Selective area evaluation method, device and equipment for deep coal bed gas and storage medium

Abstract

The invention provides a selected area evaluation method, device and equipment for deep coal bed gas and a storage medium, and belongs to the technical field of coal bed gas exploration. The method comprises the following steps: acquiring evaluation data of a target evaluation unit, and reading evaluation parameters corresponding to enriched high-yield elements from the evaluation data; determining an enrichment high-yield element probability based on the evaluation parameters, wherein the enrichment high-yield element probability is used for indicating the probability that the target evaluation unit meets a high-yield condition; and determining a selection area index based on the enrichment high-yield element probability, wherein the selection area index is used for indicating the exploration potential and the risk level of the target evaluation unit. By analyzing key enrichment high-yield factors of deep coal bed gas, including gas-bearing property, storage property, storage property and compressibility, and determining the area selection index based on the evaluation data, the risk level in each favorable area can be identified, the quantitative evaluation method for the deep coal bed gas area selection is established, and a basis is provided for exploration investment and decision making.

Description

Technical Field

[0001] The present application relates to the field of coalbed methane exploration technology, and in particular to a method, device, equipment and storage medium for selecting and evaluating deep coalbed methane. Background Art

[0002] Exploration and development practices have shown that deep coalbed methane is a new type of unconventional natural gas from within the source, which is different from shallow coalbed methane. It has the characteristics of coexistence of conventional and unconventional reservoirs, and coexistence of free gas and adsorbed gas. Its reservoir pressure is high and the gas content is high, so it needs to be exploited on a large scale with the help of horizontal well volume fracturing. Therefore, the favorable exploration areas for deep coalbed methane have the characteristics of higher hydrocarbon generation quality and better storage performance. The exploration of deep coalbed methane requires deepening the optimization of zones, identifying the main risk factors, improving the success rate of exploration wells, reducing uncertainty and risks, and facilitating high-quality exploration of coalbed methane.

[0003] Previous research results mainly focused on qualitatively describing the geological characteristics, lithofacies combinations, and enrichment rules of deep coalbed methane reservoirs, and then optimizing the favorable areas or sweet spots of deep coalbed methane. However, no systematic and comprehensive quantitative evaluation method for deep coalbed methane selection has been formed, and no selection indicators have been established. Summary of the invention

[0004] This application provides a deep coalbed methane selection evaluation method, device, equipment and storage medium, which can identify the risk level in each favorable area, establish a deep coalbed methane selection quantitative evaluation method, and provide a basis for exploration investment and decision-making. The technical solution is as follows:

[0005] On the one hand, the embodiment of the present application provides a method for selecting and evaluating deep coalbed methane, comprising:

[0006] Acquire evaluation data of the target evaluation unit, and read evaluation parameters corresponding to the enrichment and high-yield elements from the evaluation data, wherein the enrichment and high-yield elements include at least one of gas content, reservoir property, preservation property and compressibility;

[0007] Determine the probability of enriching high-yield elements based on the evaluation parameters, wherein the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition;

[0008] A selection area index is determined based on the probability of the enriched high-yield elements, and the selection area index is used to indicate the exploration potential and risk level of the target evaluation unit.

[0009] On the other hand, an embodiment of the present application provides a deep coalbed methane selection and evaluation device, comprising:

[0010] An acquisition module, used to acquire evaluation data of a target evaluation unit, and read evaluation parameters corresponding to enrichment and high-yield elements from the evaluation data, wherein the enrichment and high-yield elements include at least one of gas content, storage property, preservation property and compressibility;

[0011] A determination module, used to determine the probability of enriching high-yield elements based on the evaluation parameters, wherein the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition;

[0012] The determination module is further used to determine a selection area index based on the probability of the enriched high-yield element, and the selection area index is used to indicate the exploration potential and risk level of the target evaluation unit.

[0013] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium storing a computer program, wherein the computer program is loaded and executed by a processor to implement the method as described in the above aspects.

