Method and device for determining new well productivity of fractured gas reservoir based on imaging logging

Abstract

The application provides a method and device for determining fracture gas reservoir new well productivity based on imaging logging. The method comprises the following steps: performing single-factor correlation analysis on imaging logging fracture parameters, oil test flowback rate and deliverability test open flow capacity parameters of a target block, and constructing fracture characterization parameters; establishing a regression model of the deliverability test open flow capacity parameters and the fracture characterization parameters of the target block; determining fracture characterization parameters of a target new well according to imaging logging fracture parameters and oil test flowback rate of the target new well; and bringing the fracture characterization parameters of the target new well into the regression model to determine open flow capacity parameters of the target new well. The application can determine open flow capacity of a new well based on imaging logging fracture parameters without performing deliverability test.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir development technology, specifically to a technology for determining the production capacity of new wells in fractured gas reservoirs based on imaging logging. Background Technology

[0002] With the rapid growth of domestic natural gas demand, shallow and high-quality gas reservoirs have entered the middle and late stages of development. Newly added natural gas resources are mostly found in relatively complex reservoirs, such as tight gas reservoirs, shale gas reservoirs, and deep gas reservoirs. Among these, deep fractured gas reservoirs are a relatively complex type, mainly distributed in the Tarim Basin and Sichuan Basin. Due to the complex development of natural fractures in these reservoirs, they suffer from strong heterogeneity and significant development difficulties. In particular, deep fractured gas reservoirs in the Tarim Basin are generally characterized by deep burial, high pressure, dense matrix, well-developed fractures, complex structures, and widespread presence of edge and bottom water. This leads to problems such as large differences in production between blocks / wells, difficulty in determining the scale of recoverable reserves, and severe impacts from heterogeneous water intrusion.

[0003] Imaging logging, a current technology, can provide parameters such as fracture orientation, dip, and density along the wellbore, serving as an important basis for well completion procedures. However, for this type of gas reservoir, the cost of testing production capacity in new wells is relatively high. Summary of the Invention

[0004] In view of the high cost of new well productivity testing in existing technologies, this invention provides a method and apparatus for determining the productivity of new wells in fractured gas reservoirs based on imaging logging.

[0005] One aspect of this invention provides a method for constructing fracture characterization parameters by performing single-factor correlation analysis on imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of production capacity testing of gas wells already in production in a target block.

[0006] Establish a regression model between the unobstructed flow rate parameters and fracture characterization parameters for the production capacity test of the aforementioned gas wells;

[0007] Based on the imaging logging fracture parameters and oil flowback rate of the target new well, the fracture characterization parameters of the target new well are determined;

[0008] The fracture characterization parameters of the target new well are input into the regression model to determine the unobstructed flow rate parameters of the target new well.

[0009] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described method.

[0010] Another aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the above-described method when executing the program.

[0011] Another aspect of the present invention provides an apparatus for determining the productivity of new wells in fractured gas reservoirs based on imaging logging, comprising:

[0012] The parameter input module is used to input the imaging logging fracture parameters, oil flowback rate and production capacity test flow rate parameters of the gas wells already in production in the target block, as well as the imaging logging fracture parameters and oil flowback rate of the new target wells.

[0013] The model analysis module is used to perform single-factor correlation analysis on the imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of the production wells input by the parameter input module, and to construct fracture characterization parameters; to establish a regression model between the unobstructed flow rate parameters of the production wells and the fracture characterization parameters; and to determine the fracture characterization parameters of the target new well based on the imaging logging fracture parameters and oil flowback rate of the target new well.

[0014] The results output module is used to input the fracture characterization parameters of the target new well into the regression model to determine the unobstructed flow parameters of the target new well.

[0015] The embodiments of the present invention can determine the unobstructed flow parameters of a well without the need for new well production capacity testing, saving the cost of new well production capacity testing, solving the problem of high cost of new well production capacity testing in the prior art, and providing a basis for the rational production allocation and technical policy formulation of fractured gas reservoirs.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures described in the written description, claims, and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0019] Figure 1 A flowchart of a method for determining the productivity of new wells in fractured gas reservoirs based on imaging logging, provided in an embodiment of the present invention;

[0020] Figure 2 The example shown is a regression curve of the unobstructed flow rate and fracture characterization parameters of a gas well already in production in a target block where the method described in this embodiment is applied;

[0021] Figure 3 This is a schematic diagram of the structure of a device for determining the production capacity of new wells in fractured gas reservoirs based on imaging logging, provided in an embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0024] After research, the inventors discovered that if the unobstructed flow parameters of a new well can be determined based on the analysis of an already operational well without the need for production capacity testing, the testing costs for the new well can be significantly reduced.

