A method and related apparatus for identifying interbeds suitable for coal measures

Abstract

The application discloses a method for identifying interlayer of coal measures strata and related equipment. The method comprises the following steps: according to a pre-constructed shale interlayer identification index model, shale interlayer identification indexes of different depth positions in a target layer section are calculated; according to a pre-constructed calcareous interlayer identification index model, calcareous interlayer identification indexes of different depth positions in the target layer section are calculated; according to a pre-constructed thin coal seam identification index model, thin coal seam identification indexes of different depth positions in the target layer section are calculated; and according to the calculated shale interlayer identification indexes, calcareous interlayer identification indexes and thin coal seam identification indexes of the target layer section, positions of the shale interlayer, the calcareous interlayer and the thin coal seam in the target layer section are divided. The method effectively solves the problem that special geological bodies may cause abnormal curve responses, reduces the possibility of misjudgment, and greatly improves the identification accuracy of the interlayer.

Description

Technical Field

[0001] This invention relates to the field of reservoir evaluation technology, and in particular to a method and related equipment for identifying interlayers in coal-bearing strata. Background Technology

[0002] A barrier layer, also known as a shielding layer or impermeable layer, is a non-permeable rock layer in a reservoir that can prevent or block the movement of fluids. Barrier layers are generally relatively stable in distribution and have high resistance, effectively preventing the movement of fluids between different flow units. Interlayers, on the other hand, refer to relatively impermeable layers distributed within sandstone layers. Because the distribution of interlayers is highly unstable, they usually cannot effectively prevent or control the movement of fluids, but they have a significant impact on the seepage velocity and seepage effect of fluids within the flow unit. Conventionally, low-permeability or impermeable layers distributed in hydrocarbon-bearing systems are collectively referred to as barrier layers or interlayers.

[0003] Interlayers are relatively thin, short-length strata within the main reservoir that have significant differences in lithology or physical properties compared to the upper and lower strata. The seepage differences caused by interlayers are one of the main factors contributing to reservoir heterogeneity and affecting the distribution of remaining oil.

[0004] Existing qualitative analysis methods lack clear standards for identifying interlayers, resulting in low accuracy and significant reliance on human experience. Core observation and analysis are the most direct methods for identifying interlayers, offering the highest accuracy down to the centimeter level. They can identify interlayers within rhythmic layers and bedding planes. However, core data is often limited, failing to cover the entire well section, and is also costly. Summary of the Invention

[0005] To increase the selection of methods for identifying interlayers in coal-bearing strata and to enrich the types of products for identifying interlayers in coal-bearing strata, embodiments of the present invention provide a method and related equipment for identifying interlayers in coal-bearing strata.

[0006] In a first aspect, embodiments of the present invention provide a method for identifying interlayers in coal-bearing strata, comprising:

[0007] Based on the logging data of the target layer, the identification index of argillaceous interlayers at different depths of the target layer is calculated according to the pre-constructed argillaceous interlayer identification index model.

[0008] Based on the logging data of the target layer, the calcium interlayer identification index at different depths of the target layer is calculated according to the pre-constructed calcium interlayer identification index model.

[0009] Based on the logging data of the target section, the thin coal seam identification index at different depths of the target section is calculated according to the pre-constructed thin coal seam identification index model.

[0010] Based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum, the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum are determined.

[0011] In one or more optional embodiments, the step of determining the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum includes:

[0012] When the identification index of a clay interlayer at a certain depth in the target layer is greater than the first identification threshold, the location is identified as a clay interlayer.

[0013] When the identification index of a calcareous septum at a certain depth in the target layer is greater than the second identification threshold, the location is identified as a calcareous septum.

[0014] When the thin coal seam identification index at a certain depth in the target segment is greater than the third identification threshold, the location is identified as a thin coal seam.

[0015] When a certain depth location in the target section is simultaneously identified as two or more of the following: argillaceous interlayer, argillaceous interlayer, and thin coal seam, the final identification result is determined according to the preset priority order.

[0016] In one or more optional embodiments, the preset priority order is as follows:

[0017] Thin coal seams are larger than clay interlayers, and clay interlayers are larger than calcareous interlayers.

