Coal rock type discrimination method, device, processor and machine readable storage medium
By screening and normalizing logging curves, a discrimination curve is established, which solves the problem of insufficient accuracy in coal and rock type identification in existing technologies and achieves higher identification accuracy and applicability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-12-30
- Publication Date
- 2026-06-30
AI Technical Summary
In existing technologies, coal and rock type identification methods require too much data, and the logging curve range is affected by regional geological conditions, making it difficult to apply to all coal reservoir wells, resulting in insufficient accuracy in identifying macroscopic coal and rock types in coalbed methane reservoirs.
By determining the coal and rock types and corresponding logging curve values of core samples at multiple detection depths, an intersection map is established, type-discriminating logging curves are selected, normalized, and a discrimination curve is constructed to reduce the influence of blocks and burial depth and improve identification accuracy.
It improves the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs, increases the applicability of the method, and can promptly serve the needs of coal-bearing strata evaluation.
Smart Images
Figure CN122307769A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geological evaluation technology for coalbed methane reservoirs, specifically to a method for identifying coal and rock types, a device for identifying coal and rock types, a processor, and a machine-readable storage medium. Background Technology
[0002] Currently, the mainstream macroscopic coal and rock types can generally be divided into four types: bright coal, semi-bright coal, semi-dull coal, and dull coal. Coal reservoirs of different macroscopic coal and rock types vary to varying degrees in terms of coal quality, physical properties, porosity, permeability, gas content, and gas production after commissioning. Therefore, coal and rock type identification is an important foundational task in the exploration and development of coalbed methane. This identification process significantly impacts the qualitative and quantitative evaluation of gas content in coalbed methane reservoirs, as well as the construction techniques and engineering parameter configurations during the mining process.
[0003] Geophysical logging technology is widely used in coal reservoir property evaluation due to its high efficiency and economy. Current methods for evaluating macroscopic coal and rock types include: using multiple sets of logging data, combined with corresponding industrial analysis and microscopic component analysis, and establishing quantitative logging interpretation methods for macroscopic coal and rock types based on the naked-eye identification of fractured core samples; or using logging curves to divide intervals for evaluating macroscopic coal and rock types. However, these methods require excessive data, and the logging curve intervals are affected by regional geological conditions, making it difficult to apply to all coal reservoir wells. Applying these methods to other regions results in significant errors, affecting the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs. Summary of the Invention
[0004] To address the technical problems of existing technologies, such as excessive data requirements, the influence of regional geological conditions on well logging curve intervals, difficulty in applying to all coal reservoir wells, and significant errors when applied to other regions, thus affecting the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs, this invention provides a coal and rock type identification method, a coal and rock type identification device, a processor, and a machine-readable storage medium. This method reduces the impact of block and burial depth on individual well logging curves, increases the applicability of the method, and improves the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs, thus providing timely service and meeting the needs of coal-bearing strata evaluation.
[0005] To achieve the above objectives, the first aspect of the present invention provides a method for coal and rock type discrimination, comprising: determining the coal and rock type of core samples at multiple detection depths and various logging curve values corresponding to the core samples at each detection depth; establishing a cross-plot based on the coal and rock type of the core samples at multiple detection depths and the various logging curve values corresponding to the core samples at each detection depth; and using the cross-plot to screen out type discrimination logging curves from the logging curves corresponding to the various logging curve values, and determining the selection criteria. The discrimination error rate of each type of discrimination logging curve is calculated; the discrimination logging curves of each type are normalized to obtain the corresponding type-normalized discrimination logging curves; the discrimination curves of core samples are constructed using the discrimination error rates of each type of discrimination logging curves and the corresponding type-normalized discrimination logging curves; the discrimination interval of the discrimination curves of core samples is determined using the coal and rock types of the core samples; the discrimination curve of the core to be tested is determined, and the coal and rock types of the core to be tested are determined based on the discrimination curve and the discrimination interval of the core to be tested.
[0006] Furthermore, the coal rock types include: bright coal, semi-bright coal to semi-dull coal.
[0007] Furthermore, the logging curves can distinguish differences in coal and rock types.
[0008] Furthermore, the step of using the intersection plot to select type-discrimination logging curves from logging curves corresponding to multiple logging curve values includes: if a logging curve in the intersection plot can divide a discrimination interval for at least one coal and rock type, and within the discrimination interval for the coal and rock type divided by the logging curve, the number of core samples of that coal and rock type is greater than a set number, then the logging curve is determined as a type-discrimination logging curve.
