Coal rock reservoir temporary storage ratio determination method and device, medium and electronic equipment

Abstract

The invention discloses a coal rock reservoir temporary storage ratio determination method and device, a medium and electronic equipment. The method comprises the following steps: acquiring an actually measured temporary storage ratio and a pressure curve of the coal rock reservoir, wherein the actually measured temporary storage ratio and the pressure curve are obtained through drilling data and logging data of the coal rock reservoir; establishing a temporary storage ratio prediction curve according to the actually measured temporary storage ratio and the pressure curve; optimizing the temporary storage ratio prediction curve in combination with a multivariate attribute regression model, a probabilistic neural network model and the temporary storage ratio prediction curve; and predicting the temporary storage ratio of the coal rock reservoir based on a seismic data volume and the temporary storage ratio prediction curve. According to the method, the accuracy of predicting the temporary storage ratio of the coal rock reservoir can be improved.

Description

Background Art

[0002] At present, with the continuous deepening of exploration work in recent years, the accurate prediction of the critical storage ratio of coal-rock reservoirs has become a very important technical index in the exploration and evaluation of coalbed methane. To a certain extent, the critical storage ratio can reflect the size of reservoir energy. By predicting the critical storage ratio of the whole area, it can reflect the gas saturation degree and the size of reservoir energy to a certain extent, and can provide technical reference for exploration deployment. Existing predictions of the critical storage ratio generally require obtaining the Langmuir parameters of coal-rock and the reservoir pressure of coal seams. However, in actual exploration and production, it is often necessary to predict relevant parameters without Langmuir parameters. Based on this, how to improve the accuracy of predicting the critical storage ratio of coal-rock reservoirs is a technical problem to be solved urgently. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, medium and electronic equipment for determining the critical storage ratio of coal-rock reservoirs. This application can improve the accuracy of predicting the critical storage ratio of coal-rock reservoirs.

[0004] Other characteristics and advantages of this application will become obvious through the following detailed description, or be learned partially through the practice of this application.

[0005] According to one aspect of the embodiments of this application, a method for determining the critical storage ratio of coal-rock reservoirs is provided. The method is characterized in that the method includes: obtaining the measured critical storage ratio and pressure curve of the coal-rock reservoir, where the measured critical storage ratio and the pressure curve are obtained through the drilling data and logging data of the coal-rock reservoir; establishing a critical storage ratio prediction curve according to the measured critical storage ratio and the pressure curve; combining a multi-attribute regression model, a probabilistic neural network model, and the critical storage ratio prediction curve to optimize the critical storage ratio prediction curve; predicting the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the critical storage ratio prediction curve.

[0006] In an embodiment of this application, based on the foregoing solution, optimizing the critical storage ratio prediction curve through the multi-attribute regression model includes: for the low-frequency segment of the critical storage ratio prediction curve, obtaining the critical storage ratio corresponding to the low-frequency segment; calculating the mean square error between the critical storage ratio corresponding to the low-frequency segment and the measured critical storage ratio corresponding to the low-frequency segment; if the mean square error is greater than a preset threshold, optimizing the low-frequency segment of the critical storage ratio prediction curve according to the multi-attribute regression model and the measured critical storage ratio corresponding to the low-frequency segment.

[0007] In one embodiment of the present application, based on the foregoing solution, after optimizing the low-frequency segment of the predicted critical storage ratio curve, the method further includes: obtaining the correlation coefficient between the low-frequency segment of the predicted critical storage ratio curve and the measured critical storage ratio; if the correlation coefficient is greater than a first preset correlation coefficient, determining the low-frequency segment of the predicted critical storage ratio curve as the target prediction curve, where the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir.

[0008] In one embodiment of the present application, based on the foregoing solution, optimizing the predicted critical storage ratio curve through the probabilistic neural network model includes: for the high-frequency segment of the predicted critical storage ratio curve, determining the fitting effect parameter and the number of training data according to the probabilistic neural network model and the predicted critical storage ratio curve; optimizing the high-frequency segment of the predicted critical storage ratio curve based on the fitting effect parameter and the number of training data.

[0009] In one embodiment of the present application, based on the foregoing solution, after optimizing the high-frequency segment of the predicted critical storage ratio curve, the method further includes: obtaining the correlation coefficient between the high-frequency segment of the predicted critical storage ratio curve and the measured critical storage ratio; if the correlation coefficient is greater than a second preset correlation coefficient, determining the high-frequency segment of the predicted critical storage ratio curve as the target prediction curve, where the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir.