[0018] In one or more optional embodiments, based on well logging data of the target formation, and according to a pre-built clay interlayer identification index model, the clay interlayer identification index at different depths of the target formation is calculated, including:

[0019] Based on the compensated neutron logging values ​​of the target layer, the neutron porosity at different depths of the target layer is calculated according to Formula 1:

[0020] φ CNL =CNL+a, Formula 1;

[0021] In the formula, φ CNL denoted as neutron porosity; CNL is the compensated neutron logging value; a is the neutron porosity compensation value;

[0022] Based on the density logging values ​​of the target layer, the density porosity at different depths of the target layer is calculated according to Formula 2:

[0023]

[0024] In the formula, φ DEN Density porosity; DEN is the density logging value; b is the density porosity compensation value;

[0025] The natural gamma logging values ​​at different depths in the target formation were normalized, and the clay content at different depths in the target formation was calculated according to Formula 3:

[0026]

[0027] In the formula, V sh GCUR is the clay content; GR_nor is the normalized natural gamma logging value.

[0028] Based on the neutron porosity, density porosity, and clay content at different depths in the target layer, the clay interlayer identification index at different depths in the target layer is calculated according to Formula 4:

[0029] SHI=(φ CNL -φ DEN )×V sh , formula 4;

[0030] In the formula, SHI is the identification index of argillaceous interlayers; φ CNL Neutron porosity; φ DEN Density porosity; V sh This refers to the mud content.

[0031] In one or more optional embodiments, the well logging data based on the target formation, according to a pre-built calcareous interlayer identification index model, calculates the calcareous interlayer identification index at different depths in the target formation, including:

[0032] Based on the resistivity logging values ​​of the target layer, the resistivity difference coefficient at different depths of the target layer is calculated according to Formula 5:

[0033]

[0034] In the formula, R C RI is the resistivity difference coefficient; RXO is the shallow resistivity; RXO is the resistivity of the rinsing zone.

[0035] The density logging values, natural gamma logging values, sonic logging values, and resistivity difference coefficient of the target layer were normalized, and the calcareous interlayer identification index at different depths of the target layer was calculated according to Formula 6.

[0036] CII = 0.45DEN_nor + 0.15R C _nor-0.31GR_nor-0.25AC_nor, Formula 6;

[0037] In the formula, CII is the calcareous interlayer identification index; DEN_nor is the normalized density logging value; R C _nor is the normalized resistivity difference coefficient; GR_nor is the normalized natural gamma logging value; AC_nor is the normalized sonic logging value.

[0038] In one or more optional embodiments, the thin coal seam identification index at different depths of the target section is calculated based on the logging data of the target section and according to a pre-built thin coal seam identification index model, including:

[0039] The natural gamma logging values ​​of the target layer are normalized and combined with the density logging values ​​of the target layer. Based on Formula 7, the thin coal seam identification index at different depths in the target layer is calculated.

[0040] COAI=DEN×GR_nor, formula 7;

[0041] In the formula, COAI is the thin coal seam identification index; GR_nor is the normalized natural gamma logging value; and DEN is the density logging value.

[0042] Secondly, embodiments of the present invention provide a device for identifying interlayers in coal-bearing strata, comprising:

[0043] The first calculation module is used to calculate the identification index of argillaceous interlayers at different depths in the target layer based on the logging data of the target layer and according to the pre-built argillaceous interlayer identification index model.

[0044] The second calculation module is used to calculate the calcium interlayer identification index at different depths in the target layer based on the well logging data of the target layer and according to the pre-built calcium interlayer identification index model.

[0045] The third calculation module is used to calculate the thin coal seam identification index at different depths in the target section based on the logging data of the target section and according to the pre-built thin coal seam identification index model.

[0046] The segmentation module is used to segment the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target segment based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams.

[0047] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the interlayer identification method applicable to coal-bearing strata as described in the first aspect.

[0048] Fourthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the interlayer identification method applicable to coal-bearing strata as described in the first aspect.

[0049] Fifthly, embodiments of the present invention provide a computer program product containing instructions that, when run on a computer device, cause the computer device to execute the interlayer identification method applicable to coal-bearing strata as described in the first aspect.