[0009] Further, determining the discrimination error rate of each type of discriminant logging curve includes: determining a first number of core samples outside the discrimination interval of the coal and petrology corresponding to each type of discriminant logging curve; determining a second number of core samples within the overlapping intervals of the discrimination intervals of the coal and petrology corresponding to each type of discriminant logging curve; and determining the discrimination error rate of each type of discriminant logging curve based on the first number, the second number, and the total number of core samples. ; Among them, E i S represents the error rate of the type-discrimination logging curve. i1 S represents the first number of core samples outside the discrimination interval for each type of discriminant logging curve corresponding to the coal and petrology type. i2 S represents the second number of core samples within the overlapping intervals of the discrimination intervals corresponding to the coal and petrology types of each type of discrimination logging curve. 岩芯 denoted as the total amount of core samples, and i represents the type of logging curve for type discrimination.
[0010] Furthermore, the normalization process for each type of discrimination logging curve to obtain the corresponding type-discriminative normalized logging curve includes: ; in, To determine the type of normalized logging curves, i For type-discrimination logging curves, The minimum value of the layer segment for type-based logging curves. The maximum value of the layer segment for type-based logging curves.
[0011] Furthermore, the step of constructing a discrimination curve for a core sample using the discrimination error rate of each type of discrimination logging curve and the corresponding type-normalized logging curve includes: determining the priority coefficient of the type of discrimination logging curve based on the discrimination error rate of the type of discrimination logging curve; determining the response proportion of the type of discrimination logging curve based on the priority coefficient of the type of discrimination logging curve; and constructing a discrimination curve for the core sample based on the response proportion of each type of discrimination logging curve and the corresponding type-normalized logging curve.
[0012] Furthermore, the determination of the priority coefficient of the type-discrimination logging curve based on the discrimination error rate of the type-discrimination logging curve includes: ; Among them, Y i Priority coefficients for type-based logging curves.
[0013] Furthermore, the determination of the response proportion of the type-discrimination logging curve based on the priority coefficient of the type-discrimination logging curve includes:
[0014] Among them, P i The response percentage of the logging curve is used to determine the type.
[0015] A second aspect of the present invention provides a coal and rock type discrimination device, comprising: a determination module for determining the coal and rock type of core samples at multiple detection depths and various logging curve values corresponding to the core samples at each detection depth; and a pattern and curve determination module for establishing an intersection pattern of the coal and rock type of core samples at multiple detection depths and the various logging curve values corresponding to the core samples at each detection depth, and using the intersection pattern to select type discrimination logging curves from the logging curves corresponding to the various logging curve values, and determining the discrimination error rate of each selected type discrimination logging curve. The normalization module is used to normalize the discrimination logging curves of each type to obtain the corresponding type-normalized discrimination logging curves. The discrimination curve determination module is used to construct the discrimination curve of the core sample using the discrimination error rate of each type of discrimination logging curve and the corresponding type-normalized discrimination logging curve. The curve discrimination interval determination module is used to determine the curve discrimination interval of the core sample's discrimination curve using the coal and rock type of the core sample. The coal and rock type determination module is used to determine the discrimination curve of the core to be tested and determine the coal and rock type of the core to be tested based on the discrimination curve and the curve discrimination interval.
[0016] A third aspect of the present invention provides a processor configured to execute the coal and rock type discrimination method described above.
[0017] A fourth aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the coal and rock type discrimination method described above.
[0018] The present invention has at least the following technical effects through the technical solution provided by the present invention: The coal and petrology type discrimination method of this invention first determines the coal and petrology types of core samples at multiple detection depths and the corresponding well logging curve values for each detection depth. Based on the coal and petrology types of core samples at multiple detection depths and the corresponding well logging curve values, a cross-plot is established. Using the cross-plot, type discrimination well logging curves are selected from the well logging curves corresponding to the multiple well logging curve values, and the discrimination error rate of each selected type discrimination well logging curve is determined. Next, each type discrimination well logging curve is normalized to obtain the corresponding type discrimination normalized well logging curve. The discrimination curve of the core sample is constructed using the discrimination error rate of each type discrimination well logging curve and the corresponding type discrimination normalized well logging curve. The discrimination interval of the discrimination curve of the core sample is determined using the coal and petrology type of the core sample. The discrimination curve of the core to be tested is determined, and the coal and petrology type of the core to be tested is determined based on the discrimination curve and the discrimination interval of the core to be tested. The coal and rock type identification method provided by the present invention can reduce the influence of block and burial depth on a single logging curve, increase the applicability of the method, improve the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs, and can promptly serve and meet the needs of coal-bearing strata evaluation.
