Method and device for evaluating and predicting dynamic productivity of low-permeability tight gas reservoir gas well

Abstract

The invention discloses a method and a device for evaluating and predicting the dynamic productivity of a gas well of a low-permeability tight gas reservoir. Determining gas production indexes of the target gas well at different production times according to the gas well daily production data, the gas reservoir parameters and the gas well tubular column parameters of the target gas well; pseudo gas reservoir pressures of the target gas well at different production times are obtained; determining the open flow capacity of the target gas well according to the gas production index and the pseudo gas reservoir pressure; and fitting a relation curve of the open flow capacity and the production time, and predicting the gas well productivity of the target gas well in the future through the relation curve. According to the scheme, the gas production indexes and the formation pressure of the gas well at different times are solved by using daily production data and adopting a modern gas well dynamic analysis method, the gas well dynamic productivity evaluation and prediction method is established, the dynamic productivity change rule of the gas well can be accurately evaluated and predicted, and the gas well dynamic productivity evaluation and prediction method is established. The method is used for guiding reasonable production allocation of gas wells and optimized implementation of development schemes.

Description

Technical Field

[0001] The present invention relates to the technical field of gas reservoir development, and in particular to a method and device for evaluating and predicting the dynamic productivity of gas wells in low-permeability and tight gas reservoirs. Background Art

[0002] For conventional gas reservoirs, the productivity well test method based on the steady seepage theory is generally used to obtain the gas well productivity equation. The condition is that the gas well reaches a steady seepage state during the test process, and both the production and bottomhole pressure remain stable.

[0003] Low-permeability tight gas reservoirs are significantly different from conventional gas reservoirs. Due to their low natural productivity, they are often developed using process wells (horizontal wells or highly deviated wells) and artificial transformation methods (acidizing or fracturing). Gas wells are in an unstable seepage state for a long time, and their production capacity is highly time-sensitive. There is a large difference between short-term test capacity and long-term production capacity. The commonly used stable seepage analysis method is less applicable to this type of gas wells. Summary of the Invention

[0004] The present invention provides a method and device for evaluating and predicting the dynamic productivity of gas wells in low-permeability and tight gas reservoirs, so as to realize the evaluation and prediction of the productivity of gas wells in low-permeability and tight gas reservoirs.

[0005] According to one aspect of the present invention, a method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability tight gas reservoir is provided, comprising:

[0006] determining a gas production index at different production times of the target gas well based on daily gas well production data, gas reservoir parameters, and gas well string parameters of the target gas well;

[0007] obtaining the simulated gas reservoir pressure of the target gas well at different production times;

[0008] determining the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure;

[0009] A relationship curve between the open flow rate and the production time is fitted, and the future gas well production capacity of the target gas well is predicted based on the relationship curve.

[0010] Optionally, the daily production data of the gas well includes: oil pressure, casing pressure, daily gas production and daily water production;

[0011] The gas reservoir parameters include: gas reservoir temperature, gas reservoir original pressure, fluid, effective reservoir thickness, porosity, and gas saturation parameters;

[0012] The gas well string parameters include: gas well type and gas well string parameters.

[0013] Optionally, determining the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters includes:

[0014] A modern production decline analysis is performed on the target gas well based on the daily production data of the gas well, gas reservoir parameters and gas well string parameters, and the gas production index of the gas well at different production times is obtained by the flow material balance method.

[0015] Optionally, obtaining the simulated gas reservoir pressure of the target gas well at different production times includes:

[0016] fitting the production curve of the target gas well by an analytical model method to obtain the gas reservoir pressure of the target gas well at different production times;

[0017] The gas reservoir pressure is converted into the pseudo gas reservoir pressure.

[0018] Optionally, converting the gas reservoir pressure into the pseudo gas reservoir pressure includes:

[0019] The gas reservoir pressure is converted to the pseudo gas reservoir pressure by the following formula:

[0020]

[0021] Wherein, m(p) is the pseudo gas reservoir pressure, p is the gas pressure, μ is the natural gas viscosity; and Z is the natural gas deviation factor.

[0022] Optionally, determining the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure includes:

[0023] The open-flow rate is calculated using the following formula:

[0024] Q AOF (t)=J(t)·m(p R );

[0025] Among them, Q AOF (t) is the open-flow rate, J(t) is the gas production index of the gas well at time t, m(p R ) is the pseudo gas reservoir pressure.

[0026] Optionally, fitting the relationship curve between the open-flow rate and the production time includes:

[0027] The relationship curve is fitted by the following formula:

[0028] Q AOF (t) = a·t b ;

[0029] Among them, Q AOF(t) is the open-flow rate, a and b are fitting coefficients, and t is the production time of the target gas well.

