Method and device for predicting injection pressure of biological nanometer measure well

Abstract

The embodiment of the invention discloses a method and device for predicting the injection pressure of a biological nanometer measure well. The method comprises the following steps: calculating hydrostatic pressure according to the net height of injected fluid; calculating throttle valve pressure loss generated when the injected fluid flows through the throttle valve according to the throttle valve parameters; acquiring flowing bottomhole pressure calculation parameters, and calculating the flowing bottomhole pressure of the injection well by using the flowing bottomhole pressure calculation parameters; the flowing speed of the injected fluid in the shaft is obtained, and the on-way pressure loss is calculated according to the flowing speed; and predicting the injection pressure of the biological nanometer measure well according to the hydrostatic pressure, the pressure loss of the throttle valve, the bottomhole flowing pressure of the injection well and the on-way pressure loss. When the injection pressure is predicted, the multiple flowing bottomhole pressure calculation parameters are comprehensively considered to calculate the flowing bottomhole pressure of the injection well, the on-way pressure loss is considered, meanwhile, different calculation modes are adopted for predicting the injection pressure according to different production working conditions of a vertical well and a horizontal well, it is ensured that the prediction result better fits the actual production condition, and the prediction efficiency is improved. And prediction accuracy is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of oil and gas development, and in particular, to a method, device, computing device, computer storage medium, and computer program product for predicting the injection pressure of a biological nano measure well. Background Art

[0002] The oil displacement technology is an oil production technology used to improve the oil recovery rate. It changes the physical and chemical properties of the displacement fluid by injecting specific chemical agents or gases into the formation, thereby more effectively displacing the crude oil. It mainly includes water flooding, polymer flooding, microbial flooding, carbon dioxide flooding technology, binary composite flooding technology, etc.; the oil displacement technology is mainly used in the middle and late stages of oilfield development. When the oil production rate of natural production decreases, various oil displacement technologies are applied to improve the recovery rate. With the rapid development of nanotechnology in oil exploitation, the nano oil displacement technology that uses nanoscale materials to form a "thrombolytic agent" to open the blockage points in the pore throats of the Fuyu oil layer and allow the reservoir to produce more oil has gradually matured.

[0003] Regardless of which oil displacement technology, the injection pressure for oil displacement is an important indicator for maintaining the reservoir pressure, enabling the reservoir to have a stronger driving force, thereby increasing the exploitation speed and recovery rate of the reservoir. An appropriate injection pressure can effectively displace the crude oil into the production well, increasing the oilfield production and economic benefits; currently, the injection pressure is only roughly predicted based on aspects such as the injection speed of the fluid, the viscosity of the injected fluid, the formation thickness, and the formation permeability, making it difficult to ensure the prediction accuracy. Summary of the Invention

[0004] In view of the above problems, the present application is proposed to provide a method, device, computing device, computer storage medium, and computer program product for predicting the injection pressure of a biological nano measure well that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the embodiments of the present application, a method for predicting the injection pressure of a biological nano measure well is provided. The method includes:

[0006] Calculating the hydrostatic pressure of the fluid according to the net height of the injected fluid;

[0007] Calculating the throttle pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters;

[0008] Obtaining the bottom hole flowing pressure calculation parameters and calculating the bottom hole flowing pressure of the injection well using the bottom hole flowing pressure calculation parameters;

[0009] Obtaining the flow velocity of the injected fluid in the wellbore and calculating the frictional pressure loss according to the flow velocity;

[0010] Predict the injection pressure of biogenic nano - measure wells based on hydrostatic pressure, throttle valve pressure loss, bottom - hole flowing pressure of injection wells, and pressure loss along the way.

[0011] Furthermore, the calculation parameters of the bottom - hole flowing pressure include: formation pressure P e , injection rate q, viscosity of the injected fluid μ, initial nanoparticle concentration δ0 after injecting the nano - solution, effective period t of the biogenic nano - measure well, formation thickness h, reservoir supply radius r e and wellbore radius r w .