[0019] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description
[0020] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart of the coal and rock type discrimination method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the intersection map established in the coal and rock type discrimination method provided in the embodiments of the present invention; Figure 3 This is a diagram illustrating the macroscopic coal and rock type classification method provided in this embodiment of the invention. Figure 4 This is a schematic diagram of a coal and rock type discrimination device provided in an embodiment of the present invention. Detailed Implementation
[0021] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.
[0022] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.
[0023] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used to describe the relative positions of components in relation to the directions shown in the accompanying drawings or in relation to the vertical, perpendicular, or gravitational directions.
[0024] As described in the background section, existing semiconductor structures have poor performance. This will be explained in detail below with reference to the accompanying drawings.
[0025] Please refer to Figure 1 The first aspect of this invention provides a method for coal and rock type discrimination, comprising: S101: determining the coal and rock type of core samples at multiple detection depths and various logging curve values corresponding to the core samples at each detection depth; S102: establishing a cross-plot based on the coal and rock type of core samples at multiple detection depths and the various logging curve values corresponding to the core samples at each detection depth, and using the cross-plot to select type discrimination logging curves from the logging curves corresponding to the various logging curve values, and determining each selected type discrimination... S103: Normalize the logging curves of each type to obtain the corresponding normalized logging curves; S104: Construct the discrimination curves of the core sample using the discrimination error rates of each type of logging curves and the corresponding normalized logging curves; S105: Determine the discrimination interval of the discrimination curves of the core sample using the coal and rock type of the core sample; S106: Determine the discrimination curve of the core to be tested, and determine the coal and rock type of the core to be tested based on the discrimination curve and the discrimination interval of the core to be tested.
[0026] Specifically, the first step is to perform step S101: determine the coal and rock types of core samples at multiple detection depths and the various logging curve values corresponding to core samples at each detection depth.
[0027] Furthermore, the logging curves can distinguish differences in coal and rock types.
[0028] Furthermore, the coal rock types include: bright coal, semi-bright coal to semi-dull coal.
[0029] Specifically, in this embodiment of the invention, well logging is performed on the reservoir to obtain various initial logging curves. Since dull coal is often identified as interbedded gangue in coal rock logging and accounts for a small proportion in coal-gas development, its identification is not significant and therefore not considered. In this embodiment, coal rock types include: bright coal, semi-bright coal, and semi-dull coal. Because there are many types of initial logging curves, it is necessary to select geologically significant logging curves from these multiple initial logging curves. The logging curves should be able to distinguish the differences in coal rock types. Logging curves can be selected based on the differences in coal rock physical properties caused by the differences in coal rock types and their theoretical feedback type in the logging curves.
[0030] In one possible implementation, bright coal, semi-bright coal, and semi-dull coal differ in the content of vitreous and bright coal. The composition gradually becomes more complex from bright to semi-dull, with more impurities, such as clay. The clay content can be reflected by natural gamma ray logging, neutron logging, photoelectric absorption cross section index, and resistivity logging. The density gradually increases from bright to semi-dull, and its properties can be reflected by density logging and sonic transit time logging. The coal body structure gradually deteriorates from bright to semi-dull, and its properties can be reflected by the caliper curve. Therefore, geologically significant and relevant logging curves preferably include: GR (natural gamma ray logging), DEN (density logging), RLLD (resistivity logging), DT (sonic transit time logging), PE (photoelectric absorption cross section index), CNL (neutron logging), and CAL (caliper curve).
[0031] After selecting multiple logging curves, data from the same multiple detection depths were selected for each logging curve, and core sampling was performed at multiple detection depths of the reservoir to obtain multiple corresponding core samples, and the coal and rock types of the core samples were identified.
[0032] Next, step S102 is executed: based on the coal and rock types of core samples at multiple detection depths and the various logging curve values corresponding to core samples at each detection depth, a cross-plot is established, and the cross-plot is used to screen out type-discrimination logging curves from the logging curves corresponding to the various logging curve values, and the discrimination error rate of each type-discrimination logging curve is determined.
