A method and device for processing pumping test multi-drop data

Abstract

The application provides a pumping test multi-drop data processing method and device. The method monitors the water level change and the water outlet pipe flow in real time before the pumping test starts; the current static water level is taken as the initial water level of any drop before the drop starts; the drawdown-yield dynamic curve of the drop is generated based on the real-time collected dynamic water level and yield during the pumping of the drop; the recovery water level change curve of the drop is generated after the end of the drop; the multi-drop joint dynamic data set is constructed based on the drawdown-yield dynamic curves and the corresponding recovery water level change curves of multiple pumping drops, the hydrogeological parameters of the aquifer are inverted based on the data set, the inverted hydrogeological parameters are verified by using the recovery water level change curve, and the corrected hydrogeological parameters are output. The method realizes continuous monitoring and comprehensive analysis of the whole pumping process, and the real-time data collection and automatic inversion capability reduces manual intervention and human error.

Description

Technical Field

[0001] This application relates to the field of hydrogeological exploration and geothermal resource development technology, and more specifically, to a method and apparatus for processing multi-fall data from pumping tests. Background Technology

[0002] The exploration and development of groundwater resources is a core component of hydrogeological work, particularly in the development and utilization of geothermal resources and deep confined aquifers. Accurately obtaining the hydrogeological parameters of aquifers is of significant engineering importance. Pumping tests, as the primary means of obtaining parameters such as aquifer permeability, hydraulic conductivity, and storage capacity, are widely used in hydrogeological surveys, geothermal well evaluation, and mine water inflow prediction. With the increasing demand for deep groundwater resource development, higher requirements are being placed on the accuracy of pumping test data and the ability to perform multi-fall analysis.

[0003] Current pumping test methods typically employ submersible pumps for constant or variable flow rate pumping, with water level gauges and flow meters used to monitor dynamic water level and inflow changes in real time. The conventional approach involves pumping through multiple drawdown sections, each lasting for a specific time (e.g., 12 hours, 24 hours, 48 ​​hours), recording the static and dynamic water levels, and calculating the inflow for each section. After the test, a Qs curve is plotted based on the drawdown-inflow data for each section, and then aquifer parameters are estimated using analytical or graphical methods. For example, this pumping test used a 250QJR150-255 / 9 submersible pump to conduct three drawdown tests on a confined aquifer with depths ranging from 1172.80 to 1735.6 m, pumping for 48 hours, 24 hours, and 12 hours respectively, obtaining the correlation between drawdown and inflow.

[0004] However, existing pumping test methods have the following technical problems in the process of processing multi-fall data: traditional methods usually treat each fall as an independent test, recording drawdown and inflow separately, lacking systematic analysis methods for the dynamic correlation between multiple falls, resulting in low data utilization, and manual observation and empirical fitting methods are difficult to correct the mutual influence between falls in real time, making it impossible to make full use of the restored water level data, resulting in low accuracy of aquifer parameter inversion and poor data fusion capability. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for processing multi-fall data from pumping tests, which overcomes the shortcomings of isolated processing of each fall data in traditional methods, realizes continuous monitoring and comprehensive analysis of the entire pumping process, and reduces manual intervention and human error by enabling real-time data acquisition and automatic inversion capabilities.

[0006] Firstly, a method for processing multi-fall-height data from pumping tests is provided, which may include: Before the pumping test begins, monitor the water level changes in the well and the flow rate in the outlet pipe in real time; Before any of the multiple sequential pumping flows begins, the current static water level is used as the initial water level for that flow. During pumping in that flow, a dynamic curve of drawdown and inflow is generated based on the real-time collected dynamic water level and inflow. After the flow ends, the water level recovery process is monitored, and a recovery water level change curve for that flow is generated. Based on the dynamic curves of drawdown-inflow and corresponding recovery water level changes of multiple pumping heads, a joint dynamic dataset of multiple pumping heads is constructed. Based on a multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are inverted, and the inverted hydrogeological parameters are verified by restoring the water level change curve, and the corrected hydrogeological parameters are output.