[0010] In one embodiment of the present application, based on the foregoing solution, predicting the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the predicted critical storage ratio curve includes: determining the critical storage ratio distribution area according to the seismic data volume and the predicted critical storage ratio curve; predicting the critical storage ratio of the coal-rock reservoir based on the critical storage ratio distribution area.

[0011] In one embodiment of the present application, based on the foregoing solution, predicting the critical storage ratio of the coal-rock reservoir based on the critical storage ratio distribution area includes: obtaining the critical storage ratio distribution range corresponding to the critical storage ratio distribution area; determining the critical storage ratio corresponding to the coal-rock reservoir based on the critical storage ratio distribution range.

[0012] According to one aspect of the embodiments of the present application, a device for determining the in-situ storage ratio of a coal-rock reservoir is provided, characterized in that the device includes: an acquisition unit, configured to acquire the measured in-situ storage ratio and pressure curve of the coal-rock reservoir by analyzing the drilling data and logging data of the coal-rock reservoir; a construction unit, configured to establish an in-situ storage ratio prediction curve according to the measured in-situ storage ratio and the pressure curve; an optimization unit, configured to optimize the in-situ storage ratio prediction curve by combining a multi-attribute regression model, a probabilistic neural network model, and the in-situ storage ratio prediction curve; and a prediction unit, configured to predict the in-situ storage ratio of the coal-rock reservoir based on the seismic data volume and the in-situ storage ratio prediction curve.

[0013] According to one aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program includes executable instructions, and when the executable instructions are executed by a processor, the method described in the above embodiments is implemented.

[0014] According to one aspect of the embodiments of the present application, an electronic device is provided, including: one or more processors; a memory, configured to store the executable instructions of the processor, and when the executable instructions are executed by the one or more processors, the one or more processors implement the method described in the above embodiments.

[0015] In the present application, first, the measured in-situ storage ratio and pressure curve of the coal-rock reservoir are acquired through the drilling data and logging data of the coal-rock reservoir. Then, according to the measured in-situ storage ratio and the pressure curve, an in-situ storage ratio prediction curve for initially predicting the in-situ storage ratio of the reservoir is constructed. In order to improve the accuracy of the in-situ storage ratio prediction curve, the in-situ storage ratio prediction curve can be optimized through a multi-attribute regression model and a probabilistic neural network model. Secondly, based on the optimized in-situ storage ratio prediction curve and combined with the seismic data volume, the in-situ storage ratio of the coal-rock reservoir is predicted. Among them, the seismic data volume can be the drilling data and logging data of the coal-rock reservoir corresponding to each different coal-rock reservoir. Based on the in-situ storage ratio prediction curve determined by the present application, the prediction accuracy of the in-situ storage ratio of the coal-rock reservoir can be improved. At the same time, according to the in-situ storage ratio prediction curve, the in-situ storage ratio characteristics can be analyzed, so as to guide the in-situ storage ratio prediction work of the coal-rock reservoir.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Description of the Drawings

[0017] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. In the drawings:

[0018] Figure 1 is a flowchart of a method for determining the critical storage ratio of a coal-rock reservoir according to an embodiment of the present application;

[0019] Figure 2 is a linear regression curve based on the measured critical storage ratio and reservoir pressure according to an embodiment of the present application;

[0020] Figure 3 is a crossplot of the critical storage ratio constructed based on a multi-attribute regression model according to an embodiment of the present application;

[0021] Figure 4 is an analysis diagram of the correlation coefficient constructed based on a multi-attribute regression model according to an embodiment of the present application;

[0022] Figure 5 is a crossplot of the critical storage ratio constructed based on a probabilistic neural network model according to an embodiment of the present application;

[0023] Figure 6 is an analysis diagram of the correlation coefficient constructed based on a probabilistic neural network model according to an embodiment of the present application;

[0024] Figure 7 is a distribution area diagram of the critical storage ratio according to an embodiment of the present application;

[0025] Figure 8 is a block diagram of a device for determining the critical storage ratio of a coal-rock reservoir according to an embodiment of the present application;

[0026] Figure 9 is a schematic diagram of the system structure of an electronic device according to an embodiment of the present application. Detailed Embodiments

[0027] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0028] In addition, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0029] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the content and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0031] It should be noted that: "a plurality of" mentioned in this article refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0032] The implementation details of the technical solutions of the embodiments of the present application are elaborated in detail as follows:

