Method and device for evaluating well bottom effect of coal-bed gas well

Abstract

The invention discloses a coal-bed gas well bottom effect evaluation method and device. The method comprises the steps of obtaining a longitudinal and transverse wave velocity curve according to acoustic logging data of a to-be-evaluated well in a coal bed gas reservoir, obtaining a first brittleness index curve through a brittleness index prediction model, obtaining a second brittleness index curve based on a mineral content curve of the well, and obtaining a third brittleness index curve through integration; according to the density and mineral content data of a plurality of samples in the reservoir, constructing a reservoir anisotropic rock physical model, performing simulated mining through the model in combination with the third brittleness index curve to obtain a fourth brittleness index curve and a dynamic elastic modulus curve, and constructing a compressive strength curve in combination with the shale content curve; and performing well bottom effect evaluation on the to-be-evaluated well according to the third brittleness index curve, the fourth brittleness index curve and the compressive strength curve. According to the method, a brittleness index curve is constructed through multiple ways, and a compressive strength curve obtained through mining simulation is combined, so that reasonable evaluation of the coal-bed gas well bottom effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method and apparatus for evaluating the bottom-hole performance of coalbed methane wells. Background Technology

[0002] As a vital global resource, coal gas exploration and development technologies have become a hot topic and a major challenge in research. Besides conventional elastic properties, coal gas reservoirs also require attention to organic matter and reservoir brittleness (fractureability). Identifying reservoir brittleness directly impacts coal gas extraction, making it a crucial parameter in coal gas exploration. Accurate, cost-effective, and efficient brittleness prediction plays a key role in coal gas reservoir forecasting and development.

[0003] Existing technologies typically use sonic logging to predict the brittleness of coalbed methane reservoirs, thereby evaluating the bottom-hole performance of coalbed methane wells. Summary of the Invention

[0004] To enrich process routes and increase the options, this invention provides a method and apparatus for evaluating the bottom-hole performance of coalbed methane wells, which can reasonably and accurately evaluate the bottom-hole performance of coalbed methane wells.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating the bottom-hole performance of coalbed methane wells, comprising:

[0006] Based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, the shear wave velocity curve and the longitudinal wave velocity curve are obtained. The first brittleness index curve is obtained through the brittleness index prediction model. The second brittleness index curve is obtained based on the mineral content curve of the well to be evaluated. The third brittleness index curve is obtained by combining the first brittleness index curve and the second brittleness index curve.

[0007] Based on the density and mineral content data of multiple samples in the reservoir, an anisotropic rock physical model of the reservoir is constructed. Combined with the third brittleness index curve, mining is simulated through the model to obtain the fourth brittleness index curve and dynamic elastic modulus curve. Based on the dynamic elastic modulus curve and clay content curve, a compressive strength curve is constructed.

[0008] The bottom hole performance of the well to be evaluated is assessed based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

[0009] Secondly, embodiments of the present invention provide a coalbed methane well bottom hole performance evaluation device, comprising:

[0010] The first module for constructing the brittleness index curve is used to obtain the shear wave velocity curve and the longitudinal wave velocity curve based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, obtain the first brittleness index curve through the brittleness index prediction model, obtain the second brittleness index curve based on the mineral content curve of the well to be evaluated, and obtain the third brittleness index curve from the first brittleness index curve and the second brittleness index curve.

[0011] The second module for constructing the brittleness index curve is used to construct an anisotropic rock physical model of the reservoir based on the density and mineral content data of multiple samples in the reservoir. Combined with the third brittleness index curve, the model is used to simulate mining to obtain the fourth brittleness index curve and the dynamic elastic modulus curve. The compressive strength curve is constructed based on the dynamic elastic modulus curve and the clay content curve.

[0012] The bottom-hole performance evaluation module is used to evaluate the bottom-hole performance of the well to be evaluated based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

[0013] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned method for evaluating the bottom-hole performance of coalbed methane wells.

[0014] Fourthly, this disclosure provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned method for evaluating the bottom-hole performance of coalbed methane wells.

[0015] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0016] (1) The method for evaluating the bottom-hole effect of coalbed methane wells provided in this embodiment of the invention is based on a pre-established brittleness index prediction model. A first brittleness index curve is established based on the shear wave velocity curve and the longitudinal wave velocity curve; a second brittleness index curve is obtained from the mineral content curve; a fourth brittleness index curve is obtained by simulating mining using a reservoir anisotropic rock physics model; the brittleness index curves obtained through multiple methods are cross-validated and comprehensively analyzed; in addition, a dynamic elastic modulus curve is also obtained through simulated mining, and a compressive strength curve is constructed by combining it with the clay content curve; the bottom-hole effect of the well to be evaluated is then evaluated by combining the brittleness index curve and the compressive strength curve. This method can comprehensively and reasonably evaluate the bottom-hole effect of coalbed methane wells by combining multiple information sources and through multiple methods.

