CN116312824B - Water-bearing layer heterogeneity recognition method aiming at non-Fick dispersion of groundwater pollutants - Google Patents
Water-bearing layer heterogeneity recognition method aiming at non-Fick dispersion of groundwater pollutants Download PDFInfo
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Abstract
The invention discloses a method for identifying the heterogeneity of an aquifer dispersed in a non-Fick manner aiming at a groundwater pollutant, which comprises the steps of collecting geological exploration information of a target land block, establishing a training image of aquifer permeability area distribution, generating a permeability coefficient sample set, setting a concentration sensor to collect observation data, simulating a pollutant migration process, and jointly inverting a mass transfer coefficient and a permeability coefficient; according to the method, a permeability coefficient sample set conforming to a target land parcel geologic body space distribution mode is described by combining actual geological exploration information with a multipoint geostatistical method, and the accuracy of pollutant migration simulation is further improved by assimilating monitoring data. The method solves the problem of predicting the non-Fick dispersion phenomenon of pollutant migration in a highly heterogeneous aquifer system by utilizing observation data and a numerical simulation technology under the condition of limited exploration data.
Description
Technical Field
The invention relates to the technical field of underground pollution control and underground water simulation prediction, in particular to a method for identifying the heterogeneity of an aquifer under the phenomenon that underground water pollutants are not Fick dispersed.
Background
There is a great uncertainty in the distribution of the aquifer medium in an actual site, taking as an example the key element permeability coefficient characterizing the aquifer properties, there is typically both a high permeability region with preferential water flow channels and a low permeability lenticular region in the aquifer. The values of the high and low permeability coefficients may differ by several orders of magnitude, especially in "highly heterogeneous" media, the natural variance of the hydraulic conductivity of the aquifer is typically higher than 3.
In highly heterogeneous aquifer systems, the heterogeneity of the medium typically results in contaminants exhibiting pronounced abnormal migration characteristics or non-feik dispersion in groundwater, i.e., there is a phenomenon that is inconsistent with the migration process predicted by conventional convection-dispersion equations based on feik's law. On one hand, the distribution of the high permeability areas has connectivity in a certain direction, and the connectivity forms a preferential water flow channel, so that part of pollutants are quickly migrated; on the other hand, the pollutants enter a low-permeability area through solute mass transfer processes such as molecular diffusion, chemical adsorption and the like, and are slowly released through processes such as molecular diffusion and the like, so that the pollutants are retained and trailing for a long time is caused. (Guo Zhilin, ma Rui, zhang Yong, etc.) migration of groundwater contaminants in highly heterogeneous media. Summary of mechanism and numerical simulations [ J ]. Chinese science: earth science, 2021,51 (11): 1817-1836).
Thus, the heterogeneity of the aquifer medium is an important factor affecting groundwater remediation. At present, in an actual field, limited geological exploration data are difficult to accurately describe a high-heterogeneity aquifer, prediction analysis of non-fick dispersion of groundwater pollutants is widely and widely spread in China, and accurate prediction of situations such as early arrival or tailing retention of the pollutants existing in the aquifer is difficult.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an aquifer heterogeneity recognition method for groundwater pollutants not fick dispersion phenomenon. The method solves the problem of predicting the non-Fick dispersion phenomenon of pollutant migration in a highly heterogeneous aquifer system by utilizing observation data and a numerical simulation technology under the condition of limited exploration data.
According to the invention, the distribution conditions of the high and low permeability areas in the aquifer of the site are obtained by combining the actual geological exploration condition and a further high-efficiency calculation and analysis mode, so that the migration change of pollutants is simulated by means of a numerical model, and real-time effective decision-making auxiliary information is provided for the groundwater pollution remediation process.
