A visual fracturing physical simulation method and simulation system based on multi-source signal mutual correction

Abstract

The application discloses a kind of visual fracturing physical simulation method and simulation system based on multi-source signal mutual correction, it is related to petroleum and natural gas reservoir reconstruction technical field, wherein, simulation method includes: in the process of rock sample fracturing, the displacement characteristics, strain characteristics and energy characteristics of rock sample are acquired simultaneously;The event of detected displacement characteristics is determined as reference crack event, and the strain characteristics and energy characteristics corresponding to reference crack event are acquired;The crack event discrimination threshold is established based on the strain characteristics corresponding to reference crack event;For the event of detected energy characteristics, the corresponding strain characteristics are compared with crack event discrimination threshold;According to the comparison result, the event of strain characteristics meeting threshold is determined as effective crack event, and the event of strain characteristics not meeting threshold is determined as noise event;The application can effectively solve the problem that noise signal is difficult to suppress and the consistency of fracturing process analysis result and actual crack propagation condition is poor.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir stimulation technology, and more specifically, to a visual fracturing physical simulation method and simulation system based on multi-source signal mutual correction. Background Technology

[0002] Hydraulic fracturing is an important technical means for the stimulation of unconventional oil and gas reservoirs. It promotes the formation of rock fractures and seepage channels by injecting high-pressure fluids into the formation, thereby improving the development efficiency of oil and gas resources. In order to study the fracture initiation mechanism, propagation law and fracturing parameter response characteristics, it is usually necessary to carry out fracturing physical simulation tests under laboratory conditions.

[0003] In existing physical simulation tests of rock fracturing, fiber optic monitoring technology or acoustic emission monitoring technology is usually used to monitor the rock sample fracturing process. Fiber optic monitoring technology can obtain strain response information inside and on the surface of the rock sample, while acoustic emission monitoring technology can obtain energy information released during the initiation and propagation of cracks inside the rock sample.

[0004] However, the aforementioned monitoring technologies are usually used as independent monitoring methods. Different monitoring methods obtain data from different sources, represent different objects, and have different response characteristics. The lack of unified correlation criteria and mutual verification mechanisms makes it difficult to establish an effective correspondence between various monitoring results, thus making it difficult to accurately reflect the real physical behavior during crack propagation.

[0005] Especially during fracturing, the energy mutation signal obtained by acoustic emission monitoring is easily affected by equipment vibration, environmental noise, local friction and non-crack rupture behavior. The strain anomaly signal obtained by fiber optic monitoring may also be affected by local stress disturbance or sensing error. When crack event identification is based on a single monitoring signal, non-crack activities are easily misjudged as crack propagation events, which in turn affects the accuracy of the monitoring results.

[0006] At the same time, the initiation and propagation of cracks inside rock samples are highly concealed. Some crack activities occur only inside the rock sample and have not yet formed directly observable crack morphologies. If analysis is based solely on internal monitoring results, it is difficult to determine whether the relevant signals correspond to actual crack propagation behavior. Therefore, it is difficult to accurately classify and effectively identify crack events.

[0007] Furthermore, existing technologies typically lack the means to calibrate and verify internal monitoring signals, making it impossible to establish criteria for judging internal monitoring signals based on confirmed crack propagation events, or to suppress noise signals by utilizing the correspondence between different monitoring signals. This results in discrepancies between fracturing monitoring results and actual crack propagation.

[0008] In summary, how to solve the problems of noise signal suppression and poor consistency between fracturing process analysis results and actual crack propagation in existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, the purpose of this invention is to provide a visualization fracturing physical simulation method and system based on multi-source signal mutual correction, which can effectively solve the problems of difficulty in suppressing noise signals and poor consistency between fracturing process analysis results and actual fracture propagation.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] A visualization-based physical simulation method for fracturing based on multi-source signal cross-correction includes:

[0012] During the rock sample fracturing process, the displacement characteristics, strain characteristics, and energy characteristics of the rock sample are acquired simultaneously.

[0013] Events that detect displacement features are identified as benchmark crack events, and strain and energy features corresponding to the benchmark crack events are obtained.

[0014] A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event;

[0015] For events where energy characteristics are detected, the corresponding strain characteristics are compared with the crack event discrimination threshold.

[0016] Based on the comparison results, events whose strain characteristics meet the threshold are determined as valid crack events, and events whose strain characteristics do not meet the threshold are determined as noise events.

[0017] The strain and energy characteristics corresponding to the effective crack events are obtained for crack event identification, noise suppression, and fracturing process analysis.

[0018] In some technical solutions, the strain characteristics corresponding to the benchmark crack event include multiple strain anomaly characteristic values, and the crack event discrimination threshold is obtained statistically based on the strain characteristics corresponding to multiple benchmark crack events.

[0019] The crack event discrimination threshold is one of the minimum, average, median or weighted statistical values ​​among the strain characteristics corresponding to multiple benchmark crack events.