[0017] (2) The method for evaluating the bottom-hole effect of coalbed methane wells provided in this embodiment of the invention uses acoustic logging to detect acoustic waves and analyzes the acoustic waves detected using the waveform similarity method to obtain acoustic logging data. By using the waveform similarity method to analyze the acoustic waves detected, the correlation of the measurement data can be effectively utilized, the influence of noise on the logging data can be reduced, thereby improving the accuracy of time difference calculation and making the prediction of brittleness in coalbed methane reservoirs more accurate.

[0018] 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 particularly pointed out in the written description, claims, and drawings.

[0019] 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

[0020] 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:

[0021] Figure 1 This is a flowchart of the method for evaluating the bottom-hole effect of coalbed methane wells in an embodiment of the present invention;

[0022] Figure 2 This is a flowchart illustrating the specific implementation of the fragility index prediction model in this embodiment of the invention.

[0023] Figure 3 This is a schematic diagram of the structure of the coalbed methane well bottom effect evaluation device in an embodiment of the present invention. Detailed Implementation

[0024] 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.

[0025] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0026] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0027] The inventors discovered that existing technologies for predicting the brittleness of coalbed methane reservoirs using acoustic logging are often affected by various noises, such as interference and conversion between different modes of wave, making the analysis of logging data more difficult. This affects the accuracy and reliability of time difference calculations, leading to significant deviations in coalbed methane reservoir brittleness predictions. Furthermore, most predictions involve simple numerical calculations without practical simulations, making it difficult to verify the predicted brittleness index and increase its accuracy. Additionally, the data obtained for evaluating the bottom-hole performance of coalbed methane is not comprehensive enough, thus affecting the accuracy of the evaluation.

[0028] In view of the above-mentioned technical problems, the inventors, through further research and development, made this invention. Embodiments of this invention provide a method and apparatus for evaluating the bottom-hole performance of coalbed methane wells. By constructing brittleness index curves through multiple methods and combining them with compressive strength curves obtained from mining simulations, a reasonable evaluation of the bottom-hole performance of coalbed methane wells can be achieved.

[0029] Example

[0030] This invention provides a method for evaluating the bottom-hole performance of coalbed methane wells, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0031] Step S11: Based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, obtain the shear wave velocity curve and the longitudinal wave velocity curve, and obtain the first brittleness index curve through the brittleness index prediction model.

[0032] In some embodiments, see Figure 2 As shown, the brittleness index prediction model is pre-established through the following steps:

[0033] Step S21: Obtain the density of multiple samples within the reservoir.

[0034] The density of different rocks in the rock sample was obtained by performing density tests on the rock sample.

[0035] Step S22: Determine the brittleness index of the corresponding point of the sample based on the sonic logging data of the corresponding well.

[0036] First, the P-wave time difference of the corresponding point of the sample is obtained based on the sonic logging data of the corresponding well. Then, the S-wave time difference is obtained by using the correspondence between the P-wave time difference and the S-wave time difference.

[0037] After obtaining the P-wave and S-wave transit times, the dynamic Young's modulus and Poisson's ratio are determined from the density, P-wave transit time, and S-wave transit time at the corresponding points.

[0038]

[0039] Where, Δt s The transverse wave time difference is expressed in μs / ft; Δt p The longitudinal wave time difference is expressed in μs / ft; ρ represents the longitudinal wave time difference. d Density of rock, g / cm³ 3 X, Y, and Z are ordinary coefficients.

[0040]

[0041] E d Represents the dynamic Young's modulus; 9.299 × 10⁻⁶ 7 This indicates the conversion of Young's modulus to a coefficient in MPa; υ d This represents the dynamic Poisson's ratio.

[0042] Finally, the brittleness index at the corresponding point is determined based on the dynamic Young's modulus and Poisson's ratio.

[0043] Step S23: Based on the transverse wave velocity, longitudinal wave velocity and brittleness index of the sample, obtain the brittleness index prediction model through multivariate fitting.

[0044] The transverse and longitudinal wave velocities of the samples were also obtained based on acoustic logging data.

[0045] In some embodiments, the above-mentioned acoustic logging data are obtained in the following manner:

[0046] Acoustic logging is used to detect acoustic waves, and the acoustic waves detected are analyzed using waveform similarity methods to obtain acoustic logging data.

