KR102396659B1 - Method of produce a three-dimensional discrete fracture network model - Google Patents

Abstract

The present invention discloses a three-dimensional discrete crack network model generation method in which a multi-flow technique is introduced for spatial and temporal behavior of groundwater flowing out from a rock crusher, prediction of the interaction of groundwater-brine, and quantitative evaluation. A method for generating a three-dimensional discrete crack network model according to an embodiment of the present invention comprises: a first step (S100) in which the data collection unit 110 collects one or more parameters related to the rock crusher from the outside through the communication unit 120; The prediction operation unit 130 generates a crack network, which is simulated data of the rock crusher, based on the parameters received from the data collection unit 110, calculates the flow of fluid through the crack network, and the fluid passing through the crack network a second step (S200) of predicting the movement path of the pollutants through the flow of , and predicting the movement path of the fluid groundwater and the groundwater-brine boundary layer by applying a multiphase flow technique; and a third step (S300) in which the model generation unit 140 generates the three-dimensional discrete crack network model 10 by reflecting the data calculated and predicted from the prediction operation unit 130; by including; It is possible to accurately analyze the crack characteristics affecting the

Description

Method of produce a three-dimensional discrete fracture network model

The present invention relates to a method for generating a three-dimensional discrete crack network model, and more particularly, a multi-flow technique for spatial and temporal behavior of groundwater flowing out from a rock crusher, prediction of the interaction of groundwater-brine, and quantitative evaluation of which is introduced. It relates to a method for generating a 3D discrete crack network model.

A crack network consisting of interconnected crack elements in cracked rock with very low permeability is a major pathway for fluid flow. Such crack networks are commonly considered in rock extraction, storage and management of rock aquifers, deep geothermal energy extraction, restoration of contaminated crack bedrock, and radioactive nuclear waste repository.

However, the available information on the crack network in its natural state is very limited, which creates serious uncertainty when analyzing the effects of crack properties on fluid flow in cracked rock masses. Accordingly, the mathematical models of various methods applied to predict (or simulate) the flow of fluid in the crack bed contain uncertainty, and each model has its pros and cons.

Among the above mathematical models, the most applied mathematical model is the continuum model, in which rock is represented by a porous medium, the permeability coefficient is scale-dependent, and spatially correlated probability fields, and the geometric and physical properties of individual crack elements are clearly defined. It is a Discrete Fracture Network (DFN) model that is expressed. The above discrete crack network model generally has the advantage of simulating a wider range of fluid flow than the continuum model, but the uncertainty in constructing the input data is relatively increased because the parameters to be corrected are relatively increased accordingly. There was a problem being For example, in order to predict fluid flow through a high-reliability discrete crack network model, the variability of the pore distribution for crack elements can be included in the discrete crack network model. information was needed.

Furthermore, one of the sources of uncertainty for discrete crack network model-based modeling is the relationship between the length distribution of crack elements constituting the crack network and the pore distribution of crack elements related to the permeability of the crack network. Here, if it is assumed that the gap distribution of the cracking element controls the fluid flow in the crack network, it is possible to implicitly define the correlation between the length distribution of the cracking element and the gap distribution. The proposed models for the correlation between the length distribution and gap distribution of such crack elements include the Levy stable model, lognormal distribution model, and power series distribution model. The above models are determined through a wide range of parameters based on the size of the target area and field data. In recent studies, the power series distribution model is most frequently applied among the above models, and a wide range of exponential values used in the model has been disclosed through literature such as papers.

However, most of the existing discrete crack network model-based modeling is performed through the two-dimensional discrete crack network model, and the research on the three-dimensional discrete crack network model has not yet reached the commercialization stage and remains in the development stage.

On the other hand, the present inventor "a numerical study on the fluid flow characteristics according to the change in the length distribution of the crack elements in the flow field of a three-dimensional discrete crack network" (Woo-Chang Jung, J. Korea Water Resour. Assoc. Vol 52, No. 2 ( 2019), pp.149-161).

The present inventor's thesis is a study of fluid flow characteristics according to the length distribution of crack elements by applying a three-dimensional discrete crack network model. is set to 1.0~6.0, and the gap distribution of cracking elements is similarly generated by vomiting a power series distribution model, but the exponent range is equally applied to 2 and 5 to study the fluid flow characteristics.

However, the present inventor's thesis considers only the effect of the change in the length distribution of the crack elements constituting the crack network on the flow on the fluid in the crack network, and does not consider the effect on the change in the pore distribution of the crack elements at all. There was a problem of disseminating serious uncertainty when analyzing the effects of crack properties affecting the flow of fluid in the bedrock.