[0033] Furthermore, the step of using the intersection plot to select type-discrimination logging curves from logging curves corresponding to multiple logging curve values includes: if a logging curve in the intersection plot can divide a discrimination interval for at least one coal and rock type, and within the discrimination interval for the coal and rock type divided by the logging curve, the number of core samples of that coal and rock type is greater than a set number, then the logging curve is determined as a type-discrimination logging curve.
[0034] Further, determining the discrimination error rate of each type of discriminant logging curve includes: determining a first number of core samples outside the discrimination interval of the coal and petrology corresponding to each type of discriminant logging curve; determining a second number of core samples within the overlapping intervals of the discrimination intervals of the coal and petrology corresponding to each type of discriminant logging curve; and determining the discrimination error rate of each type of discriminant logging curve based on the first number, the second number, and the total number of core samples. ; Among them, E i S represents the error rate of the type-discrimination logging curve. i1 S represents the first number of core samples outside the discrimination interval for each type of discriminant logging curve corresponding to the coal and petrology type. i2 S represents the second number of core samples within the overlapping intervals of the discrimination intervals corresponding to the coal and petrology types of each type of discrimination logging curve. 岩芯 denoted as the total amount of core samples, and i represents the type of logging curve for type discrimination.
[0035] Specifically, in this embodiment of the invention, any two logging curves are selected from a variety of selected logging curves and used as the horizontal and vertical axes, respectively. A rectangular coordinate system is established by intersecting the two curves pairwise. Then, according to the coal and rock type of the core sample, the core sample is plotted in the rectangular coordinate system to create an intersection map. Following this method, corresponding intersection maps are created for all types of logging curves.
[0036] If a logging curve in the cross plot can distinguish at least one coal and rock type, meaning that a greater than a predetermined number of core samples of that coal and rock type (e.g., more than 90% of the core samples of that coal and rock type) are distributed within the distinguishing interval, then the logging curve is designated as a type-discriminating logging curve. If a logging curve in the cross plot cannot clearly distinguish the coal and rock type of the core samples, then the logging curve is not designated as a type-discriminating logging curve.
[0037] In one possible implementation, a system was established as follows: Figure 2 The intersection map shown is based on Figure 2 As can be seen from the four images, along the vertical axis (DG curve), bright coal is mostly distributed in the 0-50 API range, semi-bright coal is mostly distributed in the 50-100 API range, and semi-dull coal is mostly distributed in the range above 60 API. In the top left image, along the horizontal axis (RLLD curve), bright coal is mostly distributed in the range above 5000 Ω·m, semi-bright coal is mostly distributed in the range of 1000-10000 Ω·m, and semi-dull coal is mostly distributed in the range below 1000 Ω·m. In the top right image, along the horizontal axis (DEN curve), bright coal is mostly distributed in the range of 1.5 g / cm³. 3In the following intervals, most of the semi-bright coals are distributed in the range of 1.45 - 1.6 g / cm 3 In the interval of, most of the semi-dull coals are distributed in the range of 1.6 g / cm 3 and above. In the following two figures, the core samples of the three coal petrology types are cross-distributed in the horizontal axis direction. Therefore, it can be determined that the GR curve, the RLLD curve, and the DEN curve can divide the discrimination intervals of coal petrology types. The DT curve and the PE curve cannot divide the discrimination intervals of coal petrology types. At the same time, the discrimination intervals of the coal petrology types shown in Table 1 are obtained.
[0038] Table 1
[0039] Next, determine the discrimination error rate of the type discrimination logging curves: Determine all the discrimination intervals of the coal petrology types divided by each type discrimination logging curve, count the first quantity of the core samples outside all the discrimination intervals, and the second quantity of the core samples in the overlapping intervals among all the discrimination intervals. Add the first quantity and the second quantity, and then divide by the total quantity of the core samples to obtain the discrimination error rate of the type discrimination logging curves.