[0047] By analyzing the acoustic waves of the detection feedback using the waveform similarity method, the correlation of measurement data can be effectively utilized, the impact of noise on well logging data can be reduced, thereby improving the accuracy of time difference calculation and making the brittleness prediction results of rock gas reservoirs more accurate.

[0048] Step S12: Obtain the second brittleness index curve based on the mineral content curve of the well to be evaluated.

[0049] The second brittleness index curve is obtained using the following formula:

[0050]

[0051] Wherein, BI(z) is the brittleness index, V(z) is the content of matrix minerals, Vbritt(z) is the total content of brittle minerals, and Vtotal(z) is the total content of all types of minerals. The total number of brittle mineral categories in the reservoir is m, and the total number of mineral categories is n.

[0052] In this embodiment, the mineral content refers to the volume fraction of the mineral.

[0053] Step S13: Obtain the third brittleness index curve from the first brittleness index curve and the second brittleness index curve.

[0054] Step S14: Based on the density and mineral content data of multiple samples in the reservoir, construct an anisotropic rock physical model of the reservoir. Combined with the third brittleness index curve, simulate mining through the model to obtain the fourth brittleness index curve and dynamic elastic modulus curve. Construct the compressive strength curve based on the dynamic elastic modulus curve and clay content curve.

[0055] Specifically, the multiple samples in step S14 can be all the same as the multiple samples in step S21 above, or they can be partially the same, or they can all be different.

[0056] Mining is simulated by establishing an anisotropic rock physics model of the reservoir until the simulated reservoir shows signs of fracturing. The simulation is then stopped, and a set of simulation data is obtained. The simulation mining is repeated until the set number of simulations is reached, and the number of sets of simulation data is the same as the set number of simulations. Using all the obtained simulation data, the fourth brittleness index curve and the dynamic elastic modulus curve are obtained.

[0057] Based on the dynamic elastic modulus curve and clay content curve, the compressive strength curve is constructed using the following formula:

[0058] σ B =(0.0045+0.0035V) sh E d

[0059] Where, σ B V represents compressive strength, also known as peak strength, and is measured in MPa. sh Indicates clay content; E d It represents the dynamic elastic modulus.

[0060] The shale content curve here can be obtained from the natural gamma logging curve. The specific formula for calculating the shale content of the rock is as follows:

[0061]

[0062] Wherein, GCUR represents an empirical coefficient related to stratigraphic age, with 3.7 for newer strata and 2 for older strata; SH represents the relative value of natural gamma; GR represents the natural gamma logging value; GR min Represented as the natural gamma ray logging value for pure sandstone formations, i.e., the minimum natural gamma ray logging value; GR max This represents the natural gamma logging value of a pure mudstone formation, i.e., the maximum natural gamma logging value.

[0063] Step S15: Evaluate the bottom hole effect of the well to be evaluated based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

[0064] The method for evaluating the bottom-hole performance of coalbed methane wells provided in this invention is based on a pre-established brittleness index prediction model. A first brittleness index curve is established based on the shear wave velocity curve and the longitudinal wave velocity curve; a second brittleness index curve is obtained from the mineral content curve; a fourth brittleness index curve is obtained by simulating mining using a reservoir anisotropic rock physics model; the brittleness index curves obtained through multiple methods are cross-validated and comprehensively analyzed; furthermore, a dynamic elastic modulus curve is obtained simultaneously through simulated mining, and a compressive strength curve is constructed by combining it with the clay content curve; the bottom-hole performance of the well is evaluated by comprehensively considering both the brittleness index curve and the compressive strength curve. This method can comprehensively and reasonably evaluate the bottom-hole performance of coalbed methane wells by combining multiple information sources and using multiple methods.

[0065] In some embodiments, the method may further include modifying the brittleness index prediction model based on the difference between the first brittleness index curve and the second brittleness index curve, and / or the difference between the first brittleness index curve and the fourth brittleness index curve.

[0066] This invention, through the establishment of a brittleness index prediction model, enables faster prediction of the brittleness of gas reservoirs in different geological formations, reducing the workload of relevant personnel and improving prediction efficiency. Simulating mining using a three-dimensional model of the gas reservoir yields data changes from mining to fracturing. Comparing this data with calculated data allows for better verification of prediction accuracy and reduces prediction errors. Calculating the rock brittleness index using sonic logging technology combined with rock mechanics experiments allows for rapid and comprehensive data acquisition. The rock samples used are undamaged and can be reused.

