Macroscopic coal rock type prediction method, device, equipment and medium

Abstract

The invention relates to the field of coal bed gas exploitation and geological exploration, and discloses a macroscopic coal and rock type prediction method, device, equipment and medium, which can perform rock physical analysis on a first coal bed in a first stratum of a research area to determine a plurality of sub-coal beds in the first coal bed and macroscopic coal and rock type sensitive parameter groups of the plurality of sub-coal beds. And for any sub-coal seam, sampling in the sub-coal seam to obtain a plurality of rock samples, respectively detecting parameter values of the macroscopic coal rock type sensitive parameter group of each rock sample, and counting a sensitive parameter value distribution range of each sub-coal seam. And based on the sensitive parameter value distribution range of each sub-coal seam, predicting macroscopic coal rock type distribution of a second coal seam in a second stratum of the research area. According to the method, the macroscopic coal rock type of the coal seam can be predicted based on the macroscopic coal rock type sensitive parameter group of the coal seam, the macroscopic coal rock type distribution of the coal seam is obtained, the macroscopic coal rock type prediction of the coal seam is realized, and the prediction modes of the macroscopic coal rock type of the coal seam are diversified.

Description

Technical Field

[0001] The present invention relates to the field of coalbed methane mining and geological exploration, and in particular to a macroscopic coal and rock type prediction method, device, equipment and medium. Background Art

[0002] At present, oil and gas resources have entered a stage of development with equal emphasis on conventional and unconventional approaches, while the distribution of coal resources and coalbed methane exploration and development have broad prospects and are of great significance to enhancing energy security.

[0003] Coal lithotype, as a macroscopic manifestation of its primary components, influences coal seam quality. Macrolithology refers to the classification of coal lithotypes based on the overall relative gloss intensity of its macroscopic components within a coal seam. It is a type of coal lithofacies composition and, to a certain extent, reflects the composition of its macroscopic components. Macrolithology is a key parameter for evaluating coalbed methane exploration and development outcomes.

[0004] However, there are few methods for predicting the macroscopic coal rock types of coal seams in related technologies. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for predicting macro coal rock types, which are used to solve the defect of the related art that there are few methods for predicting macro coal rock types of coal seams, and diversify the methods for predicting macro coal rock types of coal seams.

[0006] In a first aspect, the present invention provides a macroscopic coal and rock type prediction method, comprising: Performing a petrophysical analysis on a first coal seam in a first stratum in a study area to determine a plurality of sub-coal seams in the first coal seam and a macro-coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein each of the sub-coal seams corresponds to a different macro-coal rock type; For any of the sub-coal seams, sampling is performed in the sub-coal seam to obtain a plurality of rock samples, and parameter values ​​of a macro coal rock type sensitive parameter group of each of the rock samples are detected respectively; According to the parameter value of the macro coal rock type sensitive parameter group of each rock sample in each sub-coal seam, statistically analyzing the distribution range of the sensitive parameter value of each sub-coal seam; Based on the distribution range of the sensitive parameter values ​​of each sub-coal seam, the macroscopic coal rock type distribution of the second coal seam in the second stratum of the study area is predicted.

[0007] Optionally, when the macro-coal rock type sensitive parameter group includes longitudinal wave impedance and gamma, the parameter value of the macro-coal rock type sensitive parameter group of the rock sample includes the longitudinal wave impedance value and gamma value of the rock sample, and the sensitive parameter value distribution range of the lithology layer includes the gamma value distribution range and longitudinal wave impedance distribution range of the lithology layer.

[0008] Optionally, the method further comprises: drawing a gamma and P-wave impedance crossplot; wherein the gamma and P-wave impedance crossplot is a planar rectangular coordinate plot, and the horizontal axis and the vertical axis are respectively a gamma axis and a P-wave impedance axis; for any of the sub-seams, drawing a corresponding data point in the gamma and P-wave impedance crossplot according to the P-wave impedance value and the gamma value of each of the rock samples in the sub-seam, and the horizontal coordinate and the vertical coordinate of the data point are respectively the gamma value and the P-wave impedance value of the rock sample; clustering each of the data points in the gamma and P-wave impedance crossplot to obtain a data point group corresponding to each of the sub-seams; for any of the sub-seams, determining a maximum horizontal coordinate, a minimum horizontal coordinate, a maximum vertical coordinate and a minimum vertical coordinate in the horizontal coordinate and the vertical coordinate of each of the data points in the data point group corresponding to the sub-seam, and determining an interval from the minimum horizontal coordinate to the maximum horizontal coordinate as a gamma value distribution range of the sub-seam, and determining an interval from the minimum vertical coordinate to the maximum vertical coordinate as a P-wave impedance distribution range of the sub-seam.