[0007] In one possible implementation, a dynamic curve of drawdown-inflow for the head pumping is generated based on real-time acquired dynamic water level and inflow rate, including: During the pumping process, the dynamic water level and water inflow are collected in real time according to the preset sampling frequency; The collected dynamic water level and inflow were filtered to eliminate instantaneous fluctuations, and dynamic change curves were plotted with time as the horizontal axis and drawdown and inflow as the vertical axes, respectively. By synchronously linking the drawdown curve and the inflow curve for the same drop height, a dynamic drawdown-inflow curve for that drop height is formed.

[0008] In one possible implementation, the water level recovery process continues to be monitored after the fall ends, generating a recovery water level change curve for the fall, including: After the initial pumping operation is completed, the water level recovery process will continue to be monitored at the same sampling frequency, and the data sequence of water level rise over time will be recorded. The data sequence is smoothed to generate the recovery water level change curve for this drop.

[0009] In one possible implementation, a multi-fall joint dynamic dataset is constructed based on the drawdown-inflow dynamic curves and corresponding recovery water level change curves of multiple pumping falls, including: Align the dynamic curves of drawdown-inflow and the corresponding recovery water level change curves for each drop section with a unified time reference. For the time interval between adjacent drops, missing data is supplemented by interpolation methods to form a multi-drop joint dynamic dataset containing the entire pumping test process; this multi-drop joint dynamic dataset includes the drawdown value, inflow value and recovery water level value corresponding to each moment.

[0010] In one possible implementation, the hydrogeological parameters include permeability coefficient, hydraulic conductivity coefficient, and water storage coefficient; Based on a multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are retrieved, including: Based on the aforementioned multi-fall joint dynamic dataset, a multi-segment fitting algorithm is used to simultaneously fit the dynamic curves of drawdown and inflow for multiple falls, thereby obtaining the drawdown-inflow fitting relationship for each fall. Based on the unsteady flow theory of confined aquifers, a theoretical formula for drawdown during pumping is established. The permeability coefficient, hydraulic conductivity, and water storage coefficient to be inverted are substituted into the theoretical formula as unknown parameters to calculate the theoretical drawdown value of each drop at different times. An objective function is constructed with the goal of minimizing the sum of squared errors between the theoretical depth of descent and the depth of descent at the corresponding time in the multi-fall-length joint dynamic dataset; Using the drawdown-inflow fitting relationship as a constraint, the objective function is iteratively solved using the least squares method or optimization algorithm to derive the hydrogeological parameters of the aquifer, including the current permeability coefficient, hydraulic conductivity, and water storage coefficient.

[0011] In one possible implementation, the recovered water level change curves from the multi-fall-length joint dynamic dataset are used to verify the inverted hydrogeological parameters, and the corrected hydrogeological parameters are output, including: The permeability coefficient, hydraulic conductivity, and water storage coefficient obtained from the inversion are substituted into the configured groundwater flow model to simulate the water level recovery process after each fall, and generate simulated recovery water level curves for each fall. The simulated recovery water level curves for each drop are compared point by point with the corresponding recovery water level change curves, and the root mean square error is calculated. If the root mean square error exceeds the preset threshold, the weight coefficients or initial parameters in the multi-segment fitting algorithm are adjusted, and the inversion is performed again until the error meets the requirements, and the final corrected hydrogeological parameters are output.

[0012] In one possible implementation, after outputting the corrected hydrogeological parameters, the method further includes: A comprehensive pumping test report is generated, which includes dynamic curves of drawdown and inflow for each drop, curves of restored water level changes, visualization charts of the joint dynamic dataset of multiple drop sections, and hydrogeological parameter values ​​obtained by inversion.

[0013] Secondly, a device for processing multi-fall data from pumping tests is provided, the device comprising: The monitoring unit is used to monitor the water level changes in the well and the flow rate in the outlet pipe in real time before the pumping test begins. The generation unit is used to take the current static water level as the initial water level of any of the multiple sequential pumping flows before the start of the flow; generate the drawdown-inflow dynamic curve of the flow based on the real-time collected dynamic water level and inflow during the pumping of the flow; and continue to monitor the water level recovery process after the flow ends to generate the recovery water level change curve of the flow. The building unit is used to construct a multi-fall joint dynamic dataset based on the dynamic curves of drawdown-inflow and the corresponding recovery water level change curves of multiple pumping falls. The output unit is used to retrieve the hydrogeological parameters of the aquifer based on the multi-fall joint dynamic dataset, and to verify the retrieved hydrogeological parameters using the restored water level change curve, and output the corrected hydrogeological parameters.

