Coal bed gas well history matching method, apparatus and device

Abstract

The embodiments of the present specification provide a coal bed gas well history matching method, an apparatus and a device, which are applied to the field of geological exploration and development. The method comprises: constructing a plurality of simulation schemes on the basis of at least one impact factor corresponding to a target coal bed gas well, wherein in different simulation schemes, the impact factors are set as corresponding simulation parameter values; separately calculating cumulative production parameters of the different simulation schemes, the cumulative production parameters comprising a cumulative gas production and a cumulative water production; determining a scheme deviation type on the basis of a comparison result between the cumulative production parameters and actual measured parameters; and performing history matching on the target coal bed gas well on the basis of a parameter adjustment strategy corresponding to the scheme deviation type.

Description

A coalbed methane well history matching method, device and equipment

[0001] Related applications

[0002] This application claims priority to the Chinese invention patent application with application number 202311634039.4 filed on November 30, 2023, and cites the entire contents disclosed in the above patent application as part of this application. Technical Field

[0003] The embodiments of this specification relate to the field of geological exploration and development technology, and in particular to a method, device and equipment for history matching of coalbed methane wells. Background Art

[0004] Coalbed methane (CBM) is an unconventional natural gas stored in coal seams. Its development not only significantly reduces coal mine gas accidents and greenhouse gas emissions, but also generates significant economic benefits as a clean energy source. During CBM extraction, CBM well history matching is often necessary. This not only calibrates understanding of geological parameters by matching production data, but also enables production forecasts, laying the foundation for development plans. Therefore, history matching is a crucial component of CBM development.

[0005] However, due to the strong heterogeneity of coal reservoirs and the particularity of coalbed methane's occurrence mechanism (primarily adsorption), history matching for coalbed methane is complex and time-consuming. It is often impossible to quickly and effectively obtain the corresponding history matching results, which can even hinder the effective implementation of development work. Therefore, a rapid history matching method for coalbed methane wells is urgently needed.

[0006] Summary of the Invention

[0007] The purpose of the embodiments of this specification is to provide a coalbed methane well history matching method, device and equipment to solve the problem of how to quickly and accurately perform history matching on coalbed methane wells.

[0008] In order to solve the above technical problems, an embodiment of this specification proposes a historical fitting method for coalbed methane wells, including: constructing multiple simulation schemes based on at least one influencing factor corresponding to a target coalbed methane well; setting the influencing factors in different simulation schemes to corresponding simulation parameter values; calculating the cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production; determining the scheme deviation type based on the comparison result between the cumulative production parameters and the actual measured parameters; and performing historical fitting on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type.

[0009] In some embodiments, the influencing factors include at least one of the following: fracture porosity, permeability, water Corey coefficient, rock compressibility, gas saturation, gas Corey coefficient, and gas content.

[0010] In some embodiments, the influencing factors each correspond to a distribution range limit; and the simulation parameter value is set within the corresponding distribution range limit.

[0011] Based on the above implementation, multiple simulation schemes are constructed according to at least one influencing factor corresponding to the target coalbed methane well, including: setting the influencing factor to be analyzed as a variable value within the distribution range limit; setting other influencing factors except the influencing factor to be analyzed as the median value within the distribution range limit.

[0012] In some embodiments, the respectively calculating cumulative production parameters of different simulation schemes includes: calculating the cumulative gas production and cumulative water production corresponding to the simulation scheme; drawing sensitivity analysis storm diagrams of the cumulative gas production and cumulative water production respectively; and determining the direction and degree of the impact of the simulation scheme on the cumulative production based on the sensitivity analysis storm diagram.

[0013] In some embodiments, determining the scheme deviation type based on the comparison result between the cumulative production parameter and the actual measured parameter includes: determining the fitting deviation based on the comparison result between the cumulative production parameter and the actual measured parameter; the fitting deviation includes a gas production fitting deviation and a water production fitting deviation; constructing a gas-water fitting deviation intersection diagram based on the gas production fitting deviation and the water production fitting deviation; determining the scheme deviation type by comparing the gas-water fitting deviation intersection diagram with a fitting deviation threshold; the fitting deviation threshold is determined based on calculation accuracy, gas field development stage, data completeness and fitting accuracy.

