Method for intelligently identifying macroscopic fracture extension trajectory in fracturing source clusters

Abstract

The present invention discloses a method for intelligently identifying the propagation trajectory of macroscopic fractures in a fracturing seismic source cluster, including: collecting the elastic wave waveforms released by rock fracture or rupture, and filtering the elastic wave waveforms; using deep learning methods to obtain the starting points of the high-quality waveforms; dividing the dynamic time step, and dividing the seismic source cluster according to the dynamic time step; dividing the seismic source data volume within a single time step into multi-level spatial unit bodies; comprehensively identifying the macroscopic fractures of each parameter to obtain the propagation trajectory of the macroscopic fractures within the unit body; reconstructing the fractures identified in each spatial unit body at two levels of space and time to obtain the propagation trajectory of the macroscopic fractures during the entire process of fracturing expansion. By distinguishing the macroscopic fractures from the secondary small-scale fracture zones, the present invention can achieve high-precision identification of the propagation trajectory of macroscopic fractures.

Description

Technical Field

[0001] The present invention relates to a rock material structure detection technology, and in particular to a method for intelligently identifying macro crack extension trajectories in a fracturing source cluster. Background Art

[0002] Fracturing, which uses high-pressure fluid to fracture rocks and drive the expansion of fractures, is the core technology for permeability enhancement of unconventional oil and gas reservoirs, permeability enhancement of hot dry rock geothermal heat storage to improve heat extraction efficiency, permeability enhancement of coal seam gas in underground mines and surrounding rock shear stress transfer, and permeability enhancement of uranium reservoirs to improve in-situ leaching efficiency. High-precision monitoring of fracturing fractures is the basis for controlling the expansion of fracturing fractures and an important support point for increasing the production of energy and strategic minerals.

[0003] Earthquakes, microseisms, ground sounds and acoustic emissions are elastic waves with frequencies ranging from low to high, which are released in fractures of different scales. The main means of monitoring hydraulic fractures is to receive elastic waves through sensors to invert the fracture position, which is collectively referred to as the source inversion of fractures. However, the physical mechanism of the initiation and fusion of a large number of small-scale fractures into macro fractures determines that the fracture morphology obtained by source inversion is clustered; by improving the accuracy of source inversion to obtain higher-precision source clusters, this method of identifying cracks is currently the main method and development trend of hydraulic fracture monitoring, but this method will cause macro fractures to be hidden in the source cluster, making it impossible to distinguish between macro fractures and secondary small-scale fracture areas, which seriously limits the accuracy of hydraulic fracture monitoring. Summary of the invention

[0004] Purpose of the invention: In view of the above problems, the purpose of the present invention is to provide a method for intelligently identifying the extension trajectory of macroscopic fractures in a fracturing source cluster, thereby realizing the intelligent identification of the extension trajectory of macroscopic fractures and secondary small-scale fracture areas in the source cluster.

[0005] Technical solution: A method for intelligently identifying macro crack extension trajectories in a fracturing source cluster of the present invention comprises the following steps:

[0006] Step 1, collecting elastic wave waveforms released by rock fracture or rupture, filtering the elastic wave waveforms using an intelligent filtering model, and recording the filtered elastic wave waveforms as high-quality waveforms;

[0007] Step 2: Use deep learning methods to obtain the head starting point of the high-quality waveform, and then obtain the arrival time of the high-quality waveform, and obtain the source point according to the arrival time;

[0008] Step 3, obtaining the earthquake source clusters of the earthquake source points, dividing the dynamic time steps, dividing the earthquake source clusters according to the dynamic time steps, and obtaining the earthquake source data volume within a single time step;

[0009] Step 4, dividing the earthquake source data volume within a single time step into multi-level spatial units, and analyzing the distribution characteristics of multiple parameters of earthquake source characteristics within the spatial units;

[0010] Step 5, identifying the macro cracks corresponding to each parameter according to the distribution characteristics, and obtaining the extension trajectory of the macro cracks in the unit body by integrating the macro cracks identified by each parameter;

[0011] Step 6: Reconstruct the identified fractures in each spatial unit at both spatial and temporal levels to obtain the macroscopic fracture extension trajectory during the entire fracturing expansion process.

[0012] Further, in step 3, dividing the dynamic time steps includes:

[0013] Dynamically identify the aggregation form of the point cluster in the earthquake source space, and determine whether the aggregation form conforms to the characteristics of non-planar turning expansion or bifurcation of the crack. If it conforms, continue to search for time points earlier than the current time until the aggregation form presents a single crack, and the corresponding time is taken as the end point of the time step;

[0014] If the source distribution shows a single form throughout the entire process of crack expansion, it is necessary to determine whether the amount of spatial source data within a single time step is sufficient for the analysis of the multi-parameter spatial distribution law of source characteristics. If not, the time step needs to be increased until the minimum threshold of spatial source data is reached.

