Model construction method and system for multi-scale characterization of micro-fractures of clastic rock compaction

Abstract

This invention relates to the field of oil and gas field development and discloses a model construction method and system for multi-scale characterization of microfractures in compacted clastic rocks. The method includes: preparing artificial rock cores under simulated formation depth and pressure conditions within a reactor mold; simultaneously acquiring acoustic wave data axially passing through the artificial rock core during the compaction process; injecting epoxy resin adhesive into the artificial rock core after preparation; and slicing the artificial rock core to obtain corresponding cast thin sections after the adhesive has completely solidified; acquiring a full-view image of the cast thin sections and obtaining the number of fractures, fracture width, number of gravels, and surface fracture ratio from the full-view image; acquiring the number of acoustic pulse frames to obtain acoustic pulse frame images corresponding to different simulation depths; projecting the obtained surface fracture ratio onto the acoustic pulse frame image based on the same simulation depth conditions, and then correlating it with the full-view image of the cast thin sections to obtain a joint distribution model of image-surface fracture ratio-acoustic pulse frame number corresponding to the simulation depth, which is the final model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development, in particular to a model construction method and system for multi-scale characterization of compaction micro-fractures of clastic rocks based on static image analysis and dynamic acoustic wave monitoring. BACKGROUND

[0002] Due to the extreme geological characteristics such as poor physical properties (such as porosity < 8% and permeability < 0.1 mD in Bohai Bay Basin), strong heterogeneity (complexity of reservoir space is 3-5 times higher than that of shallow layer), high pressure and high temperature (pressure > 70 MPa, temperature > 150℃, vertical stress gradient > 2.0 MPa / 100m), etc. in deep reservoirs. At present, there are mainly four methods for characterization of micro-fractures: ①Micro-fracture identification in reservoirs is carried out by means of cores, thin sections, scanning electron microscopy and other means. This method can intuitively and accurately determine the development of micro-fractures, but the cost is high and it is difficult to continuously characterize the development of micro-fractures. ②Micro-fracture development sensitive section is identified by using conventional logging curve combination. This method can continuously evaluate the size of micro-fractures, and the logging curve data is rich, but the precision is low due to the small size of micro-fractures. ③For specific exploration areas, micro-fractures are identified and interpreted by using the powerful computing power of computers combined with actual experimental data of the region. This method depends on a large number of training samples to ensure accuracy, and cannot be used in areas with low exploration and development degree and has poor universality of the model. ④Sensitive information analysis is carried out on actual fracture data by using electric imaging logging method. This method has good effect on large-scale fractures, but it is difficult to accurately identify micro-fractures caused by compaction.

[0003] Based on the analysis of the existing research progress, the evaluation technology of formation micro-fractures still faces multi-dimensional technical bottlenecks, mainly including economic conditions restriction, characterization precision limitation, insufficient adaptability of exploration area and lack of dynamic monitoring. SUMMARY

[0004] In view of the above problems, the present application aims to provide a model construction method and system for multi-scale characterization of compaction micro-fractures of clastic rocks, which solves the problems of economy, precision, adaptability and dynamic monitoring in characterization of micro-fractures in deep reservoirs.

[0005] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a model construction method for multi-scale characterization of microcracks in compacted clastic rocks, comprising: preparing artificial rock cores under simulated formation depth pressure conditions in a reactor mold; simultaneously acquiring acoustic wave data passing through the artificial rock core axially during the compaction and diagenesis process; injecting epoxy resin colored glue into the artificial rock core after its preparation, and slicing the artificial rock core to obtain corresponding cast thin sections after the colored glue has completely solidified; acquiring a full-view image of the cast thin sections, and obtaining the number of cracks, crack width, number of gravels, and surface crack ratio through the full-view image; acquiring the number of acoustic pulse frames, and obtaining acoustic pulse frame number images corresponding to different simulation depths; based on the same simulation depth conditions, projecting the obtained surface crack ratio onto the acoustic pulse frame number image, and then correlating it with the full-view image of the cast thin sections, finally obtaining a joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulation depth, as a model for multi-scale characterization of microcracks in compacted clastic rocks.

[0006] Furthermore, artificial rock cores were prepared within a reactor mold under simulated pressure conditions at different formation depths. During the compaction and rock-forming process, acoustic data was simultaneously collected, passing axially through the artificial rock core. Specifically:

[0007] Acoustic data were collected using a monitoring experimental device for preparing artificial rock cores by simulating compaction. The monitoring experimental device for simulating the preparation of artificial rock cores includes an acoustic wave emission and acquisition system for collecting acoustic wave data axially passing through the artificial rock core during the compaction and rock formation process.