[0014] In some embodiments, the scheme deviation type includes one of: positive deviation of gas and positive water, no deviation of gas and water, positive deviation of gas and negative water, deviation of gas and no water, negative deviation of gas and negative water, positive deviation of gas and negative water, and no deviation of gas and no water.

[0015] Based on the above implementation mode, the parameter adjustment strategy corresponding to the positive deviation of gas and water is to reduce the permeability and water Corey coefficient; the parameter adjustment strategy for the gas with water and no deviation is to reduce / increase the gas Corey coefficient and increase / decrease the gas content; the parameter adjustment strategy for the positive deviation of gas and water is to increase the fracture porosity and reduce the gas saturation; the parameter adjustment strategy for the deviation of gas without water is to increase / decrease the rock compression coefficient; the parameter adjustment strategy for the negative deviation of gas and water is to increase the permeability and water Corey coefficient; the parameter adjustment strategy for the positive deviation of gas and water is to reduce the fracture porosity and increase the gas saturation; the parameter adjustment strategy for the gas with no water and no deviation is not to adjust the parameters.

[0016] In some embodiments, the parameter adjustment strategy corresponding to the scheme deviation type is used to perform historical fitting on the target coalbed methane well, including: correcting the parameters in the simulation scheme according to the parameter adjustment strategy, and performing parameter iteration using the corrected parameters; wherein, it includes: analyzing the impact of the parameter adjustment amplitude and adjustment direction of the influencing factors on the fitting deviation; re-determining the parameter adjustment strategy based on the impact results, and repeating the process of adjusting parameters and determining the parameter adjustment strategy until the fitting deviation meets the preset requirements.

[0017] Based on the above implementation, the preset requirements include a fitting accuracy threshold; the fitting accuracy threshold is used to limit the proportion of coalbed methane wells that have gas, no water, and no deviation.

[0018] The embodiments of this specification also propose a coalbed methane well history fitting device, including: a simulation scheme construction module, used to construct multiple simulation schemes based on at least one influencing factor corresponding to the target coalbed methane well; the influencing factors in different simulation schemes are set to corresponding simulation parameter values; a cumulative production parameter calculation module, used to calculate the cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production; a deviation type determination module, used to determine the scheme deviation type based on the comparison result between the cumulative production parameters and the actual measured parameters; a history fitting module, used to perform history fitting on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type.

[0019] The embodiments of this specification also propose a coalbed methane well history matching device, including a memory and a processor; the memory is used to store computer programs / instructions; the processor is used to execute the computer programs / instructions to implement the above-mentioned coalbed methane well history matching method.

[0020] As can be seen from the technical solutions provided in the embodiments of this specification, the coalbed methane well history fitting method in the embodiments of this specification constructs a simulation scheme based on the influencing factors affecting the target coalbed methane well, and then calculates the cumulative production parameters corresponding to different simulation schemes, and then determines the scheme deviation type based on the comparison results between the cumulative production parameters and the actual measured parameters, thereby performing history fitting on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type. The above method determines the deviation type by analyzing the deviation, and thus performs history fitting on the coalbed methane well through the pre-set parameter adjustment strategy corresponding to the deviation type, which not only ensures that the fitting operation corresponds to actual production, but also improves the fitting speed, accelerates the work efficiency of the numerical simulation, and is conducive to guiding the actual production and development process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of this specification 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 recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] FIG1 is a flow chart of a coalbed methane well history matching method according to an embodiment of the present specification;

[0023] FIG2 is a schematic diagram of a single well fitting deviation intersection diagram according to an embodiment of this specification;

[0024] FIG3 is a schematic diagram of a single well fitting deviation intersection diagram according to an embodiment of this specification;

[0025] FIG4 is a schematic diagram of a single well fitting deviation intersection diagram according to an embodiment of this specification;

[0026] FIG5 is a module diagram of a history matching device for a coalbed methane well according to an embodiment of this specification. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0028] To address the aforementioned technical issues, the present invention proposes a coalbed methane well history matching method. This method can be executed by a computing device, including but not limited to servers, industrial computers, and personal computers. Specifically, as shown in Figure 1, the coalbed methane well history matching method includes the following specific implementation steps.

