Coal seam distribution high-precision prediction method, system, equipment and medium
By constructing a three-dimensional amplitude data volume and combining it with well point information, and using the maximum amplitude and waveform attributes for pattern discrimination and color palette display, the problem of large errors in traditional coal seam thickness prediction is solved, and high-precision coal seam distribution prediction is achieved.
Patent Information
- Application Number
- CN202410318660.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-20
- Publication Date
- 2025-09-23
AI Technical Summary
Traditional methods have large errors in coal seam thickness prediction, making it difficult to achieve high-precision coal seam distribution prediction.
By constructing a three-dimensional amplitude data volume, using the maximum amplitude attribute and waveform attribute for pattern discrimination and full dynamic palette display, combined with the well point reservoir thickness information, high-precision prediction of thin-intercalated reservoirs can be achieved.
It achieves precise positioning of thin interbedded reservoirs, clearly reflects the vertical and horizontal change trend characteristics, and improves the accuracy of coal seam distribution prediction.
Smart Images
Figure CN120686316A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of oilfield development, and in particular relates to a method, system, equipment and medium for high-precision prediction of coal seam distribution. Background Art
[0002] Coalbed methane (CBM) is an unconventional natural gas stored in coal reservoirs in an adsorbed state, primarily composed of methane. Coal reservoirs serve as both the source rock and reservoir for CBM. Traditional methods for analyzing coalbed thickness rely on interpolation of borehole data or the approximate relationship between amplitude parameters and coalbed thickness based on thin-bed theory. However, due to the limited number of boreholes and the large borehole spacing (over hundreds of meters), these approximate interpolated coalbed thicknesses are subject to significant errors. However, variations in seismic wavefield attribute parameters can be used to directly or indirectly reveal or describe changes in formation lithology and physical properties.
[0003] Three-dimensional seismic is increasingly used in coalbed methane exploration and development. It can not only clearly identify the structural fault characteristics of the study area, but also use three-dimensional seismic attribute data to predict the vertical and horizontal distribution patterns of coal seams with high precision.
[0004] Therefore, it is necessary to provide a new high-precision prediction method for coal seam distribution to solve the above technical problems. Summary of the Invention
[0005] The purpose of the present invention is to provide a method, system, equipment and medium for high-precision prediction of coal seam distribution in order to solve the above problems.
[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:
[0007] A high-precision prediction method for coal seam distribution includes the following steps:
[0008] Obtain original data of the target area;
[0009] constructing a three-dimensional amplitude data volume based on the original data of the target area;
[0010] Based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, pattern discrimination and full dynamic palette display are performed to display the distribution of thin-interbedded reservoirs, so as to achieve high-precision prediction of coal seam distribution.
[0011] As a further optimization scheme of the present invention, the original data of the target area includes drilling data and seismic related data; the drilling data includes logging data and coal seam core analysis data; the seismic related data includes seismic stacking migration data and seismic interpretation layer data.
[0012] As a further optimization solution of the present invention, the specific process of constructing a three-dimensional amplitude data volume based on the original data of the target area is as follows:
[0013] Based on the amplitude values of the seismic stacking migration data, a three-dimensional amplitude data volume is established by applying a three-dimensional grid interpolation technology to the spatially discrete amplitude point values.
[0014] As a further optimization solution of the present invention, based on the amplitude values of the seismic stack migration data, the specific process of applying the three-dimensional grid interpolation technology to the spatially discrete amplitude point values to establish a three-dimensional amplitude data volume is as follows:
[0015] The following objective function is selected for n irregular spatial amplitude value data points (x1, y1, z1, u1), (x2, y2, z2, u2), ... (xi, yi, zi, ui) ... (xn, yn, zn, un) in the seismic stack migration data:
[0016]
[0017] Where x represents the x-coordinate of the amplitude point space; y represents the y-coordinate of the amplitude point space; z represents the z-coordinate of the amplitude point space; u represents the amplitude point value; A represents the objective function;
[0018] The nonlinear partial differential equation can be derived from the above formula:
[0019]
[0020] Where k represents the curvature in the gradient direction;
[0021] Through mathematical derivation and iterative calculation, the three-dimensional amplitude data volume is obtained, and the expression is as follows:
[0022] U=F(x,y,z).
[0023] As a further optimization solution of the present invention, the specific process of performing pattern discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data volume to display the distribution of thin-intercalated reservoirs is as follows:
[0024] The holographic color bars are matched according to the amplitude distribution of the 3D amplitude data body, and the color configuration of the amplitude classification interval is finely adjusted according to the reservoir thickness of the well point to reflect the thickness of the thin and finely intercalated reservoir. The amplitude dynamic range of the 3D amplitude data body is adjusted according to the reservoir thickness of the well point to determine the reservoir boundary on the section and achieve the goal of finely displaying the geological target.