[0008] Furthermore, in the joint distribution model of image-face crack ratio-acoustic pulse frame number corresponding to the simulated depth, the image is the full-view image of the cast thin section of the artificial rock core obtained under simulated different depth conditions; the face crack ratio is the percentage of the crack area in each full-view image to the total area of ​​the thin section's view; and the acoustic pulse frame number is the number of acoustic pulse sequences with significant energy transitions per unit time in the acoustic wave data collected during the compaction and diagenesis of the artificial rock core, which passes through the artificial rock core axially.

[0009] Furthermore, a significant energy transition refers to a sudden increase in pulse energy compared to the background noise exceeding both the primary and secondary thresholds; The primary threshold is: pulse energy E ≥ 3σ b , σ b The standard deviation of background noise; The secondary threshold is: pulse energy. E ≥1.5 w , w This represents the average energy within the sliding window.

[0010] Furthermore, the seam ratio is: , in, This represents the area of ​​a single crack. denoted as the total area of ​​the thin section; N represents the number of cracks.

[0011] Furthermore, the number of acoustic pulse frames is:

[0012] in, N represents the number of sound pulse frames. 有效 Indicates the number of valid pulses; P i The power of a single pulse is expressed in W; dB is the average decibel value of the signal, relative to the reference power P0=1 W; Δt is the analysis time window, expressed in seconds.

[0013] Furthermore, the method for obtaining the full-view image of the cast thin section is as follows: six adjacent views of each cast thin section are taken and image analysis software is used to stitch the images together to finally form a full-view image full of characteristic minerals.

[0014] Secondly, the technical solution adopted by this invention is as follows: a model construction system for multi-scale characterization of microcracks in compacted clastic rocks, comprising: an acoustic data acquisition module, which prepares artificial rock cores under simulated pressure conditions at different formation depths in a reactor mold, and simultaneously acquires acoustic data passing through the artificial rock core axially during the compaction and diagenesis process; a casting thin-section acquisition module, which injects epoxy resin colored glue into the artificial rock core after its preparation, and slices the artificial rock core to obtain corresponding casting thin sections after the colored glue has completely solidified; a parameter acquisition module, which acquires the full-view image of the casting thin section and obtains the number of cracks, crack width, number of gravels, and surface crack ratio through the full-view image; an acoustic pulse frame number image acquisition module, which acquires the acoustic pulse frame number and obtains acoustic pulse frame number images corresponding to different simulation depths; and a model construction module, which projects the obtained surface crack ratio onto the acoustic pulse frame number image based on the same simulation depth conditions, and then correlates it with the full-view image of the casting thin section to finally obtain a joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulation depth, which serves as a model for multi-scale characterization of microcracks in compacted clastic rocks.

[0015] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0016] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention solves the problems of economy, accuracy, adaptability and dynamic monitoring in the characterization of microfractures in deep reservoirs by using a dual-modal technology of "static image analysis + dynamic acoustic monitoring". It provides an innovative tool for the efficient development of unconventional oil and gas resources and has significant academic value and economic benefits.

[0018] 2. When performing multi-scale characterization of microcracks, the model obtained in this invention verifies dynamic evolution through static structure, resulting in smaller errors and easier interpretation of the causes of complex crack networks. It correlates the peak period of APFD with the position of the thin section under the microscope to locate active crack regions. It identifies cracks >50 μm under the microscope and detects microcracks <50 μm using APFD, jointly constructing a full-scale crack network.

[0019] 3. The experimental apparatus used in this invention can realize real-time monitoring of the compaction process, thereby enabling the monitoring results to be used in conjunction with high-resolution imaging in the later stage. Attached Figure Description

[0020] Figure 1 This is a flowchart of the model construction method for multi-scale characterization of microcracks in compacted clastic rock in an embodiment of the present invention; Figure 2 This is a schematic diagram of the simulation experimental apparatus used in Embodiment 2 of the present invention; Figure 3 The images shown are before and after stitching of images under a microscope in Embodiment 2 of the present invention; where a, b, c, d, e, and f represent different fields of view images before stitching.