[0029] S110: Constructing multiple simulation schemes according to at least one influencing factor corresponding to the target coalbed methane well; the influencing factors in different simulation schemes are set as corresponding simulation parameter values.

[0030] The target CBM well can be a production well in the currently targeted work area that is producing CBM. During the production and development of a CBM well, history matching is required for the well. History matching involves calculating the history of changes in relevant dynamic indicators (e.g., past changes in relevant dynamic indicators) using acquired static parameters during oil and gas well development, comparing the calculated results with the actual dynamic indicator parameter values, and verifying the accuracy of the acquired static parameters based on the comparison results. History matching can achieve relatively accurate dynamic prediction results, thereby providing effective guidance for production and development.

[0031] Because coal reservoirs are highly heterogeneous and the coalbed methane (CBM) storage mechanism is unique, history matching for CBM wells is complex and tedious. Therefore, this specification proposes a CBM well history matching method to achieve rapid and accurate history matching for CBM wells.

[0032] First, at least one influencing factor corresponding to the target CBM well can be determined. The influencing factor can be a parameter for the CBM well that can be adjusted during the history matching process and will have a certain impact on the fitting results.

[0033] In some embodiments, the influencing factors may include at least one of fracture porosity, permeability, water Corey coefficient, rock compressibility, gas saturation, gas Corey coefficient, and gas content.

[0034] After determining the influencing factors, multiple simulation scenarios can be constructed by constructing different simulation parameter values ​​for the influencing factors. In different simulation scenarios, since the influencing factors are set to different parameter values, the fitting results of different simulation scenarios can be compared with the actual detection results to complete the corresponding history fitting process.

[0035] In some embodiments, before setting simulation parameter values ​​for the impact factors, it is also necessary to determine the distribution range limits corresponding to the impact factors. The distribution range limits are used to limit the extreme values ​​of the simulation parameter values ​​corresponding to the impact factors, thereby ensuring that the obtained impact factors can be properly applied to the simulation process. The specific size of the distribution range limits can be set according to actual application and is not detailed here.

[0036] In actual applications, the methods of assigning parameter values ​​to different influencing factors may vary. For example, based on the above implementation, the initial values ​​of the fracture porosity, water Corey coefficient, rock compressibility, gas saturation, and gas Corey coefficient are directly given, and the limit values ​​of their distribution ranges, as well as the maximum and minimum values ​​of their adjustable ranges are also given respectively. The permeability and gas content are calculated by establishing a relationship with the burial depth, that is, the permeability and gas content of each grid are not fixed values. Their adjustable ranges are also determined by analyzing the residual distribution of the measured values ​​and the calculated values ​​using the P10 and P90 probability methods, which are two trend lines related to the burial depth, respectively.

[0037] It should be noted that in actual applications, other parameters can be selected as influencing factors according to needs, and are not limited to the above examples, which will not be described in detail here.

[0038] The specific process of constructing multiple simulation schemes based on the influencing factors can be to select the influencing factors to be analyzed from the influencing factors after determining the distribution range limits corresponding to the influencing factors, and set the influencing factors to be analyzed to a variable value within the distribution range limits. The variable value can be a value set according to the requirements without clear restrictions. For example, the influencing factors to be analyzed can be set to a high or low value within this range. The purpose is to explore the specific impact of the parameter value changes of the influencing factors to be analyzed on the fitting deviation. The specific value size can be set according to the actual application situation.

[0039] In addition, other influencing factors other than the influencing factor to be analyzed are set to the median value within the distribution range limit to minimize the impact of the parameter values ​​of other influencing factors on the fitting deviation. In practical applications, other influencing factors can also be fixed to other values, such as commonly used values ​​in practical applications, to minimize the impact of other influencing factors on the fitting deviation. There is no restriction on this.

[0040] The number of simulation scenarios can also be set according to the needs of the actual application. For example, 14 sensitivity analysis simulation cases can be created as needed, and there is no limit on this.

[0041] S120: Calculate cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production.

[0042] After determining the simulation scheme, you can first calculate the cumulative production parameters for different simulation schemes. The cumulative production parameters can represent parameters related to the cumulative production obtained under this simulation scheme. Specifically, the cumulative production parameters can include cumulative gas production and cumulative water production.

