A method for optimizing the maximum nozzle size of shale gas wells

Abstract

The present invention discloses a method for optimizing the maximum choke size of a shale gas well, comprising the following steps: S1, utilizing neural network sampling and machine learning to realize automatic historical fitting of daily gas production, bottom hole pressure, and daily liquid production parameters, and inversely derive fracture parameters; S2, establishing a wellbore pipe flow model for the shale gas well; S3, inputting different choke sizes into the established model, calculating the production pressure differences corresponding to the different choke sizes, and thereby establishing a relationship between the choke size and the reservoir pressure; S4, utilizing an indoor experiment on the permeability stress sensitivity of shale artificial fractures under different production pressure differences to obtain the maximum effective stress; S5, selecting the size of the shale gas well whose production pressure differences corresponding to different choke sizes are closest to but do not exceed the maximum effective stress as the maximum choke size, etc. The present invention can obtain the maximum choke size based on the production pressure differences corresponding to different choke sizes, and provides a theoretical basis for determining a reasonable maximum choke size in the shale gas well drainage process.

Description

Technical Field

[0001] The present invention relates to the field of shale development, and more specifically, to a method for optimizing the maximum choke size of a shale gas well. Background Art

[0002] To achieve commercial development, shale gas wells require large-scale volume fracturing, where tens of thousands of cubic meters of fracturing fluid are injected into the shale reservoir. The filtration and flowback processes of the fracturing fluid are particularly important. Establishing an optimal drainage system is key to achieving reasonable flowback of fracturing fluid from shale gas wells and maximizing gas well production capacity. During the post-fracturing drainage process of shale gas wells, choke control is employed. During the drainage phase of shale gas wells in southern Sichuan, a system in which choke sizes increase gradually from small to large is commonly employed. However, the determination of the maximum choke during the drainage process is still based on experience and lacks theoretical support. A maximum choke size that is too small makes it impossible to measure the test production of a single well. A choke size that is too large can easily cause proppant breakage, embedding, and backflow, resulting in stress sensitivity and affecting the conductivity of the artificial fracture. Currently, no domestic scholars have conducted research related to the determination of the maximum choke size. Most studies focus on the flowback mechanism of fracturing fluid in shale gas wells after fracturing, which fails to integrate with engineering practice. There is a lack of a set of methods that combine choke size with history matching, production pressure difference, and stress sensitivity curve to determine the maximum choke size considering the geological engineering conditions, fracture parameters, and production characteristics of individual shale gas wells. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for optimizing the maximum choke size of a shale gas well. The method can derive the maximum choke size for different choke sizes corresponding to the production pressure difference, and provide a theoretical basis for determining a reasonable maximum choke size in the shale gas well production process.

[0004] The object of the present invention is achieved through the following solutions:

[0005] A method for optimizing the maximum choke size of a shale gas well comprises the following steps:

[0006] S1, using neural network sampling machine learning to select historical production data of shale gas wells to achieve historical fitting of daily gas production, bottom hole pressure and daily liquid production parameters, and then using the inversion algorithm to invert the fracture parameters;

[0007] S2, establishing a shale gas wellbore flow model by combining the fracture parameters and geological engineering parameters obtained from the inversion of the selected shale gas well;

[0008] S3, in the established shale gas wellbore flow model, different nozzle sizes are input and the corresponding production pressure difference of different nozzle sizes is calculated, thereby establishing the relationship between nozzle size and reservoir pressure;

[0009] S4, the maximum effective stress is obtained by using the indoor experiment on the permeability stress sensitivity of shale artificial fractures under different production pressure differences;

[0010] S5. Combine the maximum effective stress data obtained in the experiment with the production pressure difference corresponding to the nozzle size obtained from the shale gas wellbore pipe flow model, and select the size with the production pressure difference corresponding to different nozzle sizes of the shale gas well that is closest to but does not exceed the maximum effective stress as the maximum nozzle size.

[0011] Furthermore, in step S1, the inversion algorithm includes a Markov chain-Monte Carlo inversion algorithm MCMC.

[0012] Furthermore, the fracture parameters inverted using the Markov chain-Monte Carlo inversion algorithm (MCMC) include effective fracture height, fracture length, conductivity and fracture water saturation.

[0013] Furthermore, in step S2, the shale gas wellbore pipe flow model includes the following sub-models: a wellbore model, a fluid component model, an IPR model, and a nozzle flow model.