[0021] Figure 4 The images show the mirror-like appearance and characteristics of the simulated compacted granite obtained in Example 2 of this invention; wherein: A. Simulation depth 2000m, sandstone is mainly composed of intergranular pores with point contact between particles; B. Simulation depth 3000m, mainly point contact between clastic particles and predominantly primary intergranular pores; C. Simulation depth 4000m, mainly point contact between clastic particles; D. Simulation depth 5000m, clastic particles have point-to-line contact, a large number of particles are crushed, and locally show conjugate bidirectional oriented arrangement; E. Clastic particles have point-to-line contact, a large number of particles are crushed, and locally show conjugate bidirectional oriented arrangement; blue in the figures represents castings; Figure 5This is a diagram showing the acoustic pulse density distribution of granite obtained in Embodiment 2 of the present invention; Figure 6 The curves showing the change of crack density and acoustic pulse number (APFD) with depth obtained in Embodiment 2 of the present invention are shown. Detailed Implementation

[0022] To address the challenges of economy, accuracy, adaptability, and dynamic monitoring in the characterization of microfractures in deep reservoirs, this invention provides a model construction method and system for multi-scale characterization of compacted microfractures in clastic rocks. The constructed model is a joint distribution model of image-fracture ratio-acoustic pulse frame count corresponding to the simulated depth. The image is a full-view image of a cast thin section of an artificial rock core obtained under simulated depth conditions; the fracture ratio is the percentage of fracture area to the total area of ​​the thin section in each full-view image. This invention solves the challenges of economy, accuracy, adaptability, and dynamic monitoring in the characterization of microfractures in deep reservoirs through a dual-modal technology of "static image analysis + dynamic acoustic monitoring," providing an innovative tool for the efficient development of unconventional oil and gas resources.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] Example 1: In this embodiment of the invention, a monitoring experimental device for simulating compaction in the preparation of artificial rock cores is provided, such as... Figure 2 As shown, the device is mainly used for analyzing the acoustic wave propagation characteristics of rock samples or other solid materials under simulated complex stress environments.

[0026] This device achieves coordinated operation of sound wave emission, signal acquisition, pressure regulation, and data processing through a modular design, offering advantages such as high sensitivity, real-time monitoring, and adjustable multiple parameters. The following is a detailed description of the device's core components: The device includes a reactor mold 1 for holding rock core material, a pressure supply system 2 for providing pressure to the rock core material in the reactor mold 1, and a carrying system 3 for supporting the reactor mold 1; the pressure supply system 2 includes an axial pressure pump 2-1 and a pressing plug 2-2; the carrying system 3 includes a carrying platform 3-1, a fixed frame 3-2 set on the carrying platform 3-1, and a column 3-3 set on the carrying platform 3-1 for supporting the reactor mold 1; the reactor mold 1 is set between the pressing plug 2-2 and the column 3-3; it also includes an acoustic wave emission and acquisition system 4 for collecting acoustic wave data axially passing through the artificial rock core during the artificial rock core compaction and rock formation process.

[0027] In fact, the experimental setup in this embodiment can be based on an existing experimental setup, with the addition of an acoustic wave emission and acquisition system 4. This system is used to acquire acoustic wave data axially passing through the artificial rock core during the compaction and rock formation process, in order to obtain the number of acoustic pulse frames required for experimental analysis. Then, acoustic pulse frame images corresponding to different simulated depths (such as...) can be exported. Figure 5 (As shown). For example, the relevant experimental equipment of Chengdu Haohan Well Completion Rock Electricity Technology Co., Ltd. can be used.

[0028] In the above embodiments, the acoustic wave emission and acquisition system 4 includes an emission probe 4-1 disposed on the top of the reactor mold 1, an acoustic wave acquisition port 4-2 disposed on the bottom of the reactor mold 1, and an oscilloscope 4-3 connected to the emission probe 4-1 and the acoustic wave acquisition port 4-2.

[0029] In this embodiment, the transmitting probe 4-1 can use a piezoelectric ceramic or electromagnetic ultrasonic transducer as the sound wave source, which can generate high-frequency (10 kHz-1 MHz) pulse or continuous sound wave signals. The signal frequency and amplitude are precisely adjusted by the control system to ensure the stability and repeatability of the excitation waveform.

[0030] The acoustic acquisition port 4-2 contains multiple broadband piezoelectric sensor arrays (sensitivity > 1 mV / Pa) for receiving acoustic signals penetrating the rock sample. The sensor layout supports multi-mode detection of longitudinal waves, transverse waves, and surface waves, and the data is transmitted to an oscilloscope via a shielded cable.

[0031] The oscilloscope 4-3 can use a high-speed digital oscilloscope (maximum real-time sampling rate 1GSa / s, maximum storage depth 24Mpts, maximum waveform capture rate 30000wfm / s) to achieve full waveform capture of the microcrack propagation process. The built-in algorithm can automatically calculate parameters such as the number of acoustic pulse frames (see Formula 2 for the calculation method), propagation speed, and spectral characteristics.

[0032] In the above embodiments, the device further includes a control system 5 for controlling the pressure supply system 2 to simultaneously acquire acoustic wave data collected by the oscilloscope 4-3. The control system 5 can also monitor the operating status of the pressure supply system 2.