[0009] Optionally, the plurality of sub-seams include bright coal, semi-bright coal, semi-dull coal and dull coal. The method further comprises: performing seismic inversion on the second seam to determine a P-wave impedance data volume and a gamma data volume of the second seam; wherein the P-wave impedance data volume of the second seam comprises a corresponding relationship between P-wave impedance values and formation depths, and the gamma data volume comprises a corresponding relationship between gamma values and formation depths; determining distribution areas of bright coal, semi-bright coal, semi-dull coal and dull coal in the second seam based on the gamma value distribution ranges and the P-wave impedance distribution ranges of bright coal, semi-bright coal, semi-dull coal and dull coal, and based on the P-wave impedance data volume and the gamma data volume of the second seam; determining the distribution areas of bright coal, semi-bright coal, semi-dull coal and dull coal in the second seam as a macroscopic coal lithotype distribution of the second seam.

[0010] Optionally, before the rock physical analysis on the first seam in the first formation of the study area, the method further comprises: identifying the first seam in the first formation of the study area.

[0011] Optionally, the identifying the first coal seam in the first stratum of the study area comprises: obtaining a P-wave impedance and frequency crossplot of the study area, and determining a coal seam P-wave impedance distribution range of the study area based on the P-wave impedance and frequency crossplot of the study area; performing seismic P-wave impedance inversion on the first stratum to obtain a P-wave impedance data volume of the first stratum, the P-wave impedance data volume of the first stratum comprising a corresponding relationship between stratum depth and P-wave impedance; determining a corresponding target depth distribution in the P-wave impedance data volume of the first stratum according to the coal seam P-wave impedance distribution range of the study area; determining a stratum corresponding to the target depth distribution in the first stratum as a coal seam in the first stratum.

[0012] In a second aspect, the present application provides a macroscopic coal rock type prediction device, comprising: a determination unit configured to perform rock physics analysis on a first coal seam in a first stratum of a study area to determine a plurality of sub-coal seams in the first coal seam and a macroscopic coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein each of the sub-coal seams corresponds to a different macroscopic coal rock type; a sampling unit configured to, for any of the sub-coal seams, perform sampling in the sub-coal seam to obtain a plurality of rock samples; a detection unit configured to respectively detect parameter values of the macroscopic coal rock type sensitive parameter group of each of the rock samples; a statistical unit configured to, according to the parameter values of the macroscopic coal rock type sensitive parameter group of each of the rock samples in each of the sub-coal seams, statistically determine a sensitive parameter value distribution range of each of the sub-coal seams; a prediction unit configured to, based on the sensitive parameter value distribution range of each of the sub-coal seams, predict a macroscopic coal rock type distribution of a second coal seam in a second stratum of the study area.

[0013] Optionally, when the macroscopic coal rock type sensitive parameter group comprises P-wave impedance and gamma, the parameter values of the macroscopic coal rock type sensitive parameter group of the rock samples comprise P-wave impedance values and gamma values of the rock samples, and the sensitive parameter value distribution range of the lithologic layer comprises a gamma value distribution range and a P-wave impedance distribution range of the lithologic layer.

[0014] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the macroscopic coal rock type prediction method of the first aspect or any of the corresponding embodiments thereof.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for causing a computer to execute the macro coal rock type prediction method of the first aspect or any of the corresponding embodiments thereof.

[0016] The macro coal rock type prediction method, device, equipment and medium provided by the present application can perform rock physical analysis on the first coal seam in the first stratum of the study area to determine the macro coal rock type sensitive parameter set of the plurality of sub-coal seams and the plurality of sub-coal seams in the first coal seam; wherein the macro coal rock type corresponding to each sub-coal seam is different. For any sub-coal seam, sampling is performed in the sub-coal seam to obtain a plurality of rock samples, and the parameter values of the macro coal rock type sensitive parameter set of each rock sample are detected respectively. According to the parameter values of the macro coal rock type sensitive parameter set of each rock sample in each sub-coal seam, the sensitive parameter value distribution range of each sub-coal seam is counted. Based on the sensitive parameter value distribution range of each sub-coal seam, the macro coal rock type distribution of the second coal seam in the second stratum of the study area is predicted. The present application can predict the macro coal rock type of the coal seam based on the macro coal rock type sensitive parameter set of the coal seam, obtain the macro coal rock type distribution of the coal seam, realize the prediction of the macro coal rock type of the coal seam, and guarantee the prediction accuracy of the macro coal rock type of the coal seam, and diversify the prediction mode of the macro coal rock type of the coal seam. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present application or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or the related art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0018] Figure 1 A flowchart of a macro coal rock type prediction method provided by an embodiment of the present application; Figure 2 A longitudinal wave impedance and frequency crossplot provided by an embodiment of the present application; Figure 3 A coal seam prediction result schematic diagram based on longitudinal wave impedance inversion provided by an embodiment of the present application; Figure 4 A gamma and longitudinal wave impedance crossplot of different macro coal rock types provided by an embodiment of the present application; Figure 5 A macro coal rock type identification standard schematic diagram provided by an embodiment of the present application; Figure 6 A macro coal rock type prediction result schematic diagram provided by an embodiment of the present application; Figure 7A structural schematic diagram of a macroscopic coal rock type prediction device provided by an embodiment of the present application is shown. Figure 8 A structural schematic diagram of a computer device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0020] The macroscopic coal rock type prediction method of the present application will be described below in conjunction with Figures 1-6

