Intelligent identification system for fractured layer of coal measure strata

Abstract

The invention relates to the technical field of stratum identification, in particular to an intelligent identification system for a fractured layer of a coal measure stratum.The intelligent identification system comprises a sampling module, a detection module, a calibration module and an analysis module, a drilled stratum rock core is detected, the stratum rock core is divided into a plurality of samples, and vibration wave signals of all the samples are obtained; the vibration wave signals are analyzed so as to determine the crack condition of each part of the stratum rock core, whether the fractured layer exists in the stratum by each sample is comprehensively analyzed, and the fractured layer identification efficiency of the coal measure stratum is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of stratum identification, and in particular to an intelligent identification system for fractured layers in coal-bearing strata. Background Art

[0002] The identification of fractured layers in coal-bearing strata plays an important role in oil and gas exploration and development. Fractured reservoirs are the main reservoir type for deep oil and gas reservoirs and unconventional oil and gas reservoirs. For fractured reservoirs in coal-bearing strata, identifying fractured layers is an important goal of oil and gas exploration and development. Coalbed methane reservoirs have low permeability and usually require permeability enhancement through fracturing technology. Identifying fractured layers helps to improve the permeability of low-permeability coal-bearing reservoirs and realize the industrial development of coalbed methane. By identifying fractured layers, hydraulic fracturing operations can be carried out more effectively, promoting the extension and expansion of fractures, and improving the production and development efficiency of coalbed methane wells.

[0003] The identification of fractured layers in coal-bearing strata plays an important role and significance in the exploration and development of oil and gas resources, reservoir transformation, optimization of hydraulic fracturing operations, reduction of development risks, improvement of microseismic monitoring accuracy, and coal mine safety.

[0004] Chinese patent publication number: CN110259442B, discloses a method for identifying hydraulically fractured layers in coal-bearing strata, which comprises the following steps: 1, selecting coal-bearing strata to be fractured, drilling strata cores with a coring drill, conducting hydraulic fracture elastic wave detection experiments on each stratum, and obtaining hydraulic fracture elastic wave characteristics of each stratum; 2, using a neural network to train the correspondence between fracture elastic wave characteristics and coal seams, and establishing a fracture elastic wave layer identification model; 3, identifying the actual fractured layer according to the fracture elastic wave layer identification model based on the on-site hydraulic fracturing fracture elastic wave; It can be seen that the above technical solution has the following problems: it does not consider comprehensive analysis of each sample to comprehensively determine whether there is a fracture layer in the stratum, which affects the accuracy of fracture layer identification for coal-bearing strata, and further affects the fracture layer identification efficiency for coal-bearing strata. Summary of the invention

[0005] To this end, the present invention provides an intelligent identification system for fractured layers in coal-bearing strata, which is used to overcome the problem that the prior art does not consider comprehensive analysis of each sample to comprehensively determine whether there is a fractured layer in the stratum, which affects the accuracy of identification of fractured layers in coal-bearing strata and further affects the efficiency of identification of fractured layers in coal-bearing strata.

[0006] To achieve the above object, the present invention provides a coal-bearing stratum fracture layer intelligent identification system, comprising: A sampling module, which is used to perform layer processing on the drilled stratum core to obtain a number of samples; A detection module, which is used to perform pressure tests on each sample to obtain vibration wave signals at each point of the sample; A calibration module, which is connected to the detection module, and is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, and determine whether the single sample has cracks in combination with the sample depth, and mark the sample determined to have cracks as a calibration sample; The analysis module is respectively connected to the sampling module, the detection module and the calibration module, and is used to determine the calibration distribution parameters based on the calibration samples, and determine whether there is a fracture layer in the formation in combination with the calibration sample ratio.

[0007] Furthermore, the calibration module is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, including solving the average value of the amplitude of the vibration wave signal at each point of the single sample and recording it as the vibration feedback parameter.

[0008] Furthermore, the calibration module is used to determine whether a single sample has cracks based on the vibration feedback parameter, including: If the vibration feedback parameter is greater than the second preset vibration feedback parameter, it is determined that the single sample has a crack, and the single sample is marked as a calibration sample; If the vibration feedback parameter is less than or equal to the second preset vibration feedback parameter and greater than the first preset vibration feedback parameter, it is determined whether a single sample has a crack in combination with the sample depth; If the vibration feedback parameter is less than or equal to the first preset vibration feedback parameter, it is determined that there is no crack in the single sample; The calibration module is used to determine whether a single sample has cracks in combination with the sample depth, and includes: If the sample depth is less than or equal to the preset sample depth, it is determined that the single sample has cracks, and the single sample is marked as a calibration sample; If the sample depth is greater than the preset sample depth, the first preset vibration feedback parameter is adjusted to a corresponding value based on the sample depth, and whether the single sample has a crack is re-determined based on the adjusted first preset vibration feedback parameter.

