CN114547927A - Hydrothermal deposit three-dimensional quantitative prediction evaluation method based on numerical simulation - Google Patents
Hydrothermal deposit three-dimensional quantitative prediction evaluation method based on numerical simulation Download PDFInfo
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Abstract
The invention belongs to the technical field of three-dimensional geological modeling and numerical simulation, and particularly relates to a three-dimensional quantitative prediction evaluation method for a hydrothermal deposit based on numerical simulation, which comprises the following steps: s1, collecting the existing basic data of exploration data in the research area; step S2, three-dimensional modeling and grid unit division; step S3, simulating the numerical value of the ore forming process; and step S4, predicting and evaluating deep mineral resources. The method adopts the numerical simulation, three-dimensional modeling and quantitative prediction methods of the hydrothermal mineralization process to carry out the prediction and evaluation of the deep mineral products, provides reliable technical support for the extraction of favorable information of the deep mineral products and the prediction and evaluation of the mineral products, has important significance for guaranteeing the safety of mineral resources in China, and has better prospect in the exploration of the deep mineral resources.
Description
Technical Field
The invention belongs to the technical field of three-dimensional geological modeling and numerical simulation, and particularly relates to a three-dimensional quantitative prediction evaluation method for a hydrothermal deposit based on numerical simulation.
Background
Mineral resources are an important material basis for human survival and development, and the safe supply of mineral resources depends on the development level of mineral exploration technology to a great extent. Along with the continuous enhancement of the dynamics of mineral exploration and development, the earth surface ore, the shallow ore and the easily-identified ore are increasingly reduced, the mineral exploration work is changing towards the direction of searching concealed ore, deep ore and the difficultly-identified ore, and the ore finding difficulty is continuously increased. With the increase of the mining depth and the reduction of exploration engineering, less and less deep mineral formation favorable information can be obtained. How to utilize limited exploration data to carry out prediction evaluation on deep mineral products is a technical difficulty which needs to be solved currently.
As the three-dimensional modeling and the mineralization prediction can fully integrate the multi-element prospecting information, the three-dimensional modeling and quantitative prediction evaluation method becomes an important technical means for deep prospecting. The numerical simulation of the mineralization process is an important technical means for researching the mineralization process of the hydrothermal deposit, researchers have tried to apply the numerical simulation to three-dimensional mineralization prediction in recent years, but related researches are still exploring, and numerical simulation parameters which can be used for three-dimensional prediction are relatively few.
Disclosure of Invention
The invention aims to provide a hydrothermal type ore deposit three-dimensional quantitative prediction evaluation method based on numerical simulation, which adopts the methods of numerical simulation, three-dimensional modeling and quantitative prediction in the hydrothermal mineralization process to carry out the prediction evaluation of deep mineral products, provides reliable technical support for the extraction of beneficial information of the deep mineral products and the prediction evaluation of the mineral products, has important significance for guaranteeing the safety of mineral resources in China, and has better prospect in the deep mineral resource exploration.
The technical scheme for realizing the purpose of the invention is as follows:
a hydrothermal deposit three-dimensional quantitative prediction evaluation method based on numerical simulation comprises the following steps:
s1, collecting the existing basic data of exploration data in the research area;
step S2, three-dimensional modeling and grid unit division;
step S3, simulating the numerical value of the ore forming process;
and step S4, predicting and evaluating deep mineral resources.
The basic data in step S1 includes: topographic maps, survey line profiles, mid-section plans, borehole data, geophysical data, geochemical data.
The step S2 includes:
step S21: constructing a three-dimensional geological model of a research area;
step S22: and carrying out grid unit division on the three-dimensional geological model.
The step S21 specifically includes: and constructing a three-dimensional geological model of the research area by a display modeling or implicit modeling method, wherein the constructed three-dimensional geological model can be converted into a FLAC3D file format applied to FLAC3D software after gridding.