[0067] Based on the inventive concept of this invention, embodiments of this invention also provide a coalbed methane well bottom hole effect evaluation device, the structure of which is as follows: Figure 3 As shown, it includes:

[0068] The first module 31 for constructing the brittleness index curve is used to obtain the shear wave velocity curve and the longitudinal wave velocity curve based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, obtain the first brittleness index curve through the brittleness index prediction model, obtain the second brittleness index curve based on the mineral content curve of the well to be evaluated, and obtain the third brittleness index curve from the first brittleness index curve and the second brittleness index curve.

[0069] The second module 32 for constructing the brittleness index curve is used to construct an anisotropic rock physical model of the reservoir based on the density and mineral content data of multiple samples in the reservoir. Combined with the third brittleness index curve, the model is used to simulate mining to obtain the fourth brittleness index curve and the dynamic elastic modulus curve. The compressive strength curve is constructed based on the dynamic elastic modulus curve and the clay content curve.

[0070] The bottom-hole effect evaluation module 33 is used to evaluate the bottom-hole effect of the well to be evaluated based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

[0071] In some embodiments, the above-described apparatus further includes a brittleness index prediction model building module 34, for pre-building a brittleness index prediction model in the following manner:

[0072] The density of multiple samples within the reservoir is obtained, and the brittleness index of corresponding points of the samples is determined based on the sonic logging data of the corresponding wells. Based on the shear wave velocity, longitudinal wave velocity, and brittleness index of the samples, a brittleness index prediction model is obtained through multivariate fitting.

[0073] In some embodiments, the brittleness index prediction model building module 34, which determines the brittleness index of a corresponding point on the sample based on the sonic logging data of the corresponding well, is used for:

[0074] Based on the sonic logging data of the corresponding well, the P-wave transit time of the corresponding point of the sample is obtained, and the S-wave transit time is obtained by using the correspondence between the P-wave transit time and the S-wave transit time. The dynamic Young's modulus and Poisson's ratio are determined by the density, P-wave transit time and S-wave transit time of the corresponding point. The brittleness index of the corresponding point is determined based on the dynamic Young's modulus and Poisson's ratio.

[0075] In some embodiments, the above-described apparatus further includes an acoustic logging data acquisition module 35, used for:

[0076] Acoustic logging is used to detect acoustic waves, and the acoustic waves detected are analyzed using waveform similarity methods to obtain acoustic logging data.

[0077] In some embodiments, a first brittleness index curve construction module 31 is used to obtain a second brittleness index curve based on the mineral content curve of the well to be evaluated, for the purpose of:

[0078] Based on the mineral content curve of the well to be evaluated, the second brittleness index curve is obtained using the following formula:

[0079]

[0080] Wherein, BI(z) is the brittleness index, Vbritt(z) is the total content of brittle minerals, and Vtotal(z) is the total content of all types of minerals.

[0081] In some embodiments, the brittleness index curve construction second module 32, wherein the compressive strength curve is constructed based on the dynamic elastic modulus curve and the clay content curve, is used for:

[0082] Based on the dynamic elastic modulus curve and clay content curve, the compressive strength curve is constructed using the following formula:

[0083] σ B =(0.0045+0.0035V) sh E d

[0084] Where, σ B V represents compressive strength. sh E represents the clay content. d It represents the dynamic elastic modulus.

[0085] In some embodiments, a second module 32 for constructing brittleness index curves is used to obtain a fourth brittleness index curve and a dynamic elastic modulus curve through simulated mining using the model, for the purpose of:

[0086] The simulation mining is carried out using the model until the simulated reservoir experiences fracturing. The simulation is then stopped, and a set of simulation data is obtained. The simulation mining is repeated until the number of simulations reaches the set number, and a set of simulation data is obtained with the same number of sets as the set number of simulations. Using all the obtained simulation data, the fourth brittleness index curve and the dynamic elastic modulus curve are obtained.

[0087] In some embodiments, the above-described apparatus further includes a fragility index prediction model correction module 36, for:

[0088] The brittleness index prediction model is modified based on the difference between the first brittleness index curve and the second brittleness index curve, and / or the difference between the first brittleness index curve and the fourth brittleness index curve.

[0089] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0090] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned method for evaluating the bottom-hole effect of coalbed methane wells.

[0091] Based on the inventive concept of this invention, this embodiment of the invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned method for evaluating the bottom-hole effect of coalbed methane wells.