[0050] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0051] The interlayer identification method for coal-bearing strata provided in this invention addresses the differences in physical properties among three types of interlayers. Through collaborative analysis and processing of multiple well logging data, it constructs identification index models for argillaceous interlayers, calcareous interlayers, and thin coal seams, respectively. By calculating the identification indices for argillaceous, calcareous, and thin coal seams in the target stratum, the location of argillaceous, calcareous, and thin coal seams within the target stratum is determined. This method also corrects for individual well logging data, effectively solving the problem of abnormal curve responses caused by special geological bodies, reducing the possibility of misjudgment, and significantly improving the accuracy of interlayer identification.

[0052] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0055] Figure 1 This is a flowchart illustrating the interlayer identification method for coal-bearing strata provided in an embodiment of the present invention.

[0056] Figure 2 This is an example diagram of mud-based interlayer identification provided in an embodiment of the present invention;

[0057] Figure 3 This is an example diagram of calcium septum identification provided in an embodiment of the present invention;

[0058] Figure 4 This is an example diagram of thin coal seam identification provided in an embodiment of the present invention;

[0059] Figure 5 This is a structural block diagram of a layer identification device suitable for coal-bearing strata provided in an embodiment of the present invention;

[0060] Figure 6 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0061] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0062] The inventors discovered that existing qualitative analysis methods mainly fall into two categories: core observation and analysis, and identification based on well logging response characteristics. Neither method has clear standards, resulting in low identification accuracy and significant reliance on human experience. Specifically, core observation and analysis cannot achieve full well coverage and are costly. Methods for identifying interlayers based on well logging response characteristics use fixed thresholds for parameters such as porosity and permeability to define interlayers; however, certain geological bodies may cause abnormal curve responses, exceeding the threshold rules and leading to misjudgments and reduced accuracy. Therefore, an effective quantitative identification method is urgently needed to achieve a precise description of interlayer distribution.

[0063] Based on this, embodiments of the present invention provide a method for identifying interlayers in coal-bearing strata, referring to... Figure 1 As shown, it includes:

[0064] S101: Based on the logging data of the target layer, the identification index of the argillaceous interlayer at different depths of the target layer is calculated according to the pre-constructed argillaceous interlayer identification index model.

[0065] S102: Based on the logging data of the target layer, the calcium interlayer identification index at different depths of the target layer is calculated according to the pre-constructed calcium interlayer identification index model.

[0066] S103: Based on the logging data of the target section, the thin coal seam identification index at different depths of the target section is calculated according to the pre-constructed thin coal seam identification index model.

[0067] S104: Based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target section, the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target section are determined.

[0068] The inventors discovered that the mechanisms of neutron logging and density logging determine that the porosity they reflect is not as sensitive to lithology as neutron porosity and density porosity. Neutron porosity is greatly affected by fluid and porosity, while density porosity is greatly affected by the skeleton and clay content. The product of the difference between neutron porosity and density porosity and the clay content can effectively reflect the clay interlayers in sandstone reservoirs that cause a sudden increase in heterogeneity. Based on this, a clay interlayer identification index model can be constructed, as shown in Equations 1-4 below:

[0069] φ CNL =CNL+a, Formula 1;

[0070]

[0071] SHI=(φ CNL -φ DEN )×V sh , formula 4;

[0072] In formulas 1-4, φ CNL φ is the neutron porosity; CNL is the compensated neutron logging value; a is the neutron porosity compensation value; where φ is the neutron porosity. DEN Density porosity; DEN is the density logging value; b is the density porosity compensation value; V sh φ represents the clay content; GCUR is the empirical coefficient; GR_nor is the normalized natural gamma logging value; SHI is the clay interlayer identification index; φ CNL Neutron porosity; φ DEN Density porosity; V sh This refers to the mud content.

[0073] In this embodiment of the invention, the natural gamma logging values ​​can be normalized according to Formula 8 to obtain the normalized natural gamma logging values:

[0074]

[0075] In the formula, GR_nor is the normalized natural gamma logging value; GR is the natural gamma logging value; GR MAX The maximum value of the natural gamma logging value for the target well section; GR MIN The minimum value of the natural gamma logging value for the target well section.

[0076] In this embodiment of the invention, core calibration verification shows that when the identification index (SHI) of argillaceous interlayers is greater than 0.18, the argillaceous interlayer characteristics are obvious. Therefore, 0.18 can be used as the first identification threshold. When the identification index (SHI) of argillaceous interlayers at a certain depth in the target layer is greater than the first identification threshold of 0.18, that location is identified as an argillaceous interlayer.