[0014] Furthermore, in step S3, the range of the different nozzle sizes is between 3-12 mm.

[0015] Furthermore, in step S4, the maximum effective stress is 10 MPa.

[0016] The beneficial effects of the present invention include:

[0017] The present invention combines pipe flow numerical simulation with indoor gas-water two-phase seepage experiments, establishes pipe flow models and flowback models based on shale gas well geological engineering parameters, fracture parameters, and automatic history fitting, and combines them with indoor experiments. It can derive the maximum nozzle size for different nozzle sizes corresponding to the production pressure difference, solving the current problem of lack of theoretical support for determining the maximum nozzle size on site, realizing the protection of the conductivity of artificial fractures in the shale gas well production process, and providing a theoretical basis for optimizing the shale gas well production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 paying any creative work.

[0019] Figure 1 This is a flow chart of a method for determining the maximum choke size of a shale gas well according to an embodiment of the present invention;

[0020] Figure 2Schematic diagram of the daily gas production fitting results in an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of the fitting results of daily liquid production in an embodiment of the present invention;

[0022] Figure 4 Schematic diagram of the bottom hole flow pressure fitting result in an embodiment of the present invention;

[0023] Figure 5 Schematic diagram of a wellbore model in an embodiment of the present invention;

[0024] Figure 6 Schematic diagram of a fluid component model in an embodiment of the present invention;

[0025] Figure 7 Schematic diagram of an IPR model in an embodiment of the present invention;

[0026] Figure 8 Schematic diagram of a mouth flow model according to an embodiment of the present invention;

[0027] Figure 9 Schematic diagram of a wellbore pressure profile with a 12mm nozzle in an embodiment of the present invention;

[0028] Figure 10 Schematic diagram of permeability recovery curves under different effective stress conditions in an embodiment of the present invention;

[0029] Figure 11 Schematic diagram of limiting the maximum nozzle size under production pressure difference in an embodiment of the present invention;

[0030] Figure 12 Flowchart of method steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0031] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0032] like Figures 1 to 12 As shown, a method for optimizing the maximum choke size of a shale gas well includes the following steps:

[0033] S1, using neural network sampling machine learning to select the historical production data of shale gas wells to achieve historical fitting of daily gas production, bottom hole pressure and daily liquid production parameters, and then using the inversion algorithm to invert the fracture parameters; in this step, if Figures 2-4As shown in the figure, a shale gas well was selected and the Markov chain-Monte Carlo inversion algorithm (MCMC) was used to automatically sample and learn historical production data through artificial intelligence-neural network to achieve automatic historical fitting of three parameters: daily gas production, bottomhole pressure, and daily liquid production. The key fracture parameters including effective fracture height, fracture length, conductivity, and fracture water saturation were inverted (Table 1).

[0034] Table 1 Inversion results of shale gas well fracture parameters

[0035] Crack parameters Optimal value <![CDATA[P 10 Value]]> <![CDATA[P 50 Value]]> <![CDATA[P 90 Value]]> Height (m) 12.8 11.1 12.7 14.6 Half length (m) 84.3 81.7 85.4 89.3 Conductivity (md·m) 28 22.5 29 51.7 Water saturation 0.711 0.683 0.713 0.739 Width (m) 0.0907 0.0831 0.09 0.0995 Cluster efficiency 0.702 0.666 0.696 0.724

[0036] The optimal values ​​of fracture parameters obtained by inversion will be applied to the pipe flow model, making the establishment of the pipe flow model more in line with production practice.

[0037] S2, combining the fracture parameters and geological engineering parameters obtained by inversion of the selected shale gas well to establish a shale gas wellbore pipe flow model; in this step, if Figures 5 to 8 As shown in Figure 1, the pipesim software can be used to combine the geological engineering parameters and fracture inversion parameters of the selected shale gas well to establish a shale gas wellbore pipe flow model, whose sub-models include the wellbore model, fluid component model, IPR model and mouth flow model.