[0043] Calculating the cumulative production parameters for a simulation scenario can involve using a corresponding model to calculate the resulting cumulative production parameters when implementing the simulation scenario. The specific calculation process can be performed using a pre-configured calculation model. For example, a sensitivity analysis simulation model can be created to calculate the cumulative production parameters for different simulation scenarios. In practical applications, adaptive calculation methods can be selected based on specific needs, and this will not be further elaborated here.

[0044] In some embodiments, the cumulative production parameter also includes, for example, the direction and degree of the impact of the simulation scheme on cumulative production. Specifically, the cumulative water and gas production corresponding to the simulation scheme can be calculated first, and then a sensitivity analysis storm chart can be drawn based on the cumulative gas and water production to determine the direction of the impact of parameter adjustment on cumulative production (such as an increase in cumulative production, a decrease in production, or no change in production). The sensitivity index can then be calculated to quantitatively characterize the degree of impact on cumulative production.

[0045] Specifically, we can use the formula SI = D p -D n Calculate the sensitivity index of cumulative gas production and cumulative water production, where SI is the sensitivity index of cumulative gas production or cumulative water production; D p The positive cumulative production deviation of gas or water predicted for the simulation case; D n Cumulative production deviation of gas or water predicted for the simulation case.

[0046] S130: Determine the type of plan deviation based on the comparison result between the cumulative production parameter and the actual measured parameter.

[0047] After obtaining the cumulative production parameters, you can compare them with the actual measured parameters and determine the type of plan deviation based on the comparison results. The actual measured parameters can be the cumulative production parameter values ​​during the actual production process. By comparing them with the calculated cumulative production parameters, you can verify whether the parameters are normal.

[0048] Specifically, a benchmark simulation case can be predetermined, and actual measured parameters can be measured for the benchmark simulation case for comparison with the calculated cumulative production parameters. It should be noted that the actual measured parameters can be data collected by sensors from the target CBM based on the benchmark simulation case, or data calculated based on the collected data.

[0049] In some embodiments, the formula Calculate the cumulative production deviation of cumulative gas and cumulative water production, where PD is the cumulative production deviation of cumulative gas or cumulative water production; Q s The cumulative gas or water production value predicted for the sensitivity case, that is, the calculated cumulative production parameter; Q b The cumulative gas or water production values ​​predicted for the benchmark simulation case are the cumulative production parameters obtained by actual measurement.

[0050] More specifically, the fitting deviation can also be calculated based on the comparison results, using the formula Calculate the fitting deviation of cumulative gas production and cumulative water production, where SD is the fitting deviation of cumulative gas production or cumulative water production; Q p The cumulative gas or water production value predicted for the simulation case; Q o is the observed cumulative gas or water production value.

[0051] The deviation type can be determined by analyzing the comparison results. Specifically, the fitting deviation can be determined based on the comparison results between the cumulative production parameters and the actual measured parameters. Fitting deviations include gas production fitting deviation and water production fitting deviation. A gas-water fitting deviation intersection diagram is then constructed based on the gas production fitting deviation and the water production fitting deviation. The gas-water fitting deviation intersection diagram is used to distinguish different deviation types by region.

[0052] The scheme deviation type is then determined based on the comparison result between the gas-water fitting deviation intersection diagram and the fitting deviation threshold. In this embodiment, the fitting deviation threshold can be determined based on the calculated model accuracy, gas field development stage, data completeness, and fitting accuracy requirements.

[0053] In some implementations, seven deviation types can be categorized: Type I: positive gas and water deviation, Type II: gas with water and no deviation, Type III: positive gas and water deviation, Type IV: gas without water and deviation, Type V: negative gas and water deviation, Type VI: positive gas and water deviation, and Type VII: gas without water and no deviation. In this embodiment, Type II: gas with water and no deviation includes Type II1: positive gas and water deviation and Type II2: negative gas and water deviation, and Type IV: gas without water deviation includes Type IV1: positive gas and water deviation and Type IV2: negative gas and water deviation. The distribution ranges of different deviation types in the gas-water fitting deviation intersection diagram are shown in Figure 2.

[0054] The size of the deviation threshold can be set according to the needs of actual application. The example in Figure 2 sets the deviation threshold to ±20%, thereby dividing the distribution areas corresponding to different fitting deviations.