The invention aims at realizing the following technical scheme:
a method for identifying the heterogeneity of an aquifer aiming at non-fick dispersion of groundwater pollutants comprises the following steps:
s1, collecting geological exploration information of a target land block: the method comprises stratum drilling data, stratum profile drawings and permeability coefficient measurement values according to core sampling results and laboratory geotechnical experiments; and determining the most main high-permeability soil type and low-permeability soil type of the target land block based on the high-low ordering of the permeability coefficients of all soil media of the target land block;
s2, building a training image of aquifer permeability area distribution: establishing a geological model according to the stratum drilling data and stratum sectional views collected in the step S1; establishing a training image of aquifer permeability area distribution based on a prototype geologic model, wherein the training image is a two-dimensional image for quantitatively describing actual stratum distribution and geologic body geometric form;
s3, generating a permeability coefficient sample set: generating a plurality of groups of permeability coefficient samples conforming to the mode by applying a multipoint geostatistical method Direct Sampling according to the training image generated in the step S2, and forming a permeability coefficient sample set; wherein, a single sample is a two-dimensional matrix formed by N permeation coefficients in space, N e The sample set generated by the samples is N with equal possibility obtained by Sampling through a Direct Sampling method e A group permeability coefficient two-dimensional matrix;
s4, setting a concentration sensor to collect observation data: arranging a water quality detector at a target land parcel site monitoring well, wherein the water quality detector is provided with a concentration sensor, and collecting a site in-situ pollutant concentration monitoring value;
s5, simulating a pollutant migration process: the water-bearing layer of the target site is generalized by a numerical simulation technology and is brought into the permeability coefficient sample generated in the step S3 to obtain the flow velocity distribution of the simulation area of the target site, and then the flow velocity is brought into a double-zone model to simulate the migration and mass transfer process of pollutants between a movable phase and a non-movable phase, and the control method thereofThe processes are shown in formulas (1) and (2); solving the (1) and (2) to obtain the relevant value c of the concentration distribution of the target pollutant in the dynamic domain m ：
Wherein θ m Representing the porosity in the dynamic domain, θ im Representing porosity in the motionless domain; t is the migration time of the contaminant; c m Represents solute concentration in the dynamic domain, c im Represents the concentration of solutes in the motionless domain; d (D) ij Represents the hydrodynamic dispersion coefficient; v i Representing the flow rate; alpha represents the mass transfer coefficient between the two domains;
wherein K is i Is the principal component of the permeability coefficient in the corresponding coordinate direction, x i 、x j Is the position on the corresponding coordinate, h is the head;
s6, jointly inverting the mass transfer coefficient and the permeability coefficient, and specifically comprising: generating a priori data set [ X, Y ] using steps S3 and S5]Wherein the combination parameter m= [ alpha, K of the mass transfer coefficient alpha and the permeability coefficient K 1 ,K 2 ,…K N ]N represents the number of permeation coefficients, and the parameter sample set x= [ m ] 1 ,m 2 ,…m Ne ],N e A number of samples representing the dataset; predicted contaminant concentration c= [ C ] t-1 ,C t ],C t-1 、C t Respectively representing the pollutant concentrations predicted by the double-zone model at the pollutant migration time t-1 and the moment t in the step S5, and the pollutant concentration predicted value set Y= [ C ] 1 ,C 2 ,…C Ne ]The method comprises the steps of carrying out a first treatment on the surface of the Obtaining the first +1 of mass transfer coefficient and all permeability coefficient by using the iterative set smoother algorithm, namely the pollutant concentration monitoring value collected in the assimilation step S4 of the formula (4)Secondary update value:
wherein:respectively representing the first time +1 and the first time updated value of the n-th mass transfer coefficient and permeation coefficient combination parameter m; m is m pr,n An nth set of a priori parameters representing m; d, d obs,n Is the nth group concentration monitoring data subjected to noise disturbance; g (·) represents step S5; c (C) D Representing a monitoring error covariance matrix; />Represents g (. Cndot.) in parameter m l Linearization of the position, calculated by the ratio of the change of the input parameter to the corresponding change of the output concentration; beta l Representing a custom update step size, 0<β l ≤1；Δm pr Representing a priori parametric sample m pr Deviation from the mean value of the parameters>Covariance representing model parameters is calculated by a finite number of sample sets:
further, in step S2, the method for creating training images may be adjusted according to the richness of collecting the land parcel data. For example, for a field with even stratum distribution, a manual drawing method is adopted to establish a training image; for sites with geologic body spatial distribution conforming to a classical mode, the training images can be obtained by adopting relevant statistics software for sequential simulation.