[0021] As shown in the figure, the first macroscopic coal rock type prediction method proposed by the present embodiment can include the following steps: Figure 1 S101, performing rock physical analysis on a first coal seam in a first stratum in a study area to determine a plurality of sub-coal seams in the first coal seam and a macroscopic coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein each sub-coal seam corresponds to a different macroscopic coal rock type.

[0022] The study area can be a coal seam exploration and mining area.The first stratum is a stratum in the study area that includes a coal seam.

[0023] Specifically, the first coal seam is a coal seam in the first stratum. The sub-coal seams in the first coal seam are sub-layers corresponding to one macroscopic coal rock type in the first coal seam. Optionally, the plurality of sub-coal seams include bright coal, semi-bright coal, semi-dark coal and dark coal. Bright coal, semi-bright coal, semi-dark coal and dark coal are sub-coal seams corresponding to different macroscopic coal rock types.

[0024] The macroscopic coal rock type sensitive parameter group includes a plurality of sensitive parameters that have a relatively strong correlation with the macroscopic coal rock types of the plurality of sub-coal seams.

[0025] Optionally, the macroscopic coal rock type sensitive parameter group can include P-wave impedance and gamma. Of course, the macroscopic coal rock type sensitive parameter group can also include other sensitive parameters, which can be determined by a technician according to the actual situation of the actual exploration and development area.

[0026] Optionally, in other macroscopic coal rock type prediction methods proposed by the present embodiment, before the above step S101, the following step can also be included: Identifying the first coal seam in the first stratum in the study area.

[0027] ​Specifically, this embodiment can first identify the first coal seam in the first stratum in the study area.

[0028] Optionally, the identifying of the first coal seam in the first stratum in the study area includes: Obtain the longitudinal wave impedance and frequency cross-plot of the study area, and determine the distribution range of the longitudinal wave impedance of the coal seam in the study area based on the longitudinal wave impedance and frequency cross-plot of the study area; Performing seismic P-wave impedance inversion on the first stratum to obtain a P-wave impedance data volume of the first stratum, wherein the P-wave impedance data volume of the first stratum includes a corresponding relationship between stratum depth and P-wave impedance; According to the distribution range of the longitudinal wave impedance of the coal seam in the study area, the corresponding target depth distribution is determined in the longitudinal wave impedance data volume of the first stratum; The stratum corresponding to the target depth distribution in the first stratum is determined to be the coal seam in the first stratum.

[0029] Among them, such as Figure 2 As shown in FIG, the P-wave impedance vs. frequency crossplot may include a curve showing the corresponding relationship between the P-wave impedance value and the frequency in the study area. The P-wave impedance vs. frequency crossplot may also indicate the P-wave impedance distribution range corresponding to different lithologic layers, i.e., the P-wave impedance distribution range corresponding to coal seams, mudstone layers, sandstone layers, and limestone layers.

[0030] like Figure 3 As shown, this embodiment can perform longitudinal wave impedance inversion on the first formation to obtain the longitudinal wave impedance data body of the first formation, and identify the first coal seam in the first formation based on the longitudinal wave impedance data body of the first formation and the longitudinal wave impedance distribution range corresponding to different lithologic layers.

[0031] Specifically, this embodiment can determine the sub-coal seams corresponding to different macro coal rock types in the first coal seam and determine the macro coal rock type sensitive parameter group by performing petrophysical analysis on the first coal seam.

[0032] S102. For any sub-coal seam, sampling is performed in the sub-coal seam to obtain a plurality of rock samples.

[0033] Specifically, in this embodiment, rock sampling can be performed in each sub-coal seam respectively, and multiple rock samples can be sampled from each sub-coal seam.