[0012] Furthermore, if the injection well is a vertical well, calculating the bottom - hole flowing pressure of the injection well using the calculation parameters of the bottom - hole flowing pressure further includes:

[0013] Calculate the bottom - hole flowing pressure of the injection well using the following formula:

[0014]

[0015] where p wf is the bottom - hole flowing pressure of the injection well, and a and β are set coefficients.

[0016] Furthermore, calculating the pressure loss along the way according to the flow velocity further includes:

[0017] Calculate the pressure loss along the way using the following formula:

[0018] p 沿程 = λLv 2 / (2gD)

[0019] where p 沿程 is the pressure loss along the way, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the friction factor along the way;

[0020] If the flow velocity v is less than 1.2 m / s, then λ = 0.0179(1 + 0.867 / v) 0.3 / D 0.3 ;

[0021] If the flow velocity v is greater than 1.2 m / s, then λ = 0.021 / D 0.3 .

[0022] Furthermore, if the injection well is a horizontal well, calculating the bottom - hole flowing pressure of the injection well using the calculation parameters of the bottom - hole flowing pressure further includes:

[0023] Calculate the bottom - hole flowing pressure of the injection well using the following formula:

[0024]

[0025] where p wfis the bottom-hole flowing pressure of the injection well, a and β are set coefficients, and B o is the original formation volume coefficient, and L is the wellbore length.

[0026] Furthermore, calculating the frictional pressure loss along the way according to the flow velocity further includes:

[0027] Calculating the frictional pressure loss along the way using the following formula:

[0028] p 沿程 = λLv 2 / (2gD)

[0029] where p 沿程 is the frictional pressure loss along the way, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the frictional resistance coefficient along the way;

[0030] Calculating the frictional resistance coefficient λ using the following formula:

[0031] λ = f o + f p

[0032] where f o is the wall friction coefficient of ordinary pipe flow, and f p is the perforation friction coefficient generated by the inflow;

[0033]

[0034] where d is the diameter of the injection string, q L is the flow rate per unit length of the perforated wellbore wall, and n is the perforation density.

[0035] Furthermore, the value range of the throttle valve pressure loss is [0.50, 1.00] MPa.

[0036] According to another aspect of the embodiments of the present application, a device for predicting the injection pressure of a bio-nano measure well is provided. The device includes:

[0037] A hydrostatic pressure calculation module, adapted to calculate the hydrostatic pressure according to the net height of the injected fluid;

[0038] A throttle valve pressure loss calculation module, adapted to calculate the throttle valve pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters;

[0039] An injection well bottom-hole flowing pressure calculation module, adapted to obtain the bottom-hole flowing pressure calculation parameters and calculate the injection well bottom-hole flowing pressure using the bottom-hole flowing pressure calculation parameters;

[0040] A frictional pressure loss calculation module along the way, adapted to obtain the flow velocity of the injected fluid in the wellbore and calculate the frictional pressure loss along the way according to the flow velocity;

[0041] A prediction module, adapted to predict the injection pressure of a bio-nano measure well according to the hydrostatic pressure, the throttle valve pressure loss, the bottom-hole flowing pressure of the injection well, and the frictional pressure loss.

[0042] According to another aspect of the embodiments of the present application, a computing device is provided, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0043] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned method for predicting the injection pressure of a bio-nano measure well.

[0044] According to still another aspect of the embodiments of the present application, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned method for predicting the injection pressure of a bio-nano measure well.

[0045] According to yet another aspect of the embodiments of the present application, a computer program product is provided, including at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned method for predicting the injection pressure of a bio-nano measure well.

[0046] According to the method and device for predicting the injection pressure of a bio-nano measure well provided by the embodiments of the present application, when predicting the injection pressure, multiple bottom-hole flowing pressure calculation parameters are comprehensively considered to calculate the bottom-hole flowing pressure of the injection well, and the frictional pressure loss is considered. At the same time, different calculation methods are adopted for different production conditions of vertical wells and horizontal wells to predict the injection pressure, ensuring that the prediction result is more in line with the actual production situation and improving the prediction accuracy.