Further, in step S3, the generating of the permeability coefficient sample set by applying the multipoint geostatistical method involves the advanced setting of a part of parameters, including: sample number N e Variable (variable)The type, the number of training image parameters and the number N of permeability coefficients in a single sample in the target site model; the variable types include a classification variable and a continuous variable, the classification variable including: soil type, land cover category; the continuous variable includes porosity, hydraulic conductivity.
The number N of permeability coefficients is set depending on the spatial resolution of the target site range, and is generally less than 10 for improving the calculation efficiency 4 The method comprises the steps of carrying out a first treatment on the surface of the Sample number N e The setting of (2) meets the precision requirement of the subsequent inversion step, theoretically, the uncertainty of the inversion result can be reduced by the large sample number, and the sample number is generally set to be 10 for balancing the inversion precision and the calculation efficiency 2 ～10 3 。
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to an aquifer heterogeneity identification method aiming at a non-Fick dispersion phenomenon of groundwater pollutants, which can be combined with geological information and a parameter inversion method in site investigation to interpret pollutant concentration monitoring data and invert the distribution of high and low permeability areas affecting pollutant migration. The method has the specific advantages that:
1) And generating a permeability coefficient sample set which is more in line with the spatial distribution mode of the geological body of the target geological block by utilizing a training image established based on actual geological exploration information and adopting a multipoint geostatistical method, and accurately describing the distribution of a high permeability area and a low permeability lens body of the geological block. Thus, permeability coefficient samples for a particular site are generated as a priori input to the subsequent parametric inversion stage.
2) Describing a non-Fick dispersion phenomenon of pollutants by using a double-zone model, and accurately simulating migration of the pollutants in a highly heterogeneous medium; through the processing of the earlier stage, the parameters which are matched with the actual field are input into a double-area model which is suitable for describing the non-Fick dispersion characteristics of the pollutants, and the precision of the parameter inversion result is improved together.
Therefore, the method can accurately predict the migration process of the pollutants in the highly heterogeneous aquifer when carrying out risk assessment and restoration on the groundwater pollution. The method is clear in flow and easy to apply, the execution of each step is closely related to the characteristics of the target site, and the method can be adjusted in real time according to the actual measurement data on site, so that quantitative support is provided for accurately evaluating the repair effect of the polluted site, and the required manpower and material resources are reduced.
Drawings
FIG. 1 is a schematic diagram of the steps in the process of the present invention;
FIG. 2 is a schematic view of a two-dimensional horizontal section of an example contaminated site;
FIG. 3 is a graph showing the distribution of permeability coefficient values for a contaminated site according to an embodiment;
FIG. 4 is a training image of the distribution of high and low permeability areas of an embodiment field;
FIG. 5 is a schematic diagram of a random sample of osmotic coefficients generated based on example training image sampling;
FIG. 6 is a schematic illustration of concentration profiles of example contaminant migration predictions;
FIG. 7 is a schematic diagram of the inversion results of the example.
Detailed Description
Example 1
As shown in fig. 2, the two-dimensional horizontal section of the contaminated site includes production wells and monitoring wells, 16 monitoring wells are arranged in the center of the area, one monitoring well is arranged every 60m, and production wells distributed linearly are arranged at a distance of about 140m from the center of the monitoring well group to generate contaminant leakage. The monitoring well is provided with a water quality detector for regularly monitoring pollutant concentration data in the depth of the fixed well.