[0014] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0015] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0016] The present application provides a method and apparatus for processing multi-fall data from pumping tests. The method involves real-time monitoring of well water level changes and outlet flow rate before the start of the pumping test; using the current static water level as the initial water level before starting any of the multiple sequentially performed pumping falls; generating a drawdown-inflow dynamic curve for the fall based on real-time collected dynamic water level and inflow rate during pumping; monitoring the water level recovery process after the fall ends to generate a recovery water level change curve; constructing a multi-fall joint dynamic dataset based on the drawdown-inflow dynamic curves and corresponding recovery water level change curves for multiple pumping falls; retrieving the hydrogeological parameters of the aquifer based on the multi-fall joint dynamic dataset; verifying the retrieved hydrogeological parameters using the recovery water level change curves; and outputting corrected hydrogeological parameters. This method achieves continuous monitoring and comprehensive analysis of the entire pumping process, while real-time data acquisition and automatic inversion capabilities reduce manual intervention and human error. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a pumping test system provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for processing multi-fall data from a pumping test, provided in an embodiment of this application; Figure 3 A schematic diagram of a processing device for multi-fall data from a pumping test provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] The method for processing multi-fall-length pumping test data provided in this application is mainly applied to the acquisition of hydrogeological parameters of deep confined aquifers. This method can be applied to applications such as... Figure 1 The pumping test system shown may include: a submersible pump, a portable electronic water level meter, a magnetic induction flow meter, a data acquisition terminal, and a data processing server.

[0021] The submersible pump is deployed deep into the well to pump water from any section of the confined aquifer. A portable electronic water level gauge is installed at the wellhead, with its probe extending below the water level inside the well to monitor water level changes in real time. A magnetic induction flow meter is installed on the outlet pipeline to monitor the flow rate in real time. The water level gauge and flow meter are connected to a data acquisition terminal via signal lines, converting the monitored analog signals into digital signals and transmitting them synchronously to the data acquisition terminal. The data acquisition terminal connects to a data processing server via a wireless or wired network to upload the collected raw data for further processing.

[0022] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0023] Figure 2 This is a flowchart illustrating a method for processing multi-fall data from pumping tests, provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Before the pumping test begins, monitor the water level changes in the well and the flow rate of the outlet pipe in real time.

[0024] Before the pumping test began, a portable electronic water level meter and a magnetic induction flow meter were activated to monitor the water level changes in the well and the flow rate in the outlet pipe in real time. The water level meter collected water level data at a sampling frequency of 1 time per second, and the flow meter collected flow rate data at the same sampling frequency. The collected analog signals were converted into digital signals by an analog-to-digital converter and then synchronously transmitted to the data acquisition terminal via an RS485 interface. The data acquisition terminal added a timestamp to the received data and stored it in a local cache as a temporary file.

[0025] To ensure the accuracy of subsequent data processing, this application may further include: outlier detection and removal of the monitored raw water level and flow data, and the use of median filtering or Kalman filtering algorithms to remove noise interference. Specifically, the data acquisition terminal or data processing server performs sliding window processing on the received raw data sequence, such as setting the window size to 10 sampling points. For the data within each window, the median is calculated. If the deviation of a data point from the median exceeds a preset threshold (e.g., water level deviation exceeds 0.05m, flow deviation exceeds 0.5m³ / h), the point is determined to be an outlier and removed. After removal, linear interpolation of the data before and after the outlier is used to fill the gap. Simultaneously, a Kalman filtering algorithm is used to filter the continuous data stream in real time, automatically adjusting the filtering parameters according to the dynamic changes in water level and flow, eliminating instantaneous fluctuations caused by factors such as pump start-up and shutdown, and water flow disturbance.

[0026] Step S220: Before starting any of the multiple sequential pumping flows, the current static water level is taken as the initial water level of the flow; during pumping in the flow, a dynamic curve of drawdown-inflow is generated based on the real-time collected dynamic water level and inflow; after the flow ends, the water level recovery process is monitored, and a recovery water level change curve of the flow is generated.