[0047] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to be able to understand the technical means of the embodiments of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the embodiments of the present application more obvious and understandable, the following specifically describes the specific embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the embodiments of the present application. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0049] Figure 1 A flowchart showing the method for predicting the injection pressure of a bio-nano measure well according to an embodiment of the present application is shown;

[0050] Figure 2 The structural block diagram of a biological nano-measurement well injection pressure prediction device according to an embodiment of the present application is shown;

[0051] Figure 3 The structural schematic diagram of a computing device according to an embodiment of the present application is shown. Specific embodiments

[0052] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0053] Figure 1 The flowchart of a biological nano-measurement well injection pressure prediction method according to an embodiment of the present application is shown, as Figure 1 shown, the method includes the following steps:

[0054] Step S101, calculate the hydrostatic pressure according to the net height of the injected fluid.

[0055] Specifically, after injecting fluid into the injection well, the fluid density of the injected nano-solution and the net height of the injected fluid can be determined, and thus the hydrostatic pressure can be calculated according to the following formula:

[0056] p 水柱 = ρgH

[0057] where p 水柱 is the hydrostatic pressure, H is the net height of the injected fluid, ρ is the fluid density, and g is the acceleration due to gravity.

[0058] Step S102, calculate the throttle pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters.

[0059] The throttle valve is a component used to control the fluid flow rate in a fluid system. The throttle valve parameters are key data reflecting the performance and specifications of the throttle valve stipulated by the manufacturer or relevant standards. The throttle valve parameters can be pre-stored in a parameter table, and thus the throttle valve parameters corresponding to the corresponding throttle valve can be obtained from the parameter table. Among them, the throttle valve parameters mainly include: nominal pressure, nominal diameter, flow coefficient, throttle characteristic curve, etc.

[0060] The pressure loss refers to the value of the fluid pressure reduction when the fluid flows through the throttle valve due to the throttling effect of the throttle valve (the channel becomes narrower, there is resistance, etc.).

[0061] When the injected fluid flows through the throttle valve, a pressure loss will occur. Therefore, the throttle valve pressure loss p generated by the injected fluid flowing through the throttle valve can be calculated based on the throttle valve parameters. 水嘴 The specific calculation process will not be elaborated here.

[0062] Step S103: Obtain the bottom-hole flowing pressure calculation parameters and calculate the bottom-hole flowing pressure of the injection well using the bottom-hole flowing pressure calculation parameters.

[0063] The bottom-hole flowing pressure calculation parameters are the parameters required for calculating the bottom-hole flowing pressure of the injection well. Among them, the bottom-hole flowing pressure calculation parameters mainly include: formation pressure P e , injection rate q, viscosity μ of the injected fluid, initial nanoparticle concentration δ0 after injecting the nano-solution, valid period t of the biological nano-measure well, formation thickness h, reservoir supply radius r e and wellbore radius r w . The bottom-hole flowing pressure calculation parameters are pre-recorded in the parameter table, and thus the bottom-hole flowing pressure calculation parameters can be obtained from the parameter table.

[0064] Among them, the method for determining the valid period t of the biological nano-measure well can refer to the method described in the publication number: CN114427408A (Method and device for predicting the valid period of formation biological nano-pressure reduction and injection increase technology).

[0065] Injection wells are mainly divided into two types: vertical wells and horizontal wells. Therefore, the calculation process of the bottom-hole flowing pressure p wf of the injection well is divided into two cases: vertical wells and horizontal wells:

[0066] For vertical wells, the relationship between the injection rate q and the bottom-hole flowing pressure p wf of the injection well is:

[0067]

[0068] Among them, K is the formation permeability, lnK = aδ nano , δ nano represents the current nanoparticle concentration;

[0069] According to the solution described in the publication number: CN114427408A (Method and device for predicting the valid period of formation biological nano-pressure reduction and injection increase technology): Determine the volume flow rate of the biological nanoparticles dissolved in the injected fluid at any point in the formation; determine the biological nanoparticle concentration of the biological nanoparticles dissolved in the injected fluid and washed away per unit time in the pore space of the formation according to the volume flow rate to establish the correlation between the biological nanoparticle concentration and the valid period; when the biological nanoparticle concentration is 0, determine the valid period of the biological nano-pressure reduction and injection increase technology according to the correlation, and the biological nanoparticle concentration can finally be expressed as:

[0070]

[0071] Among them, δ nano is the concentration of biological nanoparticles, and a and β are set coefficients.