As shown in fig. 1, the embodiment provides a method and a process for identifying the heterogeneity of a water-bearing layer dispersed in a non-fick manner for a groundwater pollutant, which comprises the following steps:
s1, collecting geological exploration information of a target land block: including formation borehole data, formation profile, permeability coefficient measurements; the permeability coefficient K corresponds to the seepage flow velocity when the hydraulic gradient is 1, and is one of important hydraulic parameters describing the movement of pollutants along with water flow. The main high and low permeability soil types of the target land block shown in fig. 2 are respectively determined to be sandy silty soil (average permeability coefficient 2 m/d) and clay (average permeability coefficient 2E-4 m/d) based on the high and low sequences of the permeability coefficient values of all soil media of the target land block, and the generated permeability coefficient two-dimensional matrix is shown in fig. 3.
TABLE 1 summary of permeability coefficient measurements
S2, building a training image of aquifer permeability area distribution: and establishing a geological model according to the drilling data and the stratum profile collected in the step S1. Generating a two-dimensional image for quantitatively describing actual stratum distribution and geologic body geometric form by a training image building method based on a prototype geologic model; the method for establishing the training image can be adjusted according to the richness of collecting the land parcel data. For example, for a field with even stratum distribution, a manual drawing method is adopted to establish a training image; for sites with spatial distribution of geologic bodies conforming to classical modes, a training image can be obtained by adopting relevant statistical software for sequential simulation, the distribution modes of high and low permeability areas in the embodiment conform to a typical classification variable distribution model, and the training image obtained by adopting the statistical software GSLIB for sequential simulation is shown in fig. 4.
S3, generating a transmission coefficient sample set: and (3) generating a plurality of groups of permeability coefficient samples conforming to the mode by applying a Direct Sampling (DS) method according to the training image generated in the step S2. Specifically, a single sample is a two-dimensional matrix of N permeation coefficients in space, N e The sample set generated by the samples is N with equal possibility obtained by sampling by DS method e A two-dimensional matrix of permeability coefficients. The number N of the single sample permeation coefficients is set depending on the spatial subdivision resolution of the target field range, n=28×28=784 in the present embodiment; sample number N e The setting of (2) meets the precision requirement of the subsequent inversion step, theoretically, the uncertainty of the inversion result can be reduced by the large sample number, and the sample number is generally set to be 10 for balancing the inversion precision and the calculation efficiency 2 ～10 3 N in the present embodiment e =500; DS method specific implementation referring to International patent (PCT/EP 2008/009819), random samples generated from training images (FIG. 4) of this embodimentAn example is shown in fig. 5. The DS method relates to the advance setting of partial parameters when generating a permeability coefficient sample set, and comprises the following steps: n, N e Variable types (such as class variables like soil type, earth coverage class, or continuous variables like porosity, hydraulic conductivity, etc.), training image parameter numbers.
S4, setting a concentration sensor to collect observation data: arranging a water quality detector at a site monitoring well, and collecting in-situ pollutant concentration monitoring values of a site: 0.7804, 0.7723, 0.4934, 0.8006, 0.3570, 0.3118, 0.1423, 0.3956, 0.1160, 0.0121.
S5, simulating a pollutant migration process: the method comprises the steps of summarizing a water-bearing layer of a target site through a numerical simulation technology, carrying into a permeability coefficient sample generated in the step S3 to obtain flow velocity distribution of a simulation area of the target site, and then distributing the flow velocity into a double-zone model (Dual-Domain Single Rate Mass Transfer, DDMT) to simulate migration and mass transfer processes of pollutants between a movable phase and a non-movable phase, wherein a double-zone model control equation is shown in formulas (1) and (2); solving the (1) and (2) to obtain the relevant value c of the concentration distribution of the target pollutant in the dynamic domain m The concentration distribution obtained by solving the pollutant migration model in this embodiment is shown in fig. 6.