[0033] In this embodiment, the control system 5 can be based on an embedded microprocessor to coordinate the triggering timing of the transmitting probe 4-1, the loading curve of the axial pressure pump 2-1, and the synchronization of data acquisition. It can also further provide a human-machine interface (PC software) to support experimental parameter presets, process monitoring, and data storage and export.

[0034] In the above embodiments, the rock sample stage 3-1 is made of high-strength alloy material and has positioning grooves and fixing clamps on its surface for stably holding the rock sample to be tested (size range: diameter 20-100 mm, height 10-50 mm), and is equipped with a vibration damping layer to reduce external interference. The stage 3-1 is rigidly connected to the axial pressure pump 2-1 to ensure that the pressure is uniformly transmitted to the rock sample.

[0035] In the above embodiments, the axial pressure pump 2-1 integrates a servo motor and a hydraulic system, capable of applying axial pressure from 0 to 250 MPa to simulate the formation stress environment. The pressure value is controlled through closed-loop feedback with an accuracy of ±0.1 MPa, supporting both static loading and dynamic cyclic loading modes.

[0036] In summary, this device achieves synchronous analysis of stress-acoustic characteristics through the linkage of the pressure supply system 2 and the acoustic wave emission and acquisition system 4. Furthermore, external temperature or humidity control modules can be added to this device to meet diverse experimental needs. Moreover, the experimental setup can be continuously upgraded by replacing the oscilloscope with one of higher sampling rates, thereby improving the overall accuracy of the experiment and enhancing the characterization of cracks.

[0037] Example 2: In this embodiment of the invention, a model construction method for multi-scale characterization of microcracks in compacted clastic rock is provided, based on the experimental setup in Example 1. In this embodiment, as... Figure 1 As shown, the method includes the following steps: 1) Artificial rock cores were prepared in a reactor mold under pressure conditions at different formation depths, and acoustic wave data passing through the artificial rock core axially were collected simultaneously during the compaction and rock formation process. 2) After the artificial rock core is prepared, epoxy resin colored glue is injected, and after the colored glue has completely solidified, the artificial rock core is sliced ​​to obtain the corresponding casting sheet. 3) Obtain the full-view image of the cast thin section, and obtain the number of cracks, crack width, number of gravel and surface crack ratio through the full-view image; 4) Obtain the acoustic pulse frame count (APFD) and get the acoustic pulse frame count images corresponding to different simulation depths; 5) Based on the same simulated depth conditions, the obtained surface crack ratio is projected onto the acoustic pulse frame number image, and then compared with the full-view image of the cast thin section to finally obtain the joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulated depth, which serves as a model for multi-scale characterization of microcracks in compacted clastic rocks.

[0038] In step 1) above, artificial rock cores are prepared under simulated pressure conditions at different formation depths within a reactor mold. During the compaction and rock-forming process, acoustic data is simultaneously collected, passing axially through the artificial rock core. Specifically: Acoustic data were collected using a monitoring experimental device for preparing artificial rock cores by simulating compaction. The monitoring experimental device for simulating the preparation of artificial rock cores includes an acoustic wave emission and acquisition system for collecting acoustic wave data axially passing through the artificial rock core during the compaction and rock formation process.

[0039] In step 5) above, in the joint distribution model of image-face crack ratio-acoustic pulse frame number corresponding to the simulated depth, the image is the full-view image of the cast thin section of the artificial rock core obtained under simulated different depth conditions; the face crack ratio is the percentage of the crack area in each full-view image to the total area of ​​the thin section's view; and the acoustic pulse frame number is the number of acoustic pulse sequences with significant energy transitions per unit time in the acoustic wave data collected during the compaction and diagenesis of the artificial rock core, which passes through the artificial rock core axially.

[0040] In this embodiment, a significant energy transition means that the pulse energy increases beyond the primary threshold and the secondary threshold compared to the background noise.

[0041] The primary threshold is: pulse energy E ≥ 3σ. b , σ b The background noise standard deviation is used; the secondary threshold is the pulse energy. E ≥1.5 w , w This represents the average energy within the sliding window.

[0042] In the above embodiments, the seam ratio is: , in, This represents the area of ​​a single crack. denoted as the total area of ​​the thin section; N represents the number of cracks.

[0043] In the above embodiment, the number of acoustic pulse frames is:

[0044] in, N represents the number of sound pulse frames. 有效 Indicates the number of valid pulses;P i The power of a single pulse is expressed in W; dB is the average decibel value of the signal, relative to the reference power P0=1 W; Δt is the analysis time window, expressed in seconds.

[0045] In the above embodiments, the method for obtaining the full-view image of the casting thin slice is as follows: six adjacent views of each casting thin slice are taken and image analysis software is used to stitch the images together to finally form a full-view image full of characteristic minerals.

