Sequential grouting system and method suitable for complex geological conditions

Abstract

The invention belongs to the technical field of grouting construction, and provides a sequential grouting system and method suitable for complex geological conditions, and the system comprises a stratum permeability identification module which is used for obtaining stratum permeability data and grouting actual measurement data, building a grouting theoretical model, and carrying out stratum permeability inversion to obtain a stratum permeability identification result; the sequencing grouting module is used for dividing the target grouting area into a plurality of grouting units, determining sequencing grouting strategies corresponding to the grouting units according to a set grouting sequence and dynamically adjusting the sequencing grouting strategies; the slurry proportion adjusting module is used for establishing a theoretical relation model of the slurry performance and the stratum permeability, determining the target slurry performance and carrying out self-adaptive adjustment on the slurry proportion; and the control module is used for performing sequential grouting according to the dynamically-adjusted sequential grouting strategy and the self-adaptively-adjusted grout proportion. According to the scheme, precision, intelligence and collaboration of grouting construction under complex geological conditions are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grouting construction, and in particular to a sequence grouting system and method suitable for complex geological conditions. BACKGROUND

[0002] In the field of geotechnical reinforcement such as water conservancy projects, underground rail transit projects, mine tunnel projects, and the like, complex geological conditions such as fault fracture zones, high-permeability sand and gravel layers, karst development strata, and low-permeability dense rock layers alternatingly distributed regions, etc., bring severe challenges to grouting construction. When facing such geologies, the traditional grouting technology generally has the following problems:

[0003] Firstly, the identification accuracy of stratum permeability is insufficient. The existing technology relies on the pre-stage static geological exploration data, and it is difficult to reflect the dynamic changes of stratum permeability in the grouting process in real time, resulting in a lag in the judgment of the real permeability characteristics of the stratum, and failing to provide accurate basis for subsequent construction.

[0004] Secondly, the grouting sequence lacks dynamic adjustment capability. The fixed from outside to inside or from shallow to deep grouting mode is usually adopted, and targeted zoning is not combined with the stratum permeability difference, which is prone to problems such as large loss of grout in high-permeability regions and difficulty in filling grout in low-permeability regions, resulting in waste of grouting materials and poor reinforcement effect.

[0005] Thirdly, the grout proportioning adjustment is rigid. The fixed proportioning is usually set based on experience, and the key parameters such as grout concentration and viscosity cannot be adjusted in real time according to strata with different permeability characteristics, resulting in out-of-control grout diffusion range in high-permeability strata and ineffective grout penetration and filling in low-permeability strata.

[0006] Therefore, the traditional grouting scheme has the technical problems of low accuracy, poor dynamic adaptability, and insufficient flexibility and reliability when dealing with complex geological conditions. SUMMARY

[0007] The present application provides a sequence grouting system and method suitable for complex geological conditions to solve the defects of low accuracy, poor dynamic adaptability, and insufficient flexibility and reliability of the traditional grouting scheme when dealing with complex geological conditions.

[0008] In one aspect, the present application provides a sequence grouting system suitable for complex geological conditions, comprising:

[0009] A stratum permeability identification module is configured to obtain stratum permeability data and grouting measured data, establish a grouting theoretical model using the stratum permeability data, and perform stratum permeability inversion based on the grouting measured data and the grouting theoretical model to obtain a stratum permeability identification result.

[0010] The sequence grouting module is used for dividing a target grouting area into a plurality of grouting units according to the stratum permeability identification result, determining a sequence grouting strategy corresponding to the plurality of grouting units according to a set grouting sequence, and dynamically adjusting the sequence grouting strategy according to the grouting measured data.

[0011] The slurry proportioning adjustment module is used for establishing a theoretical relation model of slurry performance and stratum permeability, determining a target slurry performance according to the theoretical relation model and the stratum permeability identification result, and adaptively adjusting slurry proportioning according to the target slurry performance.

[0012] The control module is used for controlling a target actuator to operate to perform sequence grouting according to the dynamically adjusted sequence grouting strategy and the adaptively adjusted slurry proportioning.

[0013] The sequence grouting system suitable for complex geological conditions provided by the application comprises a stratum permeability identification module, a grouting model establishment module, a grouting data acquisition module, a sequence grouting module, a slurry proportioning adjustment module and a control module.

[0014] The stratum permeability identification module acquires stratum permeability data.

[0015] The grouting model establishment module establishes a grouting theoretical model according to the stratum permeability data.

[0016] The grouting data acquisition module acquires grouting measured data.

[0017] The sequence grouting module is used for dividing a target grouting area into a plurality of grouting units according to the stratum permeability identification result, determining a sequence grouting strategy corresponding to the plurality of grouting units according to a set grouting sequence, and dynamically adjusting the sequence grouting strategy according to the grouting measured data.

[0018] The grouting model establishment module establishes a grouting theoretical model according to the stratum permeability data.

[0019] The grouting model establishment module establishes a grouting theoretical model according to the stratum permeability data.

[0020] The grouting model establishment module establishes a grouting theoretical model according to the stratum permeability data.