[0015] Furthermore, in step 4, dividing the earthquake source data volume within a single time step into multi-level spatial units includes:

[0016] The earthquake source data volume in a single time step is divided into multiple first-level earthquake source unit volumes;

[0017] The primary source unit body is divided into a plurality of secondary source unit bodies;

[0018] The secondary seismic source unit volume is divided into multiple tertiary seismic source unit volumes.

[0019] Furthermore, dividing the earthquake source data volume within a single time step into multiple first-level earthquake source unit volumes includes:

[0020] For the approximately elliptical fracturing crack with the injection point as the centroid, a cylindrical coordinate system is constructed with the injection point as the origin O and the normal line of the fracturing crack surface identified by the earthquake source cluster as the axis A. The cylindrical coordinate system parameters r and θ are located on the projection surface of the fracturing crack. When the crack does not expand along the trajectory with the injection point as the centroid, the centroid of the stage expansion of the irregular fracture expansion surface is selected as the origin O, and the normal line of the fracturing crack surface identified by the earthquake source cluster is used as the axis A to establish a cylindrical coordinate system. The cylindrical coordinate system parameters r and θ are located on the projection surface of the irregular fracturing crack.

[0021] Taking the infinite plane where the axis A and the polar coordinate r are located as the characteristic plane S0, and taking two infinite planes S1 and S2 that are symmetric with the characteristic plane S0 and have the same normal as the data screening interval, the vertical distance between the plane S1 and the plane S2 is w;

[0022] When the characteristic plane S0 rotates, the planes S1 and S2 rotate together with the characteristic plane; let the characteristic plane S0 pass through the liquid injection point, and rotate the characteristic plane by an increment of α degrees until 180°, and each time it rotates, the seismic source data between the planes S1 and S2 is used as a primary seismic source unit body, and there are a total of 180 / α primary seismic source unit bodies.

[0023] Furthermore, dividing the primary seismic source unit body into multiple secondary seismic source unit bodies includes:

[0024] Establish a local space rectangular coordinate system for each obtained primary seismic source unit body. The Y-axis is in the same direction as the polar coordinate r of the cylindrical coordinate system, and the X, Y, and Z axes conform to the right-hand system specified direction. The Z-axis is the thickness direction of the primary seismic source unit body, and the X-axis is the width direction of the primary seismic source unit body. Project the seismic source in the primary seismic source unit body onto the Y-Z plane to obtain a two-dimensional seismic source data volume;

[0025] Cut the primary seismic source unit body along the Y direction to form multiple parallel lines with a spacing of D and parallel to the X axis. The seismic source point group covered between two adjacent parallel lines is used as a secondary seismic source unit body.

[0026] Furthermore, dividing the secondary seismic source unit body into multiple tertiary seismic source unit bodies includes:

[0027] Cut each secondary seismic source unit body along the X direction to form multiple parallel lines with a spacing of d and parallel to the Y axis. The seismic source point group covered between two adjacent parallel lines is used as a tertiary seismic source unit body; the size of the tertiary seismic source unit body is D×d.

[0028] Furthermore, in step 4, analyzing the distribution characteristics of multiple seismic source characteristic parameters in the spatial unit body includes:

[0029] Taking the tertiary seismic source unit body as the smallest statistical unit, counting the multiple parameters of the seismic source corresponding waveform, and then using a mathematical statistical model to determine the distribution characteristics of the multiple seismic source characteristic parameters in the secondary seismic source unit body.

[0030] Furthermore, step 5 includes:

[0031] According to the distribution law of each parameter, obtain the coordinate values of the macro-fracture formation position in the secondary seismic source unit body in the local space rectangular coordinate system of the primary seismic source unit body;

[0032] Set the picking threshold for the microcrack zones on both sides of the macroscopic crack, and obtain the coordinate values of the boundaries of the microcrack zones within the secondary source units in the local spatial rectangular coordinate system of the primary source unit according to the picking threshold;

[0033] After the formation positions of the macroscopic cracks and the boundary positions of the microcrack zones of all the secondary units within the primary source unit are determined, connect the macroscopic cracks of all the secondary units within the primary source unit in sequence and connect the boundary positions of the microcrack zones in sequence to obtain the extension trajectory of the macroscopic cracks and the formation positions of the microcrack zones within the source strip of the primary source unit.

[0034] Further, step 6 includes:

[0035] Reconstruct the tertiary source units that have completed the statistical analysis of the parameters into secondary source units, reconstruct all the secondary source units into primary source units, and reconstruct all the primary source units into the macroscopic cracks corresponding to each time step;

[0036] Connect the macroscopic cracks of the section according to the time nodes within each time step to obtain the trajectory of the macroscopic cracks.