[0046] Specifically, you can use the software CorelDRAW Standard 2021 to stitch the images. When stitching, paste two microscopic images containing the characteristic minerals. Click on the images, select "Object," and in "Properties," adjust the "Opacity" to around 50% to align the characteristic minerals in the images. Then restore the "Opacity" and repeat this process to stitch the remaining images. Figure 3 It consists of six adjacent viewports and a full viewport image stitched together from the six viewports. Besides the software mentioned above, other image processing software can also be used.

[0047] In the above embodiments, the method for dividing the evolutionary stages using the statistical model of image-surface seam ratio-acoustic pulse frame number obtained in this invention is as follows: Based on the observation that inherited fractures account for >70% and APFD values ​​are high, the location of the early compaction stage is determined; when non-inherited fractures account for 40% and APFD values ​​decline, it is determined to be the intermediate compaction stage; when newly generated non-inherited fractures account for >30% and APFD values ​​rise, it is determined to be the late compaction stage. If abnormal data is found during the experiment, it is necessary to check whether it is caused by clay filling or equipment noise, and verify whether the fractures are closed under microscopic observation to finally identify the active fractured zone in the simulated reservoir.

[0048] Example 3, based on the experimental setup in Example 1 and the method in Example 2, uses the Bozhong 19-6 Kongdian Formation as the research object to demonstrate the construction method of the multi-scale characterization model of clastic rock compaction microcracks based on static image analysis and dynamic acoustic wave monitoring. The method specifically includes the following steps: (1) Obtain core data of the exploration area (Kongdian Formation of Bozhong 19-6) (core data includes lithology, actual clast grain size and actual clast composition) to confirm that the mineral grain composition of the Kongdian Formation sandstone and conglomerate reservoir in the Bozhong 19-6 block of the study area is mainly quartz, feldspar and rock fragments. Among them, the igneous rock fragments are mainly granite rock fragments. According to the actual clast grain size and composition of the study area, the corresponding single minerals are mixed according to the corresponding composition. According to the clay mineral type and content of the study area, the clay minerals are crushed into clay grade and added. At the same time, the corresponding proportion of clast rock fragments are added and the above minerals are fully mixed. The specific materials and dosages are: quartz 16%, potassium feldspar 7%, plagioclase 14%, clay minerals 10%, and rock fragments 53%.

[0049] (2) Prepare several “artificial rock cores” according to the content of clastic components of sandstone and conglomerate in the study area (five samples are prepared in this embodiment). Pour them into beakers and add distilled water and stir evenly (note that the sand samples should be poured into the beakers in order of mass from large to small to prevent the small rock samples from sinking to the bottom of the beakers during stirring). Finally, add the required gravel to the beakers and continue stirring until evenly mixed. Soak for two days.

[0050] (3) Each sample is placed into a simulated compaction reactor mold. After placement, it is best to tap the outer wall of the reactor a few times with moderate force to simulate the directional action of sediment transport. When placing the sample, apply petroleum jelly to the surface of the reactor body that contacts the rock core to reduce friction.

[0051] (4) The simulation conditions are shown in Table 1. In the control system, set the parameters according to the conditions in the last row of Table 1, and input the target pressure (pressure corresponding to the simulation depth) and holding time.

[0052] Table 1. Compaction Simulation Experiment Conditions

[0053] (5) Control the operation of the pressure supply system, and at the same time, the sound wave emission and acquisition system starts to work.

[0054] (6) After reaching the simulated pressure conditions, stabilize the pressure for 30 minutes and then unload the pressure.

[0055] (7) Place the reactor mold together with the rock core inside into a vacuum glue injection machine and inject epoxy resin color glue. The glue must completely cover the rock core.

[0056] (8) After the colored glue in the vessel has completely solidified, the rock core inside is taken out and sliced ​​to obtain the corresponding casting thin slice. When slicing, the casting thin slice is cut at a distance of 1.5cm from the top.

[0057] (9) The five thin sections of the cast body were observed under a microscope. Since the single field of view of the microscope is limited and cannot cover all the features of the rock sample, six adjacent fields of view were taken from each thin section of the cast body and image analysis software was used for image stitching. In this embodiment, CorelDRAW Standard 2021 software was used for image stitching. The stitching process is as described in Example 2, and the stitching result is as follows: Figure 4 As shown, the stitched images were analyzed for surface seam ratio, grain size, and crack characteristics to study the evolution of microcracks caused by compaction. Multi-view stitching can eliminate edge distortion caused by video recording and improve the statistical accuracy of surface seam ratio; all six views are statistically significant.