[0021] The sequence grouting system suitable for complex geological conditions provided by the application comprises a stratum permeability identification module, a grouting model establishment module, a grouting data acquisition module, a sequence grouting module, a slurry proportioning adjustment module and a control module.

[0022] The sequence grouting system suitable for complex geological conditions provided by the application comprises a stratum permeability identification module, a grouting model establishment module, a grouting data acquisition module, a sequence grouting module, a slurry proportioning adjustment module and a control module.

[0023] The sequence grouting system suitable for complex geological conditions provided by the application comprises a stratum permeability identification module, a grouting model establishment module, a grouting data acquisition module, a sequence grouting module, a slurry proportioning adjustment module and a control module.

[0024] According to the application, the sequence grouting system for complex geological conditions comprises a sequence grouting module, a grouting data acquisition module, a control module and a slurry proportioning adjustment module.

[0025] The stratum permeability identification result of the target grouting area is clustered to obtain a preliminary clustering result.

[0026] The preliminary clustering result is quantitatively evaluated, and the preliminary clustering result is adjusted according to the quantitative evaluation result to obtain a final clustering result.

[0027] According to the final clustering result, the sub-areas with similar permeability and similar geological conditions in the target grouting area are divided into a grouting unit to obtain a plurality of grouting units.

[0028] According to the application, the sequence grouting system for complex geological conditions comprises a sequence grouting module, a grouting data acquisition module, a control module and a slurry proportioning adjustment module.

[0029] According to the set grouting sequence of low permeability first and stable area last, the sequence grouting strategy corresponding to the plurality of grouting units is determined.

[0030] If the abnormal grouting unit with concentrated grout loss or sudden increase in grout pressure is detected according to the grouting measured data, the sequence grouting strategy is dynamically adjusted according to the abnormal grouting unit.

[0031] According to the application, the sequence grouting system for complex geological conditions comprises a sequence grouting module, a grouting data acquisition module, a control module and a slurry proportioning adjustment module.

[0032] The relationship data between different slurry performance and stratum permeability under different stratum conditions is obtained.

[0033] The parameters related to stratum permeability in the relationship data are taken as input samples, and the parameters related to slurry performance in the relationship data are taken as output samples, and a pre-constructed machine learning model is trained to obtain a theoretical relationship model of slurry performance and stratum permeability.

[0034] According to the application, the sequence grouting system for complex geological conditions comprises a sequence grouting module, a grouting data acquisition module, a control module and a slurry proportioning adjustment module.

[0035] According to the grouting measured data and the preset grouting target data, the key parameter deviation and the key parameter change rate are determined.

[0036] According to the key parameter deviation and the key parameter change rate, a target control amount is obtained through fuzzy reasoning.

[0037] According to the target control amount, a key operating parameter in target actuator operation is dynamically adjusted.

[0038] According to the present application, a sequence grouting system suitable for complex geological conditions is provided, and the system further comprises a man-machine interaction module.

[0039] The man-machine interaction module is used to establish a spatial distribution map of a target grouting area, determine the grouting progress of each grouting unit, mark the grouting progress of each grouting unit in the spatial distribution map, and generate and display a grouting progress overview map.

[0040] In another aspect, the present application further provides a sequence grouting method suitable for complex geological conditions, based on any one of the sequence grouting systems suitable for complex geological conditions described above, and the method comprises:

[0041] The formation permeability data and grouting measured data are obtained through a formation permeability identification module, a grouting theoretical model is established using the formation permeability data, and formation permeability inversion is performed according to the grouting measured data and the grouting theoretical model to obtain a formation permeability identification result;

[0042] According to the formation permeability identification result, the target grouting area is divided into multiple grouting units through a sequence grouting module, the sequence grouting strategy corresponding to the multiple grouting units is determined according to a set grouting sequence, and the sequence grouting strategy is dynamically adjusted according to the grouting measured data;

[0043] A theoretical relationship model between slurry performance and formation permeability is established through a slurry proportioning adjustment module, the target slurry performance is determined according to the theoretical relationship model and the formation permeability identification result, and the slurry proportioning is adaptively adjusted according to the target slurry performance;

[0044] The target actuator is controlled to operate for sequence grouting according to the dynamically adjusted sequence grouting strategy and the adaptively adjusted slurry proportioning through a control module.

[0045] The application provides a split-sequence grouting system and method suitable for complex geological conditions, which realizes the combination of static geological data and dynamic measured data through a formation permeability identification module, can establish a grouting theoretical model based on previous data, and can invert real-time formation permeability through measured data, thereby greatly improving the accuracy and timeliness of formation identification; the split-sequence grouting module partitions according to the accurate formation permeability identification result, and dynamically adjusts the grouting strategy in combination with measured data, thereby effectively avoiding the problems of slurry loss or insufficient filling caused by the traditional fixed sequence, and improving the pertinence of grouting construction; the slurry ratio adjustment module realizes self-adaptive adjustment of slurry ratio by establishing a theoretical relationship model of slurry performance and formation permeability, thereby ensuring that different permeability characteristics of the formation can obtain optimal slurry filling effect, and reducing the waste of grouting materials; and the control module constructs a closed-loop control system of strategy, ratio and execution, thereby guaranteeing the stability and safety of the grouting construction process, and effectively avoiding the influence of abnormal conditions on the engineering quality. The scheme realizes the precision, intelligence and collaboration of grouting construction under complex geological conditions, significantly improves the grouting reinforcement quality and construction efficiency, and reduces the construction cost and safety risk. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0047] Figure 1 is a structural schematic diagram of the split-sequence grouting system suitable for complex geological conditions provided by the embodiment of the application;