[0037] Advantageous effects: Compared with the prior art, the remarkable advantages of the present invention are:

[0038] 1. The present invention can separate the secondary crack zone from the macroscopic crack surface and enhance the monitoring and recognition accuracy of three-dimensional cracks;

[0039] 2. The present invention can identify the three-dimensional fracturing crack network and its dynamic evolution through multi-stage and multi-unit division in time and space;

[0040] 3. The present invention realizes the quantitative recognition of macroscopic three-dimensional cracks and secondary small-scale crack zones by depicting the multi-parameter distribution of the transverse crack propagation direction;

[0041] 4. The present invention improves the recognition accuracy and efficiency of three-dimensional macroscopic cracks through waveform denoising, recognition of the distribution pattern of the source cluster, time step division, and characterization of the distribution pattern of source characteristic parameters. Description of the Drawings

[0042] Figure 1 It is a flow chart of a method for intelligently identifying the extension trajectory of macroscopic cracks in a fracturing source cluster;

[0043] Figure 2 It is a schematic diagram of a source cluster;

[0044] Figure 3 It is a schematic diagram of a cylindrical coordinate system and a SO-S1-S2 cross-section plane;

[0045] Figure 4Schematic diagram of introducing cylindrical coordinate system and SO-S1-S2 section plane into the source cluster;

[0046] Figure 5 It is a structural schematic diagram of the first-level source unit;

[0047] Figure 6 It is a schematic diagram of dividing the secondary seismic source unit into three-level seismic source unit bodies;

[0048] Figure 7 Schematic diagram of the boundary position of the crack area;

[0049] Figure 8 This is a schematic diagram of the actual crack formation location;

[0050] Figure 9 It is a schematic diagram of reconstruction into a first-level source unit;

[0051] Figure 10 Schematic diagram of the fracture trajectory after reconstruction at the time level. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0053] The method for intelligently identifying the extension trajectory of macroscopic fractures in the fracturing source cluster described in this embodiment is as follows: Figure 1 As shown, the method at least includes the following steps 1 to 6.

[0054] Among them, step 1 is to collect the elastic wave waveform released by rock fracture or rupture, and use the intelligent filtering model to filter the elastic wave waveform, and record the filtered elastic wave waveform as a high-quality waveform.

[0055] The acquisition of high-quality rock fracture or rupture waveforms is the basis for high-precision source inversion and waveform feature analysis. In actual engineering and experiments, interference signals, namely background noise, are inevitable. Background noise is random and difficult to eliminate by the unified rules specified by traditional filtering methods. In this example, before formally acquiring signals, rock fracture tests in the laboratory and engineering are first carried out under the condition of avoiding noise interference to obtain elastic wave waveforms released by rock fracture or rupture, and obtain waveform characteristic parameters of high-quality waveforms. Among them, waveforms without many messy interference signals and whose various parameter values ​​of the collected waveforms reach the minimum threshold value are considered to be high-quality waveforms.

[0056] Furthermore, high-quality waveforms of rock fractures or cracks are used as samples to compare with the fracture or crack waveforms collected under background noise conditions, and deep learning is carried out to filter out the background noise and obtain high-quality waveforms. Through artificial intelligence training, an intelligent filtering model in the waveform acquisition process is obtained.

[0057] In the above deep learning process, training can be carried out by a neural network, such as various neural network models including time series Transformer, diffusion model, conditional generative adversarial network, variational autoencoder, deep convolutional generative adversarial network, self-supervised learning, and contrastive learning. Through any of the above neural network models, artificial intelligence training is performed to take the waveform feature parameters of the elastic wave released by rock fracture or rupture obtained from laboratory and engineering rock fracture tests carried out under the condition of avoiding noise interference as the standard waveform. At this time, the waveform is a standard high-quality waveform without noise. The input item of the neural network is a large amount of elastic wave waveform data obtained in the test or on the engineering site, and the output item is a standard high-quality waveform. Then, the neural network model is used for training and learning, compared with the waveform feature parameters of the obtained standard waveform, and the noise interference signals other than the standard waveform are filtered out. The neural network model after training is used as an intelligent filtering model, and the elastic wave waveform can be intelligently filtered through the intelligent filtering model to obtain a high-quality waveform.

[0058] Step 2: Use the deep learning method to obtain the head starting point of the high-quality waveform, and then obtain the arrival time of the high-quality waveform, and obtain the source point according to the arrival time.

[0059] Pick up the starting points of all waveforms released by the same source, that is, the significant sudden jump points where the waveform fluctuates smoothly before the source is generated, and the arrival time of the waveform can be obtained. The arrival time is a key parameter for source inversion. However, the sudden starting time of the waveform is short and presented by multiple data points, and it is difficult to pick up the first starting point, especially when the background noise cannot be filtered out cleanly. Therefore, in this example, the deep learning method is preferably used to label the starting points of the waveforms in different background noises as the training set data, and then the neural network model is trained, and then an intelligent high-precision recognition model for the starting points of waveforms applicable to different noise levels is obtained, and the starting time can be obtained more accurately to improve the source inversion accuracy. The neural network model can be various neural network models such as time series Transformer, diffusion model, conditional generative adversarial network, variational autoencoder, deep convolutional generative adversarial network, self-supervised learning, and contrastive learning. The input item is the high-quality waveform after filtering, and the output item is the starting points of all waveforms of the same source. Through the neural network model, in-depth training for starting point recognition is carried out, and the head starting point of the high-quality waveform can be obtained by using the neural network model after training.