[0020] In some technical solutions, the effective crack events include visible crack propagation events and internal crack initiation events;

[0021] Among them, effective crack events that simultaneously possess displacement, strain, and energy characteristics are identified as visible crack propagation events, while effective crack events that possess strain and energy characteristics but lack displacement characteristics are identified as internal crack initiation events.

[0022] In some technical solutions, when an internal crack initiation event is followed by a corresponding displacement feature detected within a subsequent time window, the internal crack initiation event is traced back and confirmed as a crack propagation event.

[0023] In some technical solutions, the displacement characteristics are the abrupt displacement characteristics and / or abrupt strain characteristics of the crack leading edge region on the rock sample surface;

[0024] The displacement characteristics are obtained by acquiring rock sample surface images through an optical image acquisition module and processing the surface images using digital image correlation analysis.

[0025] In some technical solutions, the strain characteristics are strain anomalies inside and / or on the surface of the rock sample, and the strain anomalies are acquired by at least one of a distributed optical fiber sensor and a fiber optic grating sensor.

[0026] The strain characteristics include at least one of strain amplitude, strain peak value, strain rate of change, event frequency, and its spatiotemporal distribution characteristics.

[0027] In some technical solutions, the energy characteristic is the energy mutation characteristic generated during the propagation of internal cracks in the rock sample, and the energy mutation characteristic is obtained through an acoustic emission sensor;

[0028] The energy characteristics include at least one of acoustic emission energy level, ring count, event frequency, and spatiotemporal distribution characteristics.

[0029] In some technical solutions, strain and energy characteristics corresponding to the occurrence time of each effective crack event are obtained, and a crack evolution process dataset is constructed in chronological order.

[0030] The crack evolution process dataset includes feature data corresponding to at least one of the crack initiation stage, crack propagation stage, and crack penetration stage.

[0031] A simulation system, comprising:

[0032] An optical image acquisition module is used to acquire displacement characteristics during the rock sample fracturing process;

[0033] Fiber optic strain monitoring module is used to acquire strain characteristics during rock sample fracturing.

[0034] Acoustic emission monitoring module is used to acquire energy characteristics during rock sample fracturing.

[0035] The processing module is connected to the optical image acquisition module, the fiber optic strain monitoring module, and the acoustic emission monitoring module, respectively.

[0036] The processing module is configured as follows:

[0037] Determine the benchmark crack event based on displacement characteristics;

[0038] Obtain the strain and energy characteristics corresponding to the benchmark crack event;

[0039] A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event;

[0040] The strain characteristics and energy characteristics are jointly discriminated based on the crack event discrimination threshold.

[0041] Based on the discrimination results, the event is determined to be either a valid crack event or a noise event;

[0042] Obtain the strain and energy characteristics corresponding to the benchmark crack event and the effective crack event.

[0043] In some technical solutions, the optical image acquisition module includes an industrial camera, an imaging lens, and an image correction device, wherein the image correction device is used to perform distortion correction and geometric correction on the acquired image;

[0044] The fiber optic strain monitoring module includes a distributed fiber optic sensor and / or a fiber optic grating sensor, which are arranged on the surface of the rock sample, at a preset position inside the rock sample, or in the area around the fracturing hole.

[0045] The acoustic emission monitoring module includes multiple acoustic emission sensors, which are arranged on different sides and / or end faces of the rock sample to collect acoustic emission signals generated during the rock sample fracturing process.

[0046] In some technical solutions, the simulation system further includes a loading chamber for accommodating rock samples, wherein a transparent pressure-bearing observation structure is provided on at least one side of the loading chamber, and the transparent pressure-bearing observation structure includes a transparent high-pressure-bearing loading plate and a corresponding observation window;

[0047] The optical image acquisition module is located outside the observation window and facing the visible surface of the rock sample; the fiber optic strain monitoring module is deployed on the surface and / or inside the rock sample; and the acoustic emission monitoring module includes multiple acoustic emission sensors located on the outer wall of the loading chamber and / or around the rock sample.

[0048] It also includes a light source module, located outside the observation window and / or inside the loading chamber, to improve the uniformity of illumination on the visible surface of the rock sample.

[0049] The visualization fracturing physical simulation method based on multi-source signal mutual correction provided by this invention has at least the following advantages compared with the prior art:

[0050] 1. By identifying events with detected displacement characteristics as benchmark crack events and acquiring their corresponding strain and energy characteristics, a crack event discrimination threshold is established using the strain characteristics corresponding to the benchmark crack events. Furthermore, the strain characteristics corresponding to events with detected energy characteristics are compared with the crack event discrimination threshold, and valid crack events and noise events are distinguished based on the comparison results. Thus, the confirmed crack events are used to calibrate and verify the internal monitoring signals, achieving correlation discrimination between displacement, strain, and energy characteristics, improving the accuracy of crack event identification, suppressing noise events, and reducing the probability of misjudgment.