[0038] S3, in the established shale gas wellbore flow model, different nozzle sizes (3-12mm) are input to obtain the corresponding wellbore pressure profile, such as Figure 9 As shown, the relationship between nozzle size and reservoir pressure is established; in an optional embodiment, the Mukherjee and Brill empirical formula and nozzle flow calculation method are used to simulate the bottom hole flow pressure corresponding to nozzle sizes of 3mm-12mm, so that the production pressure difference corresponding to different nozzle sizes can be calculated. As shown in Table 2:

[0039] Table 2 Different nozzle sizes corresponding to production pressure difference

[0040]

[0041] S4, using the indoor experiment of permeability stress sensitivity of shale artificial fractures under different production pressure differential conditions to obtain the maximum effective stress. In this step, when the effective stress is greater than 19MPa, the proppant breaks and embeds seriously, and the damage to the reservoir permeability is difficult to recover. In this step, by conducting stress sensitivity research on artificial fractures in shale reservoirs under different production pressure differential conditions, a theoretical basis can be provided for the selection of the maximum nozzle size. The experimental results show that the maximum effective stress is 19MPa. If it exceeds this value, the reservoir damage will be difficult to recover. Figure 10 ).

[0042] S5, the maximum effective stress data obtained in the experiment is combined with the nozzle size corresponding to the production pressure difference obtained by the shale gas well bore pipe flow model, and the size of the shale gas well corresponding to the production pressure difference closest to but not exceeding the maximum effective stress (19MPa) is selected as the maximum nozzle size. In this step, the shale gas reservoir seepage and shale gas well bore pipe flow are innovatively combined. Through the analysis and comparison of the experimental results and the numerical simulation results, the nozzle size with the nozzle size corresponding to the production pressure difference closest to but not exceeding 19MPa is selected as the maximum nozzle size ( Figure 11 ).

[0043] The parts not involved in the present invention are the same as the existing technology or can be implemented by using the existing technology.

[0044] The above technical solution is only one embodiment of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the application methods and principles disclosed in the present invention, and it is not limited to the method described in the above specific embodiment of the present invention. Therefore, the method described above is only preferred and does not have a restrictive meaning.

[0045] In addition to the above examples, those skilled in the art may obtain other embodiments based on the above disclosure or by utilizing knowledge or technology in related fields to make modifications. The features of each embodiment may be interchangeable or replaced. The modifications and changes made by those skilled in the art do not depart from the spirit and scope of the present invention and should be within the scope of protection of the claims attached to the present invention.

[0046] If the functions of the present invention are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and all or part of the steps of the methods described in each embodiment of the present invention are executed in a computer device (which can be a personal computer, server, or network device, etc.) and corresponding software. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, or an optical disk. The test or actual data in the program implementation is stored in a read-only memory (RAM), a random access memory (RAM), etc.

Claims

1. A method for optimizing the maximum choke size of a shale gas well, characterized in that: Including steps: S1, using neural network sampling machine learning to select historical production data of shale gas wells to achieve historical fitting of daily gas production, bottom hole pressure and daily liquid production parameters, and then using an inversion algorithm to invert fracture parameters; the inversion algorithm includes a Markov chain-Monte Carlo inversion algorithm MCMC; S2: A shale gas wellbore flow model is established by combining the fracture parameters and geological engineering parameters derived from the inversion of the selected shale gas well. The fracture parameters derived from the Markov Chain Monte Carlo inversion algorithm (MCMC) include effective fracture height, fracture length, conductivity, and fracture water saturation. S3, in the established shale gas wellbore flow model, different nozzle sizes are input and the corresponding production pressure difference of different nozzle sizes is calculated, thereby establishing the relationship between nozzle size and reservoir pressure; S4, the maximum effective stress is obtained by using the indoor experiment on the permeability stress sensitivity of shale artificial fractures under different production pressure differences; S5. Combine the maximum effective stress data obtained in the experiment with the production pressure difference corresponding to the nozzle size obtained from the shale gas wellbore pipe flow model, and select the size with the production pressure difference corresponding to different nozzle sizes of the shale gas well that is closest to but does not exceed the maximum effective stress as the maximum nozzle size.

2. The method for optimizing the maximum choke size of a shale gas well according to claim 1, characterized in that: In step S2, the shale gas wellbore pipe flow model includes the following sub-models: wellbore model, fluid component model, IPR model and nozzle flow model.

3. The method for optimizing the maximum choke size of a shale gas well according to claim 1, characterized in that: In step S3, the range of the different nozzle sizes is between 3-12 mm.

4. The method for optimizing the maximum choke size of a shale gas well according to claim 3, characterized in that: In step S4, the maximum effective stress is 10 MPa.

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