[0034] S103. Detect the parameter values ​​of the macro coal rock type sensitive parameter group of each rock sample respectively.

[0035] Specifically, this embodiment can detect the parameter values ​​of the macro-coal rock type sensitive parameter group of any rock sample obtained by sampling. For example, when the macro-coal rock type sensitive parameter group includes longitudinal wave impedance and gamma, this embodiment can detect the longitudinal wave impedance value and gamma value of the rock sample.

[0036] S104, according to the parameter values of the macroscopic coal lithotype sensitive parameter group of each rock sample in each sub-seam, the sensitive parameter value distribution range of each sub-seam is counted.

[0037] Specifically, the embodiment can count the sensitive parameter value distribution range of each sub-seam according to the parameter values of the macroscopic coal lithotype sensitive parameter group of all rock samples obtained by sampling.

[0038] Optionally, when the macroscopic coal lithotype sensitive parameter group includes the longitudinal wave impedance and the gamma, the parameter values of the macroscopic coal lithotype sensitive parameter group of the rock sample include the longitudinal wave impedance value and the gamma value of the rock sample, and the sensitive parameter value distribution range of the lithologic layer includes the gamma value distribution range and the longitudinal wave impedance distribution range of the lithologic layer.

[0039] Optionally, the above step S104 can include: drawing a gamma and longitudinal wave impedance cross plot; wherein the gamma and longitudinal wave impedance cross plot is a plane rectangular coordinate graph, and the horizontal axis and the vertical axis are the gamma axis and the longitudinal wave impedance axis respectively; For any sub-seam, according to the longitudinal wave impedance value and the gamma value of each rock sample in the sub-seam, the corresponding data points are drawn in the gamma and longitudinal wave impedance cross plot, and the horizontal coordinate and the vertical coordinate of the data points are the gamma value and the longitudinal wave impedance value of the rock sample respectively; Each data point in the gamma and longitudinal wave impedance cross plot is clustered to obtain the data point grouping corresponding to each sub-seam; For any sub-seam, the maximum horizontal coordinate, the minimum horizontal coordinate, the maximum vertical coordinate and the minimum vertical coordinate are determined in the horizontal coordinate and the vertical coordinate of each data point of the data point grouping corresponding to the sub-seam, and the interval from the minimum horizontal coordinate to the maximum horizontal coordinate is determined as the gamma value distribution range of the sub-seam, and the interval from the minimum vertical coordinate to the maximum vertical coordinate is determined as the longitudinal wave impedance distribution range of the sub-seam.

[0040] As shown in Figure 4 , the embodiment can draw the corresponding data points in the gamma and longitudinal wave impedance cross plot respectively according to the longitudinal wave impedance value and the gamma value of each rock sample.

[0041] Optionally, as shown in Figure 5 , the gamma value distribution ranges of the above plurality of sub-seams can be continuous, and the longitudinal wave impedance distribution ranges of the above plurality of sub-seams can also be continuous.

[0042] S105, based on the sensitive parameter value distribution range of each sub-seam, the macroscopic coal lithotype distribution of the second coal seam in the second stratum of the study area is predicted.

[0043] The second stratum is a stratum containing a coal seam in the research area, and the coal seam needs to be predicted in macro coal lithotype.

[0044] The macro coal lithotype distribution of the second coal seam can include distribution areas corresponding to different macro coal lithotypes.

[0045] Specifically, the embodiment can predict the macro coal lithotype of the second coal seam according to the sensitive parameter value distribution range of all sub-coal seams, to obtain the macro coal lithotype distribution of the second coal seam.

[0046] Optionally, the embodiment can refer to the execution process of identifying the first coal seam in the first stratum to identify the second coal seam in the second stratum.

[0047] Optionally, the plurality of sub-coal seams include bright coal, semi-bright coal, semi-dull coal, and dull coal. At this time, step S105 can include: performing seismic inversion on the second coal seam to determine a P-wave impedance data body and a gamma data body of the second coal seam; wherein the P-wave impedance data body of the second coal seam includes a corresponding relationship between P-wave impedance values and stratum depths, and the gamma data body includes a corresponding relationship between gamma values and stratum depths; determining distribution areas of bright coal, semi-bright coal, semi-dull coal, and dull coal in the second coal seam based on the gamma value distribution range and the P-wave impedance distribution range of bright coal, semi-bright coal, semi-dull coal, and dull coal, and based on the P-wave impedance data body and the gamma data body of the second coal seam; determining the distribution areas of bright coal, semi-bright coal, semi-dull coal, and dull coal in the second coal seam as the macro coal lithotype distribution of the second coal seam.