[0040] In a possible implementation manner, count the discrimination intervals of the three coal petrology types of the GR curve, that is, the discrimination intervals of bright coal: GR ≤ 50 API, semi-bright coal: 50 API < GR < 100 API, and semi-dull coal: GR ≥ 60 API. Calculate the quantity of the core samples distributed outside all the discrimination intervals , and the quantity of the core samples distributed in the overlapping intervals between the above discrimination intervals , add the two quantities, and divide by the total quantity of the core samples to obtain the discrimination error rate of the GR curve . The discrimination error rate of the DEN curve and the discrimination error rate of the RLLD curve can be obtained respectively by the above method.
[0041] Next, execute step S103: Normalize each type discrimination logging curve respectively to obtain the corresponding type discrimination normalized logging curve.
[0042] Further, the normalizing each type discrimination logging curve respectively to obtain the corresponding type discrimination normalized logging curve includes: ; where, is the type discrimination normalized logging curve, <00 The maximum value of the layer segment for type-based logging curves.
[0043] Specifically, in this embodiment of the invention, the type-discrimination logging curves are normalized to obtain corresponding type-discrimination normalized logging curves. In one possible embodiment, the selected type-discrimination logging curves include: GR curves, DEN curves, and RLLD curves, and the corresponding type-discrimination normalized logging curves are as follows: ; ; ; in, The natural gamma-ray normalized logging curve is dimensionless. Natural gamma ray logging curves, in API; GR max The maximum value of the natural gamma ray logging curve for each layer, in API; GR min This represents the minimum segment value of the natural gamma ray logging curve, expressed in API. This is a density-normalized logging curve, dimensionless; This is a density logging curve, with units of g / cm³. 3 ; This represents the maximum value of the density logging segment, expressed in g / cm³. 3 ; This represents the minimum density value of the layer segment in the density logging curve, expressed in g / cm³. 3 . The resistivity-normalized logging curve is dimensionless. Resistivity curve logging, unit is Ω·m; This represents the maximum value of the resistivity logging curve for a given segment, expressed in Ω·m. This represents the minimum value of the resistivity logging section, expressed in Ω·m.
[0044] Next, proceed to step S104: use the discrimination error rate of each type of discrimination logging curve and the discrimination curve of the corresponding type of discrimination normalized logging curve.
[0045] Furthermore, the step of constructing a discrimination curve for a core sample using the discrimination error rate of each type of discrimination logging curve and the corresponding type-normalized logging curve includes: determining the priority coefficient of the type of discrimination logging curve based on the discrimination error rate of the type of discrimination logging curve; determining the response proportion of the type of discrimination logging curve based on the priority coefficient of the type of discrimination logging curve; and constructing a discrimination curve for the core sample based on the response proportion of each type of discrimination logging curve and the corresponding type-normalized logging curve.
[0046] Furthermore, the determination of the priority coefficient of the type-discrimination logging curve based on the discrimination error rate of the type-discrimination logging curve includes: ; Among them, Y i Priority coefficients for type-based logging curves.
[0047] Furthermore, the determination of the response proportion of the type-discrimination logging curve based on the priority coefficient of the type-discrimination logging curve includes:
[0048] Among them, P i The response percentage of the logging curve is used to determine the type.
[0049] Specifically, in this embodiment of the invention, the priority coefficient of the type discrimination logging curve is first determined by the discrimination error rate of the type discrimination logging curve, the response proportion of the type discrimination logging curve is determined based on the priority coefficient of the type discrimination logging curve, and then the calculation formula of the discrimination curve is constructed based on the response proportion of each type discrimination logging curve and the corresponding type discrimination normalized logging curve.
[0050] In one possible implementation, the priority coefficient is obtained in the following manner: ; ; ; in, The priority coefficient for the natural gamma logging curve; The priority coefficient for density logging curves; This represents the priority coefficient for resistivity logging curves.
[0051] The response proportion of the type-discriminating logging curve is determined based on the priority coefficient: ; ; ; in, The percentage of the response in the natural gamma logging curve. The percentage of the response in the density logging curve. This represents the percentage of the response in the resistivity logging curve.
[0052] The numerator and denominator are divided based on the positive and negative correlation between well logging curves and macroscopic coal and rock types, from dark to bright. A macroscopic coal and rock type discrimination curve is then constructed by combining the normalized well logging curves. In this embodiment, a higher resistivity logging curve (RLLD) and lower natural gamma logging curve (GR) and density logging curve (DEN) are desirable. Therefore, the RLLD correlation term is placed in the denominator, and the GR and DEN correlation terms are placed in the numerator, resulting in the discrimination curve: ; MCRI is the discrimination curve.