[0015] The technical solution provided by this application includes at least the following beneficial effects:

[0016] The present application provides a method, device, equipment and storage medium for evaluating the selection of deep coalbed methane. By analyzing the key enrichment and high-yield factors of deep coalbed methane including gas content, storage, preservation and compressibility, a selection index is determined based on the evaluation data, and the risk level in each favorable area is identified. A quantitative evaluation method for deep coalbed methane selection is established to provide a basis for exploration investment and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0018] Figure 1 It is a flow chart of a method for evaluating a deep coalbed methane zone provided by an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for evaluating a deep coalbed methane zone provided by another exemplary embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a gas content evaluation standard provided by an exemplary embodiment of the present application;

[0021] Figure 4is a schematic diagram of a reservoir evaluation standard provided by an exemplary embodiment of the present application;

[0022] Figure 5 is a schematic diagram of a preservation evaluation standard provided by an exemplary embodiment of the present application;

[0023] Figure 6 is a schematic diagram of a compressibility evaluation standard provided by an exemplary embodiment of the present application;

[0024] Figure 7 It is a structural block diagram of a deep coalbed methane selection and evaluation device provided by an exemplary embodiment of the present application;

[0025] Figure 8 It is a structural block diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0027] Embodiment 1

[0028] Please refer to Figure 1 , which shows a flow chart of a method for selecting and evaluating deep coalbed methane provided by an exemplary embodiment of the present application. The method comprises the following steps:

[0029] Step 101, obtaining evaluation data of a target evaluation unit, and reading evaluation parameters corresponding to enriched and high-yield elements from the evaluation data, wherein the enriched and high-yield elements include at least one of gas content, storage property, preservation property and compressibility.

[0030] In a possible implementation, the technician first establishes evaluation criteria before conducting the selection area evaluation, defines the enrichment and high-yield elements and the evaluation parameters corresponding to each enrichment and high-yield element. The electronic device determines the required data based on the evaluation criteria, and reads the evaluation parameters required for the selection area evaluation from the acquired evaluation data.

[0031] For example, enrichment and high-yield factors include gas content and reservoir properties. The evaluation parameters corresponding to the gas content are total gas content, free gas percentage, total organic carbon and metamorphic degree, while the evaluation parameters corresponding to the reservoir properties are coal rock thickness, porosity and permeability.

[0032] Step 102, based on the evaluation parameters, the probability of enriching high-yield elements is determined, and the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition.

[0033] The electronic device pre-stores the corresponding relationship between the evaluation parameters and the probability of enriched high-yield elements, and can determine the probability of enriched high-yield elements corresponding to each enriched high-yield element based on the specific parameter content of the read evaluation parameters. Among them, the probability of enriched high-yield elements is used to indicate the possibility that the target evaluation unit meets the high-yield conditions. Schematically, the electronic device determines that the probability of gas content is 0.7 based on the total gas content, free gas percentage, total organic carbon and degree of metamorphism read, that is, the probability that the target evaluation unit has an effective gas generation system is 0.7.

[0034] Step 103, determining a selection index based on the probability of enrichment of high-yield elements, where the selection index is used to indicate the exploration potential and risk level of the target evaluation unit.

[0035] The electronic device calculates a selection index based on the probability of enrichment and high-yield elements corresponding to at least one determined enrichment and high-yield element. The selection index is used to indicate the exploration potential and risk level of the target evaluation unit. The higher the selection index, the higher the exploration potential and the lower the risk of the target evaluation unit.

[0036] In a possible implementation, the selection index is in positive proportion to the probability of enrichment of high-yield elements. The electronic device pre-stores the correspondence between the selection index interval and the exploration potential level and risk level. The exploration potential and risk of the target evaluation unit can be determined based on the interval to which the calculated selection index belongs, thereby guiding the selection work. In the case of a large area, multiple evaluation units can be divided. Based on the selection index of each evaluation unit, the exploration potential of different units can be mastered, and favorable exploration areas with low risk and high potential can be focused on to improve the efficiency of deep coalbed methane exploration.

[0037] In summary, the present application provides a method for evaluating the selection of deep coalbed methane. By analyzing the key enrichment and high-yield factors of deep coalbed methane including gas content, storage, preservation and compressibility, the selection index is determined based on the evaluation data, and the risk level in each favorable area is identified. A quantitative evaluation method for deep coalbed methane selection is established to provide a basis for exploration investment and decision-making.

[0038] Embodiment 2

[0039] Please refer to Figure 2 , which shows a flow chart of a method for evaluating deep coalbed methane selection provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0040] Step 201, obtaining evaluation data of the target evaluation unit, and reading at least one evaluation parameter corresponding to each enrichment and high-yield factor from the evaluation data, wherein different enrichment and high-yield factors correspond to different evaluation parameters.