[0092] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0093] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0094] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0095] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0096] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0097] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0098] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first,” “second,” etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A method for evaluating the bottom-hole performance of coalbed methane wells, characterized in that, include: Based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, the shear wave velocity curve and the longitudinal wave velocity curve are obtained. The first brittleness index curve is obtained through the brittleness index prediction model. The second brittleness index curve is obtained based on the mineral content curve of the well to be evaluated. The third brittleness index curve is obtained by combining the first brittleness index curve and the second brittleness index curve. Based on the density and mineral content data of multiple samples in the reservoir, an anisotropic rock physical model of the reservoir is constructed. Combined with the third brittleness index curve, mining is simulated through the model to obtain the fourth brittleness index curve and dynamic elastic modulus curve. Based on the dynamic elastic modulus curve and clay content curve, a compressive strength curve is constructed. The bottom hole performance of the well to be evaluated is assessed based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

2. The method as described in claim 1, characterized in that, The brittleness index prediction model is pre-established in the following manner: The density of multiple samples within the reservoir is obtained, and the brittleness index of corresponding points in the samples is determined based on the sonic logging data of the corresponding wells. Based on the transverse wave velocity, longitudinal wave velocity, and brittleness index of the sample, a brittleness index prediction model is obtained through multivariate fitting.

3. The method as described in claim 2, characterized in that, The determination of the brittleness index of the corresponding point of the sample based on the sonic logging data of the corresponding well includes: Based on the sonic logging data of the corresponding well, the P-wave transit time of the corresponding point of the sample is obtained, and the S-wave transit time is obtained by using the correspondence between the P-wave transit time and the S-wave transit time. The dynamic Young's modulus and Poisson's ratio are determined by the density, P-wave transit time and S-wave transit time at the corresponding points. The brittleness index at the corresponding point is determined based on the dynamic Young's modulus and Poisson's ratio.

4. The method as described in claim 1 or 2, characterized in that, The acoustic logging data is obtained in the following manner: Acoustic logging is used to detect acoustic waves, and the acoustic waves detected are analyzed using waveform similarity methods to obtain acoustic logging data.

5. The method as described in claim 1, characterized in that, The second brittleness index curve is obtained based on the mineral content curve of the well to be evaluated, including: Based on the mineral content curve of the well to be evaluated, the second brittleness index curve is obtained using the following formula (1): In Equation (1), BI(z) is the brittleness index, Vbritt(z) is the total content of brittle minerals, and Vtotal(z) is the total content of all types of minerals.

6. The method as described in claim 1, characterized in that, The construction of the compressive strength curve based on the dynamic elastic modulus curve and the clay content curve includes: Based on the dynamic elastic modulus curve and clay content curve, the compressive strength curve is constructed using the following formula (2): s B =(0.0045+0.0035V sh )E d (2) In equation (2), σ B V represents compressive strength. sh E represents the clay content. d It represents the dynamic elastic modulus.

7. The method as described in claim 1, characterized in that, The process of simulating mining using the model to obtain the fourth brittleness index curve and dynamic elastic modulus curve includes: The simulation mining is carried out using the model until the simulated reservoir experiences fracturing. The simulation is then stopped, and a set of simulation data is obtained. The simulation mining is repeated until the number of simulations reaches the set number, and the number of sets of simulation data is the same as the set number of times. Using all the obtained simulation data, the fourth brittleness index curve and the dynamic elastic modulus curve were obtained.

8. The method according to any one of claims 1 to 3 and 5 to 7, characterized in that, Also includes: The brittleness index prediction model is modified based on the difference between the first brittleness index curve and the second brittleness index curve, and / or the difference between the first brittleness index curve and the fourth brittleness index curve.

9. A device for evaluating the bottom-hole performance of coalbed methane wells, characterized in that, include: The first module for constructing the brittleness index curve is used to obtain the shear wave velocity curve and the longitudinal wave velocity curve based on the sonic logging data of the well to be evaluated in the coalbed methane reservoir, obtain the first brittleness index curve through the brittleness index prediction model, obtain the second brittleness index curve based on the mineral content curve of the well to be evaluated, and obtain the third brittleness index curve from the first brittleness index curve and the second brittleness index curve. The second module for constructing the brittleness index curve is used to construct an anisotropic rock physical model of the reservoir based on the density and mineral content data of multiple samples in the reservoir. Combined with the third brittleness index curve, the model is used to simulate mining to obtain the fourth brittleness index curve and the dynamic elastic modulus curve. The compressive strength curve is constructed based on the dynamic elastic modulus curve and the clay content curve. The bottom-hole performance evaluation module is used to evaluate the bottom-hole performance of the well to be evaluated based on the third brittleness index curve, the fourth brittleness index curve, and the compressive strength curve.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the coalbed methane well bottom effect evaluation method according to any one of claims 1 to 8.

11. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for evaluating the bottom-hole performance of a coalbed methane well as described in any one of claims 1 to 8.

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