[0053] This invention integrates multiple logging curves into the same scale through normalization and weighting, effectively avoiding the influence of insufficient experimental data on industrial and microscopic components and different regions of coalbed methane reservoirs at different burial depths. This reduces the data requirement and also reduces the impact of blocks and burial depths on individual logging curves, increasing the applicability of the method and improving the accuracy of macroscopic coal and rock type identification in coalbed methane reservoirs. It can promptly serve and meet the needs of coal-bearing strata evaluation.
[0054] Next, proceed to step S105: determine the discrimination interval of the discrimination curve of the core sample using the coal and rock type of the core sample.
[0055] Specifically, in this embodiment of the invention, the coal and petrology types of multiple core samples are identified in the discrimination curve of the core samples, and the discrimination interval of the discrimination curve is determined according to the distribution range of the coal and petrology types of the core samples. In this embodiment, the determined discrimination interval is as follows: bright coal: MCRI < 50; semi-bright coal: 50 < MCRI < 105; semi-dull coal: 105. <MCRI<250。
[0056] Finally, step S106 is executed: the discrimination curve of the core sample to be tested is determined, and the coal and rock type of the core sample to be tested is determined based on the discrimination curve and the discrimination interval of the curve.
[0057] Specifically, in this embodiment of the invention, when determining the coal and rock type of the core sample to be tested in the target stratum, the discrimination curve of the core sample to be tested is first determined using the same method as above for determining the discrimination curve of the core sample. The determined curve discrimination interval is marked in the discrimination curve of the core sample to be tested, and the core sample to be tested is marked in the discrimination curve of the core sample to be tested. The coal and rock type of the core sample to be tested is determined based on the curve discrimination interval.
[0058] Please refer to Figure 3 The method of this application was used to perform macroscopic coal and rock type identification on coal and rock samples from example wells. The specific identification results are as follows: Figure 3 As shown in the last three results, the coal and rock types were determined by comparing the core samples from some core sampling points in this example well. The results corresponded well and the identification results met expectations.
[0059] Please refer to Figure 4 The second aspect of the present invention provides a coal and rock type discrimination device, comprising: a determination module for determining the coal and rock type of core samples at multiple detection depths and various logging curve values corresponding to the core samples at each detection depth; and a pattern and curve determination module for establishing an intersection pattern of the coal and rock type of core samples at multiple detection depths and the various logging curve values corresponding to the core samples at each detection depth, and using the intersection pattern to filter out type discrimination logging curves from the logging curves corresponding to the various logging curve values, and determining the discrimination error rate of each selected type discrimination logging curve. The system includes: a normalization module for normalizing the discrimination logging curves of each type to obtain the corresponding type-normalized discrimination logging curves; a discrimination curve determination module for constructing the discrimination curve of the core sample using the discrimination error rate of each type of discrimination logging curve and the corresponding type-normalized discrimination logging curve; a curve discrimination interval determination module for determining the curve discrimination interval of the core sample using the coal and rock type of the core sample; and a coal and rock type determination module for determining the discrimination curve of the core to be tested and determining the coal and rock type of the core to be tested based on the discrimination curve and the curve discrimination interval.
[0060] A third aspect of the present invention provides a processor configured to execute the coal and rock type discrimination method described above.
[0061] A fourth aspect of the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the coal and rock type discrimination method described above.
[0062] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.
[0063] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.
[0064] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.
Claims
1. A method for identifying coal and rock types, characterized in that, The coal and rock type identification method includes: Determine the coal and rock types of core samples at multiple detection depths and the corresponding logging curve values for each core sample at each detection depth; A cross-plot is established based on the coal and rock types of core samples from multiple detection depths and the various logging curve values corresponding to the core samples at each detection depth. The cross-plot is then used to select type-discrimination logging curves from the logging curves corresponding to the various logging curve values, and the discrimination error rate of each type-discrimination logging curve is determined. Normalize the logging curves for each type of discrimination to obtain the corresponding type-normalized logging curves. Discriminant curves for core samples are constructed using the discrimination error rates of various types of discrimination logging curves and the corresponding type-normalized logging curves. The discrimination interval of the discrimination curve of the core sample is determined by the coal and petrology of the core sample; Determine the discrimination curve of the core sample to be tested, and determine the coal and rock type of the core sample based on the discrimination curve and the discrimination interval of the curve.