[0060] After the above steps 1 and 2, not only the recognition accuracy of the source is improved, but also high-quality initial data of the source point is obtained.

[0061] Step 3: Obtain the source clusters of the seismic source points, divide the dynamic time step, and divide the source clusters according to the dynamic time step to obtain the source data volume within a single time step.

[0062] After determining the seismic source points, the source clusters of the seismic source points can be obtained through relevant instruments for fracture monitoring. However, at this time, not only the true fracture trajectories but also the secondary small-scale fracture zones exist in the source clusters, which severely limits the accuracy of hydraulic fracture monitoring. In this embodiment, the macroscopic fractures in the source clusters are separated from the secondary small-sized fracture zones in order to achieve high-precision identification of the macroscopic fracture propagation trajectories.

[0063] After obtaining the source clusters of the seismic source points, it is necessary to identify the preliminary fracture morphology depicted by the spatial source clusters, and based on this, divide the source data into multiple characteristic time stages. Usually, the time analysis step is of equal duration. However, considering the limitation that the spatial distribution of the seismic sources is too complex due to fracture bifurcation or network formation during the whole process of fracture propagation, which restricts the identification of macroscopic fractures, the setting of the time analysis step needs to be dynamically adjusted. In this embodiment, taking the fact that the seismic sources present a single fracture propagation mode within each time analysis step as the basis for dynamically adjusting the selection of the time analysis step, the whole process of complex fracture propagation is split into multiple analysis steps formed by multiple single fractures in sequence, breaking it into parts. The macroscopic true fracture surfaces hidden in the source clusters within each analysis step are identified, and finally, the fracture propagation morphology of the whole process is reconstructed at the time level. If the seismic source distribution is mainly in a single form during the whole process of fracture propagation, it is necessary to judge whether the spatial source data volume within a single time step is sufficient to support the determination of the macroscopic fracture criterion, and the adjustment of the analysis time step needs to reach the minimum threshold of the data volume.

[0064] Further, in Step 3, dividing the dynamic time step includes:

[0065] Dynamically identify the aggregation morphology of the spatial source points of the seismic source, and judge whether the aggregation morphology conforms to the characteristics of non-planar turning propagation or bifurcation of fractures. If it conforms, continue to search for the time point earlier than the current moment until the aggregation morphology presents a single fracture, and the corresponding moment is used as the end point of the time step;

[0066] If the seismic source distribution is in a single form during the whole process of fracture propagation, it is necessary to judge whether the spatial source data volume within a single time step is sufficient for the analysis of the spatial distribution law of multi-parameters of the seismic source characteristics. If it is not sufficient, the time step needs to be increased until it reaches the minimum threshold of the spatial source data volume.

[0067] Set the minimum threshold of the source data volume, and judge whether the spatial source data volume within a single time step exceeds this threshold. When it is greater than or equal to this minimum threshold value, it is considered that the spatial source data volume within a single time step is sufficient for the analysis of the spatial distribution law of multi-parameters of the seismic source characteristics, otherwise it is not.

[0068] Step 4: divide the earthquake source data volume within a single time step into multi-level spatial units, and analyze the distribution characteristics of multiple parameters of earthquake source characteristics within the spatial units.

[0069] In this step 4, the selected source point cluster will be preliminarily judged for its distribution morphology, and the spatial unit division method of the source data will be determined, which is the basis for statistics, identification and analysis of the multi-parameter distribution characteristics of the source characteristics in each spatial unit. In general, the source data body within a single time step is spatially divided into multi-regional and multi-level units, and a mathematical statistical model is constructed and analyzed for each level of the divided source unit body. The mathematical statistical model can be the more commonly used normal distribution, binomial distribution, Poisson distribution and exponential distribution, etc., but is not limited to the above models.

[0070] Furthermore, in step 4, dividing the earthquake source data volume within a single time step into multi-level spatial units includes:

[0071] The earthquake source data volume in a single time step is divided into multiple first-level earthquake source unit volumes;

[0072] The primary source unit body is divided into a plurality of secondary source unit bodies;

[0073] The secondary seismic source unit volume is divided into multiple tertiary seismic source unit volumes.

[0074] Specifically, dividing the source data volume within a single time step into multiple first-level source unit volumes includes:

[0075] like Figure 2 As shown in the figure, for an approximately elliptical fracturing crack with the injection point as the centroid, perspective ① and perspective ② are schematic diagrams of the source cluster at different perspectives. The injection point is taken as the origin O, and the normal line of the fracturing crack surface identified by the source cluster is taken as the axis A to construct a cylindrical coordinate system, as shown in Figure 3 As shown in the middle figure (a), the cylindrical coordinate system parameters r and θ are located on the projection surface of the hydraulic fracture, and the coordinates of any point M in the source cluster are expressed as (r, θ, z), where z is the ordinate in the spatial coordinate system O-XYZ. When the fracture does not extend along the trajectory with the injection point as the centroid, the centroid of the stage expansion of the irregular fracture extension surface is selected as the origin O, and the normal line of the hydraulic fracture surface identified by the source cluster is used as the axis A to establish a cylindrical coordinate system. The cylindrical coordinate system parameters r and θ are located on the projection surface of the irregular hydraulic fracture.