[0072] There is the following relationship between the concentration of biological nanoparticles in the formation and the relative permeability of water:

[0073] lnK = aδ nano

[0074] Finally, it can be obtained:

[0075] In summary, the following expression can be obtained to calculate the bottom-hole flowing pressure p of the injection well wf :

[0076]

[0077] Among them, a and β are set coefficients, and the specific value of a can be determined by the lithology of the reservoir rock.

[0078] For horizontal wells, the relationship between the injection rate q and the bottom-hole flowing pressure p of the injection well wf is:

[0079]

[0080] Therefore, the calculation formula for the bottom-hole flowing pressure p of the injection well can be obtained wf is:

[0081]

[0082] Among them, a and β are set coefficients, B o is the original formation volume factor, and L is the wellbore length.

[0083] Step S104, obtain the flow velocity of the injection fluid in the wellbore, and calculate the frictional pressure loss according to the flow velocity.

[0084] Read the real-time injection flow rate Q from the metering equipment (such as mass flowmeter, volume flowmeter) of the injection well, obtain the wellbore diameter D, calculate the flow velocity v of the injection fluid in the wellbore according to the injection flow rate Q and the wellbore diameter D, and calculate the frictional pressure loss p based on the Darcy-Weisbach formula using the flow velocity v 沿程 .

[0085] In this embodiment, the calculation process of the frictional pressure loss p 沿程 is also divided into two cases: vertical wells and horizontal wells:

[0086] For the case of vertical wells, the following expression is used to calculate the frictional pressure loss p 沿程 :

[0087] p 沿程= λLv 2 / (2gD)

[0088] Wherein, L is the wellbore length, D is the wellbore diameter, and λ is the friction factor along the path;

[0089] The calculation method of the friction factor λ along the path is as follows:

[0090] When the flow velocity v is less than 1.2 m / s, λ = 0.0179(1 + 0.867 / v) 0.3 / D 0.3

[0091] When the flow velocity v is greater than 1.2 m / s, λ = 0.021 / D 0.3 .

[0092] For the case where the flow velocity v is equal to 1.2 m / s, the calculation method of the friction factor λ along the path can be selected according to actual needs. For example, the calculation method of the friction factor λ along the path when the flow velocity v is less than 1.2 m / s can be selected, or the calculation method of the friction factor λ along the path when the flow velocity v is greater than 1.2 m / s can be selected, or other methods can be selected, or the friction factor λ along the path is a fixed value, which will not be elaborated here.

[0093] For the horizontal well case, the pressure loss p along the path 沿程 is the same as the calculation formula for the vertical well. The difference lies in the calculation of the friction factor λ along the path. Specifically, the friction factor λ along the path is calculated as follows:

[0094] λ = f o + f p

[0095] Wherein, f o is the wall friction coefficient of the ordinary pipe flow, and f p is the perforation friction coefficient generated by the inflow;

[0096]

[0097] Wherein, d is the diameter of the injection string, q L is the flow rate per unit length of the perforated wellbore wall, and n is the perforation density.

[0098] It should be noted that after the fluid injection is completed, the execution order of steps S101 - S104 is not limited.

[0099] Step S105, predict the injection pressure of the bio-nano measure well according to the fluid static pressure, throttle valve pressure loss, bottom hole flowing pressure of the injection well, and pressure loss along the path.

[0100] Specifically, the following formula can be used to predict the injection pressure of the bio-nano measure well:

[0101] p 井口 = p wf - p 水柱 + p 沿程 + p 水嘴

[0102] wherein, p 井口 is the injection pressure of the biological nano measure well, p wf is the bottom-hole flowing pressure of the injection well, p 水柱 is the hydrostatic pressure, p 沿程 is the pressure loss along the way, p 水嘴 is the pressure loss of the throttle valve.