[0025] On the full-frequency layer body, the layer slices are cut out, and according to the amplitude value interval and the color palette information, the color seismic section and the amplitude attribute plane distribution are obtained;
[0026] Regression analysis is performed on each color interval based on the coal seam thickness at the well point to calculate the reservoir thickness; the reservoir distribution on the comprehensive amplitude slice is studied to study the spatial distribution law of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
[0027] A high-precision prediction system for coal seam distribution, comprising:
[0028] Data acquisition module, used to obtain original data of the target area;
[0029] A data volume construction module, configured to construct a three-dimensional amplitude data volume based on the original data of the target area;
[0030] The coal seam distribution prediction module is used to perform pattern discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, and to display the distribution of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
[0031] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;
[0032] Memory for storing computer programs;
[0033] The processor is used to implement a high-precision prediction method for coal seam distribution when executing the program stored in the memory.
[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a high-precision prediction method for coal seam distribution.
[0035] The beneficial effects of the present invention are:
[0036] The present invention applies three-dimensional grid interpolation technology to spatially discrete amplitude point values to establish a three-dimensional amplitude data volume, and applies pattern recognition technology and fully dynamically adjustable color palette technology to assign holographic color palette information to the point values of the database. This allows the seismic data visual environment to be sliced along the layer at intervals of 1ms or selected time intervals, and obtains a plane distribution map of the seismic maximum amplitude attribute. Combined with the analysis of well data, the distribution pattern of thin-intercalated reservoirs is studied, solving the technical problems of large errors and low resolution in thin-intercalated reservoirs. This achieves the effect of reflecting both vertical and horizontal change trend characteristics, thereby enabling more accurate determination of thin-intercalated reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] Figure 1 is a flow chart of the method of the present invention;
[0038] Figure 2 Schematic diagram of coal seam seismic attribute analysis process in an embodiment of the present invention;
[0039] Figure 3 This is a schematic diagram of a three-dimensional slice display along the layer similarity coefficient in an embodiment of the present invention for quickly identifying the geological target of the fan body;
[0040] Figure 4 is a system structure block diagram in an embodiment of the present invention;
[0041] Figure 5 It is a block diagram of the device structure in an embodiment of the present invention. DETAILED DESCRIPTION
[0042] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.
[0043] like Figure 1 As shown, a high-precision prediction method for coal seam distribution includes the following steps:
[0044] Obtain original data of the target area;
[0045] constructing a three-dimensional amplitude data volume based on the original data of the target area;
[0046] Based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, pattern discrimination and full dynamic palette display are performed to display the distribution of thin-interbedded reservoirs, so as to achieve high-precision prediction of coal seam distribution.
[0047] The original data of the target area include drilling data and seismic related data; the drilling data include logging data and coal seam core test and analysis data; the seismic related data include seismic stacking migration data and seismic interpretation layer data.
[0048] The specific process of constructing a three-dimensional amplitude data volume based on the original data of the target area is as follows:
[0049] Based on the amplitude values of the seismic stacking migration data, a three-dimensional amplitude data volume is established by applying a three-dimensional grid interpolation technology to the spatially discrete amplitude point values.
[0050] Based on the amplitude values of the seismic stack migration data, the specific process of applying the three-dimensional grid interpolation technology to the spatially discrete amplitude point values to establish a three-dimensional amplitude data volume is as follows:
[0051] The following objective function is selected for n irregular spatial amplitude value data points (x1, y1, z1, u1), (x2, y2, z2, u2), ... (xi, yi, zi, ui) ... (xn, yn, zn, un) in the seismic stack migration data:
[0052]
[0053] Where x represents the x-coordinate of the amplitude point space; y represents the y-coordinate of the amplitude point space; z represents the z-coordinate of the amplitude point space; u represents the amplitude point value; A represents the objective function;
[0054] The nonlinear partial differential equation can be derived from the above formula:
[0055]
[0056] Where k represents the curvature in the gradient direction;
[0057] Through mathematical derivation and iterative calculation, the three-dimensional amplitude data volume is obtained, and the expression is as follows:
[0058] U=F(x,y,z).
[0059] The specific process of performing mode discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data volume to display the distribution of thin-intercalated reservoirs is as follows:
[0060] The holographic color bars are matched according to the amplitude distribution of the 3D amplitude data body, and the color configuration of the amplitude classification interval is finely adjusted according to the reservoir thickness of the well point to reflect the thickness of the thin and finely intercalated reservoir. The amplitude dynamic range of the 3D amplitude data body is adjusted according to the reservoir thickness of the well point to determine the reservoir boundary on the section and achieve the goal of finely displaying the geological target.
