Interventional extraction of solute transport from ionic ore bodies

Abstract

The present application relates to the field of underground fluid intelligent control, and discloses a method for intervention and extraction of ion type ore body solute migration, comprising: establishing a feedback monitoring network for a fluid injection distribution system, obtaining pressure parameters and flow parameters generated by interaction between a controlled branch and a controlled medium; calculating dynamic impedance parameters of internal seepage resistance state of the controlled medium; extracting a follow-up change rate of the dynamic impedance parameters, identifying nonlinear evolution of pore structure induced by flow resistance mutation; comparing the follow-up change rate with a preset threshold value to determine bypass overflow risk; generating a flow dynamic distribution instruction and adjusting valve opening to make the local flow field stable within a preset flow resistance gradient range, wherein the present application solves the problem of sensing loss induced by formation porosity evolution through a dynamic flow resistance feedback intervention mechanism, drives fluid to penetrate into unconsolidated blind areas, and constructs a continuous and dense seepage interception interface.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for underground fluids, and particularly relates to a method for solute transport intervention and extraction in ionic mineral bodies. Background Technology

[0002] Currently, ion-adsorption rare earth minerals are typically found in weathered crust geological environments. In-situ leaching is the mainstream mining method, which involves deploying a liquid injection network at the top of the ore body and injecting leaching agent. Gravity and pressure gradients drive the agent to permeate within the pores, thereby completing the ion exchange reaction. The resulting rare earth mother liquor is collected through a bottom collection system. Due to structural factors such as fractures and uneven weathering, the permeability inside the ore body exhibits significant spatial heterogeneity. During transport, the leaching agent tends to migrate rapidly along the dominant flow path with less resistance. This phenomenon results in the agent failing to cover low-permeability areas, reducing resource recovery efficiency. At the same time, the uncontrolled solute transport process increases the probability of mother liquor leakage into hidden channels outside the mining area, inducing groundwater pollution and mountain structural instability.

[0003] To address leakage issues, the industry employs microbial mineralization technology to construct seepage barriers. This technology induces the formation of calcium carbonate crystals to seal pores in soil and rock. However, the formation of mineralization products causes transient changes in local stratum flow resistance, altering the distribution pattern of the underground flow field. Traditional static injection methods, such as injection with constant pump pressure or constant discharge, lack physical feedback sensing of flow field evolution characteristics and cannot respond to dynamic distortions in flow resistance. Subsequent injected fluid media inevitably bypass the already sealed high flow resistance areas, overflowing along unconsolidated bypasses. This results in a point-like distribution of the seepage barrier, making it difficult to form a continuous, closed overall structure. For example, Chinese invention patent CN113933354B discloses a method for monitoring seepage in in-situ leaching of ion-type rare earth minerals. It utilizes high-density resistivity methods to collect resistivity characteristics of each monitoring line, predicting the seepage influence range through the correspondence between resistivity changes and seepage migration, thereby adjusting the injection volume.

[0004] Therefore, the technical problem to be solved by this invention is how to achieve intelligent intervention in the solute transport path of ion-type ore bodies, obtain flow resistance feedback and dynamically adjust injection parameters to enable the fluid medium to actively fill the blind area of ​​micro-fractures and build a continuous and dense seepage barrier. Summary of the Invention

[0005] This invention aims to solve the problems of uncontrollable solute transport paths and discontinuous seepage barriers caused by the lack of flow field perception during in-situ leaching of ion-adsorption rare earth ores.

[0006] In this technical solution, a method for solute transport intervention and extraction in ionic mineral bodies includes the following steps:

[0007] Step S1: Establish a feedback monitoring network for a fluid injection distribution system with multiple injection branches to obtain the pressure and flow parameters generated by the interaction between each controlled branch and the controlled medium.

[0008] Step S2: The intelligent control system collects and monitors the transient pumping pressure and transient injection flow rate transmitted by each controlled branch in the network in real time.

[0009] Step S3: Based on the real-time ratio of transient pumping pressure to transient injection flow rate, periodically calculate the dynamic impedance parameter that characterizes the internal seepage resistance state of the controlled medium.

[0010] Step S4: Extract the rate of change of dynamic impedance parameters relative to sampling time, and compare the rate of change with a preset evolution response threshold to identify the nonlinear transient change state of pore structure induced by the evolution of flow channel blockage inside the controlled medium.