[0103] In summary, the solution provided by the embodiment of the present application comprehensively considers multiple calculation parameters to calculate the bottom-hole flowing pressure p wf when predicting the injection pressure, and considers the pressure loss p 沿程 along the way. At the same time, different calculation methods are used for different production conditions of vertical wells and horizontal wells to predict the injection pressure, ensuring that the prediction results are more in line with the actual production situation and improving the prediction accuracy.

[0104] Figure 2 shows a structural block diagram of an injection pressure prediction device for a biological nano measure well according to an embodiment of the present application, as Figure 2 shown, the device includes:

[0105] A hydrostatic pressure calculation module 201, adapted to calculate the hydrostatic pressure according to the net height of the injected fluid;

[0106] A throttle valve pressure loss calculation module 202, adapted to calculate the throttle valve pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters;

[0107] An injection well bottom-hole flowing pressure calculation module 203, adapted to obtain bottom-hole flowing pressure calculation parameters and calculate the bottom-hole flowing pressure of the injection well by using the bottom-hole flowing pressure calculation parameters;

[0108] A pressure loss along the way calculation module 204, adapted to obtain the flow velocity of the injected fluid in the wellbore and calculate the pressure loss along the way according to the flow velocity;

[0109] A prediction module 205, adapted to predict the injection pressure of the biological nano measure well according to the hydrostatic pressure, the throttle valve pressure loss, the bottom-hole flowing pressure of the injection well and the pressure loss along the way.

[0110] Optionally, the bottom-hole flowing pressure calculation parameters include: formation pressure P e , injection rate q, viscosity μ of the injected fluid, initial nanoparticle concentration δ0 after injecting the nano solution, effective period t of the biological nano measure well, formation thickness h, reservoir supply radius r e and wellbore radius rw 。

[0111] Optionally, if the injection well is a vertical well, the bottom-hole flowing pressure calculation module of the injection well is further adapted to:

[0112] Calculate the bottom-hole flowing pressure of the injection well using the following formula:

[0113]

[0114] where p wf is the bottom-hole flowing pressure of the injection well, and a and β are set coefficients.

[0115] Optionally, the frictional pressure loss calculation module is further adapted to:

[0116] Calculate the frictional pressure loss using the following formula:

[0117] p 沿程 = λLv 2 / (2gD)

[0118] where p 沿程 is the frictional pressure loss, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the friction factor;

[0119] If the flow velocity v is less than 1.2 m / s, then λ = 0.0179(1 + 0.867 / v) 0.3 / D 0.3 ;

[0120] If the flow velocity v is greater than 1.2 m / s, then λ = 0.021 / D 0.3 。

[0121] Optionally, if the injection well is a horizontal well, the bottom-hole flowing pressure calculation module of the injection well is further adapted to:

[0122] Calculate the bottom-hole flowing pressure of the injection well using the following formula:

[0123]

[0124] where p wf is the bottom-hole flowing pressure of the injection well, a and β are set coefficients, B o is the original formation volume factor, and L is the wellbore length.

[0125] Optionally, the frictional pressure loss calculation module is further adapted to:

[0126] Calculate the frictional pressure loss using the following formula:

[0127] p 沿程 = λLv 2 / (2gD)

[0128] Among them, p 沿程 is the frictional pressure loss along the way, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the frictional resistance coefficient along the way;

[0129] The frictional resistance coefficient λ along the way is calculated using the following formula:

[0130] λ = f o + f p

[0131] Among them, f o is the wall friction coefficient of ordinary pipe flow, and f p is the perforation friction coefficient generated by the inflow;

[0132]

[0133] Among them, d is the diameter of the injection string, q L is the flow rate per unit length of the perforated wellbore wall, and n is the perforation density.

[0134] Optionally, the pressure loss value range of the throttle valve is [0.50, 1.00] MPa.