[0077] It should be noted that in some wells, mudstone sections may be identified as interlayers. In this case, the distinction can be made based on the thickness and the lithology of the upper and lower strata. Mudstone interlayers are generally no more than two meters thick.

[0078] In this embodiment of the invention, step S101: Based on the logging data of the target layer, the identification index of clay interlayers at different depths in the target layer is calculated according to the pre-constructed clay interlayer identification index model. Specifically, this may include:

[0079] Based on the compensated neutron logging values ​​of the target layer, the neutron porosity at different depths of the target layer is calculated according to Formula 1:

[0080] φ CNL =CNL+a, Formula 1;

[0081] In the formula, φ CNL denoted as neutron porosity; CNL is the compensated neutron logging value; a is the neutron porosity compensation value, for example, it can be 0.035;

[0082] Based on the density logging values ​​of the target layer, the density porosity at different depths of the target layer is calculated according to Formula 2:

[0083]

[0084] In the formula, φ DEN is density porosity; DEN is density logging value; b is bedrock density, for example, it can be the bedrock density of coal-bearing strata, 2.45;

[0085] The natural gamma logging values ​​at different depths in the target formation were normalized, and the clay content at different depths in the target formation was calculated according to Formula 3:

[0086]

[0087] In the formula, V sh GCUR is the clay content; GR_nor is the normalized natural gamma logging value.

[0088] Based on the neutron porosity, density porosity, and clay content at different depths in the target layer, the clay interlayer identification index at different depths in the target layer is calculated according to Formula 4:

[0089] SHI=(φ CNL -φ DEN )×V sh , formula 4;

[0090] In the formula, SHI is the identification index of argillaceous interlayers; φ CNL Neutron porosity; φ DEN Density porosity; V sh This refers to the mud content.

[0091] In this embodiment of the invention, after calculating the identification index of clay interlayers at different depths in the target layer, the clay interlayers in the target layer can be identified according to a pre-set first identification threshold. Figure 2 An example of identifying muddy interlayers was presented using a well logging composite chart.

[0092] The inventors discovered that single-type logging data is easily affected by specific geological bodies, leading to misjudgments of calcareous interlayers. Different logging data have varying sensitivities to calcareous interlayers. Based on density logging data, resistivity logging data, natural gamma logging data, and sonic logging data, the correlation between different logging data and calcareous layers was calculated using known samples. Weights were then assigned to construct a calcareous interlayer identification index model, as shown in Equation 6 below:

[0093] CII = 0.45DEN_nor + 0.15R C _nor-0.31GR_nor-0.25AC_nor, Formula 6;

[0094] In the formula, CII is the calcareous interlayer identification index; DEN_nor is the normalized density logging value; R C _nor is the normalized resistivity difference coefficient; GR_nor is the normalized natural gamma logging value; AC_nor is the normalized sonic logging value.

[0095] In this embodiment of the invention, when the shallow resistivity probed formation and the flushed zone formation are connected in parallel for conductivity, the relationship between the resistivity logging data is shown in Formula 9:

[0096]

[0097] In the formula, RI is the shallow resistivity; RXO is the resistivity of the flushed zone; and Rs refers to the resistivity of the remaining strata within the shallow resistivity detection range after removing the flushed zone.

[0098] Based on the resistivity logging data relationship shown in Formula 9, and according to Formula 5, taking the logarithm of Rs, we can obtain the resistivity difference coefficient R. C :

[0099]

[0100] In the formula, R C is the resistivity difference coefficient; RI is the shallow resistivity; RXO is the resistivity of the rinsing zone.

[0101] In this embodiment of the invention, by repeatedly testing the weight allocation and comparing the core data with the well logging interpretation results of the calcareous interlayer identification index model, the model error is corrected, and the optimal weight allocation scheme is determined. This enables the calcareous interlayer identification index model to effectively reflect the differences in the contribution of different well logging data to the identification of interlayers, thereby ensuring the ability of the calcareous interlayer identification index model to distinguish calcareous layers, reducing misjudgments, and improving the accuracy of calcareous interlayer identification.