[0011] Step S5: When the rate of change exceeds the evolution response threshold, the risk of bypass overflow caused by the generation of a dominant flow channel inside the controlled medium is determined.

[0012] Step S6: Based on the bypass overflow risk, a flow dynamic redistribution command is generated, and the flow regulation unit of the controlled branch is driven to adjust the valve opening, so that the local flow field inside the controlled medium is stabilized within the preset flow resistance gradient range. By using the local flow resistance change as a physical diversion node, the subsequent fluid medium is forcibly guided to penetrate into the low flow resistance blind zone, thereby realizing intelligent intervention in the solute transport path.

[0013] Preferably, in step S4, the process of obtaining the following rate of change includes: step S41, setting the sampling period to 0.1s to 1.0s; step S42, calculating the first-order difference value of the dynamic impedance parameter within the continuous sampling period, and using the obtained difference value as the following rate of change.

[0014] Preferably, in step S5, the evolution response threshold is dynamically calibrated using the historical pressure drop curve of the controlled medium; when the rate of change continues to rise within a preset time window and the slope exceeds the evolution response threshold, the intelligent control system determines that the transport path corresponding to the controlled branch has been physically blocked.

[0015] Preferably, in step S6, the process of generating the flow dynamic redistribution command includes: step S61, constructing a flow field topology model inside the controlled medium based on the spatial distribution gradient of the dynamic impedance parameters of each controlled branch; step S62, calculating the combination parameters of the opening degree of each flow regulating unit to make the solute coverage in the three-dimensional space reach the preset extreme value based on the flow field topology model.

[0016] Preferably, in step S6, when adjusting the valve opening, the intelligent control system acquires the fluid recovery flow rate data at the end of the fluid injection distribution system and calculates the injection-recovery balance factor; if the injection-recovery balance factor deviates from the preset safety range, the intelligent control system generates a secondary intervention command and compensates for the gain of the flow dynamic redistribution command by adjusting the action step size of the flow adjustment unit.

[0017] Preferably, before calculating the dynamic impedance parameter in step S3, the following steps are also included: step S31, obtaining the deep spatial structure data of the controlled medium obtained by high-density electrical resistivity tomography; step S32, using the deep spatial structure data to perform spatial weight correction on the initial value of the seepage resistance of the controlled medium, so as to eliminate the static deviation in calculation caused by the uneven development of the original structure of the controlled medium.

[0018] Preferably, in step S6, the valve opening is constrained by the maximum adjustment slope in the control logic; the intelligent control system adjusts the feedback gain of the flow regulation unit in real time according to the fluctuation amplitude of the transient pumping pressure to ensure the stability of the pressure network of the fluid injection distribution system during the regulation process.

[0019] Preferably, in step S2, the sampling frequency of the feedback monitoring network is not less than 10Hz to capture the microstructure evolution signal triggered in the early stage of the flow channel obstruction evolution; the pressure parameters and flow parameters of each controlled branch are aligned using a time synchronization protocol.

[0020] Preferably, the flow dynamic redistribution instruction also includes switching control logic for the extraction medium and the solidification medium; when the follow-up rate of change identifies that the dominant flow channel has been formed, the intelligent control system drives the controlled branch to inject the solidification medium, and switches to solute extraction mode after the dynamic impedance parameter rises back to the preset range.

[0021] Preferably, the intelligent intervention of the solute transport path implemented in step S6 constrains the flow channel through the continuous seepage interception interface formed inside the controlled medium; when the dynamic impedance parameters of each controlled branch in the whole region tend to be consistent and the rate of change returns to zero equilibrium state, it is determined that the solute transport path has completed global reconstruction.

[0022] Compared with existing technologies, the extraction method for solute transport interference in ionic mineral bodies of the present invention has the following advantages:

[0023] 1. In the solute transport intervention and extraction of ion-type ore bodies, the transient pumping pressure and transient injection flow rate at the injection node are collected to realize the periodic calculation of the dynamic flow resistance parameters inside the rock mass. Based on the characteristic parameter of the flow resistance time derivative to characterize the local consolidation state, the injection system has the ability to perceive the evolution characteristics of the underground flow field in real time, eliminating the lag of traditional processes when facing transient changes in formation porosity, and providing a physical feedback basis for the precise intervention of solute transport paths.