[0025] Therefore, this embodiment provides a method for determining the productivity of new wells in fractured gas reservoirs based on imaging logging, such as... Figure 1 As shown, the specific steps may include:

[0026] S102. Collect and organize imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of production capacity testing of gas wells already in production in the target block.

[0027] The aforementioned imaging logging fracture parameters may include parameters such as fracture azimuth dispersion coefficient, fracture dip dispersion coefficient, and fracture linear density.

[0028] Specifically, the crack azimuth dispersion coefficient can be determined using the following formula:

[0029]

[0030] The crack inclination dispersion coefficient can be determined using the following formula:

[0031]

[0032] The crack linear density can be determined using the following formula:

[0033]

[0034] S104. Based on the single-factor correlation analysis of imaging logging fracture parameters, oil flowback rate and unobstructed flow rate of production capacity testing of gas wells already in production in the target block, fracture characterization parameters are constructed.

[0035] The single-factor correlation analysis may include: correlation analysis of the dispersion coefficient of fracture azimuth angle in imaging logging of gas wells already in production in the target block with the unobstructed flow rate parameter of production capacity test, correlation analysis of the dispersion coefficient of fracture dip direction in imaging logging with the unobstructed flow rate parameter of production capacity test, correlation analysis of the linear density of fracture in imaging logging with the unobstructed flow rate of production capacity test, and correlation analysis of the flowback rate of oil testing of gas wells already in production in the target block with the unobstructed flow rate parameter of production capacity test, etc.

[0036] The crack characterization parameters can be composed of parameters such as crack azimuth dispersion coefficient, crack dip dispersion coefficient, crack linear density, and flowback rate, and can be determined according to the following formula:

[0037]

[0038] In the above formula, These are parameters for characterizing cracks. The crack azimuth dispersion coefficient is... The crack inclination dispersion coefficient is... The crack linear density, To test the oil backflow rate, This is the correlation function.

[0039] S106. Establish a regression model for the unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block.

[0040] The regression model refers to the regression relationship model between the unobstructed flow rate of the production capacity test and the fracture characterization parameters of the gas wells already in production in the target block, including but not limited to simple mathematical relationship models such as linear regression, exponential regression, and logarithmic regression, as well as models established based on big data analysis.

[0041] S108. Based on the imaging logging fracture parameters of the target new well, determine the fracture characterization parameters of the target new well.

[0042] Specifically, the fracture characterization parameters of the target new well can be determined based on the imaging logging fracture parameters and oil test flowback rate, including parameters such as fracture azimuth dispersion coefficient, fracture dip dispersion coefficient, fracture linear density, and flowback rate.

[0043] S110. Input the fracture characterization parameters of the target new well into the regression model of the unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block to determine the unobstructed flow rate parameters of the target new well.

[0044] This embodiment can determine the unobstructed flow rate of a well without the need for new well production capacity testing, saving the cost of new well production capacity testing, solving the problem of high cost of new well production capacity testing in the prior art, and providing a basis for the rational production allocation and technical policy formulation of fractured gas reservoirs.

[0045] Based on the same concept, another embodiment of the present invention provides an apparatus for determining the productivity of new wells in fractured gas reservoirs based on imaging logging. Since the principle by which the apparatus solves the problem is similar to that of determining the productivity of new wells in fractured gas reservoirs based on imaging logging, the implementation of the apparatus for determining the productivity of new wells in fractured gas reservoirs based on imaging logging can refer to the implementation of a method for determining the productivity of new wells in fractured gas reservoirs based on imaging logging; repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0046] like Figure 3 As shown, the device includes: a parameter input module 302, a model analysis module 304, and a model analysis module 306. The structure will be described in detail below:

[0047] The parameter input module 302 can be used to input parameters such as imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of the gas wells already in production in the target block, as well as imaging logging fracture parameters and oil flowback rate of the new target wells used by the model analysis module.

[0048] The model analysis module 304 can be used to establish a regression model of the unobstructed flow rate of production capacity testing and fracture characterization parameters of the gas wells already in production in the target block. Based on the imaging logging fracture parameters of the new target well, the fracture characterization parameters of the well are determined. Based on the single-factor correlation analysis of the imaging logging fracture parameters, flowback rate and unobstructed flow rate of production capacity testing of the gas wells already in production in the target block, fracture characterization parameters are constructed. Based on the unobstructed flow rate of production capacity testing and fracture characterization parameters of the gas wells already in production in the target block, a regression model is established. Finally, based on the imaging logging fracture parameters of the new target well, the fracture characterization parameters of the well are determined.