[0033] According to one aspect of the present application, a method for determining the critical storage ratio of a coal-rock reservoir is provided. Figure 1 FIG. is a flowchart of the method for determining the critical storage ratio of a coal-rock reservoir shown according to an embodiment of the present application. The method for determining the critical storage ratio of a coal-rock reservoir can be executed by a device with computing and processing capabilities. The method for determining the critical storage ratio of a coal-rock reservoir at least includes steps 110 to 140, which are introduced in detail as follows:

[0034] In step 110, the measured critical storage ratio and pressure curve of the coal-rock reservoir are obtained, and the measured critical storage ratio and the pressure curve are obtained from the drilling data and logging data of the coal-rock reservoir.

[0035] In this application, the critical desorption ratio of the coal-rock reservoir is the ratio of the critical desorption pressure of coalbed methane to the reservoir pressure of the coal-rock reservoir. Therefore, by analyzing the drilling data and logging data of the coal-rock reservoir, the measured critical desorption ratio and pressure curve of the coal-rock reservoir can be obtained.

[0036] Continue to refer to Figure 1 , in step 120, according to the measured critical desorption ratio and the pressure curve, a critical desorption ratio prediction curve is established.

[0037] In this application, after obtaining the measured critical desorption ratio and pressure curve of the coal-rock reservoir, a linear regression curve can be constructed based on the measured critical desorption ratio and the pressure curve, so as to analyze the measured critical desorption ratio and the reservoir pressure of the coal-rock reservoir through the linear regression curve. Refer to Figure 2 , which is the linear regression curve based on the measured critical desorption ratio and reservoir pressure shown in the embodiments of this application. In Figure 2 , it can be known that the correlation degree between the reservoir pressure and the measured critical desorption ratio is 1. And the measured critical desorption ratio corresponding to the coal-rock reservoir can be obtained through the linear regression curve and the reservoir pressure. Based on this, the linear regression curve of the constructed line can be used as the critical desorption ratio prediction curve for predicting the critical desorption ratio of the coal-rock reservoir.

[0038] Continue to refer to Figure 1 , in step 130, the critical desorption ratio prediction curve is optimized by combining the multi-attribute regression model, the probabilistic neural network model, and the critical desorption ratio prediction curve.

[0039] In this application, after obtaining the critical desorption ratio prediction curve, in order to be able to more accurately predict the critical desorption ratio of the coal-rock reservoir, the critical desorption ratio prediction curve can be optimized by the multi-attribute regression model and / or the probabilistic neural network model.

[0040] In an embodiment of this application, optimizing the critical desorption ratio prediction curve through the multi-attribute regression model specifically includes steps 131 to 133:

[0041] Step 131, for the low-frequency segment of the critical desorption ratio prediction curve, obtain the critical desorption ratio corresponding to the low-frequency segment.

[0042] Step 132, calculate the mean square error between the critical desorption ratio corresponding to the low-frequency segment and the measured critical desorption ratio corresponding to the low-frequency segment.

[0043] Step 133, if the mean square error is greater than the preset threshold, optimize the low-frequency segment of the critical desorption ratio prediction curve according to the multi-attribute regression model and the measured critical desorption ratio corresponding to the low-frequency segment.

[0044] In this embodiment, in order to improve the accuracy of the predicted critical storage ratio curve, the predicted critical storage ratio curve can be divided into a low-frequency segment and a high-frequency segment.

[0045] Referring to Figure 3 , it is a crossplot of the critical storage ratio constructed based on the multiple attribute regression model according to the embodiments of the present application. In Figure 3 , for the low-frequency segment, through the multiple attribute regression model, the relationship between the critical storage ratio of the coal-rock reservoir and each seismic attribute of the coal-rock reservoir can be established. Then, by calculating the mean square error between the critical storage ratio corresponding to the low-frequency segment and the measured critical storage ratio corresponding to the low-frequency segment, it can be determined whether the mean square error is greater than a preset threshold. If the mean square error is greater than the preset threshold, the low-frequency segment of the predicted critical storage ratio curve can be optimized according to the multiple attribute regression model and the measured critical storage ratio corresponding to the low-frequency segment.

[0046] In an embodiment of the present application, after optimizing the low-frequency segment of the predicted critical storage ratio curve, it specifically further includes steps 134 to 135:

[0047] Step 134, obtaining the correlation coefficient between the low-frequency segment of the predicted critical storage ratio curve and the measured critical storage ratio.