[0009] Furthermore, the calibration module is used to adjust the first preset vibration feedback parameter to a corresponding value based on the sample depth, wherein an increase amplitude of the first preset vibration feedback parameter determined based on the sample depth is positively correlated with the sample depth.

[0010] Furthermore, the analysis module is used to determine whether there is a fractured layer in the formation based on the calibration sample ratio, including: The analysis module is used to determine the calibration sample ratio, calculate the ratio of the calibration sample quantity to the total sample quantity, and record it as the calibration sample ratio; If the calibration sample ratio is less than or equal to the first preset sample ratio, it is determined that there is no fracture layer in the formation; If the calibration sample ratio is less than or equal to the second preset sample ratio and greater than the first preset sample ratio, determining whether there is a fracture layer in the formation in combination with the calibration distribution parameter; If the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, it is determined that there is a fractured layer in the formation, and the fractured layer is marked to complete the identification of the fractured layer in the formation; If the calibration sample ratio is greater than the third preset sample ratio, it is determined that there is an abnormality in the identification of the rupture layer, and a processing method for the detection module is determined based on the amplitude fluctuation parameter.

[0011] Furthermore, the analysis module is used to determine whether there is a fracture layer in the formation in combination with the calibration distribution parameter, including: The analysis module is used to determine the calibration distribution parameter based on the calibration samples, to obtain the distance between the calibration samples, to solve the variance of each distance, and to obtain the calibration distribution parameter; If the calibration distribution parameter is less than or equal to the preset calibration distribution parameter, it is determined that there is a fracture layer in the formation, and the fracture layer is marked to complete the identification of the fracture layer in the formation; If the calibration distribution parameter is greater than the preset calibration distribution parameter, it is determined that there is an abnormality in the identification of the rupture layer, and the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

[0012] Furthermore, the analysis module is used to adjust the number of points at which the detection module obtains the sample vibration wave signal to a corresponding value based on the calibration distribution parameter, wherein: The increase in the number of points determined based on the calibrated distribution parameters is positively correlated with the calibrated distribution parameters.

[0013] Furthermore, the analysis module is used to determine a processing method for the detection module based on the amplitude fluctuation parameter, including: The analysis module is used to determine the amplitude fluctuation parameter, solve the variance of the vibration feedback parameter of each sample, and obtain the amplitude fluctuation parameter; If the amplitude fluctuation parameter is less than or equal to the preset amplitude fluctuation parameter, it is determined that the diameter of the obtained formation core is adjusted to a corresponding value based on the amplitude fluctuation parameter; If the amplitude fluctuation parameter is greater than the preset amplitude fluctuation parameter, the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

[0014] Further, the analysis module is used to adjust the diameter of the obtained formation core to a corresponding value based on the amplitude fluctuation parameter, wherein: The increase in the diameter of the obtained formation core determined based on the amplitude fluctuation parameter is negatively correlated with the amplitude fluctuation parameter.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the drilled formation core is tested, the formation core is divided into several samples, and the vibration wave signal of each sample is obtained, each vibration wave signal is analyzed to determine the crack conditions of each part of the formation core, and each sample is comprehensively analyzed to see whether there is a fracture layer in the formation, thereby effectively improving the efficiency of identifying fracture layers in coal-bearing formations.

[0016] Further, a vibration feedback parameter is determined. The vibration feedback parameter is the average value of the amplitude of the vibration wave signal at each point of a single sample. The average value can comprehensively consider the vibration wave amplitude at different points of the core, reduce the interference of individual abnormal points, and more comprehensively reflect the overall vibration characteristics of this part of the core. In complete and crack-free rocks, the vibration wave propagates more evenly and the amplitude is relatively stable. When cracks exist, the vibration wave will refract, reflect and attenuate energy during the propagation process, resulting in changes in the amplitude, so that the average value is lower than the average value of no cracks; based on the vibration feedback parameter, it is determined whether a single sample has a crack. When the vibration feedback parameter is less than or equal to the second preset vibration feedback parameter and greater than the first preset vibration feedback parameter, it is impossible to determine whether a single sample has a crack based on the vibration feedback parameter alone. In this case, the sample depth is determined to comprehensively determine whether a single sample has a crack. While effectively improving the accuracy of identifying fractured layers in coal-bearing strata, the efficiency of identifying fractured layers in coal-bearing strata is further improved.