[0055] S140: Performing history matching on the target coalbed methane well according to a parameter adjustment strategy corresponding to the scheme deviation type.

[0056] After determining the solution deviation type, a corresponding parameter adjustment strategy can be determined according to the solution deviation type to implement a specific history matching operation.

[0057] The parameter adjustment strategy is a strategy to adjust the parameters corresponding to the corresponding influencing factors according to the different types of scheme deviations, so as to ensure that the parameters are effectively adjusted under the premise of improving the fitting deviation effect.

[0058] Specifically, based on the example of the scheme deviation type in S130, the correspondence between the parameter adjustment strategy and the deviation type can be: the parameter adjustment strategy for Class I deviation is to reduce the permeability and water Corey coefficient; the parameter adjustment strategy for Class II (including Class II1 and Class II2) deviation is to reduce / increase the gas Corey coefficient and slightly increase / slightly reduce the gas content; the parameter adjustment strategy for Class III deviation is to increase the fracture porosity and reduce the gas saturation; the parameter adjustment strategy for Class IV (including Class IV1 and Class IV2) deviation is to increase / reduce the rock compressibility; the parameter adjustment strategy for Class V deviation is to increase the permeability and water Corey coefficient; the parameter adjustment strategy for Class VI deviation is to reduce the fracture porosity and increase the gas saturation; and Class VII deviation does not require further parameter adjustment.

[0059] Accordingly, the process of performing history matching on the target coalbed methane well according to the parameter adjustment strategy may be to correct the parameters in the simulation scheme according to the parameter adjustment strategy, and perform parameter iteration using the corrected parameters.

[0060] The parameter iteration process can be based on the cumulative production parameters calculated by the simulation scheme, and a quantitative analysis of the impact of adjusting the median value to a high or low value on the cumulative gas and water production, clarifying the adjustment amplitude of each parameter, generating and running a new simulation scheme, and using the gas and water fitting deviation intersection diagram to identify the fitting results. Based on the identification results, a parameter adjustment strategy is formulated and the simulation scheme is re-run until the single well fitting deviation meets the expected requirements.

[0061] The expected requirements may include a fitting accuracy threshold that is used to limit the proportion of coalbed methane wells that have gas, no water, and no deviation.

[0062] When making specific quantitative adjustments to parameters, changes in fracture porosity, water Corey coefficient, rock compressibility, gas saturation, and gas Corey coefficient are directly assigned values ​​within the previously preset upper and lower limits. The initial values ​​of permeability and gas content are calculated using formulas. During parameter adjustment, these initial values ​​must be multiplied by a coefficient. To ensure that the multiplied gas content and permeability are within the previously preset upper and lower limits, the coefficients for permeability are determined as follows: First, the median permeability value in the grid is calculated using the fitting formula. Then, the average value of the corresponding grid in the model is calculated using the high and low trend formulas. The calculated high and low values ​​are divided by the values ​​corresponding to the fitting formula to obtain two values, namely the product coefficients for the parameter adjustment. In practical applications, the parameter adjustment method can be modified according to the specific requirements of the parameters, and there are no restrictions on this.

[0063] In actual production, coalbed methane well production is influenced not only by geological factors but also by engineering factors. Therefore, it is unscientific to fit all wells to a Class VII deviation. Therefore, a fitting accuracy threshold is set, defining the percentage of wells that fall within the Class VII deviation range as acceptable. This threshold is determined based on the quality of production data and the implementation of geological parameters. For example, when 80% of the wells have a Class VII deviation, history matching is considered complete, ensuring practical application results.

[0064] The technical effect is further illustrated by using the verification results of a specific scenario example. More than ten years of production data from 50 wells in the example gas field are fitted. The fitting results of the initial simulation case show that there are 9 wells with Class I deviation, 14 wells with Class II deviation (including 5 wells of Class II1 and 9 wells of Class II2), 6 wells with Class III deviation, 5 wells with Class IV deviation (including 0 wells of Class IV1 and 5 wells of Class IV2), 9 wells with Class V deviation, 2 wells with Class VI deviation, and 5 wells with Class VII deviation. The distribution characteristics of the deviations are shown in Figure 3.