[0048] Figure 2 is a flowchart of the split-sequence grouting method suitable for complex geological conditions provided by the embodiment of the application. DETAILED DESCRIPTION

[0049] In order to make the objects, technical solutions and advantages of the application clearer, the following will combine the drawings in the application to clearly and completely describe the technical solutions in the application. Obviously, the described embodiments are some of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0050] The following will describe the details of the split-sequence grouting system and method suitable for complex geological conditions provided by the embodiment of the application in combination with Figure 1 and Figure 2 ​

[0051] As Figure 1 shown, the sequence grouting system suitable for complex geological conditions provided by the embodiment of the application specifically comprises:

[0052] The formation permeability identification module 110 is configured to acquire formation permeability data and grouting measured data, establish a grouting theoretical model by using the formation permeability data, and perform formation permeability inversion according to the grouting measured data and the grouting theoretical model to obtain a formation permeability identification result.

[0053] The sequence grouting module 120 is configured to divide a target grouting area into a plurality of grouting units according to the formation permeability identification result, determine a sequence grouting strategy corresponding to the plurality of grouting units according to a set grouting sequence, and dynamically adjust the sequence grouting strategy according to the grouting measured data.

[0054] The slurry proportioning adjustment module 130 is configured to establish a theoretical relationship model between slurry performance and formation permeability, determine a target slurry performance according to the theoretical relationship model and the formation permeability identification result, and self-adaptively adjust slurry proportioning according to the target slurry performance.

[0055] The control module 140 is configured to control a target actuator to operate to perform sequence grouting according to the dynamically adjusted sequence grouting strategy and the self-adaptively adjusted slurry proportioning.

[0056] In an embodiment, the formation permeability identification module acquires formation permeability data, specifically comprising:

[0057] On one hand, a formation core sample is acquired, and core logging information is determined.

[0058] In actual application, the formation core sample can be acquired by drilling core, and in operation, drilling can be performed by drilling equipment according to designed drilling position and angle, in the drilling process, the integrity of the core is ensured to avoid core breakage or disturbance, after the formation core sample is taken out, detailed logging can be performed on the formation core sample, in the embodiment, the core logging information specifically comprises lithology description, fracture development condition, core recovery rate and other information, through direct observation and analysis of the formation core sample, the basic characteristics of the formation can be preliminarily understood.

[0059] On the other hand, acoustic logging information and water pressure test data of the drill hole are acquired.

[0060] It can be understood that the acoustic logging information is mainly obtained through acoustic logging technology. The acoustic logging mainly emits acoustic waves in the borehole, receives reflected and transmitted acoustic signals, and infers the physical properties and structure of the formation according to the propagation speed, amplitude and other acoustic logging information of the acoustic waves in different formations. For example, in a dense rock formation, the acoustic wave propagates faster, while in a formation containing more pores or fractures, the acoustic wave propagation speed will decrease, and the signal amplitude will also change.

[0061] The pressure water test data is mainly obtained through the pressure water test. The pressure water test is to press water into the borehole at a certain pressure, measure the amount of water and pressure change per unit time, obtain the pressure water test data, and then calculate the permeability coefficient of the formation according to the Darcy law and other related theories, so as to quantify the permeability of the formation.

[0062] Finally, the formation permeability data is obtained according to the core logging information, acoustic logging information and pressure water test data.

[0063] The embodiment can obtain more comprehensive formation permeability data through drilling, acoustic logging, pressure water test and other means.

[0064] During the grouting process, the grouting measured data including pressure fluctuation, flow change and other key information can be collected in real time through pressure sensors, flow sensors and other devices installed in the grouting pipeline and the borehole.

[0065] In an embodiment, the formation permeability identification module establishes a grouting theoretical model using the formation permeability data, specifically including:

[0066] First, the formation permeability data is integrated with the previously obtained geological exploration data to obtain integrated data.

[0067] In this embodiment, the obtained formation permeability data is integrated with the previously obtained geological exploration data to obtain more complete integrated data. The geological exploration data specifically includes regional geological structure map, stratigraphic section map, geological survey report, etc.

[0068] Then, key modeling data including slurry properties, geological structure of grouting area and grouting boundary are extracted from the integrated data.

[0069] In this step, information related to formation permeability, such as lithology distribution, fault and fracture distribution of the formation, etc., can be extracted from the integrated data as key modeling data.

[0070] In some embodiments, database management technology can be used to store the integrated data in a classified manner, and a formation permeability database is established to facilitate data query, update and analysis, and to provide comprehensive and accurate data support for subsequent grouting construction.