[0102] In this embodiment of the invention, core calibration verification showed that when the calcareous interlayer identification index (CII) is greater than 0.26, the calcareous interlayer characteristics are obvious. Therefore, 0.26 can be used as the second identification threshold. When the calcareous interlayer identification index (CII) at a certain depth in the target layer is greater than the second identification threshold of 0.26, that location is identified as a calcareous interlayer.

[0103] It should be noted that the second identification threshold of the calcium interlayer identification index (CII) in some wells has been tightened to the range of 0.28-0.32, resulting in better actual identification performance.

[0104] In this embodiment of the invention, step S102: Based on the logging data of the target layer, the calcareous interlayer identification index at different depths of the target layer is calculated according to the pre-constructed calcareous interlayer identification index model. Specifically, this may include:

[0105] Based on the resistivity logging values ​​of the target layer, the resistivity difference coefficient at different depths of the target layer is calculated according to Formula 5:

[0106]

[0107] In the formula, R C RI is the resistivity difference coefficient; RXO is the shallow resistivity; RXO is the resistivity of the rinsing zone.

[0108] The density logging values, natural gamma logging values, sonic logging values, and resistivity difference coefficient of the target layer were normalized, and the calcareous interlayer identification index at different depths of the target layer was calculated according to Formula 6.

[0109] CII = 0.45DEN_nor + 0.15R C _nor-0.31GR_nor-0.25AC_nor, Formula 6;

[0110] In the formula, CII is the calcareous interlayer identification index; DEN_nor is the normalized density logging value; R C _nor is the normalized resistivity difference coefficient; GR_nor is the normalized natural gamma logging value; AC_nor is the normalized sonic logging value.

[0111] It should be noted that the process of normalizing density logging values, sonic logging values, and the resistivity difference coefficient is well known to those skilled in the art and will not be elaborated here.

[0112] In this embodiment of the invention, after calculating the identification index of calcareous septa at different depths of the target layer, the calcareous septa in the target layer can be identified according to a pre-set second identification threshold. Figure 3 An example of identifying calcareous interlayers was presented using a well logging composite chart.

[0113] In this embodiment of the invention, the calcium interlayer identification index model can not only improve the accuracy of quantitative identification, but also expand the scope of application of calcium interlayer identification. That is, the weight of different logging data can be adjusted according to different geological characteristics. For example, in areas with high clay content, the weight of density logging values ​​can be increased to highlight density differences, so that the calcium interlayer identification index model can complete high-precision interlayer identification in different regions.

[0114] The inventors discovered that coal seams exhibit significant low density and low natural gamma characteristics. By constructing a thin coal seam identification index model based on the product relationship between density logging values ​​and natural gamma logging values, this model can quantify and amplify these characteristics, accurately identifying all coal seams, including thin coal seams between reservoirs. The thin coal seam identification index model is shown in Equation 7 below:

[0115] COAI=DEN×GR_nor, formula 7;

[0116] In the formula, COAI is the thin coal seam identification index; GR_nor is the normalized natural gamma logging value; and DEN is the density logging value.

[0117] In this embodiment of the invention, the natural gamma logging values ​​can be normalized according to Formula 8 to obtain the normalized natural gamma logging values:

[0118]

[0119] In the formula, GR_nor is the normalized natural gamma logging value; GR is the natural gamma logging value; GR MAX The maximum value of the natural gamma logging value for the target well section; GR MIN The minimum value of the natural gamma logging value for the target well section.

[0120] The thin coal seam identification index model provided in this embodiment of the invention uses normalized natural gamma logging values, which improves the comparison of multiple wells and the universality of the model. The combination of normalized natural gamma logging values ​​and density logging data achieves more robust lithology identification, improves the accuracy of thin coal seam identification, and expands the scope of application.

[0121] In this embodiment of the invention, core calibration verification shows that when the thin coal seam identification index (COAI) is less than 0.63, the coal seam characteristics are obvious. Therefore, 0.63 can be used as the third identification threshold. When the COAI of a thin coal seam at a certain depth in the target section is greater than the third identification threshold of 0.63, that location is identified as a thin coal seam.

[0122] It should be noted that in the actual process of identifying coal seams, a small amount of carbonaceous mudstone may also be identified as a coal seam. In this case, the third threshold of the thin coal seam identification index (COAI) can be appropriately tightened to the range of 0.53-0.43.