[0027] Three pumping phases of varying durations were set up sequentially: the first phase (48 hours of pumping), the second phase (24 hours of pumping), and the third phase (12 hours of pumping). Before each phase began, the data acquisition terminal automatically recorded the current static water level as the initial water level for that phase. For example, before the start of the pumping test (22:00 on November 25, 2025), the static water level was recorded as 109.76 m, which was used as the initial water level for the first phase. After the first phase ended, the water level recovered for 6 hours, and the static water level was recorded again before the start of the second phase, which was used as the initial water level for the second phase; and so on.

[0028] During each pumping phase, dynamic water level and inflow data are collected in real time to generate a dynamic curve of drawdown-inflow for that phase. Specifically, dynamic water level and inflow are collected in real time at a preset sampling frequency (e.g., once per minute). The data processing server filters the collected dynamic water level and inflow to eliminate instantaneous fluctuations. This application employs an adaptive Kalman filter algorithm to perform real-time filtering on the raw collected data. This algorithm automatically adjusts the filtering parameters based on the dynamic changes in dynamic water level and inflow. Simultaneously, the filtered data undergoes secondary smoothing using a sliding window averaging method. The window size is dynamically adjusted according to the duration of the pumping phase, with the first phase window size being 30 minutes, the second phase window size being 15 minutes, and the third phase window size being 5 minutes. By combining adaptive filtering with dynamic window smoothing, instantaneous abnormal fluctuations caused by factors such as pump start-up and shutdown, and water flow disturbances at different pumping stages are effectively eliminated, while preserving the true trend of the drawdown-inflow relationship. Then, dynamic change curves are plotted with time as the horizontal axis and drawdown and inflow as the vertical axes, and the drawdown curve and inflow curve of the same drop are correlated in time to form the drawdown-inflow dynamic curve of that drop.

[0029] Furthermore, during the real-time acquisition of dynamic water level and inflow, this application can simultaneously calculate the rate of change of drawdown (the difference between static and dynamic water levels) and inflow, generating drawdown rate curves and inflow rate curves. When the drawdown rate is detected to be consistently less than a preset threshold for more than 30 minutes, this period is marked as a stable flow period, and the start and end times and data segments of this stable flow period are marked in the drawdown-inflow dynamic curve. By introducing rate of change monitoring and automatic identification of stable flow periods, quantitative judgment of flow regime changes during pumping is achieved, providing a reliable basis for selecting stable flow data segments for subsequent hydrogeological parameter inversion, and avoiding the subjectivity and inaccuracy of manual judgment in traditional methods.

[0030] After each drop phase, the submersible pump stops operating, but the water level gauge continues to monitor the water level recovery process at the same sampling frequency (1 time / second), generating a recovery water level change curve for that drop phase. Specifically, after the pumping of that drop phase ends, the water level recovery process continues to be monitored at the same sampling frequency, recording the data sequence of water level rise over time. This data sequence is smoothed, for example using a moving average method or spline interpolation, to generate the recovery water level change curve for that drop phase. The start and end times of the recovery phase are also recorded.

[0031] Step S230: Based on the dynamic curves of drawdown-inflow and corresponding recovery water level changes of multiple pumping heads, construct a joint dynamic dataset of multiple pumping heads.

[0032] Specifically, the data processing server aligns the dynamic curves of drawdown and inflow for each pumping stage with the corresponding recovery water level change curves using a unified time base, ensuring that the time axes of all curves are continuous and non-overlapping. Since there may be time intervals between pumping stages (e.g., recovery or preparation phases), interpolation methods are used to supplement missing data for time intervals between adjacent stages, forming a multi-stage joint dynamic dataset encompassing the entire pumping test process. This multi-stage joint dynamic dataset can include: the drawdown value, inflow value, and recovery water level value corresponding to each moment.