[0071] In practical applications, the physical and chemical properties of the grouting materials used, such as cement slurry, chemical slurry, etc., can be thoroughly researched, including the density, viscosity, setting time, compressive strength, etc. of the slurry, which directly affect the flow and diffusion law of the slurry in the ground. For example, high-viscosity slurry has poor flowability and a relatively small diffusion range, and its flow resistance parameter needs to be accurately reflected in the grouting theoretical model.

[0072] At the same time, the geological structure of the grouting area can be understood in detail, including the lithology of the stratum (such as the type, hardness, and porosity of the rock), geological faults, and fracture distribution characteristics. Under different geological conditions, the penetration path and diffusion mode of the slurry differ greatly. For example, in a rock stratum with developed fractures, the slurry mainly flows along the fractures; while in loose sand stratum, the slurry penetrates through the pores, and these geological conditions need to be accurately characterized in the grouting theoretical model.

[0073] In addition, the boundary conditions in the grouting process need to be clearly defined, i.e. the grouting boundary, such as the location and aperture of the grouting hole, the application method and size of the grouting pressure, the initial pressure and saturation of the stratum, etc. These boundary conditions provide initial values and constraints for model calculation, affecting the diffusion range and pressure distribution of the slurry in the entire calculation area. For example, the size of the grouting pressure directly determines the ability of the slurry to overcome the resistance of the stratum, thereby affecting its diffusion distance.

[0074] Finally, based on the key modeling data, a grouting theoretical model is established to describe the variation characteristics of the flow parameters of the slurry in different media.

[0075] In this embodiment, based on the principles of fluid mechanics, the flow law of the slurry in different geological media can be determined, and corresponding mathematical equations can be established to describe the variation characteristics of the flow parameters such as flow rate, flow volume, and pressure distribution of the slurry, thereby obtaining the grouting theoretical model.

[0076] In an embodiment, the stratum permeability identification module is further configured to:

[0077] Firstly, obtain the grouting verification data under different conditions obtained by indoor simulation experiments and field grouting tests.

[0078] In the indoor simulation experiment stage, rock samples with different porosities can be prepared, and the porosity range of these rock samples covers multiple gradients from very low porosity to higher porosity, to comprehensively simulate the rock characteristics under different geological conditions. Then, different proportions of slurry are injected into these rock samples using high-precision grouting equipment, and measuring instruments are used to monitor the diffusion of the slurry in the rock samples in real time, and the diffusion radius of the slurry is recorded in detail. At the same time, pressure sensors are used to closely track the pressure changes during the injection process, and data on the relationship between the injection volume and pressure changes are accurately recorded.

[0079] In the field grouting test, representative grouting construction sites can be selected, and various grouting parameters can be set according to different geological structures, rock types and engineering requirements. During the grouting process, multiple monitoring points are arranged, and advanced non-destructive testing techniques such as ground penetrating radar are used to dynamically monitor the diffusion range and path of the slurry in the ground, and more real and reliable field grouting data are obtained.

[0080] Then, according to the grouting verification data, the grouting theoretical model is modified.

[0081] In this embodiment, after obtaining these grouting verification data under different conditions, the grouting theoretical model constructed in the early stage can be verified and modified. Specifically, the data obtained from the indoor simulation experiment and the field grouting test can be compared and analyzed in detail with the model prediction results. For example, for the grouting simulation experiment data of different porosity rock samples in the indoor simulation experiment, the differences between the model predicted slurry diffusion radius and the actually measured diffusion radius, and the differences between the model estimated injection amount and pressure change relationship and the experimental record data are carefully compared. By continuously adjusting various parameters in the grouting theoretical model, such as the rheological parameters of the slurry, the permeability coefficient of the rock, etc., the grouting theoretical model can more accurately reflect the complex situation in the actual grouting process, thereby providing more reliable theoretical basis for the actual grouting engineering.

[0082] In an embodiment, the sequence grouting module divides the target grouting area into multiple grouting units according to the stratum permeability identification result, specifically including:

[0083] First, the stratum permeability identification result of the target grouting area is clustered to obtain a preliminary clustering result.

[0084] Before clustering, the stratum permeability identification result can be cleaned to remove outliers and missing values. If there are missing values, methods such as mean filling and interpolation can be used for processing. Then the data is standardized or normalized to make data of different characteristics have the same scale, avoiding that some characteristics have too large influence on the clustering result due to large value.

[0085] In the clustering process, appropriate clustering algorithms can be selected according to data characteristics and actual needs, such as K-Means algorithm, DBSCAN algorithm, etc. K-Means algorithm is suitable for data with uniform distribution and regular shape; DBSCAN algorithm is more suitable for processing data with noise and uneven density.

[0086] For algorithms such as K-Means that require a pre-specified number of clusters, the elbow method, the silhouette coefficient method, etc. can be used to determine the optimal number of clusters. The elbow method is to calculate the sum of squared errors under different cluster numbers, draw the curve of the sum of squared errors and the number of clusters, and the number of clusters corresponding to the inflection point of the curve is the optimal choice; the silhouette coefficient method is to calculate the silhouette coefficient of each sample, and select the number of clusters that maximizes the average silhouette coefficient.