"A Numerical Study on the Fluid Flow Resistance According to the Change in the Length Distribution of Crack Elements in the Flow Field of a 3D Discrete Crack Network" (Woochang Jung, J. Korea Water Resour. Assoc. Vol 52, No.2(2019), pp. 149-161)

The present invention was devised to solve the above problems, and in order to accurately analyze the crack characteristics affecting the fluid flow in the cracked bedrock, the spatial and temporal behavior of groundwater and the interaction of groundwater-brine in a three-dimensional discrete crack network model. The purpose of this study is to propose a method for generating a three-dimensional discrete crack network model in which a multi-flow technique for action prediction and quantitative evaluation is introduced.

Another object of the present invention is to propose a method for generating a three-dimensional discrete crack network model that can simulate the flow of a fluid while changing the gap of the crack network, which is simulated data of a rock crusher.

On the other hand, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned are clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. it could be

As a technical method for achieving the above object, in the three-dimensional discrete crack network model generation method according to an embodiment of the present invention, the data collection unit 110 receives parameters related to the rock crusher from the outside through the communication unit 120 . A first step of collecting one or more (S100); The prediction operation unit 130 generates a crack network, which is simulated data of the rock crusher, based on the parameters received from the data collection unit 110, calculates the flow of fluid through the crack network, and the fluid passing through the crack network a second step (S200) of predicting the movement path of the pollutants through the flow of , and predicting the movement path of the fluid groundwater and the groundwater-brine boundary layer by applying a multiphase flow technique; and a third step (S300) in which the model generation unit 140 generates the three-dimensional discrete crack network model 10 by reflecting the data calculated and predicted from the prediction operation unit 130 .

And the second step (S200) is a first setting step (S201a) in which the first prediction operation module 131 sets at least one of the shape, diameter, orientation and inclination of the plurality of individual cracks 131a constituting the crack network (S201a) ; a second setting step (S201b) in which the first prediction operation module 131 sets the gap between the individual cracks 131a at the same time as the first setting step (S201a); and a crack network generation step (S202) in which the first prediction operation module 131 creates a crack network in the target region 131b as individual cracks 131a in which at least one of shape, diameter, orientation, slope and gap is set; include

In addition, the first prediction operation module 131 sets the shape of the individual crack 131a to at least one of a circle, a rectangle, and a polygon in the first setting step S201a, and the diameter of the individual crack 131a is an algebraic rule, It is set using at least one of the exponential rule and the power series rule, and the orientation and inclination of the individual cracks 131a are set using the Fisher-Von-Mises law.

And the first prediction operation module 131, in the second setting step (S201b), based on the logarithmic rule or the power series rule, the correction gap 131d corrected for the irregular gap 131c of the individual cracks 131a based on the individual cracks ( 131a).

In addition, in the second step ( S200 ), the flow path shape of the individual crack 131a is set to a rectangular cross section or a circular section in the second prediction operation module 132 , and two individual cracks 132a among the individual cracks for which the flow path shape is set , 132b) connecting individual cracks connecting step (S203); The integrated permeability coefficient calculation step in which the second prediction calculation module 132 calculates the integrated permeability coefficient at the intersection of the individual cracks 132a and 132b through the harmonic average from the center of the individual cracks 132a and 132b to the intersection point ( S204); A passing flow rate calculation step (S205) in which the second prediction calculation module 132 calculates the flow rate passing through the intersection of the individual cracks 132a and 132b; a head calculation step (S206) in which the second prediction calculation module 132 calculates a head for each of the individual cracks 132a and 132b; and a fluid flow calculation step (S207) in which the second prediction calculation module 132 calculates the gap distribution 132c and the head 132d of the crack network.

And the second prediction operation module 132 connects the two individual cracks 132a and 132b having a water head difference from the center in the individual crack connecting step S203, so that the water head difference exists in the passing flow calculation step S205. Calculate the flow rate through the intersection of the individual cracks 132a, 132b.

In addition, the second prediction calculation module 132, the intersection of the individual cracks 132a, 132b in the integrated permeability coefficient calculation step (S204) based on the gap distribution characteristics of the individual cracks 132a, 132b changed by the surrounding stress Calculate the combined pitcher coefficient of the branch.

And, in the second step (S200), the third prediction operation module 133 moves with the fluid based on the gap distribution (132c) and the head (132d) of the crack network calculated in the fluid flow operation step (S207). A movement path generation step (S208) of generating a movement path (133a) of the solute particles and the non-reacting solute particles; A solute breakthrough curve generation step (S209) in which the third prediction operation module 133 generates a solute breakthrough curve 133b at an outlet boundary where the fluid is discharged from the movement path 133a; and a pollutant movement path prediction step (S210) of predicting the movement path of the pollutant based on the movement path 133a and the solute breakthrough curve 133b.