Wherein θ m Representing porosity (-), theta in the dynamic domain im Represents the porosity (-) in the motionless domain; t is the migration time (d) of the contaminant; c m Represents solute concentration (mg/L) in the dynamic domain, c im Represents the concentration of solutes in the motionless domain; d (D) ij Represents the hydrodynamic dispersion coefficient (m 2 /d)；v i Representing darcy flow rate (m/d); alpha is the mass transfer rate (1/d) between the two domains.
Wherein K is i Is the principal component (m/d), x of the permeability coefficient in the corresponding coordinate direction i Is the position (m) on the corresponding coordinates, h is the head (m).
S6, jointly inverting the mass transfer coefficient and the permeability coefficient, and specifically comprising: generating a priori data set [ X, Y ] using steps S3 and S5]Wherein the combination parameter m= [ alpha, K of the mass transfer coefficient alpha and the permeability coefficient K 1 ,K 2 ,…K N ]N represents the number of permeation coefficients, and the parameter sample set x= [ m ] 1 ,m 2 ,…m Ne ],N e A number of samples representing the dataset; predicted contaminant concentration c= [ C ] t-1 ,C t ],C t-1 、C t Respectively representing the pollutant concentrations predicted by the double-zone model at the pollutant migration time t-1 and the moment t in the step S5, and collecting the predicted value sets of the pollutant concentrations Assimilating the monitoring data collected in the step S4 by using an iterative set smoother algorithm, namely a formula (4), and obtaining an (i+1) th updated value of the mass transfer coefficient and all permeability coefficients:
wherein:respectively representing the first time +1 and the first time updated value of the n-th mass transfer coefficient and permeation coefficient combination parameter m; m is m pr,n An nth set of a priori parameters representing m; d, d obs,n Is the nth group concentration monitoring data subjected to noise disturbance; g (-) represents the groundwater contaminant migration model in step S5; c (C) D Representing a monitoring error covariance matrix; />Representative model g (. Cndot.) is given in parameter m l Linearization of the model is calculated by the ratio of the change of the model parameters to the corresponding change of the model output; beta l Representing a custom update step size, 0<β l ≤1；Δm pr Representing a priori parametric sample m pr Deviation from the mean value of the parameters>Covariance representing model parameters is calculated by a finite number of sample sets:
the inversion result of the mass transfer coefficient α and the matching result of the observation-prediction concentration obtained in this example are shown in fig. 7.
The steps of the flow are based on monitoring data of the concentration of the pollutants in the underground water, real-time data assimilation is carried out by using an iterative set smoother algorithm, rapid interpretation of the monitoring data can be realized, pollutant substance transfer between a high-permeability priority channel region and a low-permeability lenticular region in a highly heterogeneous aquifer is quantified, and distribution of the high-permeability region and the low-permeability region is further accurately depicted by combining actual geological information. The migration condition of the pollutants is simulated by means of the numerical model, and real-time effective decision-making auxiliary information is provided for the groundwater pollution remediation process.