[0030] According to another aspect of the present invention, a device for evaluating and predicting the dynamic productivity of a gas well in a low-permeability tight gas reservoir is provided, comprising:

[0031] A gas production index determination unit, configured to determine the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters;

[0032] a pseudo-gas reservoir pressure determination unit, configured to obtain the pseudo-gas reservoir pressure of the target gas well at different production times;

[0033] an open flow determination unit, configured to determine the open flow of the target gas well according to the gas production index and the simulated gas reservoir pressure;

[0034] The relationship curve fitting unit is used to fit the relationship curve between the open flow rate and the production time, and predict the future gas well production capacity of the target gas well through the relationship curve.

[0035] According to another aspect of the present invention, an electronic device is provided, comprising:

[0036] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for dynamic productivity evaluation and prediction of gas wells in low-permeability and tight gas reservoirs as described in any embodiment of the present invention.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the dynamic productivity evaluation and prediction method for a gas well in a low-permeability tight gas reservoir as described in any embodiment of the present invention when executed.

[0038] The technical solution of the embodiment of the present invention determines the gas production index of the target gas well at different production times based on the daily production data of the target gas well, the gas reservoir parameters and the gas well string parameters; obtains the pseudo gas reservoir pressure of the target gas well at different production times; determines the open flow rate of the target gas well based on the gas production index and the pseudo gas reservoir pressure; fits the relationship curve between the open flow rate and production time, and predicts the future gas well production capacity of the target gas well through the relationship curve. The solution of the present invention utilizes daily production data and adopts modern gas well dynamic analysis methods to obtain the gas production index and formation pressure of the gas well at different times, establishes a gas well dynamic production capacity evaluation and prediction method, and can accurately evaluate and predict the dynamic production capacity change law of the gas well, which is used to guide the rational production allocation of gas wells and the optimization implementation of development plans.

[0039] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is a flow chart of a method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability tight gas reservoir provided in Example 1 of the present invention;

[0042] Figure 2 Schematic diagram of the birth period index applicable to the first embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the analytical model method applicable to the first embodiment of the present invention;

[0044] Figure 4 Schematic diagram of obtaining gas reservoir pressure applicable to the first embodiment of the present invention;

[0045] Figure 5 Schematic diagram of the relationship between natural gas pressure and pseudo-pressure applicable to the first embodiment of the present invention;

[0046] Figure 6 is a schematic diagram of a relationship curve applicable to Example 1 of the present invention;

[0047] Figure 7 This is a schematic structural diagram of a dynamic productivity evaluation and prediction device for a gas well in a low-permeability tight gas reservoir provided by a third embodiment of the present invention;

[0048] Figure 8 It is a structural schematic diagram of an electronic device for implementing the method for dynamic productivity evaluation and prediction of gas wells in low-permeability tight gas reservoirs according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] Example 1

[0052] Figure 1 This is a flow chart of a method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability and tight gas reservoir provided by the first embodiment of the present invention. This embodiment is applicable to the case of predicting the productivity of a gas well in a low-permeability and tight gas reservoir. The method can be executed by a dynamic productivity evaluation and prediction device for a gas well in a low-permeability and tight gas reservoir. The dynamic productivity evaluation and prediction device for a gas well in a low-permeability and tight gas reservoir can be implemented in the form of hardware and / or software. The dynamic productivity evaluation and prediction device for a gas well in a low-permeability and tight gas reservoir can be configured in an electronic device. Figure 1 As shown, the method includes:

[0053] S110 , determining a gas production index of the target gas well at different production times based on daily gas well production data, gas reservoir parameters, and gas well string parameters of the target gas well.

[0054] In the embodiment of the present invention, the daily production data of the gas well includes: oil pressure, casing pressure, daily gas production and daily water production;

[0055] The gas reservoir parameters include: gas reservoir temperature, gas reservoir original pressure, fluid, effective reservoir thickness, porosity, and gas saturation parameters;

[0056] The gas well string parameters include: gas well type and gas well string parameters.

[0057] Among them, as a necessary condition for capacity prediction, it is necessary to obtain the above three aspects of gas well data, including: daily production data of gas wells: including oil pressure, casing pressure, daily gas production, and daily water production; gas reservoir parameters: including gas reservoir temperature, gas reservoir original pressure, fluid, effective reservoir thickness, porosity, and gas saturation parameters; gas well string parameters include: gas well type (vertical well, fractured vertical well, horizontal well, or multi-stage fractured horizontal well) and gas well string parameters (including oil casing inner diameter and depth).

