Shale oil well productivity prediction method and system based on different flow states and electronic equipment

Abstract

The invention belongs to the technical field of petroleum and natural gas blowout drainage and mining processes, particularly relates to a shale oil well productivity prediction method and system based on different flow states and electronic equipment, and aims at solving the problems that a shale oil well productivity prediction method in the prior art is large in error and poor in adaptability. The method comprises the steps of collecting oil well production data in a historical production time period; based on oil well production data, RTA software is used for calculating the flowing bottomhole pressure; yield normalized pressure is calculated based on the wellhead pressure, the flowing bottomhole pressure and the oil well yield; taking the logarithm of the yield normalized pressure as a vertical coordinate and the logarithm of the material balance time as a horizontal coordinate to make a double logarithm diagnostic graph; determining an unstable linear flow and a quasi-steady flow based on the slope of the double logarithmic diagnostic graph; and adding the productivity condition of the unstable linear flow and the productivity condition of the quasi-steady flow to obtain the final productivity. The shale oil well productivity prediction accuracy is improved.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas self-flowing drainage technology, specifically involving a method, system, electronic equipment and storage medium for predicting the production capacity of shale oil wells based on different flow states. Background Technology

[0002] As the scale of shale oil exploration and development in China gradually expands, the accuracy of production capacity prediction methods is crucial for efficient development. However, during the exploration and development process, there are problems such as rapid and large declines in initial oil production and pressure, large differences between different layers, and complex dynamic characteristics of production. At present, scholars at home and abroad have conducted a lot of research on the production capacity decline law and production capacity prediction of fractured horizontal wells, including analytical models, semi-analytical models, numerical simulations and other methods, to analyze the dynamic laws of various production indicators of horizontal wells from different perspectives. However, most of the methods are relatively macroscopic and lack analysis of different flow states. Even if different flow states are considered, they are only applied by formula, resulting in large prediction errors. At present, the production capacity prediction methods for shale oil wells have the following limitations: (1) Most of the current production capacity prediction methods are for shale gas wells and are not very adaptable to shale oil wells, which needs further research; (2) After multi-stage fracturing and completion of shale oil wells, the fluid will produce different flow characteristics. If only one method is used to predict the entire production process of a horizontal well, it is not applicable to different flow states of shale oil well fluids; (3) Based on the flow state, different formula methods are used for fitting. If only the formula is applied, the prediction error is large.

[0003] Therefore, existing shale oil well productivity prediction methods suffer from large errors and poor adaptability. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, namely the significant errors in existing shale oil well productivity prediction methods, this invention provides a shale oil well productivity prediction method based on different flow regimes. The method includes:

[0005] Collect oil well production data within a historical production period; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells;

[0006] Based on the oil well production data, the bottom hole flowing pressure was calculated using RTA software;

[0007] Calculate production-normalized pressure based on wellhead pressure, bottomhole flowing pressure, and well production;

[0008] A double logarithmic diagnostic graph was plotted with the logarithm of normalized pressure of production on the ordinate and the logarithm of material balance time on the abscissa.

[0009] The unsteady linear flow and quasi-steady flow are determined based on the slope of the double logarithmic diagnostic plot;

[0010] The capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow are calculated separately; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

[0011] In a preferred embodiment, the method for calculating the bottom hole flowing pressure is as follows:

[0012] W2 = W1 + ρ·g·h + ΔP;

[0013] Where W2 is the bottom hole flowing pressure, W1 is the wellhead pressure, ρ is the fluid density, g is the gravitational acceleration, h is the wellbore depth, and ΔP is the friction loss.

[0014] In a preferred embodiment, the method for calculating the normalized pressure of production output is as follows:

[0015] The first difference is obtained by subtracting the wellhead pressure from the bottom hole pressure at the current moment; the normalized pressure of the production at the current moment is obtained by dividing the first difference by the well production at the current moment.

[0016] In a preferred embodiment, the method for determining the unsteady linear flow and the quasi-steady flow based on the slope of the double logarithmic diagnostic plot is as follows:

[0017] When the slope of the double logarithmic diagnostic graph is 0.5, and the length of the horizontal axis corresponding to the slope of 0.5 is greater than the first threshold, then the material equilibrium time corresponding to the slope of 0.5 is an unsteady linear flow.

[0018] When the slope of the double logarithmic diagnostic plot is 1, and the length of the horizontal axis corresponding to the slope 1 is greater than the second threshold, the material equilibrium time corresponding to the slope 1 is a pseudo-steady-state flow.

[0019] In a preferred embodiment, the method for calculating the production capacity of the unsteady linear flow is as follows:

[0020] The production capacity of the unsteady linear flow is obtained by cumulatively summing the oil well production corresponding to the material balance time of the unsteady linear flow.