[0135] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments and will not be elaborated here.

[0136] In summary, for the solution provided in the embodiment of the present application, when predicting the injection pressure, multiple calculation parameters are comprehensively considered to calculate the bottom-hole flowing pressure p wf , and the frictional pressure loss p 沿程 along the way is considered. At the same time, different calculation methods are used for different production conditions of vertical wells and horizontal wells to predict the injection pressure, ensuring that the prediction results are more in line with the actual production situation and improving the prediction accuracy.

[0137] The embodiment of the present application provides a non-volatile computer storage medium. The computer storage medium stores at least one executable instruction or computer program, and the executable instruction or computer program can enable the processor to execute the operations corresponding to the injection pressure prediction method for the biogenic nano-measurement well in any of the above method embodiments.

[0138] The embodiment of the present application provides a computer program product. The computer program product includes at least one executable instruction or computer program, and the executable instruction or computer program can enable the processor to execute the operations corresponding to the injection pressure prediction method for the biogenic nano-measurement well in any of the above method embodiments.

[0139] Figure 3 The structure diagram of the computing device embodiment of the present application is shown. The specific implementation of the computing device is not limited in the specific embodiments of the present application.

[0140] As Figure 3 shown, the computing device may include: a processor 302, a communications interface 304, a memory 306, and a communication bus 308.

[0141] Among them: the processor 302, the communications interface 304, and the memory 306 communicate with each other through the communication bus 308. The communications interface 304 is used to communicate with network elements of other devices such as clients or other servers. The processor 302 is used to execute the program 310, and specifically can execute the relevant steps in the above-mentioned embodiments of the method for predicting the injection pressure of the biological nano-measurement well for the computing device.

[0142] Specifically, the program 310 may include program code, and the program code includes computer operation instructions.

[0143] The processor 302 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. One or more processors included in the computing device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0144] The memory 306 is used to store the program 310. The memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0145] The program 310 is specifically used to cause the processor 302 to execute the method for predicting the injection pressure of the biological nano-measurement well in any of the above method embodiments. For the specific implementation of each step in the program 310, reference may be made to the corresponding steps and descriptions in the corresponding units in the above-mentioned embodiments of the method for predicting the injection pressure of the biological nano-measurement well, which will not be elaborated here. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding process descriptions in the foregoing method embodiments, which will not be elaborated here.

[0146] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. A variety of general-purpose systems may also be used in conjunction with the teachings presented herein. The structure required to construct such systems will be apparent from the above description. Additionally, the embodiments of the present application are not directed to any particular programming language. It should be understood that the content of the embodiments of the present application described herein can be implemented using a variety of programming languages, and the descriptions made above with respect to a particular language are for the purpose of disclosing the best mode of the embodiments of the present application.

[0147] In the specification provided herein, a number of specific details are set forth. However, it is understood that the embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0148] Similarly, it should be understood that, in order to streamline the present disclosure and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed embodiments of the present application require more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all of the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0149] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from those of the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0150] In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of the embodiments of the present application and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0151] Each component embodiment of the embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present application. The embodiments of the present application can also be implemented as a device or apparatus program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the embodiments of the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0152] It should be noted that the above embodiments illustrate the embodiments of the present application rather than limit the embodiments of the present application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The embodiments of the present application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

Claims

1. A method for predicting the injection pressure of a biological nano-measure well, characterized in that The method includes: Calculating the hydrostatic pressure according to the net height of the injected fluid; Calculating the throttle pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters; Obtaining the bottom-hole flowing pressure calculation parameters and calculating the bottom-hole flowing pressure of the injection well by using the bottom-hole flowing pressure calculation parameters; Obtaining the flow velocity of the injected fluid in the wellbore and calculating the frictional pressure loss according to the flow velocity; Predicting the injection pressure of the bio-nano measure well according to the hydrostatic pressure, the throttle pressure loss, the bottom-hole flowing pressure of the injection well and the frictional pressure loss.