[0049] The output module 306 can be used to determine the unobstructed flow rate of the target new well. It inputs the fracture characterization parameters of the target new well into the regression model of the unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block to determine the unobstructed flow rate parameters of the target new well.

[0050] As can be seen from the above description, the device for determining the production capacity of new wells in fractured gas reservoirs based on imaging logging provided in this embodiment fully considers the spatial discrete characteristics of fractures and the flowback characteristics of oil testing in the target block through the parameter input module 302 and the model analysis module 304, and constructs fracture characterization parameters. Based on the correlation between the fracture characterization parameters of the already produced wells in the target block and the unobstructed flow rate of the production capacity test, the unobstructed flow rate of the new well is determined by combining the imaging logging fracture parameters and the flowback rate of the target new well. This avoids the process of determining the unobstructed flow rate of the new well through production capacity testing, and greatly reduces the testing cost of the new well.

[0051] The following example illustrates how, in a specific implementation scenario, the method and apparatus for determining the production capacity of a new well in a fractured gas reservoir based on imaging logging, as described in this embodiment, are applied to determine the unobstructed flow rate of a new well (Well A) in a fractured gas reservoir in the Tarim Basin without conducting a production capacity test. The specific implementation process can be found below.

[0052] S102. Collect and organize imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of production capacity testing of gas wells already in production in the target block.

[0053] Among them, the fracture parameters mentioned above in imaging logging include parameters such as fracture azimuth dispersion coefficient, fracture dip dispersion coefficient, and fracture linear density.

[0054] Specifically, the crack azimuth dispersion coefficient can be determined using the following formula:

[0055]

[0056] Specifically, the crack inclination dispersion coefficient can be determined using the following formula:

[0057]

[0058] Specifically, the crack linear density can be determined using the following formula:

[0059]

[0060] S104. Based on the single-factor correlation analysis of imaging logging fracture parameters, oil flowback rate and unobstructed flow rate parameters of production capacity testing of gas wells already in production in the target block, fracture characterization parameters are constructed.

[0061] The single-factor correlation analysis of imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of production capacity testing for the gas wells already in production in the target block may include: correlation analysis of the dispersion coefficient of imaging logging fracture azimuth angle with unobstructed flow rate parameters of production capacity testing for the gas wells already in production in the target block; correlation analysis of the dispersion coefficient of imaging logging fracture dip direction with unobstructed flow rate parameters of production capacity testing for the gas wells already in production in the target block; correlation analysis of imaging logging fracture linear density with unobstructed flow rate parameters of production capacity testing for the gas wells already in production in the target block; and correlation analysis of the dispersion coefficient of imaging logging fracture dip direction with unobstructed flow rate parameters of production capacity testing for the gas wells already in production in the target block.

[0062] Specifically, the crack characterization parameters consist of parameters such as crack azimuth dispersion coefficient, crack dip dispersion coefficient, crack linear density, and flowback rate, and can be determined according to the following formula:

[0063]

[0064] In the above formula, These are parameters for characterizing cracks. The crack azimuth dispersion coefficient is... The crack inclination dispersion coefficient is... The crack linear density, The test oil return rate.

[0065] S106. Establish a regression model for the unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block.

[0066] The regression model of unobstructed flow rate and fracture characterization parameters of the gas wells already in production in the target block refers to the regression relationship model of unobstructed flow rate and fracture characterization parameters of the gas wells already in production in the target block, including but not limited to simple mathematical relationship models such as linear regression, exponential regression, and logarithmic regression, as well as models established based on big data analysis.

[0067] In practice, a linear regression model is used; please refer to [the relevant documentation / reference]. Figure 2 The example shown is a regression curve of the unobstructed flow rate and fracture characterization parameters of a production well in a target block provided by the embodiments of this application, applied in a scenario example.

[0068] Specifically, the regression model for the unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block is established as follows:

[0069]

[0070] In the above formula, This refers to the unobstructed flow rate of the gas well.

[0071] S108. Based on the fracture parameters from imaging logging of the target new well, determine the fracture characterization parameters of the well.

[0072] Specifically, based on the imaging logging fracture parameters and oil flowback rate of the target new well, including parameters such as fracture azimuth dispersion coefficient, fracture dip dispersion coefficient, fracture linear density, and flowback rate, the fracture characterization parameters of the well are determined.

[0073] In practice, the fracture azimuth dispersion coefficient was 0.25, the fracture dip dispersion coefficient was 0.06, the fracture linear density was 0.41, and the flowback rate was 37%. The fracture characterization parameter of the well was determined to be 1011.