[0041] In a possible implementation, each enrichment and high-yield element can be evaluated by one or more evaluation parameters.

[0042] Step 202, for each enrichment and high-yield factor, determine the parameter probability of each evaluation parameter corresponding to the enrichment and high-yield factor.

[0043] In a possible implementation, the probability of enriching high-yield elements of the enriched high-yield elements is determined by the parameter probability of the corresponding evaluation parameters, and step 202 includes the following steps:

[0044] Based on the evaluation parameters and the data type of the evaluation data, the parameter probability of the evaluation parameters is determined; wherein the data type includes confirmed data and inferred data, and the data reliability of the confirmed data is higher than the data reliability of the inferred data.

[0045] The probability that the enriched high-yield factors meet the high-yield conditions is determined by specific evaluation parameters. Therefore, technicians predefine the corresponding relationship between the parameter values ​​of the evaluation parameters and the parameter probabilities, and then determine the probability of enriching the high-yield factors based on the probabilities of each parameter.

[0046] In a possible implementation, the parameter probability is jointly determined by the parameter value and the reliability of the evaluation data. Evaluation data are roughly divided into two types, one is the confirmed data obtained by actual measurement such as well data, core data, and field test data, and the other is the inferred data based on analog data combined with a small amount of test data. Therefore, the data reliability of the confirmed data is higher than the data reliability of the inferred data. When the evaluation parameter values ​​are the same and meet the high-yield conditions, the evaluation unit whose data type is inferred data should have a lower exploration potential than the evaluation unit whose data type is confirmed data, and correspondingly, its risk is higher than the evaluation unit whose data type is confirmed data; when the evaluation parameter values ​​are the same and do not meet the high-yield conditions, since the confirmed data can confirm its risk, and the reliability of the inferred data is low, the risk of the evaluation unit whose data type is inferred data is lower than that of the evaluation unit whose data type is confirmed data, and correspondingly, the exploration potential is higher than that of the evaluation unit whose data type is confirmed data.

[0047] Specifically, based on the evaluation parameter and the data type of the evaluation data, determining the parameter probability of the evaluation parameter includes the following steps:

[0048] Step 1: In response to the evaluation parameter belonging to the low risk interval and the evaluation data being confirmation data, determining the parameter probability as the low risk probability.

[0049] Step 2: In response to the evaluation parameter belonging to a relatively low risk interval and the evaluation data being inferred data, determining the probability parameter as a relatively low risk probability.

[0050] Step three, in response to the evaluation parameter belonging to a relatively high risk interval and the evaluation data being inferred data, determining the probability parameter to be a relatively high risk probability.

[0051] Step 4: In response to the evaluation parameter belonging to the high risk interval and the evaluation data being confirmation data, the probability parameter is determined to be a high risk probability.

[0052] Among them, the probability values ​​of low risk probability, relatively low risk probability, relatively high risk probability and high risk probability decrease in turn.

[0053] Optionally, for the same evaluation parameter, the low risk interval is the same as the relatively low risk interval, or the low risk interval is different from the relatively low risk interval, or the low risk interval and the relatively low risk interval have an intersection; correspondingly, for the same evaluation parameter, the high risk interval is the same as the relatively high risk interval, or the high risk interval and the relatively high risk interval are different, or the high risk interval and the relatively high risk interval have an intersection.

[0054] Indicatively, for the probability of gas content, if the data type is confirmed data, the gas content is ≥8 cubic meters per ton and there are many sample points with a gas content exceeding 12 cubic meters per ton (for example, the number exceeds the quantity threshold or the proportion of the total number of sample points exceeds the proportion threshold), then the gas content probability is determined to be a low-risk probability; if the data type is inferred data and the gas content is ≥8 cubic meters per ton, then the gas content probability is determined to be a relatively low-risk probability.

[0055] Step 203, based on the wooden barrel theory, the minimum value of the parameter probability is determined as the probability of enriching high-yield factors.

[0056] Illustratively, for the assessment of gas content, the electronic device determines that the probability of gas content is 0.7, the probability of free gas proportion is 0.7, the probability of total organic carbon is 0.6, and the probability of metamorphic degree is 0.5, and then the probability of gas content is determined to be 0.5.

[0057] Step 204, determining a selection index based on the probability of enrichment of high-yield elements, the selection index is used to indicate the exploration potential and risk level of the target evaluation unit.