[0065] After implementing parameter adjustments in the above steps, the final fitting results show that there are 0 wells with Class I deviation, 3 wells with Class II deviation (including 1 well with Class II1 and 2 wells with Class II2), 3 wells with Class III deviation, 1 well with Class IV deviation (including 0 wells with Class IV1 and 1 well with Class IV2), 0 wells with Class V deviation, 2 wells with Class VI deviation, and 41 wells with Class VII deviation, accounting for 82%, which meets the fitting accuracy requirements. The distribution characteristics of the deviations are shown in Figure 4.

[0066] Through the introduction of the above embodiments and scenario examples, it can be seen that the coalbed methane well history fitting method constructs a simulation scheme based on the influencing factors affecting the target coalbed methane well, and then calculates the cumulative production parameters corresponding to different simulation schemes, and then determines the scheme deviation type according to the comparison results between the cumulative production parameters and the actual measured parameters, so as to perform history fitting on the target coalbed methane well according to the corresponding parameter adjustment strategy. The above method determines the deviation type by analyzing the deviation, and thus performs history fitting on the coalbed methane well through the corresponding pre-set parameter adjustment strategy, which not only ensures that the fitting operation corresponds to the actual production, but also improves the fitting speed, speeds up the work efficiency of the numerical simulation, and is conducive to guiding the actual production and development process (for example, it can be based on the results of historical fitting to adjust the development plan of the coalbed methane well, such as adjusting the parameters used for coalbed methane well mining, etc.).

[0067] Based on the above-mentioned coalbed methane well history matching method, this embodiment of the specification also proposes a coalbed methane well history matching device. The execution subject of the coalbed methane well history matching device can be a corresponding computing device. As shown in Figure 5, the coalbed methane well history matching device can include the following specific modules.

[0068] The simulation scheme construction module 510 is used to construct multiple simulation schemes according to at least one influencing factor corresponding to the target coalbed methane well; the influencing factors in different simulation schemes are set as corresponding simulation parameter values.

[0069] The cumulative production parameter calculation module 520 is used to calculate the cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production.

[0070] The deviation type determination module 530 is configured to determine the type of plan deviation based on the comparison result between the cumulative production parameters and the actual measured parameters.

[0071] The history matching module 540 is used to perform history matching on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type.

[0072] Based on the coalbed methane well history matching method corresponding to FIG1 , an embodiment of this specification provides a coalbed methane well history matching device, which includes a memory and a processor.

[0073] In this embodiment, the memory can be implemented in any appropriate manner. The memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), memory card, and the like. The computer storage medium stores computer program instructions. When the computer program instructions are executed, the program instructions or modules corresponding to the embodiment shown in FIG. 1 of this specification are implemented.

[0074] In this embodiment, the processor can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, etc. Specifically, when the processor is provided on a corresponding device, it can execute the method steps in the embodiment corresponding to FIG. 1 .

[0075] It should be noted that the coalbed methane well history fitting method, device and equipment can be applied to the field of geological exploration and development technology, and can also be applied to other technical fields except the field of geological exploration and development technology, without limitation.

[0076] Although the process flows described above include multiple operations occurring in a particular order, it should be understood that these processes may include more or fewer operations, which may be performed sequentially or in parallel (eg, using parallel processors or a multi-threaded environment).

[0077] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the function specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0078] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0080] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] Embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0082] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between the various embodiments can be referenced across them. Each embodiment focuses on the differences from the other embodiments. In particular, since the system embodiments are generally similar to the method embodiments, their description is relatively simple. For relevant parts, reference can be made to the description of the method embodiments. Throughout this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the embodiments in this specification. In this specification, the schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate the different embodiments or examples, and features of different embodiments or examples, described in this specification, without conflict.

[0083] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A coalbed methane well history matching method, characterized in that: include: Constructing multiple simulation schemes according to at least one influencing factor corresponding to the target coalbed methane well; setting the influencing factor in different simulation schemes to corresponding simulation parameter values; Calculating cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production; Determining the type of scheme deviation based on the comparison result between the cumulative production parameter and the actual measured parameter; as well as According to the parameter adjustment strategy corresponding to the scheme deviation type, historical matching is performed on the target coalbed methane well.

2. The method according to claim 1, characterized in that The influencing factors include at least one of the following: fracture porosity, permeability, water Corey coefficient, rock compressibility, gas saturation, gas Corey coefficient and gas content.