[0074] S110. Input the fracture characterization parameters of the target new well into the regression model of unobstructed flow rate and fracture characterization parameters of the production capacity test of the gas wells already in production in the target block to determine the unobstructed flow rate parameters of the target new well.

[0075] In this embodiment, specifically, the fracture characterization parameter 1011 of the new well is substituted into the regression model of the unobstructed flow rate and fracture characterization parameter of the production capacity test of the gas wells already in production in the target block. Specifically, the unobstructed flow rate of the new well is:

[0076]

[0077] Specifically, the unobstructed flow rate of the new well (Well A) was determined to be 1.56 million cubic meters per day without conducting a production capacity test.

[0078] The above scenario examples verify that the method and apparatus provided in this application for determining the production capacity of new wells in fractured gas reservoirs based on imaging logging can determine the unobstructed flow rate of a new well without the need for production capacity testing, thus saving the cost of new well production capacity testing and solving the problem of high cost of new well production capacity testing in the prior art. Furthermore, it provides a basis for the rational production allocation and technical policy formulation of fractured gas reservoirs.

[0079] It should be noted that although different specific implementation methods are mentioned in this application, this application is not limited to the situations described in industry standards or implementation methods. Slightly modified implementation methods based on certain industry standards or custom methods or implementation methods can also achieve the same, equivalent, or similar, or predictable implementation effects after modification, as described above. Implementation methods that apply these modified or modified data acquisition, processing, output, and judgment methods still fall within the scope of optional implementation methods of this application.

[0080] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of the steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded.

[0081] The devices or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into functional modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules, etc. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0082] Furthermore, those skilled in the art will recognize that, besides implementing the controller using purely computer-readable program code, the controller can achieve the same functionality by logically programming the steps in the above methods, making it available as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered both software modules implementing the method and structures within a hardware component.

[0083] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0084] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0085] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. This application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0086] Although this application has been described through several different embodiments, those skilled in the art will know that this application can have many modifications and variations without departing from the spirit of this application. It is hoped that the modifications and variations included in the appended different embodiments will not depart from the scope of protection of this application.

Claims

1. A method for determining the productivity of new wells in fractured gas reservoirs based on imaging logging, characterized in that, include: Single-factor correlation analysis was conducted on imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of production capacity testing of gas wells already in production in the target block to construct fracture characterization parameters; Establish a regression model between the unobstructed flow rate parameters and fracture characterization parameters for the production capacity test of the aforementioned gas wells; Based on the imaging logging fracture parameters and oil flowback rate of the target new well, the fracture characterization parameters of the target new well are determined; The fracture characterization parameters of the target new well are input into the regression model to determine the unobstructed flow parameters of the target new well. The imaging logging fracture parameters include: fracture azimuth dispersion coefficient, fracture dip dispersion coefficient, and fracture linear density; the single-factor correlation analysis includes: Correlation analysis was performed between the crack azimuth dispersion coefficient and the unobstructed flow rate parameter of the capacity test; correlation analysis was also performed between the crack dip dispersion coefficient and the unobstructed flow rate parameter of the capacity test. Perform a correlation analysis between the crack linear density and the unobstructed flow rate parameters of the capacity test; and A correlation analysis was conducted between the oil return rate and the unobstructed flow parameters of the production capacity test. The crack characterization parameters are determined by the following formula: in, These are parameters for characterizing cracks. The crack azimuth dispersion coefficient is... The crack inclination dispersion coefficient is... The crack linear density, The test oil return rate.

2. The method according to claim 1, characterized in that, The crack azimuth dispersion coefficient is determined by the following formula: 。 3. The method according to claim 1, characterized in that, The crack inclination dispersion coefficient is determined by the following formula: 。 4. The method according to claim 1, characterized in that, The crack linear density is determined by the following formula: 。 5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

7. An apparatus for implementing the method according to any one of claims 1 to 4, characterized in that, include: The parameter input module is used to input the imaging logging fracture parameters, oil flowback rate and production capacity test flow rate parameters of the gas wells already in production in the target block, as well as the imaging logging fracture parameters and oil flowback rate of the new target wells. The model analysis module is used to perform single-factor correlation analysis on the imaging logging fracture parameters, oil flowback rate, and unobstructed flow rate parameters of the production wells input by the parameter input module, and to construct fracture characterization parameters; and to establish a regression model between the unobstructed flow rate parameters of the production wells and the fracture characterization parameters. Based on the imaging logging fracture parameters and oil flowback rate of the target new well, the fracture characterization parameters of the target new well are determined; The results output module is used to input the fracture characterization parameters of the target new well into the regression model to determine the unobstructed flow parameters of the target new well.

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