In addition, the second step (S200) includes a pressure distribution calculation step (S211) in which the fourth prediction operation module 134 applies the multiphase flow technique to calculate the pressure distribution (134a) of the crack network; a saturation prediction step (S212) in which the fourth prediction operation module 134 predicts the saturation degree 134b of the crack network by applying the multiphase flow technique; and a groundwater flow path and boundary layer prediction step in which the fourth prediction operation module 134 predicts the movement path of groundwater and the boundary layer of groundwater-brine based on the pressure distribution 134a and saturation 134b of the crack network (S213); further includes

And the fourth prediction operation module 134 applies IMPES (Implicit Pressure Explicit Saturation) technique in the groundwater flow path and boundary layer prediction step S213 to predict the movement path of groundwater and the groundwater-salt water boundary layer.

According to an embodiment of the present invention, it is possible to accurately analyze the crack characteristics affecting the fluid flow in the cracked rock.

And, according to an embodiment of the present invention, it is possible to perform a simulation of the flow of fluid while changing the gap of the crack network, which is simulation data of the rock crusher.

On the other hand, the effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be able

1 is a block diagram showing the configuration of a manager terminal for generating a three-dimensional discrete crack network model.
FIG. 2 is a block diagram illustrating a detailed configuration of a prediction operation unit shown in FIG. 1 .
3 is a conceptual diagram of a three-dimensional discrete crack network model according to an embodiment of the present invention.
4 is a flowchart illustrating a process of a method for generating a three-dimensional discrete crack network model according to an embodiment of the present invention.
5 to 8 are views for explaining the process of configuring individual crack networks and crack networks generated in the target area.
9 to 13 are diagrams for explaining a process of calculating a flow of a fluid passing through a crack network.
14 to 16 are diagrams for explaining a process of predicting a pollutant movement path.
17 to 18 are diagrams for explaining a process of predicting a groundwater flow path and a groundwater-brine boundary layer.

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them. However, since the description of the present invention is merely an embodiment for structural or functional description, the right (the scope of the present invention) should not be construed as being limited by the embodiment described in the text. That is, the embodiment is subject to various changes Since this is possible and can take various forms, it should be understood that the scope of the present invention includes equivalents that can realize the technical idea.In addition, the object or effect presented in the present invention must include all of the object or effect of the present invention. It is not meant to include only such effects, so the scope of the present invention should not be construed as being limited thereby.

The meaning of the terms described in the present invention should be understood as follows.

Terms such as “first” and “second” are for distinguishing one component from another, and the scope of rights should not be limited by these terms. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component. When a component is referred to as being “connected to” another component, it may be directly connected to the other component, but it should be understood that other components may exist in between. On the other hand, when it is mentioned that a certain element is "directly connected" to another element, it should be understood that the other element does not exist in the middle. Meanwhile, other expressions describing the relationship between elements, that is, "between" and "between" or "neighboring to" and "directly adjacent to", etc., should be interpreted similarly.

The singular expression is to be understood to include the plural expression unless the context clearly dictates otherwise, and terms such as "comprise" or "have" are not intended to refer to the specified feature, number, step, action, component, part or any of them. It is intended to indicate that a combination exists, and it should be understood that it does not preclude the possibility of the existence or addition of one or more other features or numbers, steps, operations, components, parts, or combinations thereof.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs, unless otherwise defined. Terms defined in general used in the dictionary should be interpreted as having the same meaning in the context of the related art, and cannot be interpreted as having an ideal or excessively formal meaning unless explicitly defined in the present invention.

Hereinafter, with reference to the accompanying drawings, the configuration of the manager terminal 100 for performing the 3D discrete crack network model generating method according to an embodiment of the present invention will be described in detail.

1 is a block diagram showing the configuration of a manager terminal for generating a three-dimensional discrete crack network model, FIG. 2 is a block diagram showing the detailed configuration of the prediction operation unit shown in FIG. 1, FIG. 3 is an embodiment of the present invention It is a conceptual diagram of a 3D discrete crack network model according to an example.

1 to 3 , the manager terminal 100 includes a data collection unit 110 , a communication unit 120 , a prediction operation unit 130 , a model generation unit 140 , a notification unit 150 , and a control unit 160 . is done

The data collection unit 110 collects one or more parameters related to the rock crusher from the outside through the communication unit 120 . Here, the outside may be at least one of an external server, an external facility, and an external terminal in which parameter information related to the rock crusher is stored, and the parameter may be obtained from the rock crusher such as the inclination, width, air gap, and section strain of the rock crusher. All possible data.

The communication unit 120 is provided to collect parameters from the outside to the data collection unit 110 .