[0048] Specifically, the embodiment can obtain the P-wave impedance data body and the gamma data body of the second coal seam by performing seismic inversion on the second coal seam. According to the gamma value distribution range and the P-wave impedance distribution range of bright coal, semi-bright coal, semi-dull coal, and dull coal, the matching stratum depth distribution is found in the P-wave impedance data body and the gamma data body of the second coal seam, and then the distribution areas of bright coal, semi-bright coal, semi-dull coal, and dull coal are determined. Finally, the distribution areas of bright coal, semi-bright coal, semi-dull coal, and dull coal in the second coal seam are determined as the macro coal lithotype distribution of the second coal seam.

[0049] As shown in Figure 6 the embodiment can determine the distribution areas of bright coal, semi-bright coal, semi-dull coal, and dull coal in the second coal seam, i.e., the macro coal lithotype distribution of the second coal seam.

[0050] The macro coal rock type prediction method provided in the embodiment can perform petrophysical analysis on the first coal seam in the first stratum of the study area to determine a plurality of sub-coal seams in the first coal seam and a macro coal rock type sensitive parameter set of the plurality of sub-coal seams, wherein the macro coal rock type corresponding to each sub-coal seam is different. For any sub-coal seam, sampling is performed in the sub-coal seam to obtain a plurality of rock samples, and the parameter values of the macro coal rock type sensitive parameter set of each rock sample are detected respectively. According to the parameter values of the macro coal rock type sensitive parameter set of each rock sample in each sub-coal seam, the sensitive parameter value distribution range of each sub-coal seam is counted. Based on the sensitive parameter value distribution range of each sub-coal seam, the macro coal rock type distribution of the second coal seam in the second stratum of the study area is predicted. The embodiment can predict the macro coal rock type of the coal seam based on the macro coal rock type sensitive parameter set of the coal seam, obtain the macro coal rock type distribution of the coal seam, realize the prediction of the macro coal rock type of the coal seam, guarantee the prediction accuracy of the macro coal rock type of the coal seam, and diversify the prediction mode of the macro coal rock type of the coal seam.

[0051] In the related art, coal can be generally divided into bright coal, semi-bright coal, semi-dull coal and dull coal. Among them, bright coal and semi-bright coal have high vitrinite content, low ash content, good gas content, and are positively correlated with productivity, which determines the coalbed methane exploration and development effect and is an important parameter. The research on the macro coal rock type in the related art is mainly through core observation to qualitatively judge bright coal, semi-bright coal, semi-dull coal and dull coal, but the cost of coring is high, the number is limited, and the coal rock type cannot be predicted for wells without coring, lacking seismic prediction technology.

[0052] Based on Figure 1 , the second macro coal rock type prediction method is provided in the embodiment, which can include the following steps: The lithology types of the target stratum are analyzed, petrophysical crossplot is carried out, sensitive parameters capable of distinguishing coal seams are found, and corresponding seismic prediction technology is used to predict the coal seam.

[0053] On the basis of coal seam delineation, the classification of macro coal rock types, i.e., bright coal, semi-bright coal, semi-dull coal and dull coal, is focused on, and on this basis, petrophysical analysis is carried out to find sensitive parameters capable of distinguishing macro coal rock types and predict sensitive parameter data volume capable of reflecting coal rock types.

[0054] According to the different sensitive parameter ranges of different coal rock types, the prediction and characterization of coal rock types are completed.

[0055] Specifically, the embodiment can analyze the lithology combination of the target layer in the study area, including coal seams, mudstone, sandstone and limestone. The velocities and densities of different lithologies are different. The P-wave impedance (velocity x density) can be used to distinguish the lithology. The P-wave impedance of the coal seam is the lowest, followed by the mudstone and sandstone, and the P-wave impedance of the limestone is the highest. Therefore, based on the seismic P-wave impedance inversion, the coal seam can be predicted. Different coal rock types are focused in the coal seam to carry out prediction and rock physical analysis. It is found that the gamma and P-wave impedance double parameters can effectively distinguish different coal rock types. On the basis of determining that the gamma and P-wave impedance parameters can effectively distinguish different coal rock types, the gamma data volume and the P-wave impedance data volume are obtained through the seismic inversion method. Based on the rock physical analysis result, according to the different sensitive parameters of different coal rock types, the fine characterization of the macro coal rock type is completed The macro coal rock type prediction method proposed in the embodiment can carry out seismic prediction of the coal seam based on the rock physical analysis result. The sensitive parameters of bright coal, semi-bright coal, semi-dark coal and dark coal are focused in the coal seam. On the basis of determining the sensitive parameters, the prediction is realized according to the different threshold values of the coal rock types.