[0033] Furthermore, the process of constructing a multi-fall joint dynamic dataset can also include: normalizing the recovery water level change curves of each fall, using the water level at the end of each fall as a reference point, and converting the recovery water level into a relative rise height; jointly analyzing the relative rise height curves of different falls with the drawdown-inflow dynamic curves, calculating the water level rise rate per unit time after the end of each fall, and establishing the correlation between the rise rate and the drawdown of the previous fall, as supplementary feature data for the multi-fall joint dynamic dataset. Through recovery water level normalization and rise rate calculation, the influence of initial water level differences between different falls on the recovery process analysis is eliminated, revealing the intrinsic correlation between drawdown magnitude and recovery capacity, and providing a new data dimension for the refined evaluation of aquifer storage characteristics.

[0034] Furthermore, the process of constructing a multi-fall joint dynamic dataset also includes: based on the drawdown-inflow dynamic curves of each fall, using a clustering analysis algorithm to segment and identify data points during the pumping process, automatically dividing each fall into an initial fluctuation segment, a main drawdown segment, and a stabilizing segment; according to the segmentation results, assigning different confidence weights to different data segments, with the data points in the stabilizing segment having the highest weight and the data points in the initial fluctuation segment having the lowest weight, and using the confidence weights of each data segment as components of the multi-fall joint dynamic dataset.

[0035] Step S240: Based on the multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are inverted, and the recovered water level change curve in the multi-fall joint dynamic dataset is used to verify the inverted hydrogeological parameters, and the corrected hydrogeological parameters are output.

[0036] First, based on the multi-fall joint dynamic dataset, a multi-segment fitting algorithm is used to simultaneously fit the drawdown-inflow dynamic curves of multiple falls to obtain the drawdown-inflow fitting relationship for each fall.

[0037] Specifically, a piecewise nonlinear least squares fitting algorithm is used to perform polynomial fitting on the dynamic curve of drawdown-inflow for each drop section. The fitting order is automatically determined based on the number of data points and fluctuation characteristics of that drop section, ranging from order 2 to 5. For example, the first drop section has more data points and larger fluctuations, so a 4th-order polynomial fitting is used; the second drop section uses a 3rd-order polynomial fitting; and the third drop section uses a 2nd-order polynomial fitting. Simultaneously, a continuity constraint is introduced for the fitting curves of adjacent drops, ensuring that the difference between the fitted value at the end of the previous drop section and the fitted value at the beginning of the next drop section is less than a preset threshold (e.g., 0.1 m³ / h), thus ensuring a smooth transition of the fitting curves across multiple drops on the time axis. This method, by combining piecewise nonlinear fitting with continuity constraints, preserves the local characteristics of the drawdown-inflow relationship within each drop section while ensuring the continuity and consistency of data changes between multiple drops, avoiding abrupt changes in the fitting curves caused by drop section switching. Finally, the drawdown-inflow fitting formulas for each drop height are obtained, for example: Q=f1(s) for the first drop height, Q=f2(s) for the second drop height, and Q=f3(s) for the third drop height.

[0038] Next, based on the unsteady flow theory of confined aquifers (such as Theis's formula or Jacob's formula), a theoretical formula for drawdown during pumping is established. Since hydrogeological parameters include permeability, hydraulic conductivity, and storage coefficient, the hydrogeological parameters to be inverted—namely, the permeability K, hydraulic conductivity T, and storage coefficient S—are substituted as unknown parameters into the drawdown theoretical formula to calculate the theoretical drawdown value stheoretical(t) at different times for each drop height. For example, for confined aquifers, Theis's formula is: Where u = (r²S) / (4Tt), and W(u) is the well function. The objective function is constructed with the goal of minimizing the sum of squared errors between the theoretical drawdown value and the measured drawdown value smeasured(t) at the corresponding time in the multi-fall-length joint dynamic dataset: Where i represents the landing number, This represents the pumping period of the i-th flow.

[0039] The obtained drawdown-inflow fitting relationship is then used as a constraint to limit the range of values ​​of the parameters to be inverted or the correlation between the parameters.

[0040] Specifically, the theoretical unit drawdown inflow (ds / dQ) for each drawdown is calculated based on the fitted relationship. This theoretical unit drawdown inflow is then combined with the parameter expressions in the unsteady flow theory of confined aquifers to derive the upper and lower limits of the permeability coefficient, hydraulic conductivity, and storage coefficient, which serve as the boundaries of the feasible region for solving the objective function. For example, based on the fitted relationship for the first drawdown, the value of T should be between [Tmin, Tmax]. By deriving the parameter range constraints through the fitted relationship, the statistical characteristics of the drawdown-inflow data are transformed into the feasible region of physical parameters, effectively narrowing the search space of the inversion algorithm and improving the convergence speed and physical rationality of the parameters in the iterative solution.