The prediction operation unit 130 receives the parameters from the data collection unit 110, and a prediction operation for generating the 3D discrete crack network model 10 based on the received parameters is performed in a plurality of steps. The prediction operation unit 130 includes a first prediction operation module 131 , a second prediction operation module 132 , a third prediction operation module 133 , and a fourth prediction operation module 134 to perform a plurality of prediction operation steps. ) is provided.

The first prediction operation module 131 generates a crack network, which is simulated data of the rock crusher, based on the parameters received from the data collection unit 110 .

The second prediction operation module 132 calculates the flow of the fluid passing through the crack network generated by the first prediction operation module 131 .

The third prediction operation module 133 predicts the movement path of the contaminants based on the flow of the fluid passing through the crack network calculated from the second prediction operation module 132 .

After the process of the third prediction operation module 133 is completed, the fourth prediction operation module 134 applies a polyphase flow technique to the operation and prediction data of the first, second, and third prediction operation modules 131, 132, 133. It is applied to predict the movement path of groundwater, which is a fluid passing through the crack network, and the boundary layer of groundwater-brine.

The model generator 140 generates a three-dimensional discrete crack network model 10 in which the calculation and prediction data of the first, second, third, and fourth prediction operation modules 131, 132, 133, and 134 are reflected.

Accordingly, the three-dimensional discrete crack network model 10 can provide the result of predicting the movement path of groundwater and the boundary layer of groundwater-brine to the manager who is the user of the manager terminal 100 .

Furthermore, the three-dimensional discrete crack network model 10 is a groundwater-saltwater interaction (“Groundwater-saltwater interaction effect (Multi-phase flow)” in FIG. 3), groundwater flow (“Hydrological effect (Groundwater flow)” in FIG. )") and hydraulic stimulation ("Mechanical effect (Hydraulic stimulation)" in FIG. 3) are reflected, and it is possible to provide this to the manager.

That is, in the first, second, third, and fourth prediction operation modules 131 , 132 , 133 and 134 , the interaction between groundwater-salt water, groundwater flow, and hydraulic stimulation are reflected in the three-dimensional discrete crack network model 10 . Prediction calculation is performed as much as possible.

As such, the three-dimensional discrete crack network model 10 can accurately analyze the crack characteristics that affect the fluid flow in the cracked bedrock as the above-mentioned groundwater-salt water interaction, groundwater flow, and hydraulic stimulus are reflected.

The notification unit 150 transmits to the manager whether the prediction operation is performed by the prediction operation unit 130 and whether the 3D discrete crack network model 10 is generated by the model generation unit 140 through a notification sound or a notification message.

When the prediction operation of the prediction operation unit 130 is completed, the notification unit 150 generates a notification sound or generates a notification message to notify the completion of the prediction operation, and, in contrast, the first, second, third, and fourth predictions. When the prediction operation does not proceed from at least one prediction operation module among the operation modules 131 , 132 , 133 , and 134 , a notification sound for notifying the stop of the prediction operation may be generated or a notification message may be generated.

Here, the notification sound for the notification unit 150 to notify the completion of the prediction operation may be omitted as a relatively low tone or silence than when the pitch (Hz) informs the stop of the prediction operation, and the notification message is "Prediction operation complete" " or may be omitted. Alternatively, the notification sound for notifying the stop of the prediction operation may be selected as a sound or vibration having a pitch higher than that when notifying the completion of the prediction operation, and the notification message is "Prediction operation stop". can be provided.

In addition, the notification unit 150 generates a notification sound to notify the generation of the 3D discrete crack network model 10 when the generation of the 3D discrete crack network model 10 from the model generation unit 140 is completed. or a notification message is generated, and otherwise, when the three-dimensional discrete crack network model 10 is not generated from the three-dimensional discrete crack network model 10 from the model generation unit 140, the three-dimensional discrete crack network model (10) It is possible to generate a notification sound or generate a notification message to inform that the meaning has not been created.

Here, the notification sound for the notification unit 150 to inform the generation of the 3D discrete crack network model 10 is a relatively low sound or silence than when notifying the non-generation of the 3D discrete crack network model 10 . It can be omitted, and the notification message can be “completed model generation” or omitted, and in contrast, the notification sound for notifying the non-generation of the 3D discrete crack network model 10 has a pitch of the 3D discrete crack network model (10). It may be selected as a relatively high sound or vibration than when notifying the generation of , and a notification message may be provided as "Model not created".

Furthermore, the alarm sound for the notification unit 150 to notify the generation or non-generation of the dimensional discrete crack network model 10 is set to be relatively higher than the tone for notifying the completion of the prediction operation or the stop of the prediction operation. can That is, the pitch of the notification sound is when notifying the completion of the prediction operation, when notifying the stop of the prediction operation, when notifying the generation of the 3D discrete crack network model 10, the non-generation of the 3D discrete crack network model (10) When notifying, the pitch may increase in order.