[0123] In this embodiment of the invention, step S103: Based on the logging data of the target section, and according to the pre-constructed thin coal seam identification index model, the thin coal seam identification index at different depths of the target section is calculated, which may specifically include:

[0124] The natural gamma logging values ​​of the target layer are normalized and combined with the density logging values ​​of the target layer. Based on Formula 7, the thin coal seam identification index at different depths in the target layer is calculated.

[0125] COAI=DEN×GR_nor, formula 7;

[0126] In the formula, COAI is the thin coal seam identification index; GR_nor is the normalized natural gamma logging value; and DEN is the density logging value.

[0127] In this embodiment of the invention, after calculating the thin coal seam identification index at different depths of the target segment, the thin coal seams in the target segment can be identified according to a pre-set third identification threshold. Figure 4 An example of thin coal seam identification was presented using a well logging composite chart.

[0128] In this embodiment of the invention, step S104: Based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum, the locations of the argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum are determined. Specifically, this may include:

[0129] When the identification index of a clay interlayer at a certain depth in the target layer is greater than the first identification threshold, the location is identified as a clay interlayer.

[0130] When the identification index of a calcareous septum at a certain depth in the target layer is greater than the second identification threshold, the location is identified as a calcareous septum.

[0131] When the thin coal seam identification index at a certain depth in the target segment is greater than the third identification threshold, the location is identified as a thin coal seam.

[0132] When a certain depth location in the target section is simultaneously identified as two or more of the following: argillaceous interlayer, argillaceous interlayer, and thin coal seam, the final identification result is determined according to the preset priority order.

[0133] In an optional implementation, the preset priority order can be: thin coal seams are greater than argillaceous interlayers, and argillaceous interlayers are greater than calcareous interlayers. For example, if a certain depth location in the target segment is simultaneously identified as an argillaceous interlayer and a calcareous interlayer, then that location is identified as an argillaceous interlayer.

[0134] It should be noted that coal-bearing strata contain only three types of interlayers: argillaceous interlayers, calcareous interlayers, and thin coal seams. Other strata may also contain siliceous interlayers, but argillaceous, calcareous, and thin coal seams are the most common interlayers. Identifying these three types can address the problem of interlayer identification in coal-bearing strata, and can also address the interlayer identification problem in most other strata. Therefore, the interlayer identification method for coal-bearing strata provided in this embodiment of the invention is not only applicable to coal-bearing strata, but can also identify argillaceous, calcareous, and thin coal seams in other strata.

[0135] The interlayer identification method for coal-bearing strata provided in this invention addresses the differences in physical properties among three types of interlayers. Through collaborative analysis and processing of multiple well logging data, it constructs identification index models for argillaceous interlayers, calcareous interlayers, and thin coal seams, respectively. By calculating the identification indices for argillaceous, calcareous, and thin coal seams in the target stratum, the location of argillaceous, calcareous, and thin coal seams within the target stratum is determined. This method also corrects for individual well logging data, effectively solving the problem of abnormal curve responses caused by special geological bodies, reducing the possibility of misjudgment, and significantly improving the accuracy of interlayer identification.

[0136] To more clearly illustrate the method for identifying interlayers in coal-bearing strata provided in the embodiments of the present invention, the following specific embodiments are provided to introduce the application process of the method:

[0137] In production practice, it was found that interlayers were developed in the 2000-3000 meter section of well A01, but the specific location was unclear. Since this layer belongs to coal-bearing strata, the interlayer identification method for coal-bearing strata provided by this invention was used to identify argillaceous interlayers, calcareous interlayers, and thin coal seams.

[0138] Based on the logging data of the target well section, the identification indices for clay interlayers, calcareous interlayers, and thin coal seams at different depths in the target section were calculated using the clay interlayer identification index model, the calcareous interlayer identification index model, and the thin coal seam identification index model. Some calculation results are shown in Table 1 below:

[0139] Table 1: Partial Calculation Data of Identification Index for Three Types of Partitions

[0140]

[0141] After obtaining the identification indices corresponding to the three types of interlayers, locations with an identification index (SHI) greater than 0.18 for argillaceous interlayers were identified as argillaceous interlayers; locations with an identification index (CII) greater than 0.26 for calcareous interlayers were identified as calcareous interlayers; and locations with an identification index (COAI) less than 0.63 for thin coal seams were identified as thin coal seams. When a location at a certain depth is simultaneously identified as two or more of the three types of interlayers, the final identification result is obtained according to the priority order: thin coal seams > argillaceous interlayers, and argillaceous interlayers > calcareous interlayers.