[0024] 2. When the mineralization reaction induces a local flow resistance step, the actuator dynamically adjusts the valve opening of the target node to guide the subsequent fluid medium to be diverted to the adjacent low flow resistance area under the coupling effect of the hydraulic network. This process uses the locally generated mineralization products as physical diversion nodes, so that the bacterial liquid and cementing liquid actively search for and fill the unsealed micro-fracture blind area, and construct a highly continuous and dense seepage barrier in the three-dimensional geological space to avoid the risk of bypass overflow from the dominant flow channel.

[0025] 3. By combining the deep space model obtained from high-density electrical resistivity inversion with real-time flow resistance monitoring data, a deep coupling between detection information and operational dynamics is achieved. By combining macroscopic structural fracture identification with microscopic phase interface evolution perception, the topological layout of the injection and recovery system and the dynamic matching of injection parameters are optimized to ensure uniform coverage of the leaching agent in the complex weathering crust environment, improve resource recovery rate and mother liquor retention rate, and enable the mining process to operate under controlled fluid network management. Attached Figure Description

[0026] Figure 1 This is a flowchart of the dynamic flow resistance feedback-based extraction method for solute transport in ionic mineral bodies according to the present invention.

[0027] Figure 2 This is a diagram of the intelligent closed-loop control system architecture for the intervention of solute transport in ionic ore bodies according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0029] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0030] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0031] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0032] A method for solute transport intervention and extraction in ionic mineral bodies includes the following steps:

[0033] Step S1: Establish a feedback monitoring network for a fluid injection distribution system with multiple injection branches to obtain the pressure and flow parameters generated by the interaction between each controlled branch and the controlled medium.

[0034] Step S2: The intelligent control system collects and monitors the transient pumping pressure and transient injection flow rate transmitted by each controlled branch in the network in real time.

[0035] Step S3: Based on the real-time ratio of transient pumping pressure to transient injection flow rate, periodically calculate the dynamic impedance parameter that characterizes the internal seepage resistance state of the controlled medium.

[0036] Step S4: Extract the rate of change of dynamic impedance parameters relative to sampling time, and compare the rate of change with a preset evolution response threshold to identify the nonlinear transient change state of pore structure induced by the evolution of flow channel blockage inside the controlled medium.

[0037] Step S5: When the rate of change exceeds the evolution response threshold, the risk of bypass overflow caused by the generation of a dominant flow channel inside the controlled medium is determined.

[0038] Step S6: Based on the bypass overflow risk, a flow dynamic redistribution command is generated, and the flow regulation unit of the controlled branch is driven to adjust the valve opening, so that the local flow field inside the controlled medium is stabilized within the preset flow resistance gradient range. By using the local flow resistance change as a physical diversion node, the subsequent fluid medium is forcibly guided to penetrate into the low flow resistance blind zone, thereby realizing intelligent intervention in the solute transport path.

[0039] Preferably, in step S4, the process of obtaining the following rate of change includes: step S41, setting the sampling period to 0.1s to 1.0s; step S42, calculating the first-order difference value of the dynamic impedance parameter within the continuous sampling period, and using the obtained difference value as the following rate of change.

[0040] Preferably, in step S5, the evolution response threshold is dynamically calibrated using the historical pressure drop curve of the controlled medium; when the rate of change continues to rise within a preset time window and the slope exceeds the evolution response threshold, the intelligent control system determines that the transport path corresponding to the controlled branch has been physically blocked.

[0041] Preferably, in step S6, the process of generating the flow dynamic redistribution command includes: step S61, constructing a flow field topology model inside the controlled medium based on the spatial distribution gradient of the dynamic impedance parameters of each controlled branch; step S62, calculating the combination parameters of the opening degree of each flow regulating unit to make the solute coverage in the three-dimensional space reach the preset extreme value based on the flow field topology model.

[0042] Preferably, in step S6, when adjusting the valve opening, the intelligent control system acquires the fluid recovery flow rate data at the end of the fluid injection distribution system and calculates the injection-recovery balance factor; if the injection-recovery balance factor deviates from the preset safety range, the intelligent control system generates a secondary intervention command and compensates for the gain of the flow dynamic redistribution command by adjusting the action step size of the flow adjustment unit.

[0043] Preferably, before calculating the dynamic impedance parameter in step S3, the following steps are also included: step S31, obtaining the deep spatial structure data of the controlled medium obtained by high-density electrical resistivity tomography; step S32, using the deep spatial structure data to perform spatial weight correction on the initial value of the seepage resistance of the controlled medium, so as to eliminate the static deviation in calculation caused by the uneven development of the original structure of the controlled medium.