[0017] Furthermore, the ratio of the number of samples with cracks to the total number of samples is used to determine whether there is a fractured layer in the stratum. When the samples are analyzed, the overall characteristics of the samples reflect the overall situation of the stratum. When the calibration sample ratio is less than or equal to the first preset sample ratio, only individual samples have cracks, which are only local anomalies. When the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, the cracks are not isolated phenomena, but appear in a larger range. In this case, there are relatively common fracture conditions in the stratum, and it is determined that there is a fractured layer in the stratum. In the same stratum, the properties of the rock and the geological effects it is subjected to have a certain consistency. If cracks appear in multiple samples, it means that the stratum has experienced similar stresses or geological processes during its formation and evolution, leading to The formation of cracks leads to the existence of fractures in the formation; when the calibration sample ratio is less than or equal to the second preset sample ratio and greater than the first preset sample ratio, the calibration distribution parameter is determined to comprehensively identify the fracture layer of the formation; when the calibration distribution parameter is less than or equal to the preset calibration distribution parameter, the crack distribution is concentrated and there is a fracture layer in the formation; when the calibration distribution parameter is greater than the preset calibration distribution parameter, the calibration samples are dispersed, and there is a detection deviation due to the low number of points of the sample vibration wave signal obtained by the detection module and insufficient sampling points. At this time, the number of points is adjusted to the corresponding value, and the detection parameters for the core are adaptively adjusted according to the actual detection situation, which effectively improves the accuracy of identifying the fracture layer of the coal-bearing formation, and further improves the efficiency of identifying the fracture layer of the coal-bearing formation.

[0018] Furthermore, when the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, in this case, a large number of samples have cracks and the detection is abnormal. At this time, the amplitude fluctuation parameter is determined to perform targeted processing on the detection module. When the amplitude fluctuation parameter is less than or equal to the preset amplitude fluctuation parameter, in this case, the crack conditions of each sample are similar, and there is a situation where the core is damaged during the sampling process due to sampling abnormalities. In this case, the diameter of the obtained formation core is adjusted to improve the integrity of the core sampling; when the amplitude fluctuation parameter is greater than the preset amplitude fluctuation parameter, in this case, the amplitude deviation of the vibration wave signal of each core is large. At this time, the number of points for obtaining the sample vibration wave signal for the detection module is increased to improve the detection accuracy, thereby further improving the efficiency of identifying the fracture layer of the coal-bearing strata. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a module block diagram of the intelligent identification system for fractured layers in coal-bearing strata according to an embodiment of the present invention; Figure 2 This is a flow chart of a calibration module according to an embodiment of the present invention for determining whether a single sample has cracks based on vibration feedback parameters. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0023] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] See also Figure 1 and Figure 2 As shown, they are respectively a module block diagram of a coal-bearing stratum fracture layer intelligent identification system according to an embodiment of the present invention, and a flow chart of a calibration module for determining whether a single sample has a fracture based on vibration feedback parameters; an embodiment of the present invention provides a coal-bearing stratum fracture layer intelligent identification system, comprising: A sampling module, which is used to perform layer processing on the drilled stratum core to obtain a number of samples; A detection module, which is used to perform pressure tests on each sample to obtain vibration wave signals at each point of the sample; A calibration module, which is connected to the detection module, and is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, and determine whether the single sample has cracks in combination with the sample depth, and mark the sample determined to have cracks as a calibration sample; The analysis module is respectively connected to the sampling module, the detection module and the calibration module, and is used to determine the calibration distribution parameters based on the calibration samples, and determine whether there is a fracture layer in the formation in combination with the calibration sample ratio.

[0025] Specifically, the specific structure of the acquisition module is not limited, and may include a core cutter for cutting cores, a density meter for measuring physical properties, a porosity meter, an X-ray fluorescence spectrometer (XRF) for chemical analysis, and a logic processing component for combining the above test parameters to perform layered processing on the core. This is existing technology and will not be described in detail.