Finally, it should be noted that the above only illustrates the technical solution of the present invention and is not limiting, and although the present invention has been described in detail with reference to the preferred arrangement, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (3)
1. The method for identifying the heterogeneity of the water-bearing layer aiming at the non-fick dispersion of the groundwater pollutants is characterized by comprising the following steps:
s1, collecting geological exploration information of a target land block: including formation borehole data, formation profile, permeability coefficient measurements; and determining the most main high-permeability soil type and low-permeability soil type of the target land block based on the high-low ordering of the permeability coefficients of all soil media of the target land block;
s2, building a training image of aquifer permeability area distribution: establishing a geological model according to the stratum drilling data and stratum sectional views collected in the step S1; establishing a training image of aquifer permeability area distribution based on a prototype geologic model, wherein the training image is a two-dimensional image for quantitatively describing actual stratum distribution and geologic body geometric form;
s3, generating a permeability coefficient sample set: generating a plurality of groups of permeability coefficient samples conforming to the mode by applying a multipoint geostatistical method Direct Sampling according to the training image generated in the step S2, and forming a permeability coefficient sample set; wherein, a single sample is a two-dimensional matrix formed by N permeation coefficients in space, N e The sample set generated by the samples is N with equal possibility obtained by Sampling through a Direct Sampling method e A group permeability coefficient two-dimensional matrix;
s4, setting a concentration sensor to collect observation data: arranging a water quality detector at a target land parcel site monitoring well, wherein the water quality detector is provided with a concentration sensor, and collecting a site in-situ pollutant concentration monitoring value;
s5, simulating a pollutant migration process: the method comprises the steps of generalizing a target site aquifer through a numerical simulation technology, carrying the generalized target site aquifer into a permeability coefficient sample generated in the step S3 to obtain flow velocity distribution of a target site simulation area, carrying the flow velocity into a double-area model to simulate migration and mass transfer processes of pollutants between a movable phase and a non-movable phase, wherein control equations of the method are shown in formulas (1) and (2); solving the (1) and (2) to obtain the relevant value c of the concentration distribution of the target pollutant in the dynamic domain m ：
Wherein θ m Representing the porosity in the dynamic domain, θ im Representing porosity in the motionless domain; t is the migration time of the contaminant; c m Represents solute concentration in the dynamic domain, c im Represents the concentration of solutes in the motionless domain; d (D) ij Represents the hydrodynamic dispersion coefficient; v i Representing the flow rate; alpha represents the mass transfer coefficient between the two domains;
wherein K is i Is the principal component of the permeability coefficient in the corresponding coordinate direction, x i 、x j Is the position on the corresponding coordinate, h is the head;
s6, jointly inverting the mass transfer coefficient and the permeability coefficient, and specifically comprising: generating a priori data set [ X, Y ] using steps S3 and S5]Wherein the combination parameter m= [ alpha, K of the mass transfer coefficient alpha and the permeability coefficient K 1 ，K 2 ，...K N ]N represents the number of permeability coefficients, and the parameter sample setN e A number of samples representing the dataset; predicted contaminant concentration c= [ C ] t-1 ，C t ]，C t-1 、C t Respectively representing the pollutant concentration predicted by the double-zone model at the pollutant migration time t-1 and the moment t in the step S5, wherein the pollutant concentration predicted value set is +.> Obtaining mass transfer coefficients and all permeability coefficients by using an iterative set smoother algorithm, namely the pollutant concentration monitoring value collected in the assimilation step S4 of the formula (4)Update value for the 1+1st time:
wherein:respectively representing the first time +1 and the first time updated value of the n-th mass transfer coefficient and permeation coefficient combination parameter m; m is m pr，n An nth set of a priori parameters representing m; d, d obs，n Is the nth group concentration monitoring data subjected to noise disturbance; g (·) represents step S5; c (C) D Representing a monitoring error covariance matrix; />Represents g (. Cndot.) in parameter m l Linearization of the position, calculated by the ratio of the change of the input parameter to the corresponding change of the output concentration; beta l Representing a custom update step size, 0<β l ≤1；Δm pr Representing a priori parametric sample m pr Deviation from the mean value of the parameters>Covariance representing model parameters is calculated by a finite number of sample sets:
2. the method for identifying the heterogeneity of an aquifer for non-feik dispersion of groundwater contaminants according to claim 1, wherein in step S3, the advance setting of a part of parameters is involved before generating a set of osmotic coefficient samples, comprising: sample number N e The variable type, the number of training image parameters and the number N of permeability coefficients in a single sample in the target site model; the variable types comprise classified variables and continuous variables, and the classificationClass variables include: soil type, land cover category; the continuous variable includes porosity, hydraulic conductivity.
3. The method for identifying the heterogeneity of the aquifer for non-feik dispersion of groundwater pollutants according to claim 2, wherein the number of permeability coefficients N is set to be less than 10 4 The method comprises the steps of carrying out a first treatment on the surface of the Sample number N e Is 10 2 ～10 3 。
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