The control unit 160 controls the data collection unit 110 of the manager terminal 100 to collect parameters from the outside, and the prediction operation for generating the 3D discrete crack network model 10 from the prediction operation unit 130 is performed. A notification sound to inform the prediction operation and generation of the 3D discrete crack network model 10 from the notification unit 150 and to generate the 3D discrete crack network model 10 by the model generation unit 140 by controlling it to be performed Or, a notification message is generated.

And the control unit 160 is a 3D discrete crack network model 10 generated by the calculation and prediction data or the model generation unit 140 for generating the 3D discrete crack network model 10 predicted by the prediction operation unit 130 . ) can be controlled to be transmitted to a requesting user terminal (not shown).

Through this, the user of the user terminal (not shown) can be provided with the result of predicting the movement path of groundwater and the groundwater-brine boundary layer based on the 3D discrete crack network model 10 .

Hereinafter, a method for generating a three-dimensional discrete crack network model according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

4 is a flowchart showing a process of a method for generating a three-dimensional discrete crack network model according to an embodiment of the present invention, and FIGS. 5 to 8 are individual crack networks and a process of constructing a crack network generated in the target area. 9 to 13 are diagrams for explaining the process of calculating the flow of fluid passing through the crack network, and FIGS. 14 to 16 are diagrams for explaining the process of predicting the movement path of contaminants, 17 to 18 are diagrams for explaining a process of predicting a groundwater flow path and a groundwater-brine boundary layer.

First, the data collection unit 110 collects one or more parameters related to the rock crusher from the outside through the communication unit 120 (S100).

After collecting one or more parameters from the data collection unit 110, the prediction operation unit 130 performs a prediction operation for generating the 3D discrete crack network model 10 (S200).

The process of prediction calculation for generating the three-dimensional discrete crack network model 10 can be subdivided as follows, and specifically, a crack network generation process, a fluid flow calculation process, a pollutant movement path prediction process, It can proceed in the order of the groundwater movement path and the groundwater-salt water boundary layer prediction process.

The crack network generation process is a process made by the first prediction operation module 131, and the first prediction operation module 131 sets the shape, diameter, orientation and inclination of a plurality of individual cracks 131a constituting the crack network ( S201a), and at the same time, by setting the gap between the individual cracks 131a (S201b), the generation of the individual cracks 131a is completed (S201).

After that, the first prediction operation module 131 generates a crack network including a plurality of individual cracks 131a in which the shape, diameter, orientation, inclination and gap are set in the target region 132b ( S202 ).

In the crack network generation process, the first prediction operation module 131 may set the shape of the individual crack 131a to at least one of a circle, a rectangle, and a polygon, and in one embodiment, the shape of the individual crack 131a is set to a disk ( Disk) type can be set.

And the first prediction operation module 131 can set the diameter of each crack 131a by using at least one of logarithmic law, exponential law, and power series law, and Mises's law for the orientation and inclination of individual cracks 131a It can be set using (Fisher-Von-Mises).

In addition, the first prediction operation module 131 corrects the irregular gap 131c of the individual cracks 131a to the corrected gap 131d of a constant gap by applying the logarithmic rule or the power series rule to correct the gap of the individual cracks 131a. set The correction gap 131d may be set to a value separately input through the prediction operation unit 130 or the control unit 160 . Accordingly, it is possible to change the gap between the individual cracks 131a and the crack network by an administrator, and this is because the three-dimensional discrete crack network model 10, which will be described later, simulates the flow of fluid while changing the gap of the crack network. in order to do it

Here, the algebraic rule, the exponential rule, and the power series rule are rules commonly used in mathematical statistics, so detailed descriptions thereof will be omitted. Fisher-Born Mises's law is a law for determining orientation and inclination in three dimensions, such as a Gaussian distribution in three dimensions.

The fluid flow calculation process is performed after the crack network creation process is completed. The second prediction operation module 132 sets the flow path shape of the plurality of individual cracks 131a to a rectangular cross section or a circular cross section, and the flow path shape Connects two individual cracks 132a and 132b among the individual cracks in which is set (S203).

In the flow calculation process of the fluid, the calculation can be performed on the assumption that the fluid in the crack network moves one-dimensionally. This is to reflect the channeling effect that the movement (flow) of the fluid occurs while forming a specific one-dimensional flow path.

In the process of connecting the two individual cracks 132a and 132b, the flow path shape of the individual crack 131a may be set to a rectangular cross-section or a circular cross-section, but it is preferable to set it to a rectangular cross-section. This is because, when setting the flow path shape with a rectangular cross section, the rule of thumb that the flow rate passing through a crack of a local scale is proportional to the third power of the gap can be applied, and it is easy to measure the change in the gap through effective normal stress, etc. am.