[0048] Step 135, if the correlation coefficient is greater than a first preset correlation coefficient, determining the low-frequency segment of the predicted critical storage ratio curve as the target prediction curve, and the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir.

[0049] In this embodiment, referring to Figure 4 , it is a correlation coefficient analysis diagram constructed based on the multiple attribute regression model according to the embodiments of the present application. Figure 4 In, after optimizing the low-frequency segment of the predicted critical storage ratio curve, obtaining the correlation coefficient between the optimized low-frequency segment of the predicted critical storage ratio curve and the measured critical storage ratio corresponding to the low-frequency segment. If the correlation coefficient is greater than the first preset correlation coefficient, determining the low-frequency segment of the predicted critical storage ratio curve as the target prediction curve, and the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir. Among them, the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir. The first preset correlation coefficient is the correlation coefficient corresponding to the low-frequency segment of the predicted critical storage ratio curve established based on the measured critical storage ratio and the pressure curve.

[0050] If the correlation coefficient is less than the first preset correlation coefficient, it indicates that the accuracy of the low-frequency segment of the optimized predicted critical storage ratio curve is relatively low, and it is necessary to re-optimize the predicted critical storage ratio curve based on the multiple attribute regression model and the measured critical storage ratio. Or it is necessary to re-obtain the pressure curve of the measured critical storage ratio corresponding to the coal-rock reservoir to prevent data deviation.

[0051] In one embodiment of the present application, the probability neural network model is used to optimize the predicted curve of the temporary storage ratio, specifically including steps 136 to 137:

[0052] Step 136: For the high-frequency segment of the predicted curve of the temporary storage ratio, according to the probability neural network model and the predicted curve of the temporary storage ratio, determine the fitting effect parameter and the number of training data.

[0053] Step 137: Based on the fitting effect parameter and the number of training data, optimize the high-frequency segment of the predicted curve of the temporary storage ratio.

[0054] In the present application, in this embodiment, for the accuracy of the predicted curve of the temporary storage ratio, the predicted curve of the temporary storage ratio can be divided into a low-frequency segment and a high-frequency segment.

[0055] Refer to Figure 5 , which is a crossplot of the temporary storage ratio constructed based on the probability neural network model shown in the embodiment of the present application. In Figure 5 , for the high-frequency segment, at least one optimized predicted curve of the temporary storage ratio can be generated through the probability neural network model. Based on the probability neural network model, the optimized predicted curve of the temporary storage ratio, and the predicted curve of the temporary storage ratio, perform training on the temporary storage ratio data, so as to determine the fitting effect parameter and the number of training data. According to the fitting effect parameter and the number of training data, optimize the predicted curve of the temporary storage ratio, so as to obtain a better predicted curve of the temporary storage ratio.

[0056] In one embodiment of the present application, after optimizing the high-frequency segment of the predicted curve of the temporary storage ratio, it specifically further includes steps 138 to 139:

[0057] Step 138: Obtain the correlation coefficient between the high-frequency segment of the predicted curve of the temporary storage ratio and the measured temporary storage ratio.

[0058] Step 139: If the correlation coefficient is greater than the second preset correlation coefficient, determine that the high-frequency segment of the predicted curve of the temporary storage ratio is the target predicted curve, and the target predicted curve is used to predict the temporary storage ratio of the coal-rock reservoir.

[0059] In this embodiment, refer to Figure 6 , which is an analysis diagram of the correlation coefficient constructed based on the probability neural network model shown in the embodiment of the present application. Figure 6Among them, after optimizing the high-frequency segment of the predicted critical storage ratio curve, the correlation coefficient between the high-frequency segment of the optimized predicted critical storage ratio curve and the measured critical storage ratio corresponding to the high-frequency segment is obtained. If the correlation coefficient is greater than the second preset correlation coefficient, it is determined that the high-frequency segment of the predicted critical storage ratio curve is the target prediction curve, and the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir. Among them, the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir. The second preset correlation coefficient is the correlation coefficient corresponding to the high-frequency segment of the predicted critical storage ratio curve established based on the measured critical storage ratio and the pressure curve.

[0060] If the correlation coefficient is less than the second preset correlation coefficient, it indicates that the accuracy of the high-frequency segment of the optimized predicted critical storage ratio curve is low, and it is necessary to re-optimize the predicted critical storage ratio curve based on the probabilistic neural network model and the measured critical storage ratio. Or it is necessary to re-obtain the pressure curve of the measured critical storage ratio corresponding to the coal-rock reservoir to prevent data deviation.