[0026] Specifically, the specific structure of the detection module is not limited, and may include a plurality of vibration sensors disposed on the surface of the sample for detecting vibration waves, and a pressure applying machine for applying pressure to the sample, which will not be described in detail.

[0027] Specifically, the drilled formation core is tested, divided into several samples, and the vibration wave signal of each sample is obtained. Each vibration wave signal is analyzed to determine the crack conditions of each part of the formation core, and a comprehensive analysis is conducted on each sample to determine whether there is a fracture layer in the formation, which effectively improves the efficiency of identifying fracture layers in coal-bearing formations.

[0028] Specifically, the calibration module is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, including solving the average value of the amplitude of the vibration wave signal at each point of the single sample and recording it as the vibration feedback parameter.

[0029] Specifically, the calibration module is used to determine whether a single sample has cracks based on the vibration feedback parameter, including: If the vibration feedback parameter is greater than the second preset vibration feedback parameter, it is determined that the single sample has a crack, and the single sample is marked as a calibration sample; If the vibration feedback parameter is less than or equal to the second preset vibration feedback parameter and greater than the first preset vibration feedback parameter, it is determined whether a single sample has a crack in combination with the sample depth; If the vibration feedback parameter is less than or equal to the first preset vibration feedback parameter, it is determined that there is no crack in the single sample; The calibration module is used to determine whether a single sample has cracks in combination with the sample depth, and includes: If the sample depth is less than or equal to the preset sample depth, it is determined that the single sample has cracks, and the single sample is marked as a calibration sample; If the sample depth is greater than the preset sample depth, the first preset vibration feedback parameter is adjusted to a corresponding value based on the sample depth, and whether the single sample has a crack is re-determined based on the adjusted first preset vibration feedback parameter.

[0030] Specifically, the first preset vibration feedback parameter is selected within the interval [0.1MV, 0.32MV], and the second preset vibration feedback parameter is selected within the interval [0.58MV, 0.82MV]. MV is the average value of the vibration feedback parameters of each benchmark formation core obtained by the detection module in the historical data. The benchmark formation core is to arrange the vibration feedback parameters obtained by the detection module in the historical data in descending order, and select the first 30% of the formation cores as the benchmark formation cores.

[0031] Specifically, the preset sample depth S0 is selected within the interval [800, 1400].

[0032] Specifically, it is understood that in order to classify the fracture conditions of the samples, the number of selected reference formation cores should be no less than 100, which will not be elaborated here.

[0033] Specifically, it can be understood that the sample depth is the depth at which the sample is located in the formation.

[0034] Specifically, a vibration feedback parameter is determined, which is the average value of the amplitude of the vibration wave signal at each point of a single sample. The average value can comprehensively consider the vibration wave amplitude at different points in the core, reduce the interference of individual abnormal points, and more comprehensively reflect the overall vibration characteristics of this part of the core. In complete, crack-free rocks, the vibration wave propagates more evenly and the amplitude is relatively stable. When cracks exist, the vibration wave will refract, reflect and attenuate energy during propagation, resulting in changes in the amplitude, so that the average value is lower than the average value of no cracks. Based on the vibration feedback parameter, it is determined whether a single sample has a crack. When the vibration feedback parameter is less than or equal to the second preset vibration feedback parameter and greater than the first preset vibration feedback parameter, it is impossible to determine whether a single sample has a crack based on the vibration feedback parameter alone. In this case, the sample depth is determined to comprehensively determine whether a single sample has a crack. While effectively improving the accuracy of identifying fractured layers in coal-bearing strata, the efficiency of identifying fractured layers in coal-bearing strata is further improved.

[0035] Specifically, the calibration module is used to adjust the first preset vibration feedback parameter to a corresponding value based on the sample depth, wherein the increase amplitude of the first preset vibration feedback parameter determined based on the sample depth is positively correlated with the sample depth.

[0036] In this embodiment, optionally: comparing the sample depth to a first preset sample depth and a second preset sample depth; If the sample depth is less than or equal to the first preset sample depth, adjusting the first preset vibration feedback parameter to 1.12 times the initial first preset vibration feedback parameter; If the sample depth is less than or equal to the second preset sample depth and greater than the first preset sample depth, adjusting the first preset vibration feedback parameter to 1.22 times the initial first preset vibration feedback parameter; If the sample depth is greater than the second preset sample depth, adjusting the first preset vibration feedback parameter to 1.32 times the initial first preset vibration feedback parameter; The first preset sample depth is 2.8S0, and the second preset sample depth is 4.2S0.