[0061] On the full-frequency layer body, the layer slices are cut out, and according to the amplitude value interval and the color palette information, the color seismic section and the amplitude attribute plane distribution are obtained;
[0062] Regression analysis is performed on each color interval based on the coal seam thickness at the well point to calculate the reservoir thickness; the reservoir distribution on the comprehensive amplitude slice is studied to study the spatial distribution law of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
[0063] In this embodiment, the specific contents include:
[0064] Working Principle: The method of predicting coal seam thickness using seismic attributes is simple and highly accurate. When the coal seam thickness is less than 1 / 4 of the dominant wavelength λ of the coal seam reflection (approximately 10m), the roof and floor reflections cannot be separated, forming a composite wave, which falls into the thin seam category (most coal seams in my country fall into this category). When the coal seam thickness is greater than 10m, the so-called "thick seam" category, the coal seam can theoretically generate roof and floor reflection waves, so the coal seam reflection waves need to be frequency-separated. In practice, the dominant seismic attributes in the reflection wave field of coal seams of different thicknesses also vary. Therefore, the coal seam distribution should be displayed using a dual-parameter holographic display of seismic amplitude and waveform according to the actual geological conditions of the coal seam.
[0065] Neural network AI technology automatically calculates and classifies waveforms based on amplitude values from large to small, generating a full-frequency domain along-bedding volume. Automatically classifying waveforms based on a 90% correlation, sorted by amplitude, not only incorporates rock physics information from the amplitude but also includes rock physics information such as the thickness and lithologic composition of the rock layers covered by the waveform, resulting in more accurate results.
[0066] For n known irregular spatial amplitude data points x1, y1, z1, u1, x2, y2, z2, u2, ... xi, yi, zi, ui ... xn, yn, zn, un in three-dimensional space, the following objective function is selected:
[0067]
[0068] x: x coordinate of the amplitude point space
[0069] y: y coordinate of the amplitude point space
[0070] z: z coordinate of the amplitude point space
[0071] u: Amplitude point value
[0072] A: Objective function
[0073] By derivation, the nonlinear partial differential equation is obtained from the above formula:
[0074]
[0075] x: x coordinate of the amplitude point space
[0076] y: y coordinate of the amplitude point space
[0077] z: z coordinate of the amplitude point space
[0078] u: Amplitude point value
[0079] k: curvature in the gradient direction
[0080] Through mathematical deduction and iterative calculation, U=F(x,y,z) is obtained. The trend analysis results of the minimum tension method are used to perform mode discrimination value intervals and assign palette information on the full-frequency layer body. Layer slices are cut out to obtain the plane distribution of amplitude attributes.
[0081] The spatially discrete amplitude point values are applied with three-dimensional grid interpolation technology to establish a three-dimensional amplitude data body. AI pattern recognition technology and fully dynamic adjustable color palette technology are applied to it, and the amplitude point values of the three-dimensional amplitude data body are assigned holographic color palette information to realize the visualization environment of seismic data.
[0082] Using neural network AI technology, the amplitude and waveform are subdivided into three sections: the amplitude distribution curve, the adjustable amplitude dynamic range, and the adjustable holographic color bar. Holographic color bars are matched to the amplitude distribution, and the color of the amplitude classification intervals is finely adjusted to reflect the reservoir thickness at the well point. The amplitude dynamic range is adjusted based on the thickness of the thin-intercalated reservoir at the well point to determine the reservoir boundary on the profile, achieving subtle geological display.
[0083] On the full-frequency layer body, the layer slices are cut out, and according to the amplitude value interval and the color palette information, the color seismic profile and the amplitude attribute plane distribution are obtained.
[0084] Regression analysis is performed on each color interval based on the coal seam thickness at the well point to calculate the reservoir thickness. The reservoir distribution on the comprehensive amplitude slice is used to study the spatial distribution pattern of thin-intercalated reservoirs.
[0085] The waveforms are sorted based on amplitude values and automatically calculated and classified using AI technology, realizing dual-parameter seismic attribute analysis, filling the gap in seismic attribute analysis that only uses a single attribute parameter for prediction.
[0086] Using fully dynamic adjustable color palette technology with adjustable holographic color and adjustable dynamic range, the amplitude point values of logging data and seismic data are given to the holographic color palette information, realizing the visualization environment of seismic data ( Figure 2 ).
[0087] Working method: Use the maximum amplitude attribute and waveform attribute to perform pattern discrimination and full dynamic color palette display to achieve the effect of clearly displaying the distribution of thin-intercalated reservoirs, and the operation is simple and fast. The implementation method is to apply the three-dimensional grid interpolation technology to the spatially discrete amplitude point values to establish a three-dimensional amplitude data body, and apply the pattern discrimination technology and the full dynamic palette technology to it to give the point values of the data body holographic color palette information, so that the seismic data visual environment can be sliced along the layer at intervals of 1ms or selected time intervals to obtain the plane distribution map of the seismic maximum amplitude attribute, and combine the analysis of well data to study the distribution law of thin-intercalated reservoirs ( Figure 3 ).