[0041] Then, the objective function is iteratively solved using the least squares method or an optimization algorithm (such as the Levenberg-Marquardt algorithm). The algorithm searches within the feasible region defined by the constraints to minimize the objective function value. When the change in the objective function value is less than a preset threshold (e.g., 10...), the algorithm continues to work. -6 When the number of iterations reaches the upper limit (e.g., 100 times), it is considered to have converged, and the current permeability coefficient K, hydraulic conductivity T, and water storage coefficient S are output as preliminary inversion results.

[0042] Finally, this application can verify the inverted hydrogeological parameters and output corrected hydrogeological parameters. Specifically, this includes substituting the inverted permeability coefficient K, hydraulic conductivity T, and storage coefficient S into a configured groundwater flow model (such as a model based on the Theis recovery formula), simulating the water level recovery process after each fall, and generating simulated recovery water level curves for each fall. For example, for the recovery stage, the Theis recovery formula can be used: , where u and u' correspond to the parameters of the pumping stage and the recovery stage, respectively. t represents the time from the start of pumping, and t' represents the time from the start of pump shutdown (resumption). The simulated recovery water level curves for each drop are compared point-by-point with the corresponding recovery water level change curves, and the root mean square error (RMSE) is calculated. Where N is the number of comparison points, Let be the simulated depth value at point i. Let be the measured depth value at the i-th point.

[0043] If the root mean square error exceeds a preset threshold (e.g., 0.05m), iterative optimization is required. This application employs a hierarchical iterative optimization strategy: first, the weight coefficients of each data segment in the objective function are adjusted. Based on the assigned confidence weights, the contribution of each data point in the objective function is adjusted; for example, the objective function is modified to a weighted sum of squared errors. ,in Here, n represents the weight coefficient corresponding to the i-th data point (assigned based on confidence level), and n is the total number of data points. Let i be the theoretical depth of the i-th point. Let be the measured drawdown value at point i. Recalculate the inversion. If the accuracy requirement is still not met after three iterations following adjustments to the weighting coefficients, a parallel search strategy with multiple initial values ​​is initiated, simultaneously setting multiple sets of different initial parameter values ​​for parallel inversion calculations. For example, 10 sets of initial parameters are set simultaneously, and inversions are performed separately for each set. The parameter combination that minimizes the error between the simulated restored water level curve and the measured curve is selected from the multiple inversion results and used as the output of the current iteration step. If the requirement is still not met, a particle swarm optimization algorithm or a genetic algorithm is used to globally optimize the weighting coefficients and initial parameters. In the particle swarm optimization algorithm, the weighting coefficients and initial parameters are used as particle positions, and the fitness function (such as the reciprocal of RMSE) is used as the evaluation index. The particle velocity and position are updated iteratively until a global optimum is found or the maximum number of iterations is reached. Through the above hierarchical and progressive iterative optimization strategy, first local adjustments and then global search, computational efficiency is ensured while avoiding getting trapped in local optima, achieving high-precision solutions for parameter inversion under complex hydrogeological conditions. When the error meets the requirements, the final corrected hydrogeological parameters K, T, and S are output.

[0044] In some embodiments, after outputting the corrected hydrogeological parameters, this application may further include generating a comprehensive pumping test report. Specifically, the data processing server automatically generates the comprehensive pumping test report, which includes the following: Dynamic curves of drawdown and inflow for each drop section (including markings for steady flow periods); Recovery water level change curves for each drop; Visualization charts of multi-fall-time-inflow joint dynamic datasets (such as 3D plots of drawdown-time-inflow). The hydrogeological parameters obtained from the inversion (permeability coefficient, hydraulic conductivity, water storage coefficient); Quality evaluation indicators for the parameter inversion process (such as fitting error and convergence process graph); The comparison curve and root mean square error value for the water level verification were restored.