[0058] The number of cracks in the stitched large-field-of-view image is counted to calculate the crack ratio. The formula is as follows: , in The area of ​​a single crack. This represents the total visual area after the thin slices are stitched together.

[0059]

[0060] The area of ​​a single crack can be determined by defining the crack area using Ps, and then deriving the crack area from the defined pixel area. The crack width is a non-fixed value: the width can be measured every 10 μm along the crack direction, and the average value is taken as the representative value.

[0061] (10) The acoustic data acquired by the oscilloscope is processed, and the number of acoustic pulse frames is used as a characterization parameter. The dynamic evolution of cracks during the compaction and diagenesis process can be quantitatively characterized by the analysis of the time-domain characteristics of the acoustic signal. The number of acoustic pulse frames (APFD) is defined as the number of acoustic pulse sequences with significant energy transitions per unit time. Among them, significant energy transitions refer to the pulse energy suddenly increasing from the background noise and exceeding both the primary threshold and the secondary threshold. The primary threshold is: pulse energy E≥3σ. b , σ b Background noise standard deviation; Secondary threshold: pulse energy E ≥1.5 w , w This represents the average energy within the sliding window.

[0062] The formula is:

[0063] Where, N 有效 : Number of effective pulses; P i: Power of a single pulse (unit: W); dB: Average decibel value of the signal (relative to the reference power P0=1 W); Δt: Analysis time window (unit: seconds).

[0064] (11) Based on the same simulated depth conditions, the surface seam ratio is projected onto the acoustic pulse frame number image (e.g. Figure 5 As shown, the acoustic pulse frame count image was directly exported from the oscilloscope. This image was then compared with the full-view image of the cast thin section to obtain a statistical model of image-surface fracture ratio-acoustic pulse frame count for multi-scale characterization of microcracks in compacted clastic rock (e.g., ...). Figure 6 (As shown).

[0065] A comprehensive analysis was conducted based on the model obtained in step (11). According to the diagenesis and experimental conditions, the diagenetic process was divided into the following stages, as shown in Table 2.

[0066] Table 2 Compaction Stage Division

[0067] Image analysis within the model reveals that: overall, the gravel content remains relatively stable; therefore, the porosity parameters of various fractures are not significantly affected by the gravel content. When rocks experience stresses exceeding their critical stress in their diagenetic environment, rigid particles in medium- to coarse-grained sandstone and conglomerate are prone to fracture along the direction parallel to compressive stress, forming cracks. Brittle particles such as feldspar are easily compressed and fracture along specific directions, also forming cracks. In terms of the number of cracks, there is an overall increasing trend before 4000m, indicating widespread crack development during this stage. A significant decreasing trend occurs between 4000m and 4500m, as many cracks are fully opened under strong stress during this stage, leading to a reduction in the number of cracks. However, the number of cracks increases again between 4500m and 6000m, representing the formation of a new batch of cracks.

[0068] In terms of crack width, there is a significant decrease between 2000 and 3000 m. During this stage, most of the wider cracks from the first phase (mainly inherited cracks) have fully opened, leading to a reduction in crack width. From 3000 to 5000 m, the width gradually increases again, but then decreases sharply between 5000 and 6000 m, indicating that the second phase of cracks developed more extensively after 5000 m.

[0069] The APFD response characteristics at different compaction stages are highly consistent with microscopic observations. In the early stage of compaction, high-frequency, low-energy pulses account for a high proportion, reflecting the dense generation of microcracks dominated by mechanical compaction (inherited cracks account for >70%). Subsequently, APFD gradually decreases and the pulse energy distribution tends to flatten, corresponding to the clay mineral filling and crack closure process (non-inherited cracks account for 40%). After 4500m, the proportion of high-frequency events increases (>30%), indicating the regeneration of conjugate shear cracks (X-shaped arrangement) under high pressure stress.

[0070] Phase division based on comprehensive analysis: 1. Early compaction stage (2000~3000 m): Microscopic examination revealed that inherited cracks predominated (accounting for >70%), with the cracks being curved and varying in width. The particles were mainly in point contact, and the intergranular pores showed good connectivity.

[0071] APFD response: High value region (1191~985 frames / s, high frequency and low energy) reflects the dense generation of microcracks.

[0072] Due to mechanical compaction, the rearrangement of mineral particles causes pressure cracks to form inside rigid particles (quartz, feldspar), with the direction perpendicular to the tangent of the particle contact surface.

[0073] 2. Intermediate compaction stage (3000~5000 m): Microscopic examination revealed an increase in the proportion of non-inherited cracks (40%), with straight cracks and a transition in particle contact mode from point contact to point-line contact.

[0074] APFD response: Continuous decrease (985→499 frames / s), reflecting a slowdown or closure of crack propagation rate.