[0058] In an embodiment of the present invention, determining the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters includes:

[0059] A modern production decline analysis is performed on the target gas well based on the daily production data of the gas well, gas reservoir parameters and gas well string parameters, and the gas production index J(t) of the gas well at different production times is obtained by the flow material balance method. Figure 2 Schematic diagram of the production period index applicable to the first embodiment of the present invention.

[0060] S120: Obtain the simulated gas reservoir pressure of the target gas well at different production times.

[0061] In an embodiment of the present invention, obtaining the simulated gas reservoir pressure of the target gas well at different production times includes:

[0062] fitting the production curve of the target gas well by an analytical model method to obtain the gas reservoir pressure of the target gas well at different production times;

[0063] The gas reservoir pressure is converted into the pseudo gas reservoir pressure.

[0064] In an embodiment of the present invention, converting the gas reservoir pressure into the pseudo gas reservoir pressure includes:

[0065] The gas reservoir pressure is converted to the pseudo gas reservoir pressure by the following formula:

[0066]

[0067] Wherein, m(p) is the pseudo gas reservoir pressure, p is the gas pressure, μ is the natural gas viscosity; and Z is the natural gas deviation factor.

[0068] Figure 3This is a schematic diagram of the analytical model method applicable to the first embodiment of the present invention, through Figure 3 The model shown is fitted with the production curve to obtain the gas reservoir pressure p at different production times of the gas well. R . Figure 4 Schematic diagram of obtaining gas reservoir pressure applicable to the first embodiment of the present invention. Figure 5 FIG. 1 is a schematic diagram showing the relationship between natural gas pressure and pseudo-pressure applicable to the first embodiment of the present invention. Figure 4 、 5 As shown in the figure, according to the relationship between natural gas pressure and pseudo-pressure, the corresponding pseudo-gas reservoir pressure m(p R ).

[0069] S130: Determine the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure.

[0070] In an embodiment of the present invention, determining the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure includes:

[0071] The open-flow rate is calculated using the following formula:

[0072] Q AOF (t)=J(t)·m(p R );

[0073] Among them, Q AOF (t) is the open-flow rate, J(t) is the gas production index of the gas well at time t, m(p R ) is the pseudo gas reservoir pressure.

[0074] The gas production index and the planned production pressure difference at different times are used to calculate the corresponding gas well production capacity. The gas well production capacity refers to the maximum production capacity of the gas well, that is, the open flow rate. The relationship between the gas well production and the planned production pressure difference is as follows:

[0075] Q(t)=J(t)·Δm(p)=J(t)·[m(p) R )-m(p wf )];

[0076] In the above formula, Q(t) is the gas well production at time t, 10 4 m 3 / d; J(t) is the gas production index of the gas well at time t, (10 4 m 3 / d) / (MPa 2 / mPa.s), Δm(p) is the pseudo pressure difference, MPa 2 / mPa.s,m(p R ) is the pseudo gas reservoir pressure, MPa 2 / mPa.s;m(p wf) is the pseudo bottom hole pressure, MPa 2 / mPa.s.

[0077] Let m(p wf )=0, the open flow rate Q of the gas well can be obtained AOF (t) is expressed as:

[0078] Q AOF (t)=J(t)·m(p R ).

[0079] S140 , fitting a relationship curve between the open flow rate and the production time, and predicting the future gas well production capacity of the target gas well based on the relationship curve.

[0080] In an embodiment of the present invention, fitting the relationship curve between the open flow rate and the production time includes:

[0081] The relationship curve is fitted by the following formula:

[0082] Q AOF (t) = a·t b ;

[0083] Among them, Q AOF (t) is the open-flow rate, a and b are fitting coefficients, and t is the production time of the target gas well.

[0084] Among them, the fitted gas well production capacity Q AOF The relationship curve between (t) and production time is used to obtain the dynamic production capacity change curve, which can be extrapolated to predict the gas well production capacity in different time periods in the future. Figure 6 is a schematic diagram of a relationship curve applicable to the first embodiment of the present invention, Figure 6 The solid line segment in the figure is the relationship curve, and the dotted line segment is the predicted capacity curve after extrapolation.

[0085] The following is a detailed description of the execution process of this solution through a specific embodiment. This embodiment takes a gas well S1 as an example. The data in this embodiment are taken from the following Table 1:

[0086]

[0087]

[0088] 1. Using daily production data, reservoir parameters, and well string parameters, a modern production decline analysis was conducted on Well S1, a technologically renovated well in a low-permeability, tight gas reservoir. The flowing material balance method was used to obtain the gas production index J(t) of Well S1 at different production times (data in the second column of Table 1).