After connecting the two individual cracks 132a and 132b, the second prediction operation module 132 performs a harmonic average from the center of the two individual cracks 132a and 132b to the intersection point of the two individual cracks 132a and 132b. ) calculates the integrated pitcher coefficient of the intersection (S204).

In the process of calculating the integrated permeability coefficient at the intersection point, the second prediction calculation module 132 calculates the integrated permeability coefficient at the intersection point of the two individual cracks 132a and 132b. is calculated through the following [Equation 1].

In the above [Equation 1], g(m/sec ² ) is the gravitational acceleration, v(m ² /sec) is the coefficient of dynamic viscosity of the fluid, e(m) is the gap between the cracks, and l(m) is the thickness of the crack. is the intersection length.

And the second prediction operation module 132 calculates the integrated permeability coefficient of the intersection point of the two individual cracks 132a and 132b through the harmonic average, which is the distance from the center of the two individual cracks 132a and 132b to the intersection point. It is calculated through [Equation 2].

In the above [Equation 2], k _i and k _j are the integrated permeability coefficients of the two individual cracks (132a, 132b), and L _i and L _j are the intersection points from the center of the two individual cracks (132a, 132b). means the distance to

Furthermore, the second prediction operation module 132 is configured to calculate the integrated permeability coefficient at the intersection of the two individual cracks 132a and 132b, as shown in FIG. 11 , the irregular gap distribution of the individual cracks 132a and 132b. The individual cracks 132a and 132b are deformed into a laminate to form a predetermined gap. At this time, the laminate of the individual cracks 132a and 132b is composed of an open area in which fluid can move (“l ₁ ” and “l ₂ ” in FIG. 11 ) and a closed area in which the fluid cannot move except for the open area.

Then, a peripheral stress applied to the individual cracks 132a and 132b is applied to deform the laminated region. Here, in the lamination area, it is preferable that the open area shrinks and the closed area expands as the ambient stress is applied.

Through this, the second prediction calculation module 132 calculates the integrated permeability coefficient at the intersection of the two individual cracks 132a and 132b based on the gap distribution characteristics of the individual cracks 132a and 132b that are changed by the surrounding stress. can

After calculating the integrated permeability coefficient at the intersection of the two individual cracks 132a and 132b, the second prediction operation module 132 calculates the flow rate passing through the intersection of the two individual cracks 132a and 132b (S205).

In the process of calculating the flow rate passing through the intersection, the second prediction operation module 132 calculates the flow rate passing through the intersection of the two individual cracks 132a and 132b through the following [Equation 3].

In the above [Equation 3], Q _ij is the flow rate passing through the intersection of the two individual cracks 132a and 132b, Δh _ij is the head difference from the center of the two individual cracks 132a, 132b, and L _i and L _j mean the distance from the center of the two individual cracks 132a and 132b to the intersection point.

That is, the second prediction calculation module 132 calculates the flow rate passing through the intersection point of the individual cracks 132a and 132b having a head difference in the flow rate calculation process through the intersection point, and for this purpose, the individual cracks 132a, 132b) It may be preferable to connect two separate cracks 132a and 132b having a water head difference in the connection process.

In addition, the head difference between the two individual cracks 132a and 132b may be set to a value separately input through the prediction operation unit 130 or the control unit 160 irrespective of the parameters. Accordingly, the fluid in the crack network may be uniformly moved in one direction along the gap.

After calculating the flow rate passing through the intersection of the two individual cracks 132a and 132b, the second prediction operation module 132 calculates the head for each of the two individual cracks 132a and 132b (S206).

In the above head calculation process, the second prediction calculation module 132 performs calculations under steady-state conditions, and applies the mass conservation law to the case where n individual cracks 132b are connected to the individual cracks 132a. The head for each of the individual cracks 132a and 132b is calculated through the following [Equation 4].

In addition, the second prediction operation module 132 may calculate [Equation 4] above by applying a conjugate gradient method (or a preconditioning conjugate gradient method).

In addition, in the above head calculation process, the steady state is a state opposite to the abnormal state in which the temporal change of the gap of the individual cracks 132a and 132b reflecting the mechanical effect is applied, and the gap of the individual cracks 132a and 132b is applied. It means a state in which temporal changes are excluded.

After the head calculation for each of the two individual cracks 132a and 132b, the second prediction operation module 132 generates a gap distribution 132c and The flow of the fluid is calculated by calculating the head 132d (S207).

Here, the gap distribution 132c of the crack network has different colors depending on the gaps of the individual cracks for easy identification, and the head 132d of the crack network has different colors depending on the difference in the water head of the individual cracks.