[0056] As shown in Figure 7 , the embodiment proposes a macro coal rock type prediction device, which comprises: A determination unit 701 is configured to perform rock physical analysis on a first coal seam in a first stratum in a study area to determine a plurality of sub-coal seams in the first coal seam and a macro coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein the macro coal rock types of each sub-coal seam are different. A sampling unit 702 is configured to sample a plurality of rock samples in the sub-coal seam for any sub-coal seam. A detection unit 703 is configured to detect the parameter values of the macro coal rock type sensitive parameter group of each rock sample respectively. A statistical unit 704 is configured to statistically determine the sensitive parameter value distribution range of each sub-coal seam according to the parameter values of the macro coal rock type sensitive parameter group of each rock sample in each sub-coal seam. A prediction unit 705 is configured to predict the macro coal rock type distribution of a second coal seam in a second stratum in the study area based on the sensitive parameter value distribution range of each sub-coal seam.

[0057] It should be noted that the processing procedures of the determination unit 701, the sampling unit 702, the detection unit 703, the statistical unit 704 and the prediction unit 705 and the beneficial effects brought by them can be respectively referred to steps S101 to S105 in Figure 1 , which will not be described herein.

[0058] Optionally, when the macro coal lithotype sensitive parameter group includes the P-wave impedance and the gamma, the parameter values of the macro coal lithotype sensitive parameter group of the rock sample include the P-wave impedance value and the gamma value of the rock sample, and the sensitive parameter value distribution range of the lithologic layer includes the gamma value distribution range and the P-wave impedance distribution range of the lithologic layer.

[0059] Optionally, the statistical unit 704 is further configured to: draw a gamma-P-wave impedance crossplot; wherein the gamma-P-wave impedance crossplot is a planar rectangular coordinate graph, and the horizontal axis and the vertical axis are a gamma axis and a P-wave impedance axis, respectively; for any sub-seam, draw a corresponding data point in the gamma-P-wave impedance crossplot according to the P-wave impedance value and the gamma value of each rock sample in the sub-seam, and the horizontal coordinate and the vertical coordinate of the data point are the gamma value and the P-wave impedance value of the rock sample, respectively; cluster each data point in the gamma-P-wave impedance crossplot to obtain a data point group corresponding to each sub-seam; for any sub-seam, determine a maximum horizontal coordinate, a minimum horizontal coordinate, a maximum vertical coordinate and a minimum vertical coordinate in the horizontal coordinate and the vertical coordinate of each data point in the data point group corresponding to the sub-seam, and determine an interval from the minimum horizontal coordinate to the maximum horizontal coordinate as a gamma value distribution range of the sub-seam and an interval from the minimum vertical coordinate to the maximum vertical coordinate as a P-wave impedance distribution range of the sub-seam.

[0060] Optionally, the plurality of sub-seams include bright coal, semi-bright coal, semi-dull coal and dull coal. The prediction unit 705 is further configured to: perform seismic inversion on the second seam to determine a P-wave impedance data volume and a gamma data volume of the second seam; wherein the P-wave impedance data volume includes a corresponding relationship between the P-wave impedance value and the formation depth, and the gamma data volume includes a corresponding relationship between the gamma value and the formation depth; determine distribution areas of the bright coal, the semi-bright coal, the semi-dull coal and the dull coal in the second seam based on the gamma value distribution range and the P-wave impedance distribution range of the bright coal, the semi-bright coal, the semi-dull coal and the dull coal, and based on the P-wave impedance data volume and the gamma data volume of the second seam; determine the distribution areas of the bright coal, the semi-bright coal, the semi-dull coal and the dull coal in the second seam as a macro coal lithotype distribution of the second seam.

[0061] Optionally, the apparatus further includes: an identification unit configured to identify a first seam in a first formation of a study area before performing rock physics analysis on the first seam in the first formation of the study area.

[0062] Optionally, the identification unit is further configured to: Before rock physical analysis is performed on a first coal seam in a first stratum of a study area, a longitudinal wave impedance and frequency crossplot of the study area is obtained, and a coal seam longitudinal wave impedance distribution range of the study area is determined based on the longitudinal wave impedance and frequency crossplot of the study area; Performing seismic longitudinal wave impedance inversion on the first stratum to obtain a longitudinal wave impedance data volume of the first stratum, the longitudinal wave impedance data volume of the first stratum including a corresponding relationship between stratum depth and longitudinal wave impedance; According to the coal seam longitudinal wave impedance distribution range of the study area, a corresponding target depth distribution is determined in the longitudinal wave impedance data volume of the first stratum; The stratum corresponding to the target depth distribution in the first stratum is determined as a coal seam in the first stratum.