[0087] Subsequently, the formation permeability identification result is input into the selected clustering algorithm, and the algorithm is run for clustering analysis, and a preliminary clustering result can be obtained.

[0088] Then, the preliminary clustering result is quantitatively evaluated, and the preliminary clustering result is adjusted according to the quantitative evaluation result to obtain a final clustering result.

[0089] In this embodiment, the preliminary clustering result can be quantitatively evaluated to check the rationality of clustering. The clustering effect can be observed intuitively by visualizing methods such as drawing two-dimensional or three-dimensional scatter plots; some evaluation indexes such as Calinski-Harabasz index and Davies-Bouldin index can also be used for quantitative evaluation. If the preliminary clustering result is not ideal, the parameters of the clustering algorithm can be adjusted, such as the initial cluster center selection method in the K-Means algorithm, the maximum number of iterations, etc., or the clustering algorithm is reselected, and the clustering analysis is performed again until the final clustering result that meets the requirements is obtained.

[0090] Finally, according to the final clustering result, the sub-regions with similar permeability and similar geological conditions in the target grouting region are divided into a grouting unit, and a plurality of grouting units are obtained.

[0091] In this embodiment, according to the final clustering result, the sub-regions with similar permeability and similar geological conditions in the target grouting region can be divided into a grouting unit, and the division of the grouting unit is completed. In the division process, by fully considering the spatial distribution of the formation, the influence of the geological structure and other factors, it can be ensured that each grouting unit has relatively consistent characteristics, so as to formulate targeted grouting strategies.

[0092] In an embodiment, the sequence grouting module determines the sequence grouting strategies corresponding to the plurality of grouting units according to the set grouting sequence, and dynamically adjusts the sequence grouting strategies according to the grouting measured data, specifically including:

[0093] First, the sequence grouting strategies corresponding to the plurality of grouting units are determined according to the set grouting sequence of low permeability first and stable area last.

[0094] In this embodiment, low permeability first and high permeability later means that the grouting units corresponding to low permeability strata are grouted first. In low permeability strata, higher grouting pressure and smaller slurry flow can be used to enable the slurry to slowly penetrate into the pores and fissures of the strata and form effective consolidated bodies. When the grouting of the low permeability strata reaches a certain standard, the grouting units corresponding to high permeability strata are grouted. Because the pores of high permeability strata are larger or the fissures are developed, the slurry is prone to loss, so lower grouting pressure and larger slurry flow are used to quickly fill the strata space and reduce slurry loss. For example, in a certain project, low permeability strata such as silty clay are grouted first, the grouting pressure is controlled at 2-3 MPa, and the grouting flow is 5-10 L / min; after completion, high permeability strata such as sand and pebble are grouted, the grouting pressure is controlled at 1-2 MPa, and the grouting flow is increased to 15-20 L / min.

[0095] Weak area first and stable area later means that the weak areas with poor geological conditions and prone to problems are grouted first. For example, for weak areas such as fault fracture zones and the periphery of karst caves, the rock mass structure is loose and the stability is poor, which is the key area of grouting construction. By preferentially grouting the weak areas, the stability is enhanced, creating good conditions for subsequent grouting construction in stable areas. For example, in a certain mountain tunnel project, the grouting units that pass through the fault fracture zone are grouted first, and the segmented and multiple grouting method can be used to ensure that the fracture zone is fully reinforced, and then normal grouting construction is carried out in other relatively stable surrounding rock areas.

[0096] Afterwards, if abnormal grouting units with concentrated slurry loss or sudden increase in slurry pressure are detected according to the grouting measured data, the dynamic adjustment of the grouting sequence is carried out according to the abnormal grouting units.

[0097] During the grouting process, when concentrated slurry loss is detected, such as a sudden and significant increase in grouting flow and a continuous decrease in grouting pressure, or a sudden increase in pressure exceeding the set safety threshold, the dynamic adjustment of the grouting sequence mechanism can be triggered. Specifically, the automatic control equipment in the grouting system can be used to suspend the construction of the current abnormal grouting unit, analyze the reasons for the grouting anomaly, and re-evaluate the strata. If the slurry loss is caused by the influence of adjacent high permeability areas, the grouting sequence can be adjusted to first seal and grout the high permeability area, and then continue the construction of the original grouting unit; if the pressure surge is caused by local strata structure changes, the grouting pressure can be appropriately reduced, the slurry ratio can be adjusted, and after the grouting pressure returns to normal, the grouting sequence can be adjusted to continue the construction.

[0098] In an embodiment, the slurry ratio adjustment module establishes a theoretical relationship model between the performance of the slurry and the permeability of the strata, specifically including:

[0099] Firstly, the relationship data between different slurry properties and formation permeability under different formation conditions are obtained.