[0076] The infinite plane where the axis A and the polar coordinate r are located is the feature plane S0, and the two infinite planes S1 and S2 that are symmetric planes with the feature plane S0 and have the same normal are the data screening intervals. The vertical distance between plane S1 and plane S2 is w. Figure 3As shown in Figure (b) of the middle figure, it is a schematic diagram of the SO-S1-S2 wireless large cross-section space, and Figure (c) is a schematic diagram of introducing the SO-S1-S2 cross-section into the cylindrical coordinate system.

[0077] When the feature plane S0 rotates, the plane S1 and the plane S2 rotate together with the feature plane; let the feature plane S0 pass through the liquid injection point, and rotate the feature plane by an increment of α degrees until 180°, and each time it rotates, the seismic source data between the plane S1 and the plane S2 is used as a first-level seismic source unit body, and there are a total of 180 / α first-level seismic source unit bodies. As Figure 4 As shown in Figure (a) of the middle figure, it is a schematic diagram of introducing the cylindrical coordinate system into the seismic source cluster, and Figure (b) shows a schematic diagram of introducing the SO-S1-S2 cross-section and the cylindrical coordinate system into the seismic source cluster.

[0078] Furthermore, dividing the first-level seismic source unit body into multiple second-level seismic source unit bodies includes:

[0079] As Figure 5 As shown, a local space rectangular coordinate system O-XYZ is established for each obtained first-level seismic source unit body. The Y-axis is in the same direction as the polar coordinate r of the cylindrical coordinate system, and it is also the length direction of the first-level seismic source unit body. The X, Y, and Z axes conform to the right-hand system specified direction. The Z-axis is the thickness direction of the first-level seismic source unit body and is in the same direction as the axis A, and the X-axis is the width direction of the first-level seismic source unit body. The seismic sources in the first-level seismic source unit body are projected onto the Y-Z plane to obtain a two-dimensional seismic source data body. Figure 5 View ① in the middle figure is the projection view of the seismic source under the Y-Z projection plane, and view ② is the projection view of the seismic source under the Z-Y projection plane.

[0080] The first-level seismic source unit body is cut along the Y direction to form multiple parallel lines with a spacing of D and parallel to the X axis. The seismic source point clusters covered between two adjacent parallel lines are used as second-level seismic source unit bodies.

[0081] Furthermore, as Figure 6 shown, dividing the second-level seismic source unit body into multiple third-level seismic source unit bodies includes:

[0082] Each second-level seismic source unit body is divided along the X direction to form multiple parallel lines with a spacing of d and parallel to the Y axis. The seismic source point clusters covered between two adjacent parallel lines are used as third-level seismic source unit bodies; the size of the third-level seismic source unit body is D×d.

[0083] It should be noted that the determination of the size of the third-level seismic source unit body depends on the number of seismic sources. The number of seismic sources in the third-level seismic source unit body needs to be higher than a pre-set minimum threshold. If not satisfied, the unit body size needs to be increased; at the same time, the number of seismic sources in the third-level seismic source unit body should not exceed the pre-set maximum threshold. If exceeded, the unit size needs to be increased according to actual needs.

[0084] Furthermore, in step 4, analyzing the distribution characteristics of multiple parameters of earthquake source characteristics within the spatial unit body includes:

[0085] The third-level source unit is taken as the smallest statistical unit, and the multi-parameters of the waveform corresponding to the source are statistically analyzed. Then, the distribution characteristics of the multi-parameters of the source characteristics are determined in the second-level source unit using a mathematical statistical model.

[0086] In one example, if the statistical multiple parameters are accumulative scalars, the sum of the statistics is calculated, such as energy, number of earthquake sources, ringing count, etc.; if the sum of the statistical parameters has no physical meaning, the average value of the parameters is calculated, such as wave velocity, amplitude, frequency, earthquake source mechanism, duration, rise time, etc.

[0087] On the basis of the determination of multiple parameters of earthquake source characteristics in the third-level unit, the distribution of multiple parameters of earthquake source characteristics along the second-level unit is determined, and the distribution of multiple parameters of earthquake source characteristics in the second-level earthquake source unit is determined by using a mathematical statistical model. Typical multiple parameters of earthquake source characteristics here include energy, number of earthquake sources, ring count, wave velocity, amplitude, frequency, earthquake source mechanism, duration, rise time, etc., but are not limited to the above parameters. The mathematical statistical models used here include normal distribution, binomial distribution, Poisson distribution or exponential distribution, etc., but are not limited to the above models.