[0021] In a preferred embodiment, the method for calculating the capacity status of the quasi-steady-state flow is as follows:

[0022] In the quasi-steady-state flow stage, the Arps decreasing method is used to fit the capacity status to obtain the capacity status of the quasi-steady-state flow.

[0023] A second aspect of the present invention proposes a shale oil well productivity prediction system based on different flow regimes, the system comprising:

[0024] The data acquisition module is used to collect oil well production data during historical production periods; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells;

[0025] The bottom hole flowing pressure calculation module is used to calculate the bottom hole flowing pressure based on the oil well production data using RTA software;

[0026] The production pressure calculation module is used to calculate the normalized production pressure based on wellhead pressure, bottomhole flowing pressure, and oil well production.

[0027] The diagnostic chart module is used to create a double logarithmic diagnostic chart with the logarithm of the normalized pressure of production as the ordinate and the logarithm of the material balance time as the abscissa.

[0028] The flow regime determination module is used to determine the unsteady linear flow and the quasi-steady flow based on the slope of the double logarithmic diagnostic plot;

[0029] The capacity calculation module is used to calculate the capacity status of the unsteady linear flow and the quasi-steady flow respectively; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

[0030] A third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor for implementing the above-described method for predicting shale oil well productivity based on different flow regimes.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for execution by the computer to implement the above-described method for predicting shale oil well productivity based on different flow regimes.

[0032] The beneficial effects of this invention are:

[0033] (1) Based on the flow pattern diagnostic chart, this invention analyzes the production capacity reduction pattern at different stages and adopts a method that combines the Arps reduction method with the actual cumulative production situation to effectively improve the accuracy of shale oil well production capacity prediction.

[0034] (2) This invention takes into account various flow regimes, and the results are relatively accurate;

[0035] (3) The present invention first determined different flow states, and then adopted different capacity determination methods according to different flow states, and finally obtained accurate final capacity. Attached Figure Description

[0036] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] Figure 1 This is a schematic diagram of a shale oil well productivity prediction method based on different flow regimes according to an embodiment of the present invention;

[0038] Figure 2 This is a double logarithmic diagnostic chart according to an embodiment of the present invention;

[0039] Figure 3 In this embodiment of the invention, a double logarithmic diagnostic chart is used to diagnose the flow state of a shale oil well after blowout.

[0040] Figure 4 This is a schematic diagram of an early unsteady linear flow calculated using cumulative oil production according to an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram illustrating the use of hyperbolic decline to predict production capacity in the later quasi-steady-state flow stage;

[0042] Figure 6 This is a schematic diagram of the structure of a computer system used to implement the methods, systems, and devices of this application. Detailed Implementation

[0043] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] This invention provides a method for predicting the productivity of shale oil wells based on different flow regimes, the method comprising:

[0046] Collect oil well production data within a historical production period; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells;

[0047] Based on the oil well production data, the bottom hole flowing pressure was calculated using RTA software;

[0048] Calculate production-normalized pressure based on wellhead pressure, bottomhole flowing pressure, and well production;

[0049] A double logarithmic diagnostic graph was plotted with the logarithm of normalized pressure of production on the ordinate and the logarithm of material balance time on the abscissa.

[0050] The unsteady linear flow and quasi-steady flow are determined based on the slope of the double logarithmic diagnostic plot;

[0051] The capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow are calculated separately; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

[0052] To more clearly explain the shale oil well productivity prediction method based on different flow regimes of this invention, the following will be combined with... Figure 1 The steps in the embodiments of the present invention will be described in detail below.

[0053] The method for predicting shale oil well productivity based on different flow regimes according to the first embodiment of the present invention is described in detail below:

[0054] Collect oil well production data within a historical production period; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells;

[0055] When there are abnormalities in the collected data, preprocess the abnormal pressure and production data caused by problems such as untimely drilling plugging, wax blockage, and objects falling into the wellbore.

[0056] Based on the oil well production data, the bottom hole flowing pressure was calculated using RTA software;

[0057] In this embodiment, the method for calculating the bottom hole flowing pressure is as follows:

[0058] W2 = W1 + ρ·g·h + ΔP;

[0059] Where W2 is the bottom hole flowing pressure, W1 is the wellhead pressure, ρ is the fluid density, g is the gravitational acceleration, h is the wellbore depth, and ΔP is the friction loss.

[0060] Calculate production-normalized pressure based on wellhead pressure, bottomhole flowing pressure, and well production;

[0061] In this embodiment, the method for calculating the normalized pressure of production output is as follows:

[0062] The first difference is obtained by subtracting the wellhead pressure from the bottom hole pressure at the current moment; the normalized pressure of the production at the current moment is obtained by dividing the first difference by the well production at the current moment.