The process of predicting the movement path of the pollutants is a process that proceeds after the completion of the flow calculation process of the fluid, and the third prediction operation module 133 performs the prediction of the solute particles based on the gap distribution 132c and the head 132d of the crack network. A movement path 133a is created (S208).

Here, the solute particles move along the gaps between individual cracks and crack networks together with the fluid, and the reaction solute particles that react through contact with the fluid and unreacted solute particles that do not react even through contact with the fluid are separated. may include

After generating the movement path 133a of the solute particles, the third prediction operation module 133 generates a solute breakthrough curve 133b at the exit boundary where the fluid is discharged among the movement paths 133a of the solute particles (S209).

Here, the exit boundary is the end within the target area, that is, the boundary through which the fluid is discharged, that is, the outer part of the crack or crack network. , in one embodiment, as shown in FIG. 16 , as the contaminant concentration is measured to be high at 20.0 to 30.0 Temps (x 1000 ans), it can be predicted that the portion exceeds the allowable standard.

After generating the solute breakthrough curve 133b, the third prediction operation module 133 predicts the movement path of the contaminants based on the movement path 133a of the solute particles and the solute breakthrough curve 133b (S210).

The groundwater movement path and the groundwater-brine boundary layer prediction process is a process that is performed after the pollutant movement path prediction process is completed. The fourth prediction operation module 134 includes the first, second, and third prediction operation modules 131, 132, 133), the pressure distribution 134a of the crack network is calculated by applying the multiphase flow technique to the calculation and prediction data of 133), and the saturation degree of the crack network is predicted (S212) (S212). Predict the boundary layer of salt water (S213).

In the process of predicting the movement path of groundwater and the groundwater-salt water boundary layer, the calculation step of the pressure distribution 134a of the crack network and the prediction step of the saturation degree 134b of the crack network may be simultaneously performed.

In addition, the multi-phase flow technique applied to the fourth prediction operation module 134 to predict the movement path of groundwater and the boundary layer of groundwater-brine may be IMPES (Implicit Pressure Explicit Saturation) technique.

Here, the IMPES technique is a technique that can predict the boundary layer between two fluids that do not mix with each other, such as groundwater and salt water in a coastal aquifer.

After the prediction operation is completed from the first, second, third, and fourth prediction operation modules 131, 132, 133, and 134, the model generation unit 140 performs the first, second, third, and fourth prediction operation modules 131, 132, 133, 134) to generate a three-dimensional discrete crack network model 10 by reflecting the calculation and prediction data (S300).

The three-dimensional discrete crack network model 10 generated through the above process can be used to accurately analyze the crack characteristics affecting the fluid flow in the cracked bedrock, and for this purpose, the spatial and temporal behavior of groundwater, groundwater-brine Multiflow techniques for interaction prediction and quantitative evaluation can be introduced.

In addition, the three-dimensional discrete crack network model 10 can perform a simulation of the flow of fluid while changing the gap of the crack network, which is simulated data of the rock crusher.

The detailed description of the preferred embodiments of the present invention disclosed as described above is provided to enable any person skilled in the art to make and practice the present invention. Although the above has been described with reference to preferred embodiments of the present invention, it will be understood by those skilled in the art that various modifications and changes can be made to the present invention without departing from the scope of the present invention. For example, a person skilled in the art may use each configuration described in the above-described embodiments in a way of combining with each other. Accordingly, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The present invention may be embodied in other specific forms without departing from the spirit and essential characteristics of the present invention. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention. The present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, claims that are not explicitly cited in the claims may be combined to form an embodiment, or may be included as new claims by amendment after filing.

10: 3D discrete crack network model,
100: administrator terminal;
110: data collection unit;
120: communication department;
130: prediction operation unit,
131: a first prediction operation module;
131a: individual cracks;
131b: target area;
131c: irregular gap,
131d: calibration gap,
132: a second prediction operation module;
132a, 132b: two separate cracks;
132c: distribution of gaps in the crack network,
132d: the head of the rift network,
133: a third prediction operation module;
133a: movement path of solute particles,
133b: solute breakthrough curve,
134: a fourth prediction operation module;
134a: pressure distribution of the crack network,
134b: saturation of the crack network,
140: model generation unit,
150: notification unit,
160: control unit.