[0051] 2. By establishing the correspondence between displacement characteristics, strain characteristics, and energy characteristics, and constructing a crack event discrimination standard using benchmark crack events; obtaining the strain and energy characteristics corresponding to effective crack events, and conducting crack identification and fracturing process analysis based on effective crack events; thereby realizing the correlation verification between internal monitoring signals and crack propagation behavior, improving the reliability of crack evolution process analysis results, improving the consistency between fracturing process monitoring results and actual crack propagation, and providing a more reliable data foundation for crack propagation law research and fracturing parameter analysis.

[0052] The simulation system provided by this invention is used to implement the above-mentioned visualization fracturing physical simulation method based on multi-source signal mutual correction, and has the same beneficial effects. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0054] Figure 1 The flowchart shows the visualization physical simulation method for fracturing based on multi-source signal cross-correction provided by the present invention.

[0055] Figure 2 This is a schematic diagram of the simulation system provided by the present invention;

[0056] Figure 3 This is a schematic diagram of the simulation system provided by the present invention.

[0057] In the picture:

[0058] 1. Rock sample; 2. Loading plate; 3. Clamp; 31. Observation window; 4. Loading chamber; 5. High-pressure injection module; 6. Optical image acquisition module. Detailed Implementation

[0059] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] The core of this invention is to provide a visual fracturing physical simulation method and system based on multi-source signal mutual correction, which can effectively solve the problems of difficulty in suppressing noise signals and poor consistency between fracturing process analysis results and actual fracture propagation.

[0061] In hydraulic fracturing physical simulation experiments, fiber optic monitoring technology and acoustic emission monitoring technology are commonly used to monitor the fracturing process of rock samples in order to obtain information on the internal strain response and crack propagation energy of the rock samples. However, most existing monitoring technologies are used as independent means, lacking unified correlation criteria and mutual verification mechanisms between different monitoring signals. They are also susceptible to the influence of environmental noise, equipment vibration, and local disturbances, making it difficult to effectively distinguish between crack events and noise events. At the same time, there is a lack of reliable criteria for judging the authenticity of crack activity that occurs only inside the rock sample. As a result, the monitoring results cannot accurately reflect the real physical behavior of the crack propagation process, affecting the consistency between the analysis results of the fracturing process and the actual crack propagation situation.

[0062] In view of the above problems, such as Figure 1 As shown, this application provides a visual fracturing physical simulation method based on multi-source signal mutual correction, including:

[0063] During the rock sample fracturing process, the displacement characteristics, strain characteristics, and energy characteristics of the rock sample are acquired simultaneously.

[0064] Events that detect displacement features are identified as benchmark crack events, and strain and energy features corresponding to the benchmark crack events are obtained.

[0065] A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event;

[0066] For events where energy characteristics are detected, the corresponding strain characteristics are compared with the crack event discrimination threshold.

[0067] Based on the comparison results, events whose strain characteristics meet the threshold are determined as valid crack events, and events whose strain characteristics do not meet the threshold are determined as noise events.

[0068] The strain and energy characteristics corresponding to the effective crack events are obtained for crack event identification, noise suppression, and fracturing process analysis.

[0069] Displacement characteristics, strain characteristics, and energy characteristics are acquired simultaneously during rock sample fracturing. The displacement characteristics correspond to the crack propagation behavior on the rock sample surface, the strain characteristics correspond to the deformation response inside and / or on the surface of the rock sample, and the energy characteristics correspond to the energy release behavior during crack initiation and propagation inside the rock sample.

[0070] This application first identifies events with detected displacement characteristics as benchmark crack events. Since displacement characteristics directly correspond to crack surface propagation phenomena, they have high authenticity and reliability. Then, a crack event discrimination threshold is established using the strain characteristics corresponding to the benchmark crack event, thereby forming a discrimination mechanism that uses real crack events to calibrate internal monitoring signals. Subsequently, for events with detected energy characteristics, the corresponding strain characteristics are compared with the crack event discrimination threshold. Events that meet the threshold conditions are identified as valid crack events, while events that do not meet the threshold conditions are identified as noise events. Thus, the energy release behavior is verified using strain response.

[0071] This involves establishing a correspondence between displacement characteristics, strain characteristics, and energy characteristics to achieve calibration and verification of internal monitoring signals and noise suppression, thereby improving the accuracy of crack event identification and enhancing the consistency between the analysis results of the fracturing process and the actual crack propagation. Among these characteristics, displacement features can include not only abrupt displacement at the crack front, but also abrupt displacement gradient, changes in surface strain concentration areas, or changes in crack geometric parameters. Strain features can include not only strain amplitude, but also strain increment, strain gradient, or changes in strain energy. Energy features can include not only acoustic emission energy, but also microseismic energy or elastic wave release.

[0072] In some embodiments, the strain characteristics corresponding to the benchmark crack event include multiple strain anomaly characteristic values, and the crack event discrimination threshold is obtained statistically based on the strain characteristics corresponding to the multiple benchmark crack events.

[0073] The crack event discrimination threshold is one of the minimum, average, median or weighted statistical values ​​among the strain characteristics corresponding to multiple benchmark crack events.