[0044] Preferably, in step S6, the valve opening is constrained by the maximum adjustment slope in the control logic; the intelligent control system adjusts the feedback gain of the flow regulation unit in real time according to the fluctuation amplitude of the transient pumping pressure to ensure the stability of the pressure network of the fluid injection distribution system during the regulation process.

[0045] Preferably, in step S2, the sampling frequency of the feedback monitoring network is not less than 10Hz to capture the microstructure evolution signal triggered in the early stage of the flow channel obstruction evolution; the pressure parameters and flow parameters of each controlled branch are aligned using a time synchronization protocol.

[0046] Preferably, the flow dynamic redistribution instruction also includes switching control logic for the extraction medium and the solidification medium; when the follow-up rate of change identifies that the dominant flow channel has been formed, the intelligent control system drives the controlled branch to inject the solidification medium, and switches to solute extraction mode after the dynamic impedance parameter rises back to the preset range.

[0047] Preferably, the intelligent intervention of the solute transport path implemented in step S6 constrains the flow channel through the continuous seepage interception interface formed inside the controlled medium; when the dynamic impedance parameters of each controlled branch in the whole region tend to be consistent and the rate of change returns to zero equilibrium state, it is determined that the solute transport path has completed global reconstruction.

[0048] Example 1: In a solute transport intervention operation targeting an ion-adsorption rare earth deposit with centimeter-level fractures and a complex geological environment, the ore body exhibits spatial heterogeneity in permeability due to uneven weathering. This leads to the loss of leaching agent along the dominant flow channels formed by fractures during injection, resulting in reduced mother liquor recovery and increased raw material losses. To accurately control the underground fluid transport path, this method utilizes high-density electrical resistivity tomography (EDS) to spatially probe the target ore body to determine the water catchment path. Based on the empirical conductivity laws of porous media rock physics, the control system extracts the apparent resistivity spatial distribution matrix from the EDS output. The porosity parameters of each three-dimensional grid node are calculated using Archie's formula, and then transformed into a spatial weight matrix of the initial value of the seepage resistance of the controlled medium. This eliminates the static baseline deviation caused by the original structural heterogeneity of the strata. The porosity parameters are then converted... In the process of transforming into seepage resistance, the control system does not perform a simple numerical inverse equivalence. Instead, it introduces the Karman-Kozani physical equation based on the correction of microscopic throat tortuosity as a cross-scale transformation mechanism. Based on the empirical constant of specific surface area obtained from mercury intrusion porosimetry of historical cores of this type of weathered crust ore body, the system substitutes the single porosity scalar obtained by electrical inversion into the above equation to calculate the absolute permeability value reflecting the actual fluid cross-throat connectivity. Finally, the inverse of this absolute permeability is taken to obtain the spatial weight matrix of the initial value of seepage resistance. A feedback monitoring network is established for the fluid injection distribution system with multiple injection branches. The control system obtains the pressure and flow parameters generated by the interaction between each controlled branch and the controlled medium at a sampling frequency of 10Hz. The control system calculates the dynamic impedance parameter characterizing the seepage resistance state inside the controlled medium. The calculation formula is as follows: ;in, For dynamic impedance parameters, For transient pump-in pressure, Injecting transient flow.

[0049] During microbial mineralization, the generated calcium carbonate crystals cause nonlinear transient evolution of local formation porosity. The complex hydrodynamic environment causes this transient evolution to exhibit mechanical blockage characteristics. In the initial stage of continuous shearing by the fluid, loose calcium carbonate aggregates are generated. These free aggregates migrate with the fluid and induce transient bridging and consolidation at the downstream micropore throats, producing a flow resistance step response on a millisecond-level physical timescale. The control system calculates the dynamic impedance parameters within a continuous sampling period. Rate of change relative to sampling time The following formula is used to identify the evolution of pore structure: ;in, The rate of change of the dynamic impedance parameter. The dynamic impedance parameter for the current sampling period. This is the dynamic impedance parameter of the previous sampling period. The sampling time interval will be used by the control system to track the rate of change. With evolution response threshold Compare and evolve response thresholds Based on historical pressure drop data, when identified Furthermore, if this state remains stable within three consecutive sampling periods, it is determined that the transport path corresponding to the injection branch is physically blocked and there is a risk of bypass overflow.