[0063] The macroscopic coal rock type prediction device provided in the embodiment can perform rock physical analysis on a first coal seam in a first stratum of a study area to determine a plurality of sub-coal seams in the first coal seam and a macroscopic coal rock type sensitive parameter group of the plurality of sub-coal seams, wherein the macroscopic coal rock type of each sub-coal seam is different. For any sub-coal seam, a plurality of rock samples are obtained by sampling in the sub-coal seam, and the parameter values of the macroscopic coal rock type sensitive parameter group of each rock sample are detected respectively. According to the parameter values of the macroscopic coal rock type sensitive parameter group of each rock sample in each sub-coal seam, the sensitive parameter value distribution range of each sub-coal seam is counted. Based on the sensitive parameter value distribution range of each sub-coal seam, the macroscopic coal rock type distribution of a second coal seam in a second stratum of the study area is predicted. The embodiment can predict the macroscopic coal rock type of the coal seam based on the macroscopic coal rock type sensitive parameter group of the coal seam, obtain the macroscopic coal rock type distribution of the coal seam, realize the prediction of the macroscopic coal rock type of the coal seam, guarantee the prediction accuracy of the macroscopic coal rock type of the coal seam, and diversify the prediction mode of the macroscopic coal rock type of the coal seam.

[0064] The macroscopic coal rock type prediction device in the embodiment is presented in the form of a functional unit. The unit herein refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0065] The embodiment of the present application also provides a computer device with the above Figure 7 macroscopic coal rock type prediction device shown in the figure.

[0066] Please refer to Figure 8The computer device provided by the optional embodiment of the present application comprises one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are communicatively connected with each other by different buses, and can be installed on a common mainboard or in other manners as required. The processor can process instructions executed in the computer device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device, such as a display device coupled to the interface. In some optional embodiments, multiple processors and / or multiple buses can be used together with multiple memories if required. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 The processor 10 is taken as an example in the present application.

[0067] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further comprise a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic gate array, a generic array logic, or any combination thereof.

[0068] The memory 20 stores instructions executable by the at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0069] The memory 20 can comprise a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function. The data storage area can store data created according to the use of the computer device, and the like. In addition, the memory 20 can comprise a high-speed random access memory, and can further comprise a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some optional embodiments, the memory 20 can optionally comprise a memory remotely arranged with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0070] The memory 20 can comprise a volatile memory, such as a random access memory. The memory can also comprise a non-volatile memory, such as a flash memory, a hard disk, or a solid-state disk. The memory 20 can further comprise a combination of the above-mentioned kinds of memories.

[0071] The computer device further comprises a communication interface 30 for communication between the computer device and other devices or communication networks.

[0072] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded through a network and stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. Wherein, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor or hardware, the method shown in the above embodiments is implemented.

[0073] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A macro coal rock type prediction method, characterized in that: include: Performing a petrophysical analysis on a first coal seam in a first stratum in a study area to determine a plurality of sub-coal seams in the first coal seam and a macro-coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein each of the sub-coal seams corresponds to a different macro-coal rock type; For any of the sub-coal seams, sampling is performed in the sub-coal seam to obtain a plurality of rock samples, and parameter values ​​of a macro coal rock type sensitive parameter group of each of the rock samples are detected respectively; According to the parameter value of the macro coal rock type sensitive parameter group of each rock sample in each sub-coal seam, statistically analyzing the distribution range of the sensitive parameter value of each sub-coal seam; Based on the distribution range of the sensitive parameter values ​​of each sub-coal seam, the macroscopic coal rock type distribution of the second coal seam in the second stratum of the study area is predicted.

2. The method according to claim 1, characterized in that When the macro-coal rock type sensitive parameter group includes longitudinal wave impedance and gamma, the parameter value of the macro-coal rock type sensitive parameter group of the rock sample includes the longitudinal wave impedance value and gamma value of the rock sample, and the sensitive parameter value distribution range of the lithology layer includes the gamma value distribution range and longitudinal wave impedance distribution range of the lithology layer.