2. The method for identifying coal and rock types according to claim 1, characterized in that, Coal rock types include: bright coal, semi-bright coal to semi-dull coal.
3. The method for identifying coal and rock types according to claim 1, characterized in that, The logging curves can distinguish the differences in coal and rock types.
4. The method for identifying coal and rock types according to claim 1, characterized in that, The step of using the intersection map to filter out type-discriminating logging curves from logging curves corresponding to multiple logging curve values includes: If a logging curve in the intersection map can divide at least one coal and rock type into a discrimination interval, and the number of core samples of that coal and rock type is greater than a set number within the discrimination interval divided by the logging curve, then the logging curve is determined as a type discrimination logging curve.
5. The method for identifying coal and rock types according to claim 4, characterized in that, The determination of the discrimination error rate of each type of discriminant logging curve selected includes: Determine the first number of core samples outside the discrimination interval of each type of discriminant logging curve corresponding to the coal and rock type; Determine the second number of core samples within the overlapping intervals of the discrimination intervals corresponding to the coal and rock types of each type of discrimination logging curve; The discrimination error rate of each type of discrimination logging curve is determined based on the first quantity, the second quantity, and the total amount of core samples: ； wherein E i is the error rate of the type-discriminating logging curve, S i1 is the first number of core samples outside the discrimination interval of the coal rock type corresponding to each type-discriminating logging curve, S i2 is the second number of core samples within the mutually overlapping interval of the discrimination interval of the coal rock type corresponding to each type-discriminating logging curve, S 岩芯 is the total number of core samples, and i is the type of the type-discriminating logging curve.
6. The method for identifying coal and rock types according to claim 5, characterized in that, The process of normalizing the logging curves for each type of discrimination to obtain the corresponding type-normalized logging curves includes: ； in, To determine the type of normalized logging curves, i For type-discrimination logging curves, The minimum value of the layer segment for type-based logging curves. The maximum value of the layer segment for type-based logging curves.
7. The method for identifying coal and rock types according to claim 6, characterized in that, The method of constructing discrimination curves for core samples using the discrimination error rates of various types of discrimination logging curves and the corresponding type-normalized logging curves includes: The priority coefficient of a type-discriminating logging curve is determined based on the discrimination error rate of the type-discriminating logging curve. The response proportion of a type of discrimination logging curve is determined based on the priority coefficient of the type discrimination logging curve; Discriminant curves for core samples are constructed based on the response proportions of each type of discriminant logging curve and the corresponding type-normalized logging curves.
8. The method for determining coal and rock types according to claim 7, characterized in that, The determination of the priority coefficient of the type-discrimination logging curve based on the discrimination error rate of the type-discrimination logging curve includes: ； Among them, Y i Priority coefficients for type-based logging curves.
9. The method for identifying coal and rock types according to claim 8, characterized in that, The determination of the response proportion of a type-discriminatory logging curve based on the priority coefficient of the type-discriminatory logging curve includes: Among them, P i The response percentage of the logging curve is used to determine the type.
10. A coal and rock type discrimination device, characterized in that, The coal and rock type discrimination device includes: The determination module is used to determine the coal and rock types of core samples at multiple detection depths and the various logging curve values corresponding to core samples at each detection depth. The map and curve determination module is used to establish a cross-plot based on the coal and rock types of core samples at multiple detection depths and various logging curve values corresponding to core samples at each detection depth. The cross-plot is used to filter out type-discrimination logging curves from logging curves corresponding to various logging curve values and to determine the discrimination error rate of each type-discrimination logging curve. The normalization module is used to normalize the logging curves of each type separately to obtain the corresponding type-normalized logging curves. The discrimination curve determination module is used to construct the discrimination curve of the core sample by using the discrimination error rate of each type of discrimination logging curve and the corresponding type discrimination normalized logging curve; The curve discrimination interval determination module is used to determine the curve discrimination interval of the core sample using the coal and rock type of the core sample; The coal and rock type determination module is used to determine the discrimination curve of the rock core to be tested, and to determine the coal and rock type of the rock core to be tested based on the discrimination curve and the discrimination interval of the curve.
11. A processor, characterized in that, It is configured to perform the coal and rock type discrimination method according to any one of claims 1 to 9.
12. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the coal and rock type discrimination method as described in any one of claims 1 to 9.