[0045] Furthermore, this application may also include forward simulation and quality evaluation steps, including: The corrected hydrogeological parameters are substituted into the unsteady flow theory model of the confined aquifer to simulate the complete pumping and recovery process of each drop height, generating simulated drawdown and recovery curves for the entire process.

[0046] Compare the simulated curve with the measured curve using multiple indicators, including peak error, phase error, and trend consistency coefficient (such as calculating the correlation coefficient R²).

[0047] A parameter inversion quality evaluation report is generated based on the comparison results. If the quality evaluation index is lower than the preset standard (e.g., R² < 0.9), the data quality of this pumping test is marked as abnormal, and supplementary pumping tests or repetition of specific drop heights are recommended.

[0048] This application achieves comprehensive verification and quantitative quality assessment of the inversion results through forward simulation and multi-index quality evaluation.

[0049] Corresponding to the above method, embodiments of this application also provide a processing device for multi-fall data from pumping tests, such as... Figure 3 As shown, the device includes: The monitoring unit 310 is used to monitor the water level changes in the well and the flow rate in the outlet pipe in real time before the pumping test begins. The generation unit 320 is used to take the current static water level as the initial water level of any of the multiple sequential pumping flows before the start of the flow; generate the drawdown-flow dynamic curve of the flow based on the real-time collected dynamic water level and flow rate during the pumping flow; and continue to monitor the water level recovery process after the flow ends to generate the recovery water level change curve of the flow. Building unit 330 is used to construct a multi-fall joint dynamic dataset based on the dynamic curves of drawdown-inflow and the corresponding recovery water level change curves of multiple pumping falls. Output unit 340 is used to invert the hydrogeological parameters of the aquifer based on the multi-fall joint dynamic dataset, and to verify the inverted hydrogeological parameters using the restored water level change curve, and output the corrected hydrogeological parameters.

[0050] The functions of each functional unit of the pumping test multi-fall data processing device provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the pumping test multi-fall data processing device provided in the embodiments of this application will not be repeated here.

[0051] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0052] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Before the pumping test begins, monitor the water level changes in the well and the flow rate in the outlet pipe in real time; Before any of the multiple sequential pumping flows begins, the current static water level is used as the initial water level for that flow. During pumping in that flow, a dynamic curve of drawdown and inflow is generated based on the real-time collected dynamic water level and inflow. After the flow ends, the water level recovery process is monitored, and a recovery water level change curve for that flow is generated. Based on the dynamic curves of drawdown-inflow and corresponding recovery water level changes of multiple pumping heads, a joint dynamic dataset of multiple pumping heads is constructed. Based on a multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are inverted, and the inverted hydrogeological parameters are verified by restoring the water level change curve, and the corrected hydrogeological parameters are output.

[0053] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0054] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0055] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0056] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0057] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0058] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the processing method for multi-fall data of pumping tests described in any of the above embodiments.

[0059] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for processing multi-fall data of pumping tests as described in any of the above embodiments.

[0060] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0065] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for processing multi-fall data from pumping tests, characterized in that, The method includes: Before the pumping test begins, monitor the water level changes in the well and the flow rate in the outlet pipe in real time; Before any of the multiple sequential pumping flows begins, the current static water level is used as the initial water level for that flow. During pumping in that flow, a dynamic curve of drawdown and inflow is generated based on the real-time collected dynamic water level and inflow. After the flow ends, the water level recovery process is monitored, and a recovery water level change curve for that flow is generated. Based on the dynamic curves of drawdown-inflow and corresponding recovery water level changes of multiple pumping heads, a joint dynamic dataset of multiple pumping heads is constructed. Based on a multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are inverted, and the inverted hydrogeological parameters are verified by restoring the water level change curve, and the corrected hydrogeological parameters are output.

2. The method as described in claim 1, characterized in that, In this pumping operation, a dynamic curve of drawdown-inflow is generated based on real-time collected dynamic water level and inflow rate, including: During the pumping process, the dynamic water level and water inflow are collected in real time according to the preset sampling frequency; The collected dynamic water level and inflow were filtered to eliminate instantaneous fluctuations, and dynamic change curves were plotted with time as the horizontal axis and drawdown and inflow as the vertical axes, respectively. By synchronously linking the drawdown curve and the inflow curve for the same drop height, a dynamic drawdown-inflow curve for that drop height is formed.