[0075] Quartz particles undergo pressure solution to form suture lines, while clay minerals fill early-stage cracks. The plastic flow of clay minerals fills these early-stage cracks, leading to the closure of microcracks.

[0076] 3. Late compaction stage (5000~6000 m): Microscopic examination revealed that inherited and non-inherited cracks coexisted (each accounting for 50%), with newly formed cracks extending along the foliation surface. Grain contact was mainly line contact, with local conjugate bidirectional arrangement.

[0077] APFD response: Secondary porosity and microcrack regeneration, quartz particles generate X-shaped shear cracks under high confining pressure.

[0078]

[0079] like Figure 6 As shown, the number of acoustic pulse frames obtained in this embodiment is consistent with the statistical data obtained under the microscope: Crack ratio: increases before 4000m (mechanical compaction is dominant), decreases from 4000 to 4500m (filling and closing), and increases again from 4500 to 6000m (new crack regeneration).

[0080] APFD trend: high value in the early stage → decline in the middle stage → rebound in the late stage, consistent with the evolution of microscopic crack types.

[0081] The advantages of using the model obtained by the method of this invention for comprehensive characterization are: static structure verification of dynamic evolution, smaller error, and easier interpretation of the causes of complex crack networks; correlation between APFD peak periods and microscopic thin section positions to locate active crack regions; microscopic identification of cracks >50 μm and APFD detection of microcracks <50 μm, jointly constructing a full-scale crack network; modular device design to adapt to various experimental conditions; real-time monitoring and high-resolution imaging work together to overcome the limitations of traditional methods.

[0082] Example 4: In this embodiment of the invention, a model building system for multi-scale characterization of microcracks in compacted clastic rock is provided, comprising: The acoustic data acquisition module simulates different formation depths and pressure conditions to prepare artificial rock cores inside the reactor mold, and simultaneously acquires acoustic data that passes through the artificial rock core axially during the compaction and rock formation process. The casting sheet acquisition module involves injecting epoxy resin colored glue after the artificial rock core is prepared, and then slicing the artificial rock core to obtain the corresponding casting sheet after the colored glue has completely solidified. The parameter acquisition module acquires a full-view image of the cast thin section and obtains the number of cracks, crack width, number of gravel, and surface crack ratio through the full-view image; The acoustic pulse frame number image acquisition module acquires the acoustic pulse frame number and obtains acoustic pulse frame number images corresponding to different simulation depths; The model building module, based on the same simulation depth conditions, projects the obtained surface crack ratio onto the acoustic pulse frame number image, and then compares it with the full-view image of the cast thin section to finally obtain a joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulation depth, which serves as a model for multi-scale characterization of microcracks in compacted clastic rocks.

[0083] In the above embodiments, artificial rock cores are prepared under simulated pressure conditions at different formation depths within a reactor mold. During the compaction and rock formation process, acoustic data is simultaneously collected from the artificial rock core axially. Specifically: Acoustic data were collected using a monitoring experimental device for preparing artificial rock cores by simulating compaction. The monitoring experimental device for simulating the preparation of artificial rock cores includes an acoustic wave emission and acquisition system for collecting acoustic wave data axially passing through the artificial rock core during the compaction and rock formation process.

[0084] In the above embodiments, in the joint distribution model of image-face crack ratio-acoustic pulse frame number corresponding to the simulated depth, the image is the full-view image of the cast thin section of the artificial rock core obtained under simulated different depth conditions; the face crack ratio is the percentage of the crack area in each full-view image to the total area of ​​the thin section's view; and the acoustic pulse frame number is the number of acoustic pulse sequences with significant energy transitions per unit time in the acoustic wave data collected during the compaction and diagenesis of the artificial rock core, which passes through the artificial rock core axially.

[0085] In the above embodiments, a significant energy transition refers to a pulse energy that increases rapidly compared to the background noise, exceeding both the primary and secondary thresholds. The primary threshold is: pulse energy E ≥ 3σ b , σ b The standard deviation of background noise; The secondary threshold is: pulse energy. E ≥1.5 w , w This represents the average energy within the sliding window.

[0086] In the above embodiments, the seam ratio is: , in, This represents the area of ​​a single crack. The total area of ​​the thin section is denoted by N; N represents the number of cracks.

[0087] In the above embodiment, the number of acoustic pulse frames is:

[0088] in, N represents the number of sound pulse frames. 有效 Indicates the number of valid pulses; P i The power of a single pulse is expressed in W; dB is the average decibel value of the signal, relative to the reference power P0=1 W; Δt is the analysis time window, expressed in seconds.