[0037] Specifically, whether the single sample has cracks is re-determined based on the adjusted first preset vibration feedback parameter, including if the vibration feedback parameter is greater than the adjusted first preset vibration feedback parameter, then it is determined that the single sample has cracks, and the single sample is marked as a calibration sample; if the vibration feedback parameter is less than or equal to the adjusted first preset vibration feedback parameter, then it is determined that the single sample does not have cracks.

[0038] Specifically, the analysis module is used to determine whether there is a fracture layer in the formation based on the calibration sample ratio, including: The analysis module is used to determine the calibration sample ratio, calculate the ratio of the calibration sample quantity to the total sample quantity, and record it as the calibration sample ratio; If the calibration sample ratio is less than or equal to the first preset sample ratio, it is determined that there is no fracture layer in the formation; If the calibration sample ratio is less than or equal to the second preset sample ratio and greater than the first preset sample ratio, determining whether there is a fracture layer in the formation in combination with the calibration distribution parameter; If the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, it is determined that there is a fractured layer in the formation, and the fractured layer is marked to complete the identification of the fractured layer in the formation; If the calibration sample ratio is greater than the third preset sample ratio, it is determined that there is an abnormality in the identification of the rupture layer, and a processing method for the detection module is determined based on the amplitude fluctuation parameter.

[0039] Specifically, the first preset sample ratio is selected within the interval [0.01, 0.2], the second preset sample ratio is selected within the interval [0.4, 0.5], and the third preset sample ratio is selected within the interval [0.8, 0.9].

[0040] Specifically, the analysis module is used to determine whether there is a fracture layer in the formation in combination with the calibrated distribution parameters, including: The analysis module is used to determine the calibration distribution parameter based on the calibration samples, to obtain the distance between the calibration samples, to solve the variance of each distance, and to obtain the calibration distribution parameter; If the calibration distribution parameter is less than or equal to the preset calibration distribution parameter, it is determined that there is a fracture layer in the formation, and the fracture layer is marked to complete the identification of the fracture layer in the formation; If the calibration distribution parameter is greater than the preset calibration distribution parameter, it is determined that there is an abnormality in the identification of the rupture layer, and the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

[0041] Specifically, the preset calibration distribution parameter L0 is selected within the interval [0.78F0, 0.83F0], and F0 is the average value of the calibration distribution parameters of each benchmark formation core obtained from historical data.

[0042] Specifically, the ratio of the number of samples with cracks to the total number of samples is used to determine whether there is a fractured layer in the stratum. When the samples are analyzed, the overall characteristics of the samples reflect the overall situation of the stratum. When the calibration sample ratio is less than or equal to the first preset sample ratio, only individual samples have cracks, which are only local anomalies. When the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, the cracks are not isolated phenomena, but appear in a larger range. In this case, there are relatively common fracture conditions in the stratum, and it is determined that there is a fractured layer in the stratum. In the same stratum, the properties of the rock and the geological effects it is subjected to have a certain consistency. If cracks appear in multiple samples, it means that the stratum has experienced similar stresses or geological processes during its formation and evolution, leading to The formation of cracks leads to the existence of fractures in the formation; when the calibration sample ratio is less than or equal to the second preset sample ratio and greater than the first preset sample ratio, the calibration distribution parameter is determined to comprehensively identify the fracture layer of the formation; when the calibration distribution parameter is less than or equal to the preset calibration distribution parameter, the crack distribution is concentrated and there is a fracture layer in the formation; when the calibration distribution parameter is greater than the preset calibration distribution parameter, the calibration samples are dispersed, and there is a detection deviation due to the low number of points of the sample vibration wave signal obtained by the detection module and insufficient sampling points. At this time, the number of points is adjusted to the corresponding value, and the detection parameters for the core are adaptively adjusted according to the actual detection situation, which effectively improves the accuracy of identifying the fracture layer of the coal-bearing formation, and further improves the efficiency of identifying the fracture layer of the coal-bearing formation.

[0043] Specifically, the analysis module is used to adjust the number of points at which the detection module obtains the sample vibration wave signal to a corresponding value based on the calibration distribution parameter, wherein: The increase in the number of points determined based on the calibrated distribution parameters is positively correlated with the calibrated distribution parameters.