[0063] A double logarithmic diagnostic plot is created with the logarithm of normalized pressure at production as the ordinate and the logarithm of material equilibrium time as the abscissa. On this double logarithmic diagnostic plot, curves at different stages can be fitted with straight lines of a specific slope; for example... Figure 2 As shown, the unsteady linear flow (slope 1 / 2), quasi-quasi-steady flow (slope 1), bilinear flow (slope 1 / 2), quasi-radial flow (slope 0), and quasi-steady flow (slope 1) are all considered. Except for the unsteady linear flow (slope 1 / 2) and the quasi-steady flow (slope 1), the other states are transient and are not considered in this paper.

[0064] The unsteady linear flow and quasi-steady flow are determined based on the slope of the double logarithmic diagnostic plot;

[0065] In this embodiment, the method for determining the unsteady linear flow and quasi-steady flow based on the slope of the double logarithmic diagnostic plot is as follows:

[0066] When the slope of the double logarithmic diagnostic graph is 0.5, and the length of the horizontal axis corresponding to the slope of 0.5 is greater than the first threshold, then the material equilibrium time corresponding to the slope of 0.5 is an unsteady linear flow.

[0067] When the slope of the double logarithmic diagnostic plot is 1, and the length of the horizontal axis corresponding to the slope 1 is greater than the second threshold, the material equilibrium time corresponding to the slope 1 is a pseudo-steady-state flow.

[0068] The first and second thresholds can be equal. This is only to express that a slope of 0.5 and a slope of 1 are not instantaneous times. For reference, the values ​​of the first and second thresholds are as follows: In the corresponding double logarithmic diagnostic graph, there are n slope changes. The length of the horizontal axis corresponding to each slope change is different, that is, the corresponding material equilibrium time is different. Then the length of the third longest horizontal axis can be used as a reference value for the first and second thresholds.

[0069] The capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow are calculated separately; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

[0070] In this embodiment, the method for calculating the production capacity of the unsteady linear flow is as follows: the production capacity of the unsteady linear flow is obtained by cumulatively superimposing the oil well production corresponding to the material balance time of the unsteady linear flow.

[0071] In this embodiment, the method for calculating the capacity status of the quasi-steady-state flow is as follows:

[0072] In the quasi-steady-state flow stage, the Arps decline method is used to fit the capacity status to obtain the capacity status of the quasi-steady-state flow. The Arps decline method is currently the most widely used capacity decline method. This method is applicable to the quasi-steady-state flow stage and is divided into exponential decline (decline exponent = 0), hyperbolic decline (0 < decline exponent < 1), and harmonic decline (decline exponent = 1).

[0073] To more clearly explain the shale oil well productivity prediction method based on different flow regimes of the present invention, an example is provided, which includes:

[0074] Take two shale oil wells with relatively long production times as examples.

[0075] (1) Data correction;

[0076] We acquired production data after the well blowout and corrected for sudden increases in pressure and production to ensure the entire production process was free from disruptive factors.

[0077] (2) Flow regime diagnosis;

[0078] Using double logarithmic diagnostic charts to diagnose the flow regime after blowout in shale oil wells, such as... Figure 3 As can be seen, the slope of the early data points is 1 / 2, indicating a linear flow stage; the slope of the later data points is 1, indicating a quasi-steady-state flow stage.

[0079] (3) Production capacity forecast;

[0080] Based on the flow regime diagnosed through the above steps, the duration of the early unsteady linear flow in this well was determined to be 369 days. Figure 4 As shown, then the cumulative oil production is calculated using the early unsteady linear flow, such as... Figure 5 As shown, hyperbolic decreasing prediction is used in the later quasi-steady-state flow stage, and the combination of the two results is the final production capacity status.

[0081] As shown in Table 1, the production capacity prediction results obtained by this method are compared with the actual production capacity, and the goodness of fit is about 0.96, which effectively improves the accuracy of shale oil well production capacity prediction and provides support for reservoir engineering scheme preparation and lifting process optimization design.

[0082] Table 1. Statistical Table of Capacity Forecasting Results

[0083] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0084] The second embodiment of the present invention provides a shale oil well productivity prediction system based on different flow regimes, the system comprising:

[0085] The data acquisition module is used to collect oil well production data during historical production periods; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells;

[0086] The bottom hole flowing pressure calculation module is used to calculate the bottom hole flowing pressure based on the oil well production data using RTA software;

[0087] The production pressure calculation module is used to calculate the normalized production pressure based on wellhead pressure, bottomhole flowing pressure, and oil well production.

[0088] The diagnostic chart module is used to create a double logarithmic diagnostic chart with the logarithm of the normalized pressure of production as the ordinate and the logarithm of the material balance time as the abscissa.