3. The method as described in claim 1, characterized in that, After the initial settling point ends, the water level recovery process is continuously monitored, generating a recovery water level change curve for that settling point, including: After the initial pumping operation is completed, continue to monitor the water level recovery process at the same sampling frequency and record the data sequence of water level rise over time. The data sequence is smoothed to generate the recovery water level change curve for this drop.

4. The method as described in claim 1, characterized in that, Based on the dynamic curves of drawdown-inflow and corresponding recovery water level changes for multiple pumping heights, a joint dynamic dataset for multiple pumping heights is constructed, including: Align the dynamic curves of drawdown-inflow and the corresponding recovery water level change curves for each drop section with a unified time reference. For the time interval between adjacent drops, missing data is supplemented by interpolation methods to form a multi-drop joint dynamic dataset containing the entire pumping test process; this multi-drop joint dynamic dataset includes the drawdown value, inflow value and recovery water level value corresponding to each moment.

5. The method as described in claim 4, characterized in that, The hydrogeological parameters include permeability coefficient, hydraulic conductivity, and water storage coefficient; Based on a multi-fall joint dynamic dataset, the hydrogeological parameters of the aquifer are inverted, including: Based on the aforementioned multi-fall joint dynamic dataset, a multi-segment fitting algorithm is used to simultaneously fit the dynamic curves of drawdown and inflow for multiple falls, thereby obtaining the drawdown-inflow fitting relationship for each fall. Based on the unsteady flow theory of confined aquifers, a theoretical formula for drawdown during pumping is established. The permeability coefficient, hydraulic conductivity, and water storage coefficient to be inverted are substituted into the theoretical formula as unknown parameters to calculate the theoretical drawdown value of each drop at different times. An objective function is constructed with the goal of minimizing the sum of squared errors between the theoretical depth of descent and the depth of descent at the corresponding time in the multi-fall-length joint dynamic dataset; Using the drawdown-inflow fitting relationship as a constraint, the objective function is iteratively solved using the least squares method or optimization algorithm to derive the hydrogeological parameters of the aquifer, including the current permeability coefficient, hydraulic conductivity, and water storage coefficient.

6. The method as described in claim 5, characterized in that, The recovered water level change curves from the multi-fall-length joint dynamic dataset are used to verify the inverted hydrogeological parameters, and the corrected hydrogeological parameters are output, including: The permeability coefficient, hydraulic conductivity, and water storage coefficient obtained from the inversion are substituted into the configured groundwater flow model to simulate the water level recovery process after each fall, and generate simulated recovery water level curves for each fall. The simulated recovery water level curves for each drop are compared point by point with the corresponding recovery water level change curves, and the root mean square error is calculated. If the root mean square error exceeds the preset threshold, the weight coefficients or initial parameters in the multi-segment fitting algorithm are adjusted, and the inversion is performed again until the error meets the requirements, and the final corrected hydrogeological parameters are output.

7. The method as described in claim 1, characterized in that, After outputting the corrected hydrogeological parameters, the method further includes: A comprehensive pumping test report is generated, which includes dynamic curves of drawdown and inflow for each drop, curves of restored water level changes, visualization charts of the joint dynamic dataset of multiple drop sections, and hydrogeological parameter values ​​obtained by inversion.

8. A device for processing multi-fall data from pumping tests, characterized in that, The device includes: The monitoring unit is used to monitor the water level changes in the well and the flow rate in the outlet pipe in real time before the pumping test begins. The generation unit is used to take the current static water level as the initial water level of any of the multiple sequential pumping flows before the start of the flow; generate the drawdown-inflow dynamic curve of the flow based on the real-time collected dynamic water level and inflow during the pumping of the flow; and continue to monitor the water level recovery process after the flow ends to generate the recovery water level change curve of the flow. The building unit is used to construct a multi-fall joint dynamic dataset based on the dynamic curves of drawdown-inflow and the corresponding recovery water level change curves of multiple pumping falls. The output unit is used to retrieve the hydrogeological parameters of the aquifer based on the multi-fall joint dynamic dataset, and to verify the retrieved hydrogeological parameters using the restored water level change curve, and output the corrected hydrogeological parameters.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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