2. The method for predicting the injection pressure of a biological nano-measure well according to claim 1, wherein The bottom-hole flowing pressure calculation parameters include: formation pressure P e , injection rate q, injection fluid viscosity μ, initial nanoparticle concentration δ0 after injecting the nano-solution, effective period t of the biogenic nano-treatment well, formation thickness h, reservoir supply radius r e , and wellbore radius r w .

3. The method for predicting the injection pressure of a biological nano measure well according to claim 2, wherein If the injection well is a vertical well, the calculating the bottom-hole flowing pressure of the injection well by using the bottom-hole flowing pressure calculation parameters further includes: Calculating the bottom-hole flowing pressure of the injection well by using the following formula: Among them, p wf is the bottom-hole flowing pressure of the injection well, and a and β are set coefficients.

4. The method for predicting the injection pressure of a biological nano-measure well according to claim 3, wherein The calculating the frictional pressure loss according to the flow velocity further includes: Calculating the frictional pressure loss by using the following formula: p 沿程 = λLv 2 / (2gD) where p 沿程 is the frictional pressure loss, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the frictional resistance coefficient; If the flow velocity v is less than 1.2 m / s, then λ = 0.0179(1 + 0.867 / v) 0.3 / D 0.3 ; If the flow velocity v is greater than 1.2 m / s, then λ = 0.021 / D 0.3 .

5. The method for predicting the injection pressure of a biological nano-measure well according to claim 2, characterized in that, If the injection well is a horizontal well, the calculating the bottom-hole flowing pressure of the injection well by using the bottom-hole flowing pressure calculation parameters further includes: Calculating the bottom-hole flowing pressure of the injection well by using the following formula: Among them, p wf is the bottom-hole flowing pressure of the injection well, a and β are set coefficients, B o is the original formation volume factor, and L is the wellbore length.

6. The method for predicting the injection pressure of a biological nano measure well according to claim 5, wherein The calculating the frictional pressure loss according to the flow velocity further includes: Calculating the frictional pressure loss by using the following formula: p 沿程 = λLv 2 / (2gD) where p 沿程 is the frictional pressure loss, v is the flow velocity, g is the acceleration due to gravity, L is the wellbore length, D is the wellbore diameter, and λ is the frictional resistance coefficient; Calculating the friction factor λ by using the following formula: λ = f o + f p Among them, f o is the wall friction coefficient of ordinary pipe flow, and f p is the perforation friction coefficient generated by the inflow; Among them, d is the diameter of the injection string, q L is the flow rate per unit length of the perforated wellbore wall, and n is the perforation density.

7. A device for predicting the injection pressure of a biological nano-measurement well, characterized in that, The device includes: A hydrostatic pressure calculation module, adapted to calculate the hydrostatic pressure according to the net height of the injected fluid; A throttle pressure loss calculation module, adapted to calculate the throttle pressure loss generated by the injected fluid flowing through the throttle valve according to the throttle valve parameters; An injection well bottom-hole flowing pressure calculation module, adapted to obtain the bottom-hole flowing pressure calculation parameters and calculate the bottom-hole flowing pressure of the injection well by using the bottom-hole flowing pressure calculation parameters; A frictional pressure loss calculation module, adapted to obtain the flow velocity of the injected fluid in the wellbore and calculate the frictional pressure loss according to the flow velocity; A prediction module, adapted to predict the injection pressure of the bio-nano measure well according to the hydrostatic pressure, the throttle pressure loss, the bottom-hole flowing pressure of the injection well and the frictional pressure loss.

8. A computing device, comprising: A processor, a memory, a communication interface and a communication bus, and the processor, the memory and the communication interface complete communication with each other through the communication bus; The memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operations corresponding to the method for predicting the injection pressure of the bio-nano measure well according to any one of claims 1-6.

9. A computer storage medium, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute the operations corresponding to the method for predicting the injection pressure of the bio-nano measure well according to any one of claims 1-6.

10. A computer program product, including at least one executable instruction, and the executable instruction enables a processor to execute the operations corresponding to the method for predicting the injection pressure of the bio-nano measure well according to any one of claims 1-6.

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