[0058] In a possible implementation, the enrichment and high-yield factors include gas content, reservoir, preservation and compressibility. The selection index is the product of the probabilities of the enrichment and high-yield factors, and its expression is as follows:

[0059] PEI=Pg×Pr×Ps×Pf

[0060] PEI is the deep coalbed methane selection index; Pg is the gas-bearing probability; Pr is the reservoir probability; Ps is the preservation probability; and Pf is the compressibility probability.

[0061] Based on the selection index, the deep coalbed methane exploration potential level and risk within the evaluation unit can be quantitatively determined, providing a basis for strategic selection. Schematically, the relationship between the selection index and the exploration potential and risk level is as follows:

[0062] (1) Class I - low exploration risk: 0.3 <= PEI, great potential for deep coalbed methane exploration.

[0063] (2) Class II - lower exploration risk: 0.2 <= PEI < 0.3, with greater potential for deep coalbed methane exploration.

[0064] (3) Class III - Higher exploration risk: 0.1<=PEI<0.2, low potential for deep coalbed methane exploration.

[0065] (4) Class IV - high exploration risk: PEI < 0.1, low potential for deep coalbed methane exploration.

[0066] As exploration work progresses and geological understanding deepens, the selection parameter evaluation standards and selection result classification standards need to be updated and adjusted in a timely manner to effectively guide the selection work.

[0067] In the embodiments of the present application, by analyzing the geological and engineering characteristics of deep coalbed methane accumulation, evaluation parameter standards for gas content, storage, preservation and compressibility are established, the existence probability of each parameter and key element in the evaluation unit and the selection index are quantitatively evaluated, and the classification and grading of deep coalbed methane exploration potential are achieved, so as to grasp the exploration potential of different regions, identify the main risks in different favorable areas, circle the most favorable areas in the study area, focus on favorable exploration areas with low risks and high potential, and provide a basis for exploration investment and decision-making.

[0068] Embodiment 3

[0069] Based on the above embodiments, the embodiments of the present application schematically illustrate a definition and classification standard of exploration potential and risk:

[0070] The enrichment and high-yield factors include gas content. The evaluation parameters corresponding to gas content include at least one of gas content, free gas percentage, total organic carbon, vitrinite content and metamorphic degree. The probability of gas content is used to indicate the possibility of the existence of an effective gas generation system in the target evaluation unit.

[0071] and / or,

[0072] The enrichment and high-yield factors include reservoir properties, and the evaluation parameters corresponding to reservoir properties include at least one of coal rock thickness, porosity and permeability. The reservoir property probability is used to indicate the possibility that the target evaluation unit has coal rock reservoirs of the quantity and quality required for deep coalbed methane exploration;

[0073] and / or,

[0074] The enrichment and high-yield factors include preservation. The evaluation parameters corresponding to preservation include at least one of the lithology combination, structural characteristics, hydrological characteristics and pressure coefficient. The preservation probability is used to indicate the possibility of the existence of an effective closed system in the target evaluation unit.

[0075] and / or,

[0076] The enrichment and high-yield factors include compressibility. The evaluation parameters corresponding to compressibility include at least one of burial depth, coal body structure and ground stress. The compressibility probability is used to indicate the fracturing performance of the target evaluation unit.

[0077] Figures 3 to 6 The probability classification standards of enriched and high-yield elements based on gas content, reservoir quality, preservation and compressibility are shown respectively.

[0078] In a possible implementation, the value of the parameter probability may be the maximum value, minimum value or middle value of the probability interval, or the electronic device may further refine the correspondence between the parameter probability and the parameter value and the data type, so that the parameter probability corresponds to the parameter value and the data type one by one. This application embodiment does not limit this.

[0079] Embodiment 4

[0080] Please refer to Figure 7 , which shows a structural block diagram of a deep coalbed methane selection and evaluation device provided by an exemplary embodiment of the present application, the device comprising:

[0081] The acquisition module 701 is used to acquire the evaluation data of the target evaluation unit, and read the evaluation parameters corresponding to the enrichment and high-yield elements from the evaluation data, wherein the enrichment and high-yield elements include at least one of gas content, reservoir property, preservation property and compressibility;

[0082] A determination module 702 is used to determine the probability of enriching high-yield elements based on the evaluation parameters, and the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition;

[0083] The determination module 702 is further used to determine a selection area index based on the probability of the enriched high-yield element, and the selection area index is used to indicate the exploration potential and risk level of the target evaluation unit.