[0044] In this embodiment, optionally: Comparing the calibration distribution parameter with a first preset distribution comparison threshold and a second preset distribution comparison threshold; If the calibration distribution parameter is less than or equal to the first preset distribution comparison threshold, the number of points is increased to 1.12 times the initial number of points; If the calibration distribution parameter is less than or equal to the second preset distribution comparison threshold and greater than the first preset distribution comparison threshold, the number of points is increased to 1.22 times the initial number of points; If the calibration distribution parameter is greater than the second preset distribution comparison threshold, the number of points is increased to 1.32 times the initial number of points; The first preset distribution comparison threshold is 1.9L0, and the second preset distribution comparison threshold is 2.3L0.

[0045] Specifically, the analysis module is used to determine a processing method for the detection module based on the amplitude fluctuation parameter, including: The analysis module is used to determine the amplitude fluctuation parameter, solve the variance of the vibration feedback parameter of each sample, and obtain the amplitude fluctuation parameter; If the amplitude fluctuation parameter is less than or equal to the preset amplitude fluctuation parameter, it is determined that the diameter of the obtained formation core is adjusted to a corresponding value based on the amplitude fluctuation parameter; If the amplitude fluctuation parameter is greater than the preset amplitude fluctuation parameter, the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

[0046] Specifically, the preset amplitude fluctuation parameter D0 is selected within the interval [0.62MV, 0.73MV].

[0047] Specifically, when the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, in this case, a large number of samples have cracks and the detection is abnormal. At this time, the amplitude fluctuation parameter is determined to perform targeted processing on the detection module. When the amplitude fluctuation parameter is less than or equal to the preset amplitude fluctuation parameter, in this case, the crack conditions of each sample are similar, and there is a situation where the core is damaged during the sampling process due to sampling abnormalities. In this case, the diameter of the obtained formation core is adjusted to improve the integrity of the core sampling; when the amplitude fluctuation parameter is greater than the preset amplitude fluctuation parameter, in this case, the amplitude deviation of the vibration wave signal of each core is large. At this time, the number of points for obtaining the sample vibration wave signal for the detection module is increased to improve the detection accuracy, thereby further improving the efficiency of identifying the fracture layer of the coal-bearing strata.

[0048] Specifically, the analysis module is used to adjust the diameter of the obtained formation core to a corresponding value based on the amplitude fluctuation parameter, wherein: The increase in the diameter of the obtained formation core determined based on the amplitude fluctuation parameter is negatively correlated with the amplitude fluctuation parameter.

[0049] In this embodiment, optionally: Comparing the amplitude fluctuation parameter with a first preset fluctuation comparison threshold and a second preset fluctuation comparison threshold; If the amplitude fluctuation parameter is less than or equal to the first preset fluctuation comparison threshold, the diameter of the formation core is adjusted to 1.33 times the initial diameter; If the amplitude fluctuation parameter is less than or equal to the second preset fluctuation comparison threshold and greater than the first preset fluctuation comparison threshold, the diameter of the formation core is adjusted to 1.23 times the initial diameter; If the amplitude fluctuation parameter is greater than the second preset fluctuation comparison threshold, the diameter of the formation core is adjusted to 1.13 times the initial diameter; The first preset fluctuation comparison threshold is 0.3D0, and the second preset fluctuation comparison threshold is 0.8D0.

[0050] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent identification system for fractured layers in coal-bearing strata, characterized in that: include: A sampling module, which is used to perform layer processing on the drilled stratum core to obtain a number of samples; A detection module, which is used to perform pressure tests on each sample to obtain vibration wave signals at each point of the sample; A calibration module, which is connected to the detection module, and is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, and determine whether the single sample has cracks in combination with the sample depth, and mark the sample determined to have cracks as a calibration sample; An analysis module, which is connected to the sampling module, the detection module and the calibration module respectively, and is used to determine the calibration distribution parameters based on the calibration samples, and determine whether there is a fracture layer in the formation in combination with the calibration sample ratio; The analysis module is used to determine whether there is a fracture layer in the formation by combining the calibration distribution parameters, and includes: The analysis module is used to determine the calibration distribution parameter based on the calibration samples, to obtain the distance between the calibration samples, to solve the variance of each distance, and to obtain the calibration distribution parameter; If the calibration distribution parameter is less than or equal to the preset calibration distribution parameter, it is determined that there is a fracture layer in the formation, and the fracture layer is marked to complete the identification of the fracture layer in the formation; If the calibration distribution parameter is greater than the preset calibration distribution parameter, it is determined that there is an abnormality in the identification of the rupture layer, and the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

2. The coal-bearing stratum fracture layer intelligent identification system according to claim 1 is characterized in that: The calibration module is used to determine the vibration feedback parameter based on each vibration wave signal of a single sample, including solving the average value of the amplitude of the vibration wave signal of each point of the single sample and recording it as the vibration feedback parameter.