Claims (10)

In the method of generating a three-dimensional discrete crack network model (10),
A first step (S100) of the data collection unit 110 collecting one or more parameters related to the rock crusher from the outside through the communication unit 120;
The prediction operation unit 130 generates a crack network that is simulated data of a rock crusher based on the parameters received from the data collection unit 110, calculates the flow of fluid through the crack network, and generates the crack network. a second step (S200) of predicting the movement path of the contaminants through the flow of the passing fluid, and predicting the movement path of the groundwater, which is the fluid, and the boundary layer of the groundwater-brine by applying a multiphase flow technique; and
A third step (S300) in which the model generation unit 140 generates the three-dimensional discrete crack network model 10 by reflecting the data calculated and predicted from the prediction operation unit 130; A method for generating a dimensional discrete crack network model. The method of claim 1,
The second step (S200) is,
a first setting step (S201a) in which the first prediction operation module 131 sets at least one of the shape, diameter, orientation and inclination of a plurality of individual cracks 131a constituting the crack network;
a second setting step (S201b) in which the first prediction operation module 131 sets a gap between the individual cracks (131a) at the same time as the first setting step (S201a); and
a crack network generation step (S202) in which the first prediction operation module 131 generates a crack network in the target region 131b as individual cracks 131a in which at least one of the shape, diameter, orientation, inclination and gap is set; A three-dimensional discrete crack network model generation method, characterized in that it comprises a. 3. The method of claim 2,
The first prediction operation module 131,
In the first setting step (S201a), the shape of the individual crack 131a is set to at least one of a circle, a rectangle, and a polygon, and the diameter of the individual crack 131a is at least one of a logarithmic rule, an exponential rule, and a power series rule. 3D discrete crack network model generation method, characterized in that the orientation and inclination of the individual cracks (131a) are set using Fisher-Von-Mises' law (Fisher-Von-Mises). 3. The method of claim 2,
The first prediction operation module 131,
Setting the corrected gap 131d corrected for the irregular gap 131c of the individual crack 131a as the gap of the individual crack 131a based on the logarithmic rule or the power series rule in the second setting step S201b A method for generating a 3D discrete crack network model characterized by its characteristics. 3. The method of claim 2,
The second step (S200) is,
The second prediction operation module 132 sets the flow path shape of the individual crack 131a to a rectangular cross section or a circular section, and an individual crack connecting two individual cracks 132a and 132b among the individual cracks for which the flow path shape is set. connection step (S203);
The second prediction calculation module 132 calculates the integrated permeability coefficient at the intersection of the individual cracks 132a and 132b through the harmonic average from the center of the individual cracks 132a and 132b to the intersection point. calculation step (S204);
a passing flow rate calculation step (S205) in which the second prediction calculation module 132 calculates the flow rate passing through the intersection of the individual cracks (132a, 132b);
a head calculation step (S206) in which the second prediction calculation module 132 calculates a head for each of the individual cracks 132a and 132b; and
3D discrete crack network model generation, characterized in that it further comprises a fluid flow calculation step (S207) in which the second prediction operation module 132 calculates the gap distribution 132c and the head 132d of the crack network. Way. 6. The method of claim 5,
The second prediction operation module 132,
By connecting the two individual cracks 132a and 132b having a water head difference from the center in the individual crack connecting step S203, the individual cracks 132a and 132b having the water head difference in the passing flow calculation step S205 are connected. A three-dimensional discrete crack network model generation method, characterized in that the flow rate passing through the intersection is calculated. 6. The method of claim 5,
The second prediction operation module 132,
Calculating the integrated permeability coefficient at the intersection of the individual cracks 132a and 132b in the integrated permeability coefficient calculation step (S204) based on the gap distribution characteristics of the individual cracks 132a and 132b that are changed by the surrounding stress A method for generating a three-dimensional discrete crack network model with 6. The method of claim 5,
The second step (S200) is,
Reactive solute particles and non-reacting solutes moved with the fluid by the third prediction operation module 133 based on the gap distribution 132c and the head 132d of the crack network calculated in the fluid flow calculation step S207 A movement path generation step (S208) of generating the movement path (133a) of the particles;
a solute breakthrough curve generation step (S209) in which the third prediction operation module 133 generates a solute breakthrough curve 133b at an outlet boundary where the fluid is discharged among the movement path 133a; and
Contaminant movement path prediction step (S210) of predicting the movement path of the pollutant based on the movement path (133a) and the solute breakthrough curve (133b); 3D discrete crack network model generation, characterized in that it further comprises a Way. 9. The method of claim 8,
The second step (S200) is,
a pressure distribution calculation step (S211) in which the fourth prediction operation module 134 calculates the pressure distribution 134a of the crack network by applying the multi-phase flow technique;
a saturation prediction step (S212) in which the fourth prediction operation module 134 predicts the saturation degree 134b of the crack network by applying the multi-phase flow technique; and
The groundwater flow path and boundary layer prediction step ( S213); 3D discrete crack network model generating method, characterized in that it further comprises. 10. The method of claim 9,
The fourth prediction operation module 134,
In order to predict the movement path of the groundwater and the groundwater-brine boundary layer, the IMPES (Implicit Pressure Explicit Saturation) technique is applied in the groundwater flow path and boundary layer prediction step (S213). creation method.

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