[0142] Example 2

[0143] Based on the same inventive concept, embodiments of the present invention also provide a device for identifying interlayers in coal-bearing strata, referring to... Figure 5 As shown, it includes:

[0144] The first calculation module 101 is used to calculate the identification index of argillaceous interlayers at different depths in the target layer based on the logging data of the target layer and according to the pre-constructed argillaceous interlayer identification index model.

[0145] The second calculation module 102 is used to calculate the calcium interlayer identification index at different depths in the target layer based on the logging data of the target layer and according to the pre-built calcium interlayer identification index model.

[0146] The third calculation module 103 is used to calculate the thin coal seam identification index at different depths in the target section based on the logging data of the target section and according to the pre-built thin coal seam identification index model.

[0147] The segmentation module 104 is used to segment the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target segment based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams.

[0148] Example 3

[0149] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the interlayer identification method applicable to coal-bearing strata as described in Embodiment 1.

[0150] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; portable compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0151] Example 4

[0152] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the interlayer identification method applicable to coal-bearing strata as described in Embodiment 1.

[0153] Figure 6A possible structural diagram of the computer device involved in the above embodiments is shown. The computer device includes a processor 1002 and a communication interface 1003. The processor 1002 is used to control and manage the operation of the computer device, for example, executing the alarm association method described above, and / or other processes of the technology described herein. The communication interface 1003 is used to support communication between the computer device and other network entities, for example, executing the steps performed by the communication unit 902 described above. The computer device may also include a memory 1001 and a bus 1004, the memory 1001 being used to store the program code and data of the computer device.

[0154] The memory 1001 may be a memory in a computer device, and the memory may include: volatile memory, such as random access memory; the memory may also include: non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include: a combination of the above types of memory.

[0155] The processor 1002 described above can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0156] Bus 1004 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 1004 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0157] Example 5

[0158] Based on the same inventive concept, this embodiment of the invention also provides a computer program product containing instructions that, when the computer program product is run on a computer device, cause the computer device to execute the interlayer identification method applicable to coal-bearing strata as described in Embodiment 1.

[0159] The principles by which the above-described apparatus, client, medium, related equipment, and system in this embodiment solve the problem are similar to those of the aforementioned method. Therefore, their implementation can refer to the implementation of the aforementioned method, and repeated details will not be repeated.

[0160] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0161] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for identifying interlayers in coal-bearing strata, characterized in that, include: Based on the logging data of the target layer, the identification index of argillaceous interlayers at different depths of the target layer is calculated according to the pre-constructed argillaceous interlayer identification index model. Based on the logging data of the target layer, the calcium interlayer identification index at different depths of the target layer is calculated according to the pre-constructed calcium interlayer identification index model. Based on the logging data of the target section, the thin coal seam identification index at different depths of the target section is calculated according to the pre-constructed thin coal seam identification index model. Based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum, the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target stratum are determined.

2. The method for identifying interlayers in coal-bearing strata according to claim 1, characterized in that, The step of determining the locations of clay interlayers, calcareous interlayers, and thin coal seams within the target stratum based on the calculated identification indices of clay interlayers, calcareous interlayers, and thin coal seams includes: When the identification index of a clay interlayer at a certain depth in the target layer is greater than the first identification threshold, the location is identified as a clay interlayer. When the identification index of a calcareous septum at a certain depth in the target layer is greater than the second identification threshold, the location is identified as a calcareous septum. When the thin coal seam identification index at a certain depth in the target segment is greater than the third identification threshold, the location is identified as a thin coal seam. When a certain depth location in the target section is simultaneously identified as two or more of the following: argillaceous interlayer, calcareous interlayer, and thin coal seam, the final identification result is determined according to the preset priority order.