[0088] like Figure 4 As shown, an embodiment of the present disclosure provides a high-precision prediction system for coal seam distribution, comprising:
[0089] The data acquisition module 11 is used to obtain the original data of the target area;
[0090] A data volume construction module 12 is used to construct a three-dimensional amplitude data volume based on the original data of the target area;
[0091] The coal seam distribution prediction module 13 is used to perform pattern discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, and to display the distribution of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
[0092] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.
[0093] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without paying any creative work.
[0094] In the above embodiment, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is run.
[0095] See also Figure 5 The electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;
[0096] Memory 1130, for storing computer programs;
[0097] The processor 1110 is configured to implement the following high-precision coal seam distribution prediction method when executing the program stored in the memory 1130 .
[0098] The communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.
[0099] The communication interface 1120 is used for communication between the electronic device and other devices.
[0100] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory 1130 may be at least one storage device located away from the processor 1110.
[0101] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
[0102] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned high-precision coal seam distribution prediction method.
[0103] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments, or may exist independently without being incorporated into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the high-precision coal seam distribution prediction method according to the embodiments of the present disclosure.
[0104] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0105] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.
Claims
1. A high-precision prediction method for coal seam distribution, characterized in that: The following steps are involved: Obtain original data of the target area; constructing a three-dimensional amplitude data volume based on the original data of the target area; Based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, pattern discrimination and full dynamic palette display are performed to display the distribution of thin-interbedded reservoirs, so as to achieve high-precision prediction of coal seam distribution.
2. A high-precision prediction method for coal seam distribution according to claim 1, characterized in that: The original data of the target area include drilling data and seismic related data; the drilling data include logging data and coal seam core test and analysis data; the seismic related data include seismic stacking migration data and seismic interpretation layer data.
3. A high-precision prediction method for coal seam distribution according to claim 1, characterized in that: The specific process of constructing a three-dimensional amplitude data volume based on the original data of the target area is as follows: Based on the amplitude values of the seismic stacking migration data, a three-dimensional amplitude data volume is established by applying a three-dimensional grid interpolation technology to the spatially discrete amplitude point values.
4. A high-precision prediction method for coal seam distribution according to claim 3, characterized in that: Based on the amplitude values of the seismic stack migration data, the specific process of applying the three-dimensional grid interpolation technology to the spatially discrete amplitude point values to establish a three-dimensional amplitude data volume is as follows: The following objective function is selected for n irregular spatial amplitude value data points (x1, y1, z1, u1), (x2, y2, z2, u2), ... (xi, yi, zi, ui) ... (xn, yn, zn, un) in the seismic stack migration data: Where x represents the x-coordinate of the amplitude point space; y represents the y-coordinate of the amplitude point space; z represents the z-coordinate of the amplitude point space; u represents the amplitude point value; A represents the objective function; The nonlinear partial differential equation can be derived from the above formula: Where k represents the curvature in the gradient direction; Through mathematical derivation and iterative calculation, the three-dimensional amplitude data volume is obtained, and the expression is as follows: U=F(x,y,z).
5. A high-precision prediction method for coal seam distribution according to claim 1, characterized in that: The specific process of performing mode discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data volume to display the distribution of thin-intercalated reservoirs is as follows: The holographic color bars are matched according to the amplitude distribution of the 3D amplitude data body, and the color configuration of the amplitude classification interval is finely adjusted according to the reservoir thickness of the well point to reflect the thickness of the thin and finely intercalated reservoir. The amplitude dynamic range of the 3D amplitude data body is adjusted according to the reservoir thickness of the well point to determine the reservoir boundary on the section and achieve the goal of finely displaying the geological target. On the full-frequency layer body, the layer slices are cut out, and according to the amplitude value interval and the color palette information, the color seismic section and the amplitude attribute plane distribution are obtained; Regression analysis is performed on each color interval based on the coal seam thickness at the well point to calculate the reservoir thickness; the reservoir distribution on the comprehensive amplitude slice is studied to study the spatial distribution law of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
6. A high-precision prediction system for coal seam distribution, characterized in that: include: Data acquisition module, used to obtain original data of the target area; A data volume construction module, configured to construct a three-dimensional amplitude data volume based on the original data of the target area; The coal seam distribution prediction module is used to perform pattern discrimination and full dynamic palette display based on the maximum amplitude attribute and waveform attribute of the three-dimensional amplitude data body, and to display the distribution of thin-intercalated reservoirs to achieve high-precision prediction of coal seam distribution.
7. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. Memory for storing computer programs; The processor is used to implement the high-precision coal seam distribution prediction method described in any one of claims 1 to 5 when executing the program stored in the memory.
8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for high-precision prediction of coal seam distribution according to any one of claims 1 to 5 is implemented.