[0089] 2. Use the analytical model method to fit the production curve of well S1 to obtain the gas reservoir pressure p at different production times of the gas wellR (Data in the third column of Table 1), according to the relationship between natural gas pressure and pseudo-pressure, the corresponding pseudo-gas reservoir pressure m(p R )(Data in the 4th column of Table 1).

[0090] 3. Using the gas production index J(t) and the simulated production pressure difference m(p) corresponding to different production times of S1 well, R ), calculate the corresponding gas well production capacity Q AOF (t) (data in column 5 of Table 1).

[0091] 4. Fitting the gas well productivity Q of well S1 AOF The relationship curve between (t) and production time t ( Figure 6 The solid line segment in the figure) gives the dynamic productivity change curve of well S1 as follows:

[0092] Q AOF (t) = 429.37 × t -0.649 ;

[0093] The extrapolation of the curve can be used to predict the gas well production capacity at different times in the future, as mentioned above. Figure 6 The dashed line segment is shown. It can be predicted that the open flow rate of S1 well will be 6.35×10 4 m 3 / d, and the open-circuit flow rate after 3 years of operation is 4.88×10 4 m 3 / d.

[0094] This method utilizes daily gas well production data and modern gas well dynamic analysis methods to establish a dynamic production capacity evaluation and prediction method for process-modified wells in low-permeability, tight gas reservoirs. This method can accurately evaluate and predict the dynamic production capacity changes of gas wells, guiding the rational allocation of gas well production and optimizing the implementation of gas reservoir development plans. Compared with existing methods, the solution provided by this invention has the following characteristics:

[0095] (1) The solution of the present invention only requires daily production data of gas wells, which is easy to process and does not require a lot of testing time and money;

[0096] (2) The solution of the present invention does not require the gas well to reach a "stable" or "quasi-stable" seepage state. Unlike the commonly used productivity test well testing method, it is more suitable for process transformation wells in low-permeability tight gas reservoirs;

[0097] (3) The scheme of the present invention can evaluate and predict the dynamic production capacity of the entire gas well production process, and has forward-looking guiding significance for optimizing gas well production allocation.

[0098] Example 2

[0099] Figure 7This is a schematic diagram of the structure of a dynamic productivity evaluation and prediction device for a low permeability tight gas reservoir provided by the second embodiment of the present invention. Figure 7 As shown, the device includes:

[0100] A gas production index determining unit 710 is configured to determine the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters;

[0101] The pseudo gas reservoir pressure determination unit 720 is used to obtain the pseudo gas reservoir pressure of the target gas well at different production times;

[0102] an open flow determination unit 730, configured to determine the open flow of the target gas well according to the gas production index and the simulated gas reservoir pressure;

[0103] The relationship curve fitting unit 740 is used to fit the relationship curve between the open flow rate and the production time, and predict the future gas well production capacity of the target gas well through the relationship curve.

[0104] Optionally, the daily production data of the gas well includes: oil pressure, casing pressure, daily gas production and daily water production;

[0105] The gas reservoir parameters include: gas reservoir temperature, gas reservoir original pressure, fluid, effective reservoir thickness, porosity, and gas saturation parameters;

[0106] The gas well string parameters include: gas well type and gas well string parameters.

[0107] Optionally, the gas production index determining unit 710 is configured to execute:

[0108] A modern production decline analysis is performed on the target gas well based on the daily production data of the gas well, gas reservoir parameters and gas well string parameters, and the gas production index of the gas well at different production times is obtained by the flow material balance method.

[0109] Optionally, the simulated gas reservoir pressure determination unit 720 is configured to: fit the production curve of the target gas well by an analytical model method to obtain the gas reservoir pressure of the target gas well at different production times;

[0110] The gas reservoir pressure is converted into the pseudo gas reservoir pressure.

[0111] Optionally, the pseudo gas reservoir pressure determining unit 720, when converting the gas reservoir pressure into the pseudo gas reservoir pressure, specifically performs:

[0112] The gas reservoir pressure is converted to the pseudo gas reservoir pressure by the following formula:

[0113]

[0114] Wherein, m(p) is the pseudo gas reservoir pressure, p is the gas pressure, μ is the natural gas viscosity; and Z is the natural gas deviation factor.