3. The method for identifying interlayers in coal-bearing strata according to claim 2, characterized in that, The preset priority order is as follows: Thin coal seams are larger than clay interlayers, and clay interlayers are larger than calcareous interlayers.

4. The method for identifying interlayers in coal-bearing strata according to claim 1, characterized in that, Based on well logging data from the target formation, and according to a pre-constructed argillaceous interlayer identification index model, the argillaceous interlayer identification index at different depths within the target formation is calculated, including: Based on the compensated neutron logging values ​​of the target layer, the neutron porosity at different depths of the target layer is calculated according to Formula 1: φ CNL =CNL+a, Formula 1; In the formula, φ CNL denoted as neutron porosity; CNL is the compensated neutron logging value; a is the neutron porosity compensation value; Based on the density logging values ​​of the target layer, the density porosity at different depths of the target layer is calculated according to Formula 2: In the formula, φ DEN Density porosity; DEN is the density logging value; b is the density porosity compensation value; The natural gamma logging values ​​at different depths in the target formation were normalized, and the clay content at different depths in the target formation was calculated according to Formula 3: In the formula, V sh GCUR is the clay content; GR_nor is the normalized natural gamma logging value. Based on the neutron porosity, density porosity, and clay content at different depths in the target layer, the clay interlayer identification index at different depths in the target layer is calculated according to Formula 4: SHI=(φ CNL -φ DEN )×V sh Official 4; In the formula, SHI is the identification index of argillaceous interlayers; φ CNL Neutron porosity; φ DEN Density porosity; V sh This refers to the mud content.

5. The method for identifying interlayers in coal-bearing strata according to claim 1, characterized in that, Based on the well logging data of the target formation, and according to a pre-constructed calcareous interlayer identification index model, the calcareous interlayer identification index at different depths of the target formation is calculated, including: Based on the resistivity logging values ​​of the target layer, the resistivity difference coefficient at different depths of the target layer is calculated according to Formula 5: In the formula, R C RI is the resistivity difference coefficient; RXO is the shallow resistivity; RXO is the resistivity of the rinsing zone. The density logging values, natural gamma logging values, sonic logging values, and resistivity difference coefficient of the target layer were normalized, and the calcareous interlayer identification index at different depths of the target layer was calculated according to Formula 6. CII = 0.45DEN_nor + 0.15R C _nor-0.31GR_nor-0.25AC_nor, Formula 6; In the formula, CII is the calcium interlayer identification index; DEN_nor is the normalized density logging value; R C _nor is the normalized resistivity difference coefficient; GR_nor is the normalized natural gamma logging value; AC_nor is the normalized sonic logging value.

6. The method for identifying interlayers in coal-bearing strata according to claim 1, characterized in that, Based on the well logging data of the target section, and according to the pre-constructed thin coal seam identification index model, the thin coal seam identification index at different depths of the target section is calculated, including: The natural gamma logging values ​​of the target layer are normalized and combined with the density logging values ​​of the target layer. Based on Formula 7, the thin coal seam identification index at different depths in the target layer is calculated: COAI=DEN×GR_nor, formula 7; In the formula, COAI is the thin coal seam identification index; GR_nor is the normalized natural gamma logging value; and DEN is the density logging value.

7. A device for identifying interlayers in coal-bearing strata, characterized in that, include: The first calculation module is used to calculate the identification index of argillaceous interlayers at different depths in the target layer based on the logging data of the target layer and according to the pre-built argillaceous interlayer identification index model. The second calculation module is used to calculate the calcium interlayer identification index at different depths in the target layer based on the well logging data of the target layer and according to the pre-built calcium interlayer identification index model. The third calculation module is used to calculate the thin coal seam identification index at different depths in the target section based on the logging data of the target section and according to the pre-built thin coal seam identification index model. The segmentation module is used to segment the locations of argillaceous interlayers, calcareous interlayers, and thin coal seams in the target segment based on the calculated identification indices of argillaceous interlayers, calcareous interlayers, and thin coal seams.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the interlayer identification method applicable to coal-bearing strata as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the interlayer identification method applicable to coal-bearing strata as described in any one of claims 1-6.

10. A computer program product containing instructions, characterized in that, When the computer program product is run on a computer device, it causes the computer device to perform the interlayer identification method applicable to coal-bearing strata as described in any one of claims 1-6.

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