[0088] When analyzing the source parameter distribution of the secondary unit, the source characteristic parameters of some tertiary units will be abnormally high or low, which will reduce the accuracy of the mathematical model in describing the distribution of source characteristic parameters in the secondary unit. Therefore, the artificial intelligence method can be used for intelligent identification, such as pre-setting a reasonable reference range of parameter characteristic values, and then pre-setting a reasonable error range, inputting the characteristic parameter data of the three-level source unit body obtained by analysis into the artificial intelligence method, and then the artificial intelligence method is used for screening and identification, and the data within the set parameter reference range is output, and the remaining characteristic parameters that are too high or too low are identified and removed, and the input is a reasonable characteristic parameter value. For each three-level source unit body with abnormal source characteristic parameters in the secondary source unit body, the data in the unit body is eliminated, and then the mathematical statistics of the source characteristic parameter distribution of the secondary source unit body are performed.

[0089] Step 5, identifying the macro cracks corresponding to each parameter based on the distribution characteristics, and obtaining the extension trajectory of the macro cracks in the unit body by integrating the macro cracks identified by various parameters.

[0090] Further, step 5 includes:

[0091] According to the distribution law of each parameter, the coordinate value of the macro crack formation position in the secondary source unit body in the local space rectangular coordinate system of the primary source unit body is obtained;

[0092] Set the picking threshold for the microcrack zones on both sides of the macroscopic crack, and obtain the coordinate values of the boundaries of the microcrack zones within the secondary seismic source unit body in the local space rectangular coordinate system of the primary seismic source unit body;

[0093] After the formation positions of the macroscopic cracks and the boundary positions of the microcrack zones of all secondary units within the primary seismic source unit body are determined, connect the macroscopic cracks of all secondary units within the primary seismic source unit body in sequence and connect the boundary positions of the microcrack zones in sequence, to obtain the extension trajectory of the macroscopic cracks and the formation positions of the microcrack zones within the seismic source strip of the primary seismic source unit body. As Figure 7 The boundary position of the crack zone is shown by the dotted line contour in the figure, and the corresponding distribution models at positions ① and ② are respectively shown in the figure.

[0094] For each secondary seismic source unit body, the types of mathematical statistical models and model parameters obtained by different parameters vary due to the differences in the multi-factor parameter distribution laws, but the coordinates corresponding to the peak or valley value of the model are the formation positions of the macroscopic cracks. For example, if the characteristic parameters within the secondary seismic source unit body follow a normal distribution model, the coordinates corresponding to the peak point of the normal distribution model are the formation positions of the macroscopic true cracks of the secondary seismic source unit body, as Figure 8 shown; if the frequency or wave velocity within the secondary seismic source unit body follows a distribution model with high values on both sides and low values in the middle, the position corresponding to the valley value is the formation position of the macroscopic crack, and thus the coordinate values of the formation position of the macroscopic crack in the local coordinate system of the primary seismic source unit body can be obtained. In Figure 8 the distribution models at positions ① and ② are also respectively shown.

[0095] According to the test results in the laboratory and mechanical theory, determine the picking threshold for the microcrack zones on both sides of the macroscopic crack. For example, if the acoustic emission energy within the secondary seismic source unit body follows a normal distribution function, integrating along both sides of the model symmetry axis can obtain the cumulative acoustic emission energy within the selected spatial range. If the proportion of the integrated energy to the total energy within the secondary seismic source unit body reaches a certain threshold p, such as 90%, the integration boundary is the boundary of the microcrack zone, and thus the coordinate values at the boundary of the microcrack zone can be determined. For example, if the acoustic emission wave velocity has a distribution characteristic of high values on both sides and low values in the middle within the secondary seismic source unit body, according to the test results and theoretical analysis, taking the wave velocity v0 as the threshold, the area with wave velocity lower than v0 is selected as the microcrack zone. Further, the coordinate values of the boundary of the microcrack zone within the secondary seismic source unit body in the local coordinate system of the primary seismic source unit body can be obtained.

[0096] After the formation positions of the macroscopic cracks and the boundary positions of the microcrack zones of all secondary units within the primary seismic source unit body are determined, connect the macroscopic cracks and the boundary positions of the microcrack zones of all secondary units within the primary seismic source unit body in sequence, then the macroscopic cracks and the microcrack zones within the seismic source strip of the primary seismic source unit body can be obtained.

[0097] In addition, for the multi-characteristic parameters of the energy of the seismic source, the number of seismic sources, the ring count, the wave velocity, the amplitude, the frequency, the seismic source mechanism, the duration, and the rise time, the macroscopic crack extension trajectory and the formation position of the micro-crack zone can be obtained through the above methods. To obtain the final recognition result for the crack trajectory identified by integrating multiple characteristic quantities, first, the recognition results of the seismic source characteristic parameters with recognition errors exceeding the threshold are excluded. Using the crack trajectories identified by different seismic source characteristic parameters as samples, and then a deep learning method is used for training to obtain the weights of the crack trajectories identified by different seismic source characteristic parameters, a deep learning model is obtained, and then the final macroscopic crack extension trajectory and micro-crack zone in the unit body are determined. The structure of this deep learning model is not limited.