[0100] In this embodiment, the relationship data between different slurry properties and formation permeability under different formation conditions can be obtained through a large number of indoor tests and field tests. In the indoor test, the pore structure and permeability characteristics of various formations can be simulated, and slurry with different concentrations and viscosities can be configured for permeability test to record the diffusion range and permeability speed of the slurry in the simulated formation. In the field test, representative grouting units can be selected for grouting construction with different slurry ratios, and the performance of the slurry in the actual formation can be observed and monitored, such as the filling effect and consolidation strength of the slurry.

[0101] Then, the parameters related to the formation permeability in the relationship data are taken as input samples, and the parameters related to the slurry properties in the relationship data are taken as output samples, and the pre-constructed machine learning model is trained to obtain a theoretical relationship model between the slurry properties and the formation permeability.

[0102] In actual application, data analysis and modeling techniques such as multiple linear regression analysis and neural network modeling can be used to establish a theoretical relationship model between the slurry properties and the formation permeability. In this embodiment, a neural network is used as the main architecture to establish a machine learning model, and the formation permeability-related parameters such as the formation permeability coefficient and the porosity are taken as input samples, and the slurry properties-related parameters such as the slurry concentration, viscosity, and diffusion radius are taken as output samples. Through training and learning of the sample data, the machine learning model can accurately reflect the internal relationship between the two. In actual application, when the permeability coefficient is within a certain range, the theoretical relationship model can output the corresponding optimal slurry concentration and viscosity value.

[0103] During the grouting process, when the formation permeability changes, the slurry concentration, viscosity, diffusion radius, and other parameters can be output and dynamically adjusted according to the established theoretical relationship model. For example, for high-permeability formations such as sandstone or strongly fractured rock, low-concentration slurry can be used, and the water-cement ratio can be controlled between 1.5-2.0 to enhance the flowability and permeability of the slurry, so that the slurry can quickly fill the formation pores and fractures. For low-permeability formations such as clay or dense shale, high-concentration slurry can be used, and the water-cement ratio can be controlled between 0.8-1.2 to improve the viscosity and filling effect of the slurry, ensuring that it can effectively diffuse and consolidate in low-permeability formations.

[0104] In an embodiment, the control module can be further configured to:

[0105] Firstly, the key parameter deviation and the key parameter change rate are determined according to the grouting measured data and the preset grouting target data.

[0106] In this embodiment, a high-precision pressure sensor can be installed in the grouting pipeline to collect the grouting pressure in real time. The accuracy of the sensor should reach ±0.01 MPa to accurately capture the slight changes in grouting pressure. A flow sensor can also be installed at the outlet of the grouting pump and the entrance of the borehole to monitor the grouting flow in real time. The measurement error of the flow sensor should be controlled within ±2%. At the same time, a density sensor can be installed in the slurry mixing pool to detect the slurry density in real time and ensure the stability of the slurry quality. The above sensors can transmit the collected grouting measured data to the control module in real time through wired or wireless transmission.

[0107] It can be understood that for the key parameters such as grouting pressure, grouting flow, and grouting density in the grouting measured data, the difference between each key parameter and the corresponding target value in the grouting target data can be obtained to get the key parameter deviation, and the corresponding adjacent key parameter at the adjacent sampling time can be determined to determine the key parameter change rate.

[0108] Then, the target control amount is obtained by fuzzy reasoning according to the key parameter deviation and the key parameter change rate.

[0109] In practical applications, a fuzzy control algorithm can be used to infer the corresponding target control amount through a fuzzy rule base according to the key parameter deviation and the key parameter change rate in the grouting process, such as adjusting the speed of the grouting pump to change the grouting pressure and the grouting flow. For example, when the grouting pressure deviation is large and the grouting pressure change rate is large, the fuzzy control algorithm determines that the speed of the grouting pump needs to be significantly reduced to avoid accidents caused by excessive pressure.

[0110] In some embodiments, a neural network model can also be used to optimize the grouting parameters. Specifically, the neural network model pre-establishes a mapping relationship model between the grouting measured data and the grouting effect by learning and training a large amount of historical grouting data. In the actual grouting process, the real-time collected grouting measured data can be input into the trained neural network model, which can output the grouting parameter prediction value. The grouting parameter prediction value is compared with the set grouting parameter target value, and the grouting parameters such as the slurry ratio and the grouting pressure are adjusted according to the comparison result to achieve the optimal grouting effect.

[0111] In some embodiments, a genetic algorithm can also be used to optimize the grouting parameters by simulating the selection, crossover, and mutation operations in the biological evolution process. First, the grouting parameters are encoded to form an initial population. Then, the fitness of each individual is calculated according to the evaluation indicators of the grouting effect, such as the grouting quality and the grouting cost. Next, through selection, crossover, and mutation operations, a new population is generated, and the optimization is iterated constantly to finally obtain the optimal combination of grouting parameters.

[0112] Finally, according to the target control quantity, the key operating parameters in the operation of the target actuator are dynamically adjusted.

[0113] In this embodiment, the target actuator can be a grouting pump, and the target control quantity can be the rotating speed of the grouting pump.