3. The method according to claim 1, characterized in that The influencing factors respectively correspond to distribution range limits; the simulation parameter values ​​are set within the corresponding distribution range limits.

4. The method according to claim 3, characterized in that The method of constructing multiple simulation schemes according to at least one influencing factor corresponding to the target coalbed methane well includes: Setting the impact factor to be analyzed to a variable value within the distribution range limit; and The other influencing factors except the influencing factors to be analyzed are set to the median within the distribution range limit.

5. The method according to claim 1, characterized in that The cumulative production parameters of different simulation schemes are calculated respectively, including: Calculate the cumulative gas and water production corresponding to the simulation scheme; Draw sensitivity analysis storm diagrams for cumulative gas and water production respectively; and The direction and degree of the impact of the simulation scheme on cumulative production are determined based on the sensitivity analysis storm diagram.

6. The method according to claim 1, characterized in that The determining of the scheme deviation type according to the comparison result between the cumulative production parameter and the actual measured parameter includes: Determine the fitting deviation according to the comparison result between the cumulative production parameter and the actual measured parameter; the fitting deviation includes the gas production fitting deviation and the water production fitting deviation; Constructing a gas-water fitting deviation intersection diagram based on the gas production fitting deviation and the water production fitting deviation; and The scheme deviation type is determined by comparing the gas-water fitting deviation intersection diagram with the fitting deviation threshold; the fitting deviation threshold is determined based on calculation accuracy, gas field development stage, data completeness and fitting accuracy.

7. The method according to claim 1, characterized in that The scheme deviation type includes one of gas-positive-water-positive deviation, gas-with-water-no deviation, gas-positive-water-negative deviation, gas-without-water-bias, gas-negative-water-negative deviation, gas-negative-water-positive deviation, and gas-without-water-no deviation.

8. The method according to claim 7, characterized in that The parameter adjustment strategy corresponding to the positive gas and positive water deviation is to reduce the permeability and water Corey coefficient; The parameter adjustment strategy for the gas with water without deviation is to reduce / increase the gas Corey coefficient and increase / decrease the gas content; The parameter adjustment strategy for the gas-positive-water-negative deviation is to increase fracture porosity and reduce gas saturation; The parameter adjustment strategy for the gas-water-free deviation is to increase / decrease the rock compressibility coefficient; The parameter adjustment strategy for the gas-negative and water-negative deviations is to increase the permeability and the water Corey coefficient; The parameter adjustment strategy for the gas-negative-water-positive deviation is to reduce fracture porosity and increase gas saturation; and The parameter adjustment strategy for gas without water and without deviation is to perform no parameter adjustment.

9. The method according to claim 1, characterized in that The step of performing history matching on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type includes: The parameters in the simulation scheme are corrected according to the parameter adjustment strategy, and the corrected parameters are used for parameter iteration; which includes: analyzing the impact of the parameter adjustment amplitude and adjustment direction of the influencing factor on the fitting deviation; re-determining the parameter adjustment strategy based on the impact result, and repeating the process of adjusting parameters and determining the parameter adjustment strategy until the fitting deviation meets the preset requirements.

10. The method according to claim 9, characterized in that The preset requirements include a fitting accuracy threshold; the fitting accuracy threshold is used to limit the proportion of coalbed methane wells that are gas-free, water-free and deviation-free.

11. A coalbed methane well history matching device, characterized in that: include: A simulation scheme building module, used to build multiple simulation schemes according to at least one influencing factor corresponding to a target coalbed methane well; The influencing factors in different simulation schemes are set to corresponding simulation parameter values; A cumulative production parameter calculation module, used to calculate the cumulative production parameters of different simulation schemes respectively; the cumulative production parameters include cumulative gas production and cumulative water production; A deviation type determination module, used to determine the type of scheme deviation based on the comparison result between the cumulative production parameter and the actual measured parameter; as well as The history matching module is used to perform history matching on the target coalbed methane well according to the parameter adjustment strategy corresponding to the scheme deviation type.

12. A coalbed methane well history matching device, characterized in that: The method comprises a memory and a processor; the memory is used to store computer programs / instructions; and the processor is used to execute the computer programs / instructions to implement the method according to any one of claims 1 to 10.

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