[0050] The control system generates a dynamic flow distribution command based on the judgment result, driving the flow regulation unit to lower the valve opening of the corresponding branch. This utilizes the sudden change in flow resistance caused by local mineralization as a physical diversion node, guiding the subsequent fluid medium to permeate into the low-flow-resistance blind zone in the adjacent area. In this dynamic distribution and regulation mechanism, the control system stabilizes the local flow field within a preset flow resistance gradient range. This preset flow resistance gradient range is determined by using the initial flow resistance benchmark value, determined by the system's base permeability test during the initial injection phase, as a reference base, and taking 1.2 to 1.5 times this benchmark value as the gradient safety factor. The upper and lower limits of the entire range are used to ensure that the local seepage pressure after forced diversion can overcome the capillary resistance of adjacent micropore throats and is lower than the critical fracturing pressure limit that induces formation micro-splitting damage. This active intervention through flow field evolution feedback enables the injection trajectory to achieve dynamic adaptive mapping in three-dimensional geological space, constructing a continuous and dense seepage interception interface in the dominant flow channel. Ultimately, the mother liquor recovery rate of the ore body is increased to more than 85%, and the permeability of the bottom plate fracture area is reduced by 90%, enabling the extraction operation of ion-adsorption rare earth ore to operate under controlled fluid network management.

[0051] Example 2: To verify the path control accuracy of the solute transport intervention extraction method for ion-adsorption ore bodies in fractured environments, a three-dimensional seepage physical simulation platform was constructed. This platform includes a transparent plexiglass tank with internal dimensions of 2000mm × 2000mm × 1500mm. The tank is filled with ore samples layered according to the geological characteristics of ion-adsorption rare earth deposits, and a simulated fracture layer composed of coarse sand is pre-embedded to form the initial dominant flow channel. The pressure measurement sensor equipped with the three-dimensional seepage physical simulation platform has a measurement range of 0 to 1 MPa and a full-scale accuracy of 0.1%. The flow meter has a sampling resolution of 0.01 L / min and supports signal output of no less than 20 Hz, thereby ensuring the physical characterization of the underground fluid medium's transport state. For the aforementioned simulation conditions, the sampling frequency was... The frequency is set to 10Hz. This parameter's value is determined by balancing the feature extraction accuracy of the seepage pressure signal with the system's data processing load. If the sampling frequency... Below 5 Hz, due to the transient and sudden nature of pore structure evolution induced by microbial mineralization, the system cannot capture dynamic impedance parameters. The first-order difference characteristic leads to a delayed decision response; if the sampling frequency... Above 50Hz, high-frequency mechanical noise generated by the reciprocating motion of the pump unit would be introduced, masking the evolution trend of seepage resistance. Therefore, 10Hz was determined as the sampling frequency. To enable monitoring of seepage conditions.

[0052] In the initial stage of the experiment, a magnesium sulfate solution with a concentration of 0.5 mol / L was injected into the simulated ore body, and the initial pumping pressure was maintained at 0.15 MPa. At this time, the raw flow data collected by the feedback monitoring network fluctuated, and the transient injection flow rate... The flow rate jumps between 1.42 L / min and 1.48 L / min, reflecting environmental disturbances caused by the superposition of a non-uniform flow field and pumping pulses. The initial dynamic impedance parameters calculated by the control system... The mean value was 0.103 MPa·min / L. The system was switched to microbial inoculum injection mode, and the evolution response threshold was determined based on gradient pressure drop calibration data. The value is set to 0.015 MPa·min / (L·s), which is used to identify the rate of change with the flow. The magnitude jump was used to distinguish normal flow fluctuations from pore blockage; when the microbial mineralization reaction lasted for 125 s, the rate of change within the dominant flow channel was observed. The value rapidly increased from an initial 0.002 to 0.018, and then stabilized within the range of 0.018 to 0.021 over the next 5 sampling periods. This value exceeded the evolution response threshold. At this point, the control system determines that a nonlinear evolution of the pore structure has occurred, and then generates a dynamic flow distribution command, driving the valve opening of the target branch to decrease from 100% to 35%. Under this intervention, the transient pumping pressure of the injection branch is observed. The pressure increased from 0.15 MPa and stabilized at 0.28 MPa, while the transient injection flow rate of the controlled branch... When the flow rate is reduced to below 0.65 L / min, this reconfiguration of pressure and flow parameters drives the fluid medium to penetrate into the surrounding high-resistivity blind zone, providing a basis for verifying the evolution response threshold. To ensure the reasonableness of the defined range, two control groups exceeding the range were established, and the evolution response threshold was considered. When set to 0.05, due to insufficient recognition sensitivity, the system failed to identify the increase in local seepage resistance, resulting in continuous bypass overflow of the leaching agent along the mineralization interface edge. Ultimately, the blockage rate of the dominant flow channel was only 42.5%. When the evolution response threshold... When the value is set to 0.005, the system is falsely triggered by background noise, causing the valve to be frequently adjusted before the cracks are effectively sealed. The mother liquor recovery rate of the simulated ore body decreased from 86.8% in the test group to 61.2%. This comparative data confirms that the parameter range used has a synergistic effect in balancing identification accuracy and control stability.