[0074] A fracture event discrimination threshold is formed by statistically analyzing the strain anomaly characteristic values ​​corresponding to multiple benchmark fracture events. Since this threshold is derived from confirmed real fracture propagation events, it is more adaptable to different lithologies, confining pressure conditions, and fracturing conditions compared to a pre-set fixed threshold. Using the minimum value as the discrimination threshold can improve the fracture event detection rate, while using the average or median value can improve the discrimination stability. Using weighted statistical values ​​can highlight the strain response characteristics corresponding to key fracture events. Therefore, it can balance sensitivity and accuracy according to actual needs.

[0075] In some cases, using segmented statistical thresholds, adaptive thresholds, or dynamically updated thresholds can achieve the same function.

[0076] In some embodiments, the effective crack events include visible crack propagation events and internal crack initiation events;

[0077] Among them, effective crack events that simultaneously possess displacement, strain, and energy characteristics are identified as visible crack propagation events, while effective crack events that possess strain and energy characteristics but lack displacement characteristics are identified as internal crack initiation events.

[0078] By dividing effective crack events into visible crack propagation events and internal crack initiation events, the stage-by-stage identification of the crack evolution process is achieved.

[0079] Among them, events that simultaneously exhibit displacement, strain, and energy characteristics indicate that crack propagation has occurred on the rock sample surface, accompanied by internal deformation and energy release, and can therefore be identified as visible crack propagation events.

[0080] Events that only exhibit strain and energy characteristics but not displacement characteristics indicate that crack activity has already occurred inside the rock sample but has not yet extended to the surface, and are therefore identified as internal crack initiation events. This classification mechanism enables early identification of internal crack activity, improves the monitoring capability of crack incubation process, and establishes the evolutionary relationship between crack initiation, crack propagation, and crack penetration.

[0081] In some embodiments, when an internal crack initiation event is followed by a corresponding displacement feature detected in a subsequent time window, the internal crack initiation event is traced back and confirmed as a crack propagation event.

[0082] A crack event tracing and confirmation mechanism is introduced. When an internal crack initiation event is detected with corresponding displacement characteristics in a subsequent time window, the previous internal crack initiation event is traced and confirmed as a crack propagation event. That is, by taking advantage of the continuous evolution of the crack propagation process, the correlation between internal crack activity and subsequent surface crack propagation is confirmed, thereby avoiding misjudgment or omission due to the crack not yet propagating to the surface, and improving the completeness and accuracy of the crack propagation process reconstruction results.

[0083] In some embodiments, the displacement characteristics are abrupt displacement characteristics and / or abrupt strain characteristics in the crack front region of the rock sample surface;

[0084] The displacement characteristics are obtained by acquiring rock sample surface images through an optical image acquisition module and processing the surface images using digital image correlation analysis.

[0085] Images of the rock sample surface are acquired using an optical image acquisition module, and the displacement and / or strain abrupt change characteristics of the fracture leading edge region are obtained using a digital image correlation (DIC) method. The optical image acquisition module may include an industrial camera, a high-speed camera, or a linear array camera, and the digital image correlation analysis method may employ a two-dimensional digital image correlation (2D-DIC) algorithm, a three-dimensional digital image correlation (3D-DIC) algorithm, or a sub-pixel matching algorithm. Since the displacement characteristics are directly derived from the actual fracture propagation behavior on the rock sample surface, they can serve as an important criterion for determining real fracture events, thereby improving the reliability of the subsequent threshold establishment process.

[0086] In some embodiments, the strain characteristics are strain anomalies inside and / or on the surface of the rock sample, and the strain anomalies are acquired by at least one of a distributed optical fiber sensor and a fiber optic grating sensor.

[0087] The strain characteristics include at least one of strain amplitude, strain peak value, strain rate of change, event frequency, and its spatiotemporal distribution characteristics.

[0088] Strain anomaly characteristics are acquired by deploying distributed fiber optic sensors or fiber optic grating sensors on the surface, inside, or around the fracture pores of rock samples. The distributed fibers can be arranged along the length of the rock sample, the direction of the principal stress of the fracture, or the preset direction of crack propagation. The fiber optic gratings can be arranged in a series array structure or a distributed measuring point structure. By acquiring parameters such as strain amplitude, strain peak value, strain rate of change, and event frequency, the stress concentration and crack propagation behavior inside the rock sample can be characterized, providing continuous strain response data for crack event identification.

[0089] In some embodiments, the energy characteristic is an energy mutation characteristic generated during the propagation of internal cracks in the rock sample, and the energy mutation characteristic is acquired by an acoustic emission sensor;

[0090] The energy characteristics include at least one of acoustic emission energy level, ring count, event frequency, and spatiotemporal distribution characteristics.

[0091] Multiple acoustic emission sensors are used to collect elastic wave signals generated during the propagation of internal cracks in rock samples, and the acoustic emission energy level, ring count, event frequency, and spatiotemporal distribution characteristics are extracted. Multiple acoustic emission sensors can be arranged around the rock sample to form a positioning array, or arranged in layers along the loading direction to form a monitoring network. Since acoustic emission signals can sensitively reflect the initiation and propagation process of microcracks, they can provide early warning information for crack event identification. At the same time, after joint verification with strain characteristics, it can significantly reduce misjudgments caused by environmental noise.