[0114] In actual application, when the control module detects that the pressure suddenly rises and exceeds the set upper limit value of the safety pressure, such as 20% of the normal pressure, or the flow suddenly drops and is lower than the set minimum flow threshold, such as 50% of the normal flow, the alarm mechanism can be automatically triggered. Specifically, an audible and visual alarm device can be used to send an alarm signal to the operator, and abnormal information and alarm reasons can be displayed on the monitoring interface.

[0115] At the same time, an emergency plan can be immediately started, such as suspending the grouting construction, and analyzing the abnormal reasons. If the pressure suddenly rises due to the stratum blockage, measures such as high-pressure water flushing of the borehole or adjustment of the slurry ratio can be adopted; if the flow suddenly drops due to insufficient supply of slurry, the slurry mixing and conveying equipment can be checked, and the slurry can be supplemented in time. After the problem is solved, the grouting construction can be resumed.

[0116] In an embodiment, the above-mentioned grouting system with multiple sequences suitable for complex geological conditions can further comprise a human-computer interaction module.

[0117] The human-computer interaction module is used to establish a spatial distribution map of the target grouting area, determine the grouting progress of each grouting unit, mark the grouting progress of each grouting unit in the spatial distribution map, and generate and display a grouting progress overview map.

[0118] In this embodiment, the grouting progress of each grouting unit can be presented in a graphical form on a visual interface, so as to intuitively show the real-time progress of the grouting construction. On the visual interface, the spatial distribution map of the target grouting area is used as the background, and different colored lines, icons, etc. are used to represent the construction states of different grouting units, such as completed grouting, ongoing grouting, and grouting to be performed. The key parameters of each grouting unit, such as grouting pressure, grouting flow, and slurry ratio, and important information, such as cumulative grouting amount and grouting time, are displayed in real time. The operator can monitor and intervene in the grouting process through the operation buttons on the interface, such as suspending, restarting, and adjusting the grouting parameters.

[0119] In some embodiments, an expert system assisted decision-making knowledge base can be established, which contains a large number of grouting engineering cases, geological knowledge, grouting technical specifications and expert experience, etc. Through knowledge extraction and representation technology, these knowledge can be stored in the knowledge base in the form of rules, frameworks, etc. When problems occur or decisions need to be made during grouting, the reasoning mechanism can search and match in the expert system assisted decision-making knowledge base according to the real-time collected data and user input information, and use reasoning algorithms such as forward reasoning and backward reasoning, to obtain reasonable decision-making suggestions, providing reference for the operating personnel. For example, when encountering abnormal stratum conditions, appropriate grouting strategy adjustment suggestions can be given according to similar cases and experience in the expert system assisted decision-making knowledge base.

[0120] In practical applications, a special interface program can also be developed to realize the docking with the BIM (Building Information Modeling) and GIS (Geographic Information System) platforms. In the BIM platform, the three-dimensional model of the grouting engineering can be integrated with other engineering component models to realize three-dimensional visual display and management of the grouting construction process. Through the GIS platform access, the geographic information and geological information of the target grouting area can be combined with the grouting construction data to realize spatial analysis and decision support of the grouting process. For example, on the GIS platform, the topography, stratum distribution, and the influence of grouting construction on the surrounding environment can be intuitively viewed, thereby providing comprehensive information support for the planning and management of grouting construction.

[0121] Based on the same overall inventive concept, the present application also protects a sub-sequence grouting method suitable for complex geological conditions. The sub-sequence grouting method suitable for complex geological conditions provided by the present application is described as follows, which can be mutually referred to the sub-sequence grouting system suitable for complex geological conditions described above.

[0122] As shown in Figure 2 The sub-sequence grouting method suitable for complex geological conditions provided by the present application is implemented based on the sub-sequence grouting system suitable for complex geological conditions provided by the above embodiments, which specifically includes:

[0123] Step 210: Obtain stratum permeability data and grouting measured data through a stratum permeability identification module, establish a grouting theoretical model using the stratum permeability data, and perform stratum permeability inversion according to the grouting measured data and the grouting theoretical model to obtain a stratum permeability identification result.

[0124] Step 220: dividing the target grouting area into multiple grouting units according to the stratum permeability identification result by the sequence grouting module, determining the sequence grouting strategy corresponding to the multiple grouting units according to the set grouting sequence, and dynamically adjusting the sequence grouting strategy according to the grouting measured data.

[0125] Step 230: establishing a theoretical relationship model between the slurry performance and the stratum permeability by the slurry proportioning adjustment module, determining the target slurry performance according to the theoretical relationship model and the stratum permeability identification result, and self-adaptively adjusting the slurry proportioning according to the target slurry performance.

[0126] Step 240: controlling the target actuator to operate to perform the sequence grouting according to the dynamically adjusted sequence grouting strategy and the self-adaptively adjusted slurry proportioning by the control module.

[0127] As to the method in the above-mentioned embodiments, the specific implementation manners of each step have been described in detail in the embodiments of the system, and thus will not be described in detail here.