[0089] The flow regime determination module is used to determine the unsteady linear flow and the quasi-steady flow based on the slope of the double logarithmic diagnostic plot;

[0090] The capacity calculation module is used to calculate the capacity status of the unsteady linear flow and the quasi-steady flow respectively; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

[0091] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] It should be noted that the shale oil well productivity prediction system based on different flow regimes provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0093] An electronic device according to a third embodiment of the present invention includes:

[0094] At least one processor; and

[0095] A memory communicatively connected to at least one of the processors; wherein,

[0096] The memory stores instructions that can be executed by the processor to implement the above-described method for predicting shale oil well productivity based on different flow regimes.

[0097] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described method for predicting the production capacity of shale oil wells based on different flow regimes.

[0098] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic devices and storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0100] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system for implementing the methods, systems, and devices of this application. Figure 6 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 6As shown, the computer system includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 602 or programs loaded from storage section 608 into Random Access Memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An Input / Output (I / O) interface 605 is also connected to the bus 604.

[0102] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0103] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0104] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0107] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0108] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for predicting the productivity of shale oil wells based on different flow regimes, characterized in that, The method includes: Collect oil well production data within a historical production period; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells; Based on the oil well production data, the bottom hole flowing pressure was calculated using RTA software; Calculate production-normalized pressure based on wellhead pressure, bottomhole flowing pressure, and well production; A double logarithmic diagnostic graph was plotted with the logarithm of normalized pressure of production on the ordinate and the logarithm of material balance time on the abscissa. The unsteady linear flow and quasi-steady flow are determined based on the slope of the double logarithmic diagnostic plot; The capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow are calculated separately; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

2. The shale oil well productivity prediction method based on different flow regimes according to claim 1, characterized in that, The method for calculating bottom hole flowing pressure is as follows: W2 = W1 + ρ·g·h + ΔP; Where W2 is the bottom hole flowing pressure, W1 is the wellhead pressure, ρ is the fluid density, g is the gravitational acceleration, h is the wellbore depth, and ΔP is the friction loss.

3. The shale oil well productivity prediction method based on different flow regimes according to claim 2, characterized in that, The method for calculating normalized pressure on production is as follows: The first difference is obtained by subtracting the wellhead pressure from the bottom hole pressure at the current moment; the normalized pressure of the production at the current moment is obtained by dividing the first difference by the well production at the current moment.

4. The shale oil well productivity prediction method based on different flow regimes according to claim 3, characterized in that, The method for determining unsteady linear flow and quasi-steady flow based on the slope of the double logarithmic diagnostic plot is as follows: When the slope of the double logarithmic diagnostic graph is 0.5, and the length of the horizontal axis corresponding to the slope of 0.5 is greater than the first threshold, then the material equilibrium time corresponding to the slope of 0.5 is an unsteady linear flow. When the slope of the double logarithmic diagnostic plot is 1, and the length of the horizontal axis corresponding to the slope 1 is greater than the second threshold, the material equilibrium time corresponding to the slope 1 is a pseudo-steady-state flow.

5. The shale oil well productivity prediction method based on different flow regimes according to claim 4, characterized in that, The method for calculating the production capacity of the unsteady linear flow is as follows: The production capacity of the unsteady linear flow is obtained by cumulatively summing the oil well production corresponding to the material balance time of the unsteady linear flow.

6. The shale oil well productivity prediction method based on different flow regimes according to claim 5, characterized in that, The method for calculating the capacity status of the quasi-steady-state flow is as follows: In the quasi-steady-state flow stage, the Arps decreasing method is used to fit the capacity status to obtain the capacity status of the quasi-steady-state flow.

7. A shale oil well productivity prediction system based on different flow regimes, characterized in that, The system includes: The data acquisition module is used to collect oil well production data during historical production periods; the oil well production data includes wellhead pressure, material balance time, oil well production, wellbore depth, fluid density, and friction loss of shale oil wells; The bottom hole flowing pressure calculation module is used to calculate the bottom hole flowing pressure based on the oil well production data using RTA software; The production pressure calculation module is used to calculate the normalized production pressure based on wellhead pressure, bottomhole flowing pressure, and oil well production. The diagnostic chart module is used to create a double logarithmic diagnostic chart with the logarithm of the normalized pressure of production as the ordinate and the logarithm of the material balance time as the abscissa. The flow regime determination module is used to determine the unsteady linear flow and the quasi-steady flow based on the slope of the double logarithmic diagnostic plot; The capacity calculation module is used to calculate the capacity status of the unsteady linear flow and the quasi-steady flow respectively; the final capacity is obtained by adding the capacity status of the unsteady linear flow and the capacity status of the quasi-steady flow.

8. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor to implement the shale oil well productivity prediction method based on different flow regimes as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are executed by the computer to implement the shale oil well productivity prediction method based on different flow regimes as described in any one of claims 1-6.

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