[0092] In some embodiments, strain characteristics and energy characteristics corresponding to the occurrence time of each effective crack event are obtained, and a crack evolution process dataset is constructed in chronological order.

[0093] The crack evolution process dataset includes feature data corresponding to at least one of the crack initiation stage, crack propagation stage, and crack penetration stage.

[0094] By acquiring the strain and energy characteristics corresponding to the occurrence time of each effective fracture event and constructing a fracture evolution process dataset in chronological order, the entire process of fracture initiation, fracture propagation, and fracture penetration can be recorded. This dataset can form a historical database of fracture evolution, providing basic data support for subsequent research on fracture propagation laws, fracturing mechanism analysis, and fracturing parameter inversion. It can also be extended to a machine learning training sample library or a fracturing model calibration database.

[0095] In some embodiments, the fracturing physical simulation method includes the following steps:

[0096] S101: Perform sample pretreatment, including selecting a natural rock sample or a similar material sample as rock sample 1, uniformly spraying a base color on the visible surface of rock sample 1, and spraying high-contrast micro-specks to form a surface texture suitable for digital image correlation calculation; at the same time, embedding and / or attaching fiber optic sensors inside and / or attaching them to the surface of rock sample 1 according to monitoring requirements, to provide basic conditions for subsequent displacement field inversion and strain monitoring.

[0097] S102: Confining pressure loading, including installing rock sample 1 in loading chamber 4, controlling clamp 3 to apply confining pressure to rock sample 1, the confining pressure loading method can be true triaxial or quasi-true triaxial loading method, the loading path can be at least one of constant speed loading, segmented constant load or cyclic loading, in order to simulate different reservoir stress environments and establish a stable initial stress state.

[0098] S103: Fracturing injection, including controlling the high-pressure injection module 5 to apply fracturing fluid pressure according to a preset displacement law to the rock sample 1. The fracturing fluid can be selected from water, slickwater, low-viscosity gel, cross-linking fluid or other suitable fluids, and tracer particles, fluorescent particles or visualization particles can be added as needed to enhance the identifiability of the fracture propagation process.

[0099] S104: Multi-source synchronous acquisition, including controlling the optical image acquisition module 6 to acquire surface images of rock sample 1 during the fracturing process, controlling the acoustic emission monitoring module to acquire acoustic emission signals generated during the initiation and propagation of internal cracks in rock sample 1, and controlling the fiber optic strain monitoring module to acquire strain signals inside and / or on the surface of rock sample 1.

[0100] It is preferable to use a unified clock source, a unified trigger signal, or a synchronous acquisition controller to achieve multi-source synchronous sampling.

[0101] S105: Image signal correction, including refraction error correction, distortion correction, and spatial calibration of surface images acquired under confining pressure to obtain the surface displacement field and / or strain field during fracturing, and extracting the displacement abrupt change characteristics of the fracture front region; during the real-time monitoring stage, using pre-calibrated conversion coefficients, historical experimental databases, or initial empirical coefficients, the DIC correction results under confining pressure are cross-corrected online with the fiber strain response; after fracturing is completed and pressure is released, rock sample surface images are acquired again, and the fracture front displacement and / or propagation length under pressure release is calculated based on DIC, and the fracture front displacement conversion coefficient is inverted and updated according to the corrected displacement results under confining pressure, the DIC displacement results under pressure release, and the fiber strain calculation results.

[0102] S106: Fracturing event determination, including joint comparison of DIC, optical fiber and acoustic emission signals under a unified time reference, and identification of visible crack propagation events, internal crack initiation events and noise events based on consistency criteria.

[0103] S107: Results output, including the crack initiation time, crack propagation path, crack propagation rate, and corresponding pressure, acoustic emission energy, and strain evolution data of rock sample 1 based on the identification results. It can also generate crack front evolution map, strain concentration zone distribution map, and acoustic emission event location map for subsequent crack morphology interpretation, parameter inversion, and fracturing process optimization.

[0104] In this embodiment, the processing module performs joint processing on the surface images, fiber optic strain signals and acoustic emission signals collected during the fracturing test to improve the accuracy of identifying real crack events and reduce the impact of environmental noise, equipment vibration and occasional interference on the test results.

[0105] The fiber optic strain monitoring module and the digital image acquisition module (DIC) monitor different objects and represent different physical quantities. The fiber optic strain monitoring module is mainly used to acquire strain response information inside and / or on the surface of the rock sample to characterize strain concentration, microcrack incubation, and crack propagation behavior during the loading process. The digital image acquisition module is mainly used to acquire image signals of the visible surface of the rock sample and further obtain information such as surface displacement field, surface strain field, and crack front propagation length to characterize the crack geometric propagation process. Therefore, the fiber optic strain monitoring and digital image acquisition modules are not redundant detections, but rather complementary characterize the fracturing process from different observation dimensions and different physical quantities. The combined use of the two can improve the reliability of crack identification and path inversion.