[0053] Example 3: In a clustered solute transport intervention system consisting of 12 injection branches, the pore structure of the formation corresponding to each branch exhibits inconsistency due to differences in the weathering degree of the ore layer. During the startup phase, the system faces the technical challenge of unifying the initial seepage resistance benchmark. If a fixed threshold value is used for overflow determination across the entire mining area, false alarms are easily triggered in low-permeability ore layers or missed alarms occur in dominant flow channels. To determine the benchmark for each controlled branch, the control system drives each branch to inject the benchmark medium at a constant low flow rate during the initial injection phase before injecting the microbial solution, and calculates the dynamic impedance parameters of each branch. The control system extracts dynamic impedance parameters within a preset time period. Based on the statistical characteristics, calculate the mean impedance of each branch. with standard deviation Evolution response threshold The calculation formula is as follows: ;in, For the evolution response threshold, This represents the standard deviation of the dynamic impedance parameter during the initial injection phase. As the confidence factor, The value range is set between 3 and 5, and this calibration method sets the threshold value. It is anchored to the initial physical flow field characteristics acquired in real time by the sensor.

[0054] When the system identifies a specific branch rate of change Continuously exceeding its corresponding evolution response threshold At that time, the control system generates an opening reduction command for that branch. To maintain stable global injection pressure and suppress hydraulic shocks caused by sudden changes in flow rate in a single branch, the system adjusts the opening according to the other controlled branches. Current dynamic impedance parameters The allocation of traffic compensation amount involves the following calculation process: obtaining the target branch. The flow drop value at the risk assessment point is calculated by dividing this flow drop value by the reciprocal of the controlled branch impedance. The weight ratio is allocated to the opening increment of each actuator. According to the law of conservation of fluid mass in the pipeline network, the control system synchronously collects the transient recovery flow rate of the end-of-line collection network. The ratio of the total recovery flow rate integral to the total injection flow rate integral within a preset sliding time window is determined as the injection-recovery balance factor. When the injection-recovery balance factor is detected to be lower than the set safety lower limit threshold, the control system performs closed-loop proportional feedback calculation. The difference between the safety lower limit threshold and the current injection-recovery balance factor is multiplied by a preset proportional gain constant to calculate the action step compensation. This action step compensation is then added to the opening increment of the actuator. To avoid using a macroscopic global integral... The system directly controls the spatiotemporal control misalignment caused by the local transient single branch by the lag parameter. Before performing the superposition operation, the system introduces a time axis mapping allocation mechanism based on the physical lag of formation seepage. The control system extracts the empirical time delay feature value based on the effective seepage water collection path length from the injection wellhead to the end of the liquid collection network of the specific branch. The calculated macroscopic action step size compensation is discretized into multiple micro-operation step size commands in the time domain and distributed with equidistant delay within a complete time delay feature period. This makes the global macroscopic signal accurately self-consistent with the physical response rate of the local transient flow field of the branch after dimensionality reduction.

[0055] When the No. 3 injection branch experiences localized physical blockage due to mineralization reaction, triggering the flow distribution logic, its valve opening is reduced from 80% to 20%. Simultaneously, the system identifies that the No. 5 and No. 6 branches, which are topologically adjacent to it, are in a low flow resistance state. Subsequently, it drives the valve opening of the No. 5 branch to increase from 45% to 62%, and the valve opening of the No. 6 branch to increase from 50% to 68%. Under this control logic, the fluctuation range of the global pumping pressure is maintained within 5% of the initial baseline value, avoiding pipeline damage caused by transient changes in flow resistance, and guiding the cementing liquid to permeate into the low flow resistance blind zone. This method achieves continuous closure of the seepage interception barrier, and the effective reactant utilization rate of the entire mining area reaches over 92%.