3. The method according to claim 2, characterized in that The statistical distribution range of the sensitive parameter values ​​of each sub-coal seam is calculated based on the parameter values ​​of the macro coal rock type sensitive parameter group of each rock sample in each sub-coal seam, including: Draw a gamma and longitudinal wave impedance cross plot; wherein the gamma and longitudinal wave impedance cross plot is a plane rectangular coordinate plot, and the horizontal axis and the vertical axis are the gamma axis and the longitudinal wave impedance axis respectively; For any of the sub-coal seams, corresponding data points are plotted in the gamma and longitudinal wave impedance cross-plot according to the longitudinal wave impedance value and gamma value of each of the rock samples in the sub-coal seam, wherein the abscissa and ordinate of the data points are the gamma value and longitudinal wave impedance value of the rock sample, respectively; Clustering each of the data points in the gamma-ray and longitudinal wave impedance cross-plot to obtain a data point group corresponding to each sub-coal seam; For any of the sub-coal seams, the maximum horizontal coordinate, minimum horizontal coordinate, maximum vertical coordinate and minimum vertical coordinate are determined in the horizontal coordinate and vertical coordinate of each of the data points in the data point group corresponding to the sub-coal seam, and the interval from the minimum horizontal coordinate to the maximum horizontal coordinate is determined as the gamma value distribution range of the sub-coal seam, and the interval from the minimum vertical coordinate to the maximum vertical coordinate is determined as the longitudinal wave impedance distribution range of the sub-coal seam.

4. The method according to claim 2, characterized in that The plurality of sub-coal seams include bright coal, semi-bright coal, semi-dark coal and dark coal; The predicting of the macroscopic coal rock type distribution of the second coal seam in the second stratum of the study area based on the distribution range of the sensitive parameter value of each sub-coal seam includes: Performing seismic inversion on the second coal seam to determine a P-wave impedance data volume and a gamma data volume of the second coal seam; wherein the P-wave impedance data volume of the second coal seam includes a correspondence between P-wave impedance values ​​and formation depths, and the gamma data volume includes a correspondence between gamma values ​​and formation depths; determining distribution areas of bright coal, semi-bright coal, semi-dark coal, and dark coal in the second coal seam based on the gamma value distribution ranges and longitudinal wave impedance distribution ranges of the bright coal, semi-bright coal, semi-dark coal, and dark coal, respectively, and based on the longitudinal wave impedance data volume and gamma data volume of the second coal seam; The distribution areas of bright coal, semi-bright coal, semi-dark coal and dark coal in the second coal seam are determined as the macro coal rock type distribution of the second coal seam.

5. The method according to any one of claims 1 to 4, characterized in that Before performing petrophysical analysis on the first coal seam in the first formation in the study area, the method further includes: The first coal seam is identified in a first formation in the study area.

6. The method according to claim 5, characterized in that The step of identifying the first coal seam in the first stratum in the study area includes: Obtaining a crossplot of longitudinal wave impedance and frequency in the study area, and determining a distribution range of longitudinal wave impedance of the coal seam in the study area based on the crossplot of longitudinal wave impedance and frequency in the study area; Performing seismic P-wave impedance inversion on the first stratum to obtain a P-wave impedance data volume of the first stratum, wherein the P-wave impedance data volume of the first stratum includes a correspondence between stratum depth and P-wave impedance; Determining a corresponding target depth distribution in the P-wave impedance data volume of the first formation according to a distribution range of P-wave impedance of the coal seam in the study area; The stratum corresponding to the target depth distribution in the first stratum is determined to be a coal seam in the first stratum.

7. A macro coal rock type prediction device, characterized in that: include: a determination unit, configured to perform a petrophysical analysis on a first coal seam in a first stratum in a study area to determine a plurality of sub-coal seams in the first coal seam and a macro-coal rock type sensitive parameter group of the plurality of sub-coal seams; wherein each of the sub-coal seams corresponds to a different macro-coal rock type; a sampling unit, configured to sample any of the sub-coal seams in the sub-coal seam to obtain a plurality of rock samples; A detection unit, used to respectively detect the parameter values ​​of the macro coal rock type sensitive parameter group of each rock sample; a statistical unit, configured to calculate a distribution range of sensitive parameter values ​​of each sub-coal seam according to the parameter value of the macro coal rock type sensitive parameter group of each rock sample in each sub-coal seam; The prediction unit is used to predict the macro coal rock type distribution of the second coal seam in the second stratum of the study area based on the distribution range of the sensitive parameter value of each sub-coal seam.

8. The device according to claim 7, characterized in that When the macro-coal rock type sensitive parameter group includes longitudinal wave impedance and gamma, the parameter value of the macro-coal rock type sensitive parameter group of the rock sample includes the longitudinal wave impedance value and gamma value of the rock sample, and the sensitive parameter value distribution range of the lithology layer includes the gamma value distribution range and longitudinal wave impedance distribution range of the lithology layer.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the macroscopic coal rock type prediction method according to any one of claims 1 to 6 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the macroscopic coal and rock type prediction method according to any one of claims 1 to 6.

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