[0098] Step 6: Reconstruct the cracks identified in each spatial unit body at both the spatial and temporal levels to obtain the macroscopic crack extension trajectory during the entire fracturing propagation process.

[0099] After the data analysis of the characteristic parameters of the cracks is completed, overall reconstruction is performed on each divided region and each level of unit. At the temporal level and the spatial level, first, the smallest unit body is reconstructed, and then layer-by-layer reconstruction is performed step by step from the smallest to the largest unit body. By performing multi-level and multi-directional analysis on the target seismic source body data, accurate identification of the true crack trajectory can be achieved. After the mathematical statistical model analysis of the three-level seismic source unit body is completed, all the three-level seismic source unit bodies are reconstructed and synthesized into the two-level seismic source unit body in the way of subdivision; similarly, after the analysis of the two-level seismic source unit body is completed, it is reconstructed into the one-level seismic source unit body according to the subdivision path, as Figure 9 shown, where the perspective ① and the perspective ② are the projection diagrams of the one-level seismic source unit body after reconstruction under different perspectives; after the reconstruction of the one-level seismic source unit body is completed, reconstruction is performed at the temporal level according to the division of time steps, and finally, a complete crack trajectory is formed.

[0100] Specifically, step 6 includes:

[0101] Reconstruct the three-level seismic source unit body that has completed the statistical analysis of the parameters into the two-level seismic source unit body, reconstruct all the two-level seismic source unit bodies into the one-level seismic source unit body, and reconstruct all the one-level seismic source unit bodies into the section macroscopic cracks corresponding to each time step. At this time, the reconstruction of the macroscopic crack region corresponding to the single time step divided is completed, realizing the reconstruction of each level of unit body divided within each time period at the spatial level. Then, connect the section macroscopic cracks according to the time nodes within each time step to complete the crack reconstruction at the temporal level and obtain the trajectory of the macroscopic crack, as Figure 10 shown, where the perspective ① and the perspective ② are the projection diagrams of the cracks after reconstruction at the temporal level under different perspectives.

[0102] This method proposes a method for intelligently identifying the extension trajectory of macroscopic fractures in a fracturing seismic source cluster, upgrading the description of the fracture extension trajectory from "only optimizing the seismic source location" to "identifying macroscopic fractures hidden in the seismic source cluster", introducing artificial intelligence to improve the recognition accuracy and efficiency, distinguishing macroscopic fractures from secondary small-scale fracture zones, and achieving high-precision recognition of the macroscopic fracture propagation trajectory. The seismic sources depicting the fracturing fractures are divided into multiple stages and multiple regions (units) in space and time, the spatial distributions of multiple parameters of the seismic source characteristics in the sub-regions are analyzed to find macroscopic fractures, and finally multi-region reconstruction and multi-stage reconstruction are carried out to obtain the macroscopic fractures hidden in the seismic source cluster; artificial intelligence algorithms are introduced in multiple stages of the recognition process.

[0103] The fracture recognition method proposed by the present invention is not only applicable to the recognition of fracturing fractures, but also can be applied to the fracture monitoring of any rock material, and is applicable to the trajectory recognition of two-dimensional and three-dimensional fractures. Regarding earthquakes, microseisms, ground sounds, and acoustic emissions, they are all elastic waves with frequencies ranging from low to high, and their essences are the same, only the frequency differences. Therefore, this method is also applicable to earthquake, microseism, ground sound, and acoustic emission monitoring.

Claims

1. A method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster, characterized in that: The steps include: Step 1, collecting elastic wave waveforms released by rock fracture or rupture, filtering the elastic wave waveforms using an intelligent filtering model, and recording the filtered elastic wave waveforms as high-quality waveforms; Step 2: Use deep learning methods to obtain the head starting point of the high-quality waveform, and then obtain the arrival time of the high-quality waveform, and obtain the source point according to the arrival time; Step 3, obtaining the earthquake source clusters of the earthquake source points, dividing the dynamic time steps, dividing the earthquake source clusters according to the dynamic time steps, and obtaining the earthquake source data volume within a single time step; Step 4, dividing the earthquake source data volume within a single time step into multi-level spatial units, and analyzing the distribution characteristics of multiple parameters of earthquake source characteristics within the spatial units; Step 5, identifying the macro cracks corresponding to each parameter according to the distribution characteristics, and obtaining the extension trajectory of the macro cracks in the unit body by integrating the macro cracks identified by each parameter; specifically including: According to the distribution law of each parameter, the coordinate value of the macro crack formation position in the secondary source unit body in the local space rectangular coordinate system of the primary source unit body is obtained; Set the picking threshold of the microcrack area on both sides of the macrocrack, and obtain the coordinate value of the boundary of the microcrack area in the secondary source unit in the local space rectangular coordinate system of the primary source unit according to the picking threshold; When the macro crack formation positions and micro crack zone boundary positions of all secondary units in the primary source unit are determined, the macro cracks of all secondary units in the primary source unit are connected in sequence, and the micro crack zone boundary positions are connected in sequence to obtain the macro crack extension trajectory and micro crack zone formation position in the primary source unit's source strip; Step 6, reconstructing the identified fractures in each spatial unit at both spatial and temporal levels to obtain the macroscopic fracture extension trajectory during the whole fracturing extension process; specifically including: Reconstruct the third-level seismic source unit body that has completed the statistical analysis of the parameters into the second-level seismic source unit body, reconstruct all the second-level seismic source unit bodies into the first-level seismic source unit bodies, and reconstruct all the first-level seismic source unit bodies into the segment macro cracks corresponding to each time step; The macroscopic fractures of the segments are connected according to the time nodes within each time step to obtain the trajectory of the macroscopic fractures.

2. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 1, characterized in that: In step 3, dividing the dynamic time steps includes: Dynamically identify the aggregation form of the point cluster in the earthquake source space, and determine whether the aggregation form conforms to the characteristics of non-planar turning expansion or bifurcation of the crack. If it conforms, continue to search for time points earlier than the current time until the aggregation form presents a single crack, and the corresponding time is taken as the end point of the time step; If the source distribution shows a single form throughout the entire process of crack expansion, it is necessary to determine whether the amount of spatial source data within a single time step is sufficient for the analysis of the multi-parameter spatial distribution law of source characteristics. If not, the time step needs to be increased until the minimum threshold of spatial source data is reached.

3. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 1, characterized in that: In step 4, dividing the earthquake source data volume within a single time step into multi-level spatial units includes: The earthquake source data volume in a single time step is divided into multiple first-level earthquake source unit volumes; The primary source unit body is divided into a plurality of secondary source unit bodies; The secondary seismic source unit volume is divided into multiple tertiary seismic source unit volumes.

4. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 3, characterized in that: The source data volume in a single time step is divided into multiple first-level source units, including: For the approximately elliptical fracturing crack with the injection point as the centroid, a cylindrical coordinate system is constructed with the injection point as the origin O and the normal line of the fracturing crack surface identified by the earthquake source cluster as the axis A. The cylindrical coordinate system parameters r and θ are located on the projection surface of the fracturing crack. When the crack does not expand along the trajectory with the injection point as the centroid, the centroid of the stage expansion of the irregular fracture expansion surface is selected as the origin O, and the normal line of the fracturing crack surface identified by the earthquake source cluster is used as the axis A to establish a cylindrical coordinate system. The cylindrical coordinate system parameters r and θ are located on the projection surface of the irregular fracturing crack. The infinite plane where the axis A and the polar coordinate r are located is the feature plane S0, and the two infinite planes S1 and S2 that are symmetric planes with the feature plane S0 and have the same normal are the data screening intervals, and the vertical distance between plane S1 and plane S2 is w; When the characteristic surface S0 rotates, plane S1 and plane S2 rotate with the characteristic surface; let the characteristic surface S0 pass through the injection point, and rotate the characteristic surface in increments of α degrees until it reaches 180°. During each rotation, the source data between plane S1 and plane S2 are used as the first-level source unit body, and the total number of first-level source unit bodies is 180 / α.

5. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 4, characterized in that: The first-level seismic source unit body is divided into multiple second-level seismic source unit bodies including: A local spatial rectangular coordinate system is established for each first-level seismic source unit body obtained, the Y axis is in the same direction as the polar coordinate r of the cylindrical coordinate system, the X, Y and Z axes conform to the directions specified by the right-hand system, the Z axis is the thickness direction of the first-level seismic source unit body, and the X axis is the width direction of the first-level seismic source unit body. The seismic source in the first-level seismic source unit body is projected onto the YZ plane to obtain a two-dimensional seismic source data volume; The first-level seismic source unit body is cut along the Y direction to form multiple parallel lines with a spacing of D and parallel to the X-axis, among which the seismic source point cluster covered between two adjacent parallel lines is regarded as the second-level seismic source unit body.

6. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 5, characterized in that: The secondary source unit body is divided into multiple tertiary source unit bodies including: Each secondary source unit body is split along the X direction to form multiple parallel lines with a spacing of d and parallel to the Y axis, where the source point cluster covered between two adjacent parallel lines is the tertiary source unit body; the size of the tertiary source unit body is D×d.

7. The method for intelligently identifying macroscopic fracture extension trajectories in a fracturing source cluster according to claim 6, characterized in that: In step 4, the distribution characteristics of multiple parameters of earthquake source characteristics within the spatial unit are analyzed, including: The third-level source unit is taken as the smallest statistical unit, and the multi-parameters of the waveform corresponding to the source are statistically analyzed. Then, the distribution characteristics of the multi-parameters of the source characteristics are determined in the second-level source unit using a mathematical statistical model.

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