[0056] Example 4: In the scenario of operating on ion-adsorption rare earth ore bodies with fluctuating geological parameters and low permeability, before injecting microbial inoculum, the system drives the pump group to inject a reference fluid into the target ore layer, and performs step-by-step pressurization in a pressure range of 0.05MPa to 0.25MPa; the feedback monitoring network collects the transient pumping pressure and transient injection flow rate of each controlled branch, and the control system calculates the dynamic impedance parameters. With the rate of change The control system determines the rate of change of motion within a preset time period. Standard deviation And select a confidence factor between 3 and 5 based on the background noise level. ; Calculate the evolution response threshold Its formula is ,in, For the evolution response threshold, As the confidence factor, The standard deviation of the rate of change during the initial injection phase; this calibration procedure will evolve the response threshold. It is anchored to the physical flow field characteristics obtained from sampling.

[0057] In a hydraulic network with multiple parallel branches, when a local physical blockage occurs in an injection branch due to a mineralization reaction, the control system acquires the three-dimensional topological distance between the controlled branches. And utilize three-dimensional topological distance The reciprocal determines the spatial coupling weight. Based on this, the control system constructs a three-dimensional flow field topology map inside the controlled medium by combining the dynamic impedance parameters of each branch. To achieve the preset extreme value of solute coverage in three-dimensional space, the system takes minimizing the variance of the dynamic impedance parameter distribution among nodes in the entire region as the objective function, uses the extracted spatial coupling weights as network constraints, and employs a particle swarm optimization algorithm to iteratively solve for the optimal flow distribution ratio that can converge the objective function. Based on the flow resistance characteristic curve of the actuator, this is transformed into specific opening combination parameters for each flow regulation unit. The control system generates flow compensation commands for adjacent branches, transferring the pressure surplus to the spatial coupling weights. The low flow resistance region in the high-level range is allocated; the flow regulation unit drives each actuator according to the calculated opening increment to complete the balance adjustment of the injection pressure in the pipeline system; this method maintains the overall consolidation rate of the seepage interception barrier under different geological structures.

[0058] Example 5: In the intervention condition of solute migration in weathered crust ore bodies with uncertain injection pressure fluctuation frequency distribution, in order to suppress the high-frequency interference component introduced by the mechanical pulsation of the pump group and extract the essential signal that can characterize the evolution of formation porosity, the control system, after acquiring the original signals of transient pumping pressure and transient injection flow rate, adopts a method with an order of 2 and a cutoff frequency... A digital low-pass filter is set to 1.5 times the upper limit of the signal's main frequency bandwidth. For the dynamic extraction step of the aforementioned upper limit of the signal's main frequency bandwidth, the system collects a 10-second raw discrete sequence of pressure fluctuations during the baseline pre-filling stage and performs a Fast Fourier Transform on it to calculate the frequency domain power spectral density curve. Within the frequency domain, the frequency critical point corresponding to the smooth rightward decay of the power spectral density energy amplitude to 5% of the main peak energy is selected and calibrated as the upper limit of the signal's main frequency bandwidth to adapt to the current system hardware attributes. After signal preprocessing, the system initiates a 300-second baseline calibration mode, monitoring the dynamic impedance parameters within 10 consecutive sampling windows in real time. The coefficient of variation is used to determine the quasi-steady state of the flow channel when it is below 0.02. The mean of the rate of change of this steady state interval is used as the zero-point offset to subtract the value, so as to determine the initial physical field reference for eliminating the residual dynamic interference of the system hardware.

[0059] In long-cycle liquid injection conditions where ambient temperature fluctuations cause zero-point drift in the sensor, the control system employs a feedback compensation method based on a sliding time window. Every 3600-second operating cycle, the system extracts the minimum impedance reference value under fully open flow conditions and compares it with the initial calibration value. When the deviation exceeds 3% of the initial calibration value, the system corrects the evolution response threshold based on a linear scaling factor determined by the current ambient temperature deviation. The non-real flow resistance jump term caused by the evolution of physical component characteristics over time is eliminated; the linear scaling factor is determined by the product of the absolute deviation between the environmental monitoring temperature and the standard calibration temperature and the sensor sensitivity drift coefficient, and this value is used as a dynamic multiplier to directly act on the benchmark term of the evolution response threshold; this method limits the false alarm rate of the nonlinear evolution determination of the formation pore structure to within 0.8%, so that the construction state of the seepage interception barrier in mineral layers at different depths meets the preset technical indicators.