[0128] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A sequential grouting system suitable for complex geological conditions, characterized in that, include: The formation permeability identification module is used to acquire formation permeability data and grouting measurement data, establish a grouting theoretical model using the formation permeability data, and perform formation permeability inversion based on the grouting measurement data and the grouting theoretical model to obtain the formation permeability identification result. The sequential grouting module is used to divide the target grouting area into multiple grouting units based on the formation permeability identification results, determine the sequential grouting strategy corresponding to the multiple grouting units according to the set grouting sequence, and dynamically adjust the sequential grouting strategy based on the grouting measurement data. The slurry ratio adjustment module is used to establish a theoretical relationship model between slurry performance and formation permeability, determine the target slurry performance based on the theoretical relationship model and the formation permeability identification results, and adaptively adjust the slurry ratio according to the target slurry performance. The control module is used to control the operation of the target actuator to perform sequential grouting according to the dynamically adjusted sequential grouting strategy and the adaptively adjusted grout ratio.

2. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The formation permeability identification module acquires formation permeability data, including: Obtain stratigraphic core samples and determine core logging information; Acquire sonic logging information and water pressure test data from the borehole; Based on the core logging information, the sonic logging information, and the water pressure test data, formation permeability data are obtained.

3. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The formation permeability identification module uses the formation permeability data to establish a grouting theoretical model, including: The formation permeability data is integrated with the previously obtained geological exploration data to obtain integrated data; Extract key modeling data, including grout properties, geological structure of the grouting area, and grouting boundary, from the integrated data; Based on the key modeling data, a grouting theoretical model is established to describe the variation characteristics of grout flow parameters in different media.

4. The sequential grouting system suitable for complex geological conditions according to claim 3, characterized in that, The formation permeability identification module is also used for: Obtain grouting verification data under different conditions using indoor simulation experiments and on-site grouting tests; Based on the grouting verification data, the grouting theoretical model is revised.

5. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The sequenced grouting module divides the target grouting area into multiple grouting units based on the formation permeability identification results, including: Clustering was performed on the formation permeability identification results of the target grouting area to obtain preliminary clustering results; The preliminary clustering results are quantitatively evaluated, and the preliminary clustering results are adjusted based on the quantitative evaluation results to obtain the final clustering results; Based on the final clustering results, sub-regions with similar permeability and geological conditions within the target grouting area are divided into a grouting unit, resulting in multiple grouting units.

6. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The sequential grouting module determines the sequential grouting strategy corresponding to the multiple grouting units according to a set grouting sequence, and dynamically adjusts the sequential grouting strategy based on the measured grouting data, including: Based on the grouting sequence of low-permeability followed by high-permeability and weak zones followed by stable zones, the sequential grouting strategy corresponding to the multiple grouting units is determined. If, based on the measured grouting data, an abnormal grouting unit is detected that exhibits concentrated grout loss or a sudden increase in grout pressure, then the sequential grouting strategy is dynamically adjusted based on the abnormal grouting unit.

7. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The slurry proportioning adjustment module establishes a theoretical relationship model between slurry performance and formation permeability, including: To obtain data on the relationship between different slurry properties and formation permeability under different formation conditions; The parameters related to formation permeability in the relational data are used as input samples, and the parameters related to slurry performance in the relational data are used as output samples. The pre-constructed machine learning model is trained to obtain a theoretical relationship model between slurry performance and formation permeability.

8. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The control module is also used for: Based on the measured grouting data and the preset grouting target data, the deviation of key parameters and the rate of change of key parameters are determined; Based on the deviation of the key parameters and the rate of change of the key parameters, the target control quantity is obtained through fuzzy inference; According to the target control quantity, the key operating parameters of the target actuator are dynamically adjusted.

9. The sequential grouting system suitable for complex geological conditions according to claim 1, characterized in that, The system also includes: a human-computer interaction module; The human-computer interaction module is used to establish a spatial distribution map of the target grouting area, determine the grouting progress of each grouting unit, mark the grouting progress of each grouting unit on the spatial distribution map, and generate and display an overview map of the grouting progress.

10. A sequential grouting method suitable for complex geological conditions, characterized in that, Based on the sequential grouting system suitable for complex geological conditions as described in any one of claims 1 to 9, the method comprises: The formation permeability identification module acquires formation permeability data and grouting measurement data, uses the formation permeability data to establish a grouting theoretical model, and performs formation permeability inversion based on the grouting measurement data and the grouting theoretical model to obtain the formation permeability identification result. Based on the formation permeability identification results, the sequence grouting module divides the target grouting area into multiple grouting units, determines the sequence grouting strategy corresponding to the multiple grouting units according to the set grouting sequence, and dynamically adjusts the sequence grouting strategy based on the grouting measurement data. A theoretical relationship model between slurry performance and formation permeability is established through a slurry ratio adjustment module. Based on the theoretical relationship model and the formation permeability identification results, the target slurry performance is determined, and the slurry ratio is adaptively adjusted according to the target slurry performance. The control module controls the target actuator to perform sequential grouting according to the dynamically adjusted sequential grouting strategy and the adaptively adjusted grout ratio.

Images