predicting the effect of bionano oil recovery technology, characterized in that: include: Step S1: Obtain basic data of the target area and construct a corresponding three-dimensional geological model; Step S2: constructing a numerical model to characterize the bio-nano oil recovery technology using reservoir numerical simulation software; Step S3: Utilizing the numerical model to simulate and optimize process parameters, and constructing a process parameter optimization chart; Step S4: predicting the effect of bio-nano oil recovery technology and characterizing its mechanism; Wherein, step S2 includes: step S21: using indoor physical modeling experiments to obtain basic data and obtain corresponding keywords; step S22: writing keywords ENDSCALE, ENPTPC, SOWCR, TRMMULTC and TRMMULTT into the fluid parameter file, and writing historical production data, perforation data and completion data of the target area and keywords WTRACER, ACTIONW, ARITHMETIC, ENDACTIO and WBHZONE into the production parameter file; step S23: constructing the numerical model according to the fluid parameter file, the production parameter file, the diffusion reaction formula of the bio-nanoparticles and the adsorption reaction formula of the bio-nanoparticles; wherein the initial concentration of the bio-nanoparticles and the concentration of the bio-nanoparticles after injection are characterized by WTRACER; and the average porosity and average permeability of the reservoir in the target area are characterized by WBHZONE; Among them, ENDSCALE and ENPTPC represent the calibration of the phase permeability endpoint; SOWCR represents the relationship between the concentration of bio-nanoparticles and the residual oil saturation in the phase permeability curve; TRMMULTC represents the relationship between the concentration of bio-nanoparticles and the relative permeability of the oil phase; TRMMULTT represents the relationship between the retention time of bio-nanoparticles in the formation and the permeability; ACTIONW represents the start of an activity, ARITHMETIC represents a mathematical operation, and ENDACTIO represents the end of an activity.

2. The method according to claim 1, characterized in that SOWCR is calculated by the following formula (1): （1） in, δ is the bionanoparticle concentration, unit is %; V is the volume of biological nanoparticles, in m 3 ; g is the coefficient affected by the physical properties of the formation, ranging from 0 to 1; r p is the radius of the bio-nanoparticles reaching the formation, in m; h is the stratum thickness, in m; φ is the density of bionanoparticles, in g / cm 3 ; S or is the residual oil saturation of the formation, the unit is %; TRMMULTC is calculated by the following formula (2): （2） in, K r is the relative permeability of the oil phase, the unit is %; b is a coefficient determined by the lithology of the reservoir rock, ranging from 1 to 10; f is the coefficient related to the diffusion rate of the biological nanoparticle system, ranging from 0 to 1; δ is the bionanoparticle concentration, unit is %; TRMMULTT is calculated by the following formula (3): （3） in, t is the bionanoparticle formation retention time, in years; c is the coefficient related to the adsorption and desorption rate of the bio-nanoparticle system, ranging from 1 to 100; δ 0 is the initial concentration of bio-nanoparticles, the unit is %; a Increase the coefficient of bio-nano injection capacity, ,in, k D is the dimensionless average permeability from the wellbore to the injection front, r e is the supply radius of the injection well, in m, r w is the borehole radius of the injection well, in m; r p is the radius of the bio-nanoparticles reaching the formation, in m.

3. The method according to claim 1, characterized in that The diffusion reaction equation of biological nanoparticles is in, nanoFH For biological nanoparticles, nanoSH The particle size range is 10~20nm nanoparticles. nanoXF The particle size range is 30~50nm nanoparticles.

4. The method according to claim 1, characterized in that: The adsorption reaction equation of biological nanoparticles is Where k is the reaction rate constant, ranging from 1 to 10.

5. The method according to claim 1, characterized in that The step S3 comprises: Step S31: perform orthogonal design to obtain different solutions; Step S32: using the numerical model to predict the oil increase results of different schemes; Step S33: Compare the oil increase results of different schemes to obtain the optimal scheme.

6. A device for predicting the effect of bio-nano oil displacement technology, characterized in that: include An acquisition module is used to obtain basic data of the target area; Model building module, used to build corresponding three-dimensional geological model according to basic data of target area; and to build numerical model representing bio-nano oil recovery technology by using reservoir numerical simulation software; An optimization module, used to use the numerical model to simulate and optimize process parameters and construct a process parameter optimization chart; Effect prediction module, used to predict the effect of bio-nano oil recovery technology and characterize its mechanism; Wherein, using reservoir numerical simulation software to construct a numerical model for characterizing bio-nano oil recovery technology includes the following steps: step S21: using indoor physical modeling experiments to obtain basic data and obtain corresponding keywords; step S22: writing keywords ENDSCALE, ENPTPC, SOWCR, TRMMULTC and TRMMULTT into the fluid parameter file, and writing historical production data, perforation data and completion data of the target area and keywords WTRACER, ACTIONW, ARITHMETIC, ENDACTIO and WBHZONE into the production parameter file; step S23: constructing the numerical model according to the fluid parameter file, the production parameter file, the diffusion reaction formula of bio-nanoparticles and the adsorption reaction formula of bio-nanoparticles; wherein the initial concentration of bio-nanoparticles and the concentration of bio-nanoparticles after injection are characterized by WTRACER; the average reservoir porosity and average permeability of the target area are characterized by WBHZONE; Among them, ENDSCALE and ENPTPC represent the calibration of the phase permeability endpoint; SOWCR represents the relationship between the concentration of bio-nanoparticles and the residual oil saturation in the phase permeability curve; TRMMULTC represents the relationship between the concentration of bio-nanoparticles and the relative permeability of the oil phase; TRMMULTT represents the relationship between the retention time of bio-nanoparticles in the formation and the permeability; ACTIONW represents the start of an activity, ARITHMETIC represents a mathematical operation, and ENDACTIO represents the end of an activity.

7. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the effect prediction method of the bio-nano oil displacement technology according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the effect prediction method of the bio-nano oil displacement technology according to any one of claims 1 to 5.

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