[0106] In addition to the visualization fracturing physical simulation method based on multi-source signal cross-correction disclosed in the above embodiments, this application also provides a simulation system, including:

[0107] An optical image acquisition module is used to acquire displacement characteristics during the rock sample fracturing process;

[0108] Fiber optic strain monitoring module is used to acquire strain characteristics during rock sample fracturing.

[0109] Acoustic emission monitoring module is used to acquire energy characteristics during rock sample fracturing.

[0110] The processing module is connected to the optical image acquisition module, the fiber optic strain monitoring module, and the acoustic emission monitoring module, respectively.

[0111] The processing module is configured as follows:

[0112] Determine the benchmark crack event based on displacement characteristics;

[0113] Obtain the strain and energy characteristics corresponding to the benchmark crack event;

[0114] A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event;

[0115] The strain characteristics and energy characteristics are jointly discriminated based on the crack event discrimination threshold.

[0116] Based on the discrimination results, the event is determined to be either a valid crack event or a noise event;

[0117] Obtain the strain and energy characteristics corresponding to the benchmark crack event and the effective crack event.

[0118] The simulation system includes an optical image acquisition module, a fiber optic strain monitoring module, an acoustic emission monitoring module, and a processing module. The processing module is communicatively connected to the optical image acquisition module, the fiber optic strain monitoring module, and the acoustic emission monitoring module, and performs functions such as benchmark crack event identification, threshold establishment, event discrimination, and data analysis. Through the collaborative work of the hardware monitoring unit and the data processing unit, the system achieves automatic acquisition, automatic calibration, and automatic analysis of multi-source monitoring signals, thereby improving the automation level and monitoring reliability of the fracturing physical simulation test.

[0119] In some embodiments, the optical image acquisition module includes an industrial camera, an imaging lens, and an image correction device, wherein the image correction device is used to perform distortion correction and geometric correction on the acquired image;

[0120] The fiber optic strain monitoring module includes a distributed fiber optic sensor and / or a fiber optic grating sensor, which are arranged on the surface of the rock sample, at a preset position inside the rock sample, or in the area around the fracturing hole.

[0121] The acoustic emission monitoring module includes multiple acoustic emission sensors, which are arranged on different sides and / or end faces of the rock sample to collect acoustic emission signals generated during the rock sample fracturing process.

[0122] An industrial camera is positioned with its imaging lens facing the visible surface of the rock sample, and an image correction device is used to correct optical distortions and geometric errors caused by the transparent pressure-bearing structure.

[0123] Distributed fiber optic sensors and fiber Bragg grating sensors form continuous monitoring networks and discrete monitoring networks, respectively.

[0124] Multiple acoustic emission sensors are distributed on different sides and ends of the rock sample to form a three-dimensional monitoring array;

[0125] The above structural arrangement enables multi-dimensional synchronous monitoring of the rock sample surface, rock sample interior, and crack propagation area, improving the integrity and spatial coverage of monitoring data.

[0126] In some embodiments, the simulation system further includes a loading chamber for accommodating rock samples, wherein a transparent pressure-bearing observation structure is provided on at least one side of the loading chamber, and the transparent pressure-bearing observation structure includes a transparent high-pressure-bearing loading plate and a corresponding observation window;

[0127] The optical image acquisition module is located outside the observation window and facing the visible surface of the rock sample; the fiber optic strain monitoring module is deployed on the surface and / or inside the rock sample; and the acoustic emission monitoring module includes multiple acoustic emission sensors located on the outer wall of the loading chamber and / or around the rock sample.

[0128] It also includes a light source module, located outside the observation window and / or inside the loading chamber, to improve the uniformity of illumination on the visible surface of the rock sample.

[0129] In some cases, the loading chamber is preferably a cubic or cuboid pressure-bearing structure. The transparent pressure-bearing observation structure includes a transparent high-pressure-bearing loading plate and an observation window. The transparent high-pressure-bearing loading plate can be made of sapphire, high-strength quartz glass, or transparent ceramic material and is fixed to the side wall of the loading chamber by a clamping structure. The optical image acquisition module is located outside the observation window, and the light source module is located outside the observation window and / or inside the loading chamber to form a uniform lighting environment. The fiber optic strain monitoring module is deployed inside and / or on the surface of the rock sample, and the acoustic emission sensor is installed on the outer wall of the loading chamber or around the rock sample to form a surrounding monitoring structure. The transparent pressure-bearing observation structure enables visualization of the crack propagation process, and the multi-source monitoring structure enables real-time perception of internal crack activity, thereby providing a reliable hardware foundation for crack event identification, noise suppression, and fracturing process analysis.

[0130] This application provides a simulation system for implementing the above-mentioned visualization fracturing physical simulation method based on multi-source signal mutual correction, including a loading chamber 4 for fixing and confining rock sample 1, and a loading plate 2 and a clamp 3 are arranged on the side wall of the loading chamber 4 from the inside to the outside.