[0115] Optionally, the unimpeded flow determination unit 730 is configured to execute:

[0116] The open-flow rate is calculated using the following formula:

[0117] Q AOF (t)=J(t)·m(p R );

[0118] Among them, Q AOF (t) is the open-flow rate, J(t) is the gas production index of the gas well at time t, m(p R ) is the pseudo gas reservoir pressure.

[0119] Optionally, the relationship curve fitting unit 740 is configured to perform:

[0120] The relationship curve is fitted by the following formula:

[0121] Q AOF (t) = a·t b ;

[0122] Among them, Q AOF (t) is the open-flow rate, a and b are fitting coefficients, and t is the production time of the target gas well.

[0123] The dynamic productivity evaluation and prediction device for low-permeability and tight gas reservoir wells provided in an embodiment of the present invention can execute the dynamic productivity evaluation and prediction method for low-permeability and tight gas reservoir wells provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0124] Example 3

[0125] Figure 8 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0126] like Figure 8As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0127] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0128] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for dynamic productivity evaluation and prediction of gas wells in low-permeability tight gas reservoirs.

[0129] In some embodiments, the method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability and tight gas reservoir may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability and tight gas reservoir described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability and tight gas reservoir by any other appropriate means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0136] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0137] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for evaluating and predicting the dynamic productivity of gas wells in low-permeability tight gas reservoirs, characterized by: include: determining a gas production index at different production times of the target gas well based on daily gas well production data, gas reservoir parameters, and gas well string parameters of the target gas well; Obtaining the simulated gas reservoir pressure of the target gas well at different production times; determining the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure; A relationship curve between the open flow rate and the production time is fitted, and the future gas well production capacity of the target gas well is predicted based on the relationship curve.

2. The method according to claim 1, characterized in that The daily production data of the gas wells include: oil pressure, casing pressure, daily gas production and daily water production; The gas reservoir parameters include: gas reservoir temperature, gas reservoir original pressure, fluid, effective reservoir thickness, porosity, and gas saturation parameters; The gas well string parameters include: gas well type and gas well string parameters.

3. The method according to claim 1, characterized in that The step of determining the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters includes: A modern production decline analysis is performed on the target gas well based on the daily production data of the gas well, gas reservoir parameters and gas well string parameters, and the gas production index of the gas well at different production times is obtained by the flow material balance method.

4. The method according to claim 1, wherein The obtaining of the simulated gas reservoir pressure of the target gas well at different production times includes: fitting the production curve of the target gas well by an analytical model method to obtain the gas reservoir pressure of the target gas well at different production times; The gas reservoir pressure is converted into the pseudo gas reservoir pressure.

5. The method according to claim 4, characterized in that The converting the gas reservoir pressure into the pseudo gas reservoir pressure includes: The gas reservoir pressure is converted to the pseudo gas reservoir pressure by the following formula: Wherein, m(p) is the pseudo gas reservoir pressure, p is the gas pressure, μ is the natural gas viscosity; and Z is the natural gas deviation factor.

6. The method according to claim 1, characterized in that Determining the open flow rate of the target gas well according to the gas production index and the simulated gas reservoir pressure includes: The open-flow rate is calculated using the following formula: Q AOF (t)=J(t)·m(p R ); Among them, Q AOF (t) is the open-flow rate, J(t) is the gas production index of the gas well at time t, m(p R ) is the pseudo gas reservoir pressure.

7. The method according to claim 1, characterized in that The fitting of the relationship curve between the open-flow rate and the production time includes: The relationship curve is fitted by the following formula: Q AOF (t)=a·t b ; Among them, Q AOF (t) is the open-flow rate, a and b are fitting coefficients, and t is the production time of the target gas well.

8. A dynamic productivity evaluation and prediction device for gas wells in low-permeability tight gas reservoirs, characterized in that: include: A gas production index determination unit, configured to determine the gas production index of the target gas well at different production times based on the daily production data of the target gas well, gas reservoir parameters, and gas well string parameters; a pseudo-gas reservoir pressure determination unit, configured to obtain the pseudo-gas reservoir pressure of the target gas well at different production times; an open flow determination unit, configured to determine the open flow of the target gas well according to the gas production index and the simulated gas reservoir pressure; The relationship curve fitting unit is used to fit the relationship curve between the open flow rate and the production time, and predict the future gas well production capacity of the target gas well through the relationship curve.

9. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for dynamic productivity evaluation and prediction of gas wells in low-permeability and tight gas reservoirs according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for evaluating and predicting the dynamic productivity of a gas well in a low-permeability tight gas reservoir according to any one of claims 1 to 7 when executed.

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