[0060] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A method for extracting solutes from ionic mineral bodies by intervening in their transport, characterized in that, Includes the following steps: Step S1: Establish a feedback monitoring network for a fluid injection distribution system with multiple injection branches to obtain the pressure and flow parameters generated by the interaction between each controlled branch and the controlled medium. Step S2: The intelligent control system collects and monitors the transient pumping pressure and transient injection flow rate transmitted by each controlled branch in the network in real time. Step S3: Based on the real-time ratio of transient pumping pressure to transient injection flow rate, periodically calculate the dynamic impedance parameter that characterizes the internal seepage resistance state of the controlled medium. Step S4: Extract the rate of change of dynamic impedance parameters relative to sampling time, and compare the rate of change with a preset evolution response threshold to identify the nonlinear transient change state of pore structure induced by the evolution of flow channel blockage inside the controlled medium. Step S5: When the rate of change exceeds the evolution response threshold, the risk of bypass overflow caused by the generation of a dominant flow channel inside the controlled medium is determined. Step S6: Based on the bypass overflow risk, a flow dynamic redistribution command is generated, and the flow regulation unit of the controlled branch is driven to adjust the valve opening, so that the local flow field inside the controlled medium is stabilized within the preset flow resistance gradient range. By using the local flow resistance change as a physical diversion node, the subsequent fluid medium is forcibly guided to penetrate into the low flow resistance blind zone, thereby realizing intelligent intervention in the solute transport path.

2. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S4, the process of obtaining the following rate of change includes: step S41, setting the sampling period to 0.1s to 1.0s; step S42, calculating the first-order difference value of the dynamic impedance parameter within the continuous sampling period, and using the obtained difference value as the following rate of change.

3. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S5, the evolution response threshold is dynamically calibrated using the historical pressure drop curve of the controlled medium. When the rate of change continues to rise within a preset time window and the slope exceeds the evolution response threshold, the intelligent control system determines that the transport path corresponding to the controlled branch has been physically blocked.

4. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S6, the process of generating the flow dynamic redistribution command includes: step S61, constructing a flow field topology model inside the controlled medium based on the spatial distribution gradient of the dynamic impedance parameters of each controlled branch; step S62, calculating the combination parameters of the opening degree of each flow regulation unit to make the solute coverage in the three-dimensional space reach the preset extreme value based on the flow field topology model.

5. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S6, when adjusting the valve opening, the intelligent control system acquires the fluid recovery flow rate data at the end of the fluid injection distribution system and calculates the injection-recovery balance factor. If the injection-recovery balance factor deviates from the preset safety range, the intelligent control system generates a secondary intervention command and compensates for the gain of the flow dynamic redistribution command by adjusting the action step size of the flow adjustment unit.

6. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, Before calculating the dynamic impedance parameters in step S3, the following steps are also included: Step S31, obtaining the deep spatial structure data of the controlled medium obtained by high-density electrical resistivity tomography; Step S32, using the deep spatial structure data to perform spatial weight correction on the initial value of the seepage resistance of the controlled medium, so as to eliminate the static deviation in calculation caused by the uneven development of the original structure of the controlled medium.

7. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S6, the valve opening is constrained by the maximum adjustment slope in the control logic; the intelligent control system adjusts the feedback gain of the flow regulation unit in real time according to the fluctuation amplitude of the transient pumping pressure to ensure the stability of the pressure network of the fluid injection distribution system during the regulation process.

8. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, In step S2, the sampling frequency of the feedback monitoring network is not less than 10Hz to capture the microstructure evolution signal caused by the initial stage of the flow channel obstruction evolution; the pressure parameters and flow parameters of each controlled branch are aligned using a time synchronization protocol.

9. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, The flow dynamic redistribution command also includes switching control logic for the extraction medium and the solidification medium; when the follow-up rate of change detects that the dominant flow channel has been formed, the intelligent control system drives the controlled branch to inject the solidification medium, and switches to solute extraction mode after the dynamic impedance parameter rises back to the preset range.

10. The method for solute transport intervention and extraction from ionic mineral bodies according to claim 1, characterized in that, Step S6 enables intelligent intervention in the solute transport path by constraining the flow channel through a continuous interception interface formed inside the controlled medium. When the dynamic impedance parameters of all controlled branches in the entire region tend to be consistent and the rate of change returns to zero equilibrium, it is determined that the solute transport path has completed global reconstruction.

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