[0084] Optionally, the acquisition module 701 is further used to:

[0085] Reading at least one evaluation parameter corresponding to each enrichment and high-yield factor from the evaluation data, wherein different enrichment and high-yield factors correspond to different evaluation parameters;

[0086] The determining module 702 is further configured to:

[0087] For each enrichment and high-yield factor, determining the parameter probability of each evaluation parameter corresponding to the enrichment and high-yield factor;

[0088] Based on the barrel theory, the minimum value among the parameter probabilities is determined as the probability of enriching high-yield factors.

[0089] Optionally, the determining module 702 is further configured to:

[0090] Determining a parameter probability of the evaluation parameter based on the evaluation parameter and the data type of the evaluation data;

[0091] The data types include confirmed data and inferred data, and the data reliability of the confirmed data is higher than the data reliability of the inferred data.

[0092] Optionally, the determining module 702 is further configured to:

[0093] In response to the evaluation parameter belonging to a low risk interval and the evaluation data being the confirmation data, determining the parameter probability to be a low risk probability;

[0094] In response to the evaluation parameter belonging to a relatively low risk interval and the evaluation data being the inferred data, determining the probability parameter to be a relatively low risk probability;

[0095] In response to the evaluation parameter belonging to the high risk interval and the evaluation data being the inferred data, determining the probability parameter to be a relatively high risk probability;

[0096] In response to the evaluation parameter belonging to a relatively high risk interval and the evaluation data being the confirmation data, determining the probability parameter to be a high risk probability;

[0097] Among them, the probability values ​​of the low risk probability, the relatively low risk probability, the relatively high risk probability and the high risk probability decrease in sequence.

[0098] Optionally, the enrichment and high-yield factors include the gas content, and the evaluation parameters corresponding to the gas content include at least one of the gas content, free gas ratio, total organic carbon, vitrinite content and metamorphic degree, and the gas content probability is used to indicate the possibility of the existence of an effective gas generation system in the target evaluation unit.

[0099] Optionally, the enriched and high-yield factors include reservoir properties, and the evaluation parameters corresponding to the reservoir properties include at least one of coal rock thickness, porosity and permeability. The reservoir property probability is used to indicate the possibility of the existence of coal rock reservoirs of the quantity and quality required for deep coalbed methane exploration in the target evaluation unit.

[0100] Optionally, the enrichment and high-yield factors include preservation, and the evaluation parameters corresponding to the preservation include at least one of lithological combination, structural characteristics, hydrological characteristics and pressure coefficient, and the preservation probability is used to indicate the possibility of the existence of an effective closed system in the target evaluation unit.

[0101] Optionally, the enrichment and high-yield factors include compressibility, the evaluation parameters corresponding to the compressibility include at least one of burial depth, coal body structure and ground stress, and the compressibility probability is used to indicate the fracturing performance of the target evaluation unit.

[0102] Optionally, the selection area index is the product of the probabilities of each enriched high-yield element.

[0103] The present application provides a deep coalbed methane zone selection and evaluation device, which analyzes the key enrichment and high-yield factors of deep coalbed methane, including gas content, storage, preservation and compressibility, determines the zone selection index based on the evaluation data, can identify the risk level in each favorable area, and establishes a deep coalbed methane zone selection quantitative evaluation method to provide a basis for exploration investment and decision-making.

[0104] An embodiment of the present application provides an electronic device; Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the electronic device 800 includes: a processor 801, at least one communication bus 802, a user interface 803, at least one external communication interface 804, and a memory 805. The communication bus 802 is configured to realize the connection and communication between these components. The user interface 803 may include a display screen, and the external communication interface 804 may include a standard wired interface and a wireless interface. The processor 801 is configured to execute the program of the deep coalbed methane selection evaluation method stored in the memory to implement the steps in the method provided in the above embodiment.

[0105] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0106] The embodiment of the present application further provides a computer program product, which runs on a processor of a computer device, so that the computer device executes the method described in the above embodiment.