[0131] At least one sidewall loading plate 2 is a transparent high-pressure loading plate, and the corresponding clamp 3 is provided with an observation window 31;

[0132] The optical image acquisition module 6 is located outside the loading chamber 4 of the simulation system and is used to acquire surface image change data of rock sample 1 during the fracturing process through the observation window 31 of the simulation system.

[0133] The fiber optic strain monitoring module, located in loading chamber 4, is used to collect strain data of the interior and / or surface of rock sample 1 during the fracturing process.

[0134] The acoustic emission monitoring module is located in loading chamber 4 and is used to collect acoustic emission signals during the fracturing process of rock sample 1.

[0135] The processing module is used to store and / or compare the surface image change data acquired by the optical image acquisition module 6 and the acoustic emission signals acquired by the acoustic emission monitoring module.

[0136] like Figure 2As shown, the sidewall of the loading chamber 4 includes a loading plate 2 and a clamp 3 from the inside to the outside. The clamp 3 is subjected to pressure by an external loading device, thereby applying a positive pressure matching the actual formation conditions to the rock sample 1 in the loading chamber 4, so that the specimen simulates the stress balance state of the formation. At least one sidewall loading plate 2 is a transparent high-pressure loading plate and the corresponding clamp 3 is equipped with an observation window 31, so that the surface feature changes of the rock sample 1 corresponding to the sidewall can be directly observed, that is, the surface image change data of the rock sample 1 during the fracturing process can be directly acquired by the optical image acquisition module 6.

[0137] The transparent high-pressure-bearing loading plate is typically made of high-strength transparent pressure-bearing material and / or sapphire material. It has high rigidity and can be used with external loading equipment to apply target confining pressure to rock sample 1 to simulate the real environment of rock sample 1 downhole, thereby improving the consistency between the fracturing data of rock sample 1 and the actual downhole data during hydraulic fracturing physical simulation. The thickness, light transmittance and pressure-bearing level of the transparent high-pressure-bearing loading plate can be selected according to the target confining pressure range and imaging accuracy requirements.

[0138] like Figure 3 As shown, by setting an optical image acquisition module 6 outside the loading chamber 4, the characteristic data of the corresponding side surface of the rock sample 1 during the hydraulic fracturing physical simulation test can be directly and intuitively collected through the observation window 31 and the transparent high-pressure loading plate in a non-contact manner.

[0139] The optical image acquisition module 6 typically includes an industrial camera and a low-distortion lens, which captures the displacement characteristics of the fracture front end during the fracturing process with high spatiotemporal resolution and outputs the displacement field and geometric parameters for subsequent data processing and analysis.

[0140] Simultaneously, the acoustic emission monitoring module acquires acoustic emission signals during the fracturing physical simulation process to monitor the internal fracture behavior of the rock. Specifically, multiple sensors installed in the loading chamber 4 capture elastic wave signals generated by internal fractures in rock sample 1, and the location of crack initiation or propagation inside rock sample 1 is calculated based on the time difference between the signals captured by different sensors.

[0141] Furthermore, by combining the surface image change data acquired by the optical image acquisition module 6 and the acoustic emission signal acquired by the acoustic emission monitoring module through the processing module, mutual verification and noise suppression are achieved, thereby improving the accuracy and precision of crack monitoring results during the fracturing simulation process.

[0142] In some embodiments, the loading chamber 4 is a cubic space structure, and the loading plate 2 of at least one side wall in each axis system is a transparent high-pressure loading plate, and the corresponding clamp 3 is provided with an observation window 31.

[0143] like Figure 2 and Figure 3As shown, the loading chamber 4 with a cubic spatial structure can meet the physical simulation test of hydraulic fracturing of the cubic rock sample 1. Moreover, the cubic structure makes it easy for the external loading equipment to uniformly apply confining pressure to each axis of the rock sample 1 through the clamp 3, thus restoring the rock in the real environment downhole.

[0144] Furthermore, in each axis system, at least one sidewall corresponding to the loading plate 2 is set as a transparent high-pressure loading plate and the surface of the corresponding clamp 3 is set with an observation window 31, that is, the crack features of at least one surface in each axis system direction of the rock sample 1 can be observed intuitively, that is, directly acquired by the optical image acquisition module 6.

[0145] In some embodiments, the simulation system further includes a light source module disposed in the loading chamber 4, which is used to increase the surface illumination intensity of the rock sample 1.

[0146] In some cases, a light source module is installed in the loading chamber 4. This is usually a cold light source or an equivalent stable light source located at the bottom of the transparent high-pressure loading plate to improve the uniformity of illumination and contrast of the image during the visualization observation process, thereby obtaining a clearer observation effect.

[0147] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0148] The foregoing has provided a detailed description of the visualization fracturing physical simulation method and simulation system based on multi-source signal mutual correction provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A visual fracturing physical simulation method based on multi-source signal cross-correction, characterized in that, include: During the rock sample fracturing process, the displacement characteristics, strain characteristics, and energy characteristics of the rock sample are acquired simultaneously. Events that detect displacement features are identified as benchmark crack events, and strain and energy features corresponding to the benchmark crack events are obtained. A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event; For events where energy characteristics are detected, the corresponding strain characteristics are compared with the crack event discrimination threshold. Based on the comparison results, events whose strain characteristics meet the threshold are determined as valid crack events, and events whose strain characteristics do not meet the threshold are determined as noise events. The strain and energy characteristics corresponding to the effective fracture events are obtained for fracture event identification, noise suppression, and fracturing process analysis. The displacement characteristic refers to the abrupt displacement characteristics of the crack leading edge region on the rock sample surface.

2. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, The strain characteristics corresponding to the benchmark crack event include multiple strain anomaly characteristic values, and the crack event discrimination threshold is obtained statistically based on the strain characteristics corresponding to the multiple benchmark crack events. The crack event discrimination threshold is one of the minimum, average, median or weighted statistical values ​​among the strain characteristics corresponding to multiple benchmark crack events.

3. The visualized fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, The effective crack events include visible crack propagation events and internal crack initiation events; Among them, effective crack events that simultaneously possess displacement, strain, and energy characteristics are identified as visible crack propagation events, while effective crack events that possess strain and energy characteristics but lack displacement characteristics are identified as internal crack initiation events.

4. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 3, characterized in that, When an internal crack initiation event is followed by a corresponding displacement feature detected within a subsequent time window, the internal crack initiation event is traced back and confirmed as a crack propagation event.

5. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, The displacement characteristics are obtained by acquiring rock sample surface images through an optical image acquisition module and processing the surface images using digital image correlation analysis.

6. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, The strain characteristics are strain anomalies inside and / or on the surface of the rock sample, and the strain anomalies are acquired by at least one of a distributed optical fiber sensor and a fiber optic grating sensor. The strain characteristics include at least one of strain amplitude, strain peak value, strain rate of change, event frequency, and its spatiotemporal distribution characteristics.

7. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, The energy characteristic is the energy mutation characteristic generated during the propagation of internal cracks in the rock sample, and the energy mutation characteristic is acquired by an acoustic emission sensor; The energy characteristics include at least one of acoustic emission energy level, ring count, event frequency, and spatiotemporal distribution characteristics.

8. The visualization fracturing physical simulation method based on multi-source signal mutual correction according to claim 1, characterized in that, Obtain the strain and energy characteristics corresponding to the occurrence time of each valid crack event, and construct a crack evolution process dataset in chronological order; The crack evolution process dataset includes feature data corresponding to at least one of the crack initiation stage, crack propagation stage, and crack penetration stage.

9. A simulation system, characterized in that, include: An optical image acquisition module is used to acquire displacement characteristics during the rock sample fracturing process, wherein the displacement characteristics are the abrupt displacement characteristics of the crack leading edge region on the rock sample surface; Fiber optic strain monitoring module is used to acquire strain characteristics during rock sample fracturing. Acoustic emission monitoring module is used to acquire energy characteristics during rock sample fracturing. The processing module is connected to the optical image acquisition module, the fiber optic strain monitoring module, and the acoustic emission monitoring module, respectively. The processing module is configured as follows: Determine the benchmark crack event based on displacement characteristics; Simultaneously acquire the displacement characteristics, strain characteristics, and energy characteristics corresponding to the benchmark crack event; A crack event discrimination threshold is established based on the strain characteristics corresponding to the benchmark crack event; The strain characteristics and energy characteristics are jointly discriminated based on the crack event discrimination threshold. Based on the discrimination results, the event is determined to be either a valid crack event or a noise event; Obtain the strain and energy characteristics corresponding to the benchmark crack event and the effective crack event; The joint discrimination includes, for events with detected energy characteristics, comparing the corresponding strain characteristics with the crack event discrimination threshold, determining events that meet the threshold conditions as valid crack events, and determining events that do not meet the threshold conditions as noise events.

10. The simulation system according to claim 9, characterized in that, The optical image acquisition module includes an industrial camera, an imaging lens, and an image correction device, which is used to perform distortion correction and geometric correction on the acquired image. The fiber optic strain monitoring module includes a distributed fiber optic sensor and / or a fiber optic grating sensor, which are arranged on the surface of the rock sample, at a preset position inside the rock sample, or in the area around the fracturing hole. The acoustic emission monitoring module includes multiple acoustic emission sensors, which are arranged on different sides and / or end faces of the rock sample to collect acoustic emission signals generated during the rock sample fracturing process.

11. The simulation system according to claim 9, characterized in that, It also includes a loading chamber for accommodating rock samples, wherein a transparent pressure-bearing observation structure is provided on at least one side of the loading chamber, and the transparent pressure-bearing observation structure includes a transparent high pressure-bearing loading plate and a corresponding observation window; The optical image acquisition module is located outside the observation window and facing the visible surface of the rock sample; the fiber optic strain monitoring module is deployed on the surface and / or inside the rock sample; and the acoustic emission monitoring module includes multiple acoustic emission sensors located on the outer wall of the loading chamber and / or around the rock sample. It also includes a light source module, located outside the observation window and / or inside the loading chamber, to improve the uniformity of illumination on the visible surface of the rock sample.

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