3. The coal-bearing stratum fracture layer intelligent identification system according to claim 2 is characterized in that: The calibration module is used to determine whether a single sample has cracks based on the vibration feedback parameter, including: If the vibration feedback parameter is greater than the second preset vibration feedback parameter, it is determined that the single sample has a crack, and the single sample is marked as a calibration sample; If the vibration feedback parameter is less than or equal to the second preset vibration feedback parameter and greater than the first preset vibration feedback parameter, it is determined whether a single sample has a crack in combination with the sample depth; If the vibration feedback parameter is less than or equal to the first preset vibration feedback parameter, it is determined that there is no crack in the single sample; The first preset vibration feedback parameter is less than the second preset vibration feedback parameter; The calibration module is used to determine whether a single sample has cracks in combination with the sample depth, and includes: If the sample depth is less than or equal to the preset sample depth, it is determined that the single sample has cracks, and the single sample is marked as a calibration sample; If the sample depth is greater than the preset sample depth, the first preset vibration feedback parameter is adjusted to a corresponding value based on the sample depth, and whether the single sample has a crack is re-determined based on the adjusted first preset vibration feedback parameter.

4. The coal-bearing stratum fracture layer intelligent identification system according to claim 3 is characterized in that: The calibration module is used to adjust the first preset vibration feedback parameter to a corresponding value based on the sample depth, wherein the increase amplitude of the first preset vibration feedback parameter determined based on the sample depth is positively correlated with the sample depth.

5. The coal-bearing stratum fracture layer intelligent identification system according to claim 4 is characterized in that: The analysis module is used to determine whether there is a fracture layer in the formation based on the calibration sample ratio, including: The analysis module is used to determine the calibration sample ratio, calculate the ratio of the calibration sample quantity to the total sample quantity, and record it as the calibration sample ratio; If the calibration sample ratio is less than or equal to the first preset sample ratio, it is determined that there is no fracture layer in the formation; If the calibration sample ratio is less than or equal to the second preset sample ratio and greater than the first preset sample ratio, determining whether there is a fracture layer in the formation in combination with the calibration distribution parameter; If the calibration sample ratio is less than or equal to the third preset sample ratio and greater than the second preset sample ratio, it is determined that there is a fractured layer in the formation, and the fractured layer is marked to complete the identification of the fractured layer in the formation; If the calibration sample ratio is greater than the third preset sample ratio, it is determined that there is an abnormality in the identification of the fracture layer, and a processing method for the detection module is determined based on the amplitude fluctuation parameter; The first preset sample ratio is smaller than the second preset sample ratio, and the second preset sample ratio is smaller than the third preset sample ratio.

6. The coal-bearing stratum fracture layer intelligent identification system according to claim 5 is characterized in that: The analysis module is used to adjust the number of points at which the detection module obtains the sample vibration wave signal to a corresponding value based on the calibration distribution parameter, wherein: The increase in the number of points determined based on the calibrated distribution parameters is positively correlated with the calibrated distribution parameters.

7. The coal-bearing stratum fracture layer intelligent identification system according to claim 6 is characterized in that: The analysis module is used to determine a processing method for the detection module based on the amplitude fluctuation parameter, including: The analysis module is used to determine the amplitude fluctuation parameter, solve the variance of the vibration feedback parameter of each sample, and obtain the amplitude fluctuation parameter; If the amplitude fluctuation parameter is less than or equal to the preset amplitude fluctuation parameter, it is determined that the diameter of the obtained formation core is adjusted to a corresponding value based on the amplitude fluctuation parameter; If the amplitude fluctuation parameter is greater than the preset amplitude fluctuation parameter, the number of points at which the detection module obtains the sample vibration wave signal is adjusted to a corresponding value based on the calibration distribution parameter.

8. The coal-bearing stratum fracture layer intelligent identification system according to claim 7 is characterized in that: The analysis module is used to adjust the diameter of the obtained formation core to a corresponding value based on the amplitude fluctuation parameter, wherein: The increase in the diameter of the obtained formation core determined based on the amplitude fluctuation parameter is negatively correlated with the amplitude fluctuation parameter.

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