[0089] In the above embodiments, the method for obtaining the full-view image of the casting thin slice is as follows: six adjacent views of each casting thin slice are taken and image analysis software is used to stitch the images together to finally form a full-view image full of characteristic minerals.

[0090] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0091] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0092] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] In one embodiment of the present invention, a computer program product is provided, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0094] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model construction method for multi-scale characterization of microcracks in compacted clastic rocks, characterized in that, include: Artificial rock cores were prepared in a reactor mold under pressure conditions at different formation depths, and acoustic data were collected axially through the artificial rock cores during the compaction and rock formation process. After the artificial rock core is prepared, epoxy resin colored glue is injected, and after the colored glue has completely solidified, the artificial rock core is sliced ​​to obtain the corresponding casting sheet. Obtain a full-view image of the cast thin section, and obtain the number of cracks, crack width, number of gravel, and surface crack ratio from the full-view image; The number of acoustic pulse frames was obtained, and images of the number of acoustic pulse frames corresponding to different simulation depths were obtained. Based on the same simulated depth conditions, the obtained surface crack ratio is projected onto the acoustic pulse frame number image, and then compared with the full-view image of the cast thin section. Finally, a joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulated depth is obtained, which serves as a model for multi-scale characterization of microcracks in compacted clastic rocks.

2. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 1, characterized in that, Artificial rock cores were prepared within a reactor mold under simulated pressure conditions at different formation depths. During the compaction and lithification process, acoustic data was simultaneously collected from the artificial rock core, passing axially through it. Specifically: Acoustic data were collected using a monitoring experimental device for preparing artificial rock cores by simulating compaction. The monitoring experimental device for simulating the preparation of artificial rock cores includes an acoustic wave emission and acquisition system for collecting acoustic wave data axially passing through the artificial rock core during the compaction and rock formation process.

3. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 1, characterized in that, In the joint distribution model of image-face crack ratio-acoustic pulse frame count corresponding to the simulated depth, the image is the full-view image of the cast thin section of the artificial rock core obtained under simulated different depth conditions; the face crack ratio is the percentage of the crack area to the total area of ​​the thin section in each full-view image; and the acoustic pulse frame count is the number of acoustic pulse sequences with significant energy transitions per unit time in the acoustic wave data collected during the compaction and diagenesis of the artificial rock core that pass through the artificial rock core axially.

4. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 3, characterized in that, Significant energy transitions refer to a sudden increase in pulse energy compared to the background noise exceeding both the primary and secondary thresholds. The primary threshold is: pulse energy E ≥ 3σ b , σ b The standard deviation of background noise; The secondary threshold is: pulse energy. E ≥1.5 w , w This represents the average energy within the sliding window.

5. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 1, characterized in that, The seam ratio is: ， in, This represents the area of ​​a single crack. denoted as the total area of ​​the thin section; N represents the number of cracks.

6. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 1, characterized in that, The number of sound pulse frames is: in, N represents the number of sound pulse frames. 有效 Indicates the number of valid pulses; P i The power of a single pulse is expressed in W; dB is the average decibel value of the signal, relative to the reference power P0=1 W; Δt is the analysis time window, expressed in seconds.

7. The model construction method for multi-scale characterization of compacted microcracks in clastic rock as described in claim 1, characterized in that, The method for obtaining the full-view image of the cast thin section is as follows: six adjacent views are taken from each cast thin section and image analysis software is used to stitch the images together to form a full-view image full of characteristic minerals.

8. A model construction system for multi-scale characterization of microcracks in compacted clastic rocks, characterized in that, include: The acoustic data acquisition module simulates different formation depths and pressure conditions to prepare artificial rock cores inside the reactor mold, and simultaneously acquires acoustic data that passes through the artificial rock core axially during the compaction and rock formation process. The casting sheet acquisition module involves injecting epoxy resin colored glue after the artificial rock core is prepared, and then slicing the artificial rock core to obtain the corresponding casting sheet after the colored glue has completely solidified. The parameter acquisition module acquires a full-view image of the cast thin section and obtains the number of cracks, crack width, number of gravel, and surface crack ratio through the full-view image; The acoustic pulse frame number image acquisition module acquires the acoustic pulse frame number and obtains acoustic pulse frame number images corresponding to different simulation depths; The model building module, based on the same simulation depth conditions, projects the obtained surface crack ratio onto the acoustic pulse frame number image, and then compares it with the full-view image of the cast thin section to finally obtain a joint distribution model of image-surface crack ratio-acoustic pulse frame number corresponding to the simulation depth, which serves as a model for multi-scale characterization of microcracks in compacted clastic rocks.

9. A computer-readable storage medium for storing one or more programs, characterized in that, One or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, memory, and one or more programs, wherein the one or more programs are stored in memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described in claims 1 to 7.

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