[0061] Continue to refer to Figure 1 In step 140, based on the seismic data volume and the predicted critical storage ratio curve, the critical storage ratio of the coal-rock reservoir is predicted.

[0062] In this application, after determining the predicted critical storage ratio curve, the critical storage ratio in each coal-rock reservoir can be analyzed in combination with the seismic data volume to analyze the distribution of coalbed methane and the reserves of coalbed methane in each coal-rock reservoir.

[0063] In this embodiment, the prediction of the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the predicted critical storage ratio curve specifically includes steps 141 to 142:

[0064] Step 141, determine the critical storage ratio distribution area according to the seismic data volume and the predicted critical storage ratio curve.

[0065] Step 142, predict the critical storage ratio of the coal-rock reservoir based on the critical storage ratio distribution area.

[0066] In this embodiment, refer to Figure 7 which is the critical storage ratio distribution area map shown in the embodiment of this application. Figure 7 Among them, according to the calculated predicted critical storage ratio curve and the seismic data volume, the critical storage ratio distribution area is determined. Among them, the critical storage ratio distribution area can be divided into a first-class area, a second-class area, and a third-class area. The critical storage ratio corresponding to the first-class area is greater than the critical storage ratio corresponding to the second-class area, and the critical storage ratio corresponding to the second-class area is greater than the critical storage ratio corresponding to the third-class area.

[0067] In addition, the seismic data volume may include drilling data and logging data corresponding to each coal-rock reservoir. Therefore, according to the distribution area of the critical storage ratio, the critical storage ratio of different coal-rock reservoirs can be predicted.

[0068] Further, in an embodiment of the present application, predicting the critical storage ratio of the coal-rock reservoir based on the distribution area of the critical storage ratio may further include the following steps: obtaining the distribution range of the critical storage ratio corresponding to the distribution area of the critical storage ratio; determining the critical storage ratio corresponding to the coal-rock reservoir based on the distribution range of the critical storage ratio. For example, continuing to refer to Figure 7 , it is determined that the distribution area of the critical storage ratio of the coal-rock reservoir is the three types of areas. The distribution range of the critical storage ratio corresponding to the three types of areas is 0.3 - 0.6. Therefore, the critical storage ratio corresponding to the coal-rock reservoir can be analyzed according to the determined distribution range of the critical storage ratio.

[0069] The following introduces the device embodiments of the present application, which can be used to execute the method for determining the critical storage ratio of the coal-rock reservoir in the above embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the embodiments of the method for determining the critical storage ratio of the coal-rock reservoir above in the present application.

[0070] Figure 8 It is a block diagram of a device for determining the critical storage ratio of a coal-rock reservoir shown according to an embodiment of the present application.

[0071] Referring to Figure 8 As shown, according to an embodiment of the present application, a device 800 for determining the critical storage ratio of a coal-rock reservoir, the device 800 includes: an acquisition unit 801, configured to acquire the measured critical storage ratio and pressure curve of the coal-rock reservoir by analyzing the drilling data and logging data of the coal-rock reservoir; a construction unit 802, configured to establish a critical storage ratio prediction curve according to the measured critical storage ratio and the pressure curve; an optimization unit 803, configured to optimize the critical storage ratio prediction curve by combining a multi-attribute regression model, a probabilistic neural network model, and the critical storage ratio prediction curve; a prediction unit 804, configured to predict the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the critical storage ratio prediction curve.

[0072] On the other hand, the present application also provides a computer-readable storage medium, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementation manners, various aspects of the present application may also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present application described in the "Exemplary Method" section above of the present specification.

[0073] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0074] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0075] The program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0076] As another aspect, the present application also provides an electronic device capable of implementing the above method.

[0077] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, method, or program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuits", "modules", or "systems" here.

[0078] Figure 9 For a schematic diagram of the system structure of the electronic device shown in the embodiments of the present application, refer to the following Figure 9 to describe the electronic device 900 according to this embodiment of the present application. Figure 9 The electronic device 900 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0079] As shown in Figure 9As shown, the electronic device 900 is presented in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one of the above-mentioned processing units 910, at least one of the above-mentioned storage units 920, and a bus 930 that connects different system components (including the storage unit 920 and the processing unit 910).

[0080] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 910, so that the processing unit 910 executes the steps according to various exemplary embodiments of the present application described in the "Embodiment Method" section of the present specification above.

[0081] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 921 and / or a cache storage unit 922, and may further include a read-only storage unit (ROM) 923.