[0107] It should be noted here that the description of the above storage medium, electronic device, and remote control embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0108] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0110] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0111] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0113] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0114] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0115] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for selecting and evaluating deep coalbed methane, characterized in that: include: Acquire evaluation data of the target evaluation unit, and read evaluation parameters corresponding to the enrichment and high-yield elements from the evaluation data, wherein the enrichment and high-yield elements include at least one of gas content, reservoir property, preservation property and compressibility; Determine the probability of enriching high-yield elements based on the evaluation parameters, wherein the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition; A selection area index is determined based on the probability of the enriched high-yield elements, and the selection area index is used to indicate the exploration potential and risk level of the target evaluation unit.

2. The method according to claim 1, characterized in that The step of reading the evaluation parameters corresponding to the enriched high-yield elements from the evaluation data includes: Reading at least one evaluation parameter corresponding to each enrichment and high-yield factor from the evaluation data, wherein different enrichment and high-yield factors correspond to different evaluation parameters; Determining the probability of enriching high-yield elements based on the evaluation parameters includes: For each enrichment and high-yield factor, determining the parameter probability of each evaluation parameter corresponding to the enrichment and high-yield factor; Based on the barrel theory, the minimum value among the parameter probabilities is determined as the probability of enriching high-yield factors.

3. The method according to claim 2, characterized in that The parameter probability of each evaluation parameter corresponding to the enriched high-yield element is determined, including: Determining a parameter probability of the evaluation parameter based on the evaluation parameter and the data type of the evaluation data; The data types include confirmed data and inferred data, and the data reliability of the confirmed data is higher than the data reliability of the inferred data.

4. The method according to claim 3, characterized in that The determining the parameter probability of the evaluation parameter based on the evaluation parameter and the data type of the evaluation data includes: In response to the evaluation parameter belonging to a low risk interval and the evaluation data being the confirmation data, determining the parameter probability to be a low risk probability; In response to the evaluation parameter belonging to a relatively low risk interval and the evaluation data being the inferred data, determining the probability parameter to be a relatively low risk probability; In response to the evaluation parameter belonging to a relatively high risk interval and the evaluation data being the inferred data, determining the probability parameter to be a relatively high risk probability; In response to the evaluation parameter belonging to the high risk interval and the evaluation data being the confirmation data, determining the probability parameter to be a high risk probability; Among them, the probability values ​​of the low risk probability, the relatively low risk probability, the relatively high risk probability and the high risk probability decrease in sequence.

5. The method according to any one of claims 1 to 4, characterized in that: The enrichment and high-yield factors include the gas content, and the evaluation parameters corresponding to the gas content include at least one of the gas content, free gas ratio, total organic carbon, vitrinite content and metamorphic degree. The gas content probability is used to indicate the possibility of the existence of an effective gas generation system in the target evaluation unit.

6. The method according to any one of claims 1 to 4, characterized in that: The enriched and high-yield factors include reservoir properties, and the evaluation parameters corresponding to the reservoir properties include at least one of coal rock thickness, porosity and permeability. The reservoir property probability is used to indicate the possibility of the existence of coal rock reservoirs of the quantity and quality required for deep coalbed methane exploration in the target evaluation unit.

7. The method according to any one of claims 1 to 4, characterized in that: The enrichment and high-yield factors include preservation, and the evaluation parameters corresponding to the preservation include at least one of lithology combination, structural characteristics, hydrological characteristics and pressure coefficient. The preservation probability is used to indicate the possibility of the existence of an effective closed system in the target evaluation unit.

8. The method according to any one of claims 1 to 4, characterized in that: The enrichment and high-yield factors include compressibility, and the evaluation parameters corresponding to the compressibility include at least one of burial depth, coal body structure and ground stress. The compressibility probability is used to indicate the fracturing performance of the target evaluation unit.

9. The method according to any one of claims 1 to 4, characterized in that: The selection area index is the product of the probabilities of the enriched high-yield elements.

10. A deep coalbed methane selection and evaluation device, characterized in that: include: An acquisition module, used to acquire evaluation data of a target evaluation unit, and read evaluation parameters corresponding to enrichment and high-yield elements from the evaluation data, wherein the enrichment and high-yield elements include at least one of gas content, storage property, preservation property and compressibility; A determination module, used to determine the probability of enriching high-yield elements based on the evaluation parameters, wherein the probability of enriching high-yield elements is used to indicate the probability that the target evaluation unit meets the high-yield condition; The determination module is further used to determine a selection area index based on the probability of the enriched high-yield element, and the selection area index is used to indicate the exploration potential and risk level of the target evaluation unit.

11. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 9.

Images