[0082] The storage unit 920 may also include a program / utilities 924 having a set (at least one) of program modules 925. Such program modules 925 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0083] The bus 930 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0084] The electronic device 900 may also communicate with one or more external devices 1200 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through an input / output (I / O) interface 950. And, the electronic device 900 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 960. As shown in the figure, the network adapter 960 communicates with other modules of the electronic device 900 through the bus 930. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0085] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0086] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0087] It should be understood that the present application is not limited to the exact structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for determining the temporary storage ratio of a coal-rock reservoir, characterized in that, The method includes: Obtaining the measured critical storage ratio and pressure curve of the coal-rock reservoir, where the measured critical storage ratio and the pressure curve are obtained from the drilling data and logging data of the coal-rock reservoir; Establishing a critical storage ratio prediction curve according to the measured critical storage ratio and the pressure curve; Combining a multi-attribute regression model, a probabilistic neural network model, and the critical storage ratio prediction curve to optimize the critical storage ratio prediction curve; Predicting the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the critical storage ratio prediction curve.

2. The method according to claim 1, characterized in that, Optimizing the critical storage ratio prediction curve through the multi-attribute regression model, including: For the low-frequency segment of the critical storage ratio prediction curve, obtaining the critical storage ratio corresponding to the low-frequency segment; Calculating the mean square error between the critical storage ratio corresponding to the low-frequency segment and the measured critical storage ratio corresponding to the low-frequency segment; If the mean square error is greater than a preset threshold, optimizing the low-frequency segment of the critical storage ratio prediction curve according to the multi-attribute regression model and the measured critical storage ratio corresponding to the low-frequency segment.

3. The method according to claim 2, characterized in that, After optimizing the low-frequency segment of the critical storage ratio prediction curve, the method further includes: Obtaining the correlation coefficient between the low-frequency segment of the critical storage ratio prediction curve and the measured critical storage ratio; If the correlation coefficient is greater than a first preset correlation coefficient, determining the low-frequency segment of the critical storage ratio prediction curve as the target prediction curve, and the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir.

4. The method according to claim 1, characterized in that, Optimizing the critical storage ratio prediction curve through the probabilistic neural network model, including: For the high-frequency segment of the critical storage ratio prediction curve, determining the fitting effect parameter and the number of training data according to the probabilistic neural network model and the critical storage ratio prediction curve; Optimizing the high-frequency segment of the critical storage ratio prediction curve based on the fitting effect parameter and the number of training data.

5. The method according to claim 4, characterized in that, After optimizing the high-frequency segment of the critical storage ratio prediction curve, the method further includes: Obtaining the correlation coefficient between the high-frequency segment of the critical storage ratio prediction curve and the measured critical storage ratio; If the correlation coefficient is greater than a second preset correlation coefficient, determining the high-frequency segment of the critical storage ratio prediction curve as the target prediction curve, and the target prediction curve is used to predict the critical storage ratio of the coal-rock reservoir.

6. The method according to claim 1, characterized in that, The predicting the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the critical storage ratio prediction curve includes: Determining the critical storage ratio distribution area according to the seismic data volume and the critical storage ratio prediction curve; Predicting the critical storage ratio of the coal-rock reservoir based on the critical storage ratio distribution area.

7. The method according to claim 6, characterized in that, The predicting the critical storage ratio of the coal-rock reservoir based on the critical storage ratio distribution area includes: Obtaining the critical storage ratio distribution range corresponding to the critical storage ratio distribution area; Determining the critical storage ratio corresponding to the coal-rock reservoir based on the critical storage ratio distribution range.

8. A device for determining the temporary storage ratio of a coal-rock reservoir, characterized in that, The device includes: An acquisition unit, configured to obtain the measured critical storage ratio and pressure curve of the coal-rock reservoir by analyzing the drilling data and logging data of the coal-rock reservoir; A construction unit, configured to establish a critical storage ratio prediction curve according to the measured critical storage ratio and the pressure curve; An optimization unit for optimizing the predicted critical storage ratio curve by combining a multivariate attribute regression model, a probabilistic neural network model, and the predicted critical storage ratio curve; A prediction unit for predicting the critical storage ratio of the coal-rock reservoir based on the seismic data volume and the predicted critical storage ratio curve.

9. A computer-readable storage medium, characterized in that, At least one program code is stored in the computer-readable storage medium, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories. At least one program code is stored in the one or more memories, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 7.

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