The step S22 specifically includes: building block models of a research area, building attributes of each geologic body, and then exporting the block models into a table file in a csv format, a table file in the csv format and a FLAC3D file in a FLAC3D format, wherein all blocks in the respective files are stored in the form of columns of X, Y, Z and attributes, and the serial number of each unit block is a row.
The step S3 includes:
step S31, assigning geologic body parameters;
step S32, setting initial conditions and boundary conditions;
step S33, solving a simulation result and adjusting parameters;
and step S34, deriving a simulation result.
The geologic body parameters in step S31 include: density, bulk modulus, shear modulus, cohesion, tensile strength, internal friction angle, expansion angle, porosity, permeability, thermal conductivity.
The step S32 specifically includes: and researching the geological background of a research area, determining the stress state and the temperature field during mineralization and setting simulation initial conditions.
The simulating the initial conditions in the step S32 includes: initial temperature of each geological unit, initial pressure of each geological unit, initial stress magnitude and direction of each boundary of the model.
The step S33 specifically includes: and solving a simulation result, comparing and analyzing the simulation result and the actual geological condition of the research area, verifying whether the simulation parameter setting is reasonable, if the parameter setting is unreasonable, repeating the steps S31 and S32, and readjusting the parameters and the initial conditions until the simulation result meets the actual geological condition.
The method for judging whether the simulation parameter setting is reasonable in step S33 includes:
(1) whether the numerically-simulated ore formation trend is similar to the actual exploration condition or not;
(2) numerically simulating whether the spatial position of the ore body is the same as the actual condition of the ore deposit or not;
(3) whether the variation trend of the temperature and the pressure in the numerical simulation process conforms to the existing ore-forming theory or not.
The step S34 specifically includes: and (4) obtaining geological abnormal parameters through the simulation of an ore forming process, and exporting the geological abnormal parameters in a csv format file.
The step S4 includes:
step S41, importing a simulation result;
step S42, extracting abnormal variable information;
step S43: the ore formation is beneficial to target area delineation.
The step S41 specifically includes: and importing the geological abnormal parameters into the established block model.
The step S42 specifically includes: and (4) counting the relation between the known ore body and the value range of the geological abnormal parameter, and determining the mineralization favorable interval of the geological abnormal parameter.
The step S43 specifically includes: and quantitatively extracting geological abnormal parameter variable blocks in a specific value range, combining favorable geological abnormal parameter values in the process of mineral formation, extracting blocks with deep parts favorable for mineral formation, and delineating favorable target areas of the mineral formation.
The invention has the beneficial technical effects that:
1. the numerical simulation method provided by the invention can directly use the three-dimensional geological model constructed by the traditional mining software (such as Surpac software), can effectively utilize the existing three-dimensional model of the mine, avoids the huge workload of repeated modeling required by numerical simulation, effectively lowers the enterprise cost and improves the working efficiency.
2. The invention successfully realizes the conversion between the Surpac software three-dimensional attribute model and the FLAC3D file by utilizing the csv format file, thereby developing the research on mineral deposit science by utilizing the powerful force-heat-flow coupling function of the FLAC3D software, applying the numerical simulation based on the finite difference method to the research on the mineral forming process, and being beneficial to the exploration and progress of deep prediction of the mineral deposit and the research on the mineral forming theory.
3. According to the method, the favorable mining intervals of pp, temp and vsr are determined by counting the relationship between the known ore body and the value ranges of pp, temp and vsr variables, and then pp, temp and vsr variable blocks in the favorable interval range are quantitatively extracted, so that the numerical simulation result is successfully applied to deep mineral prediction, the problem that less useful information is caused because deep three-dimensional prediction can only depend on geological, geophysical and geophysical abnormal information to carry out prediction is effectively solved, and the reliability of the prediction result is improved.
Drawings
FIG. 1 is a diagram illustrating the effect of setting the initial conditions in a certain research area according to the present invention;
FIG. 2 is a graph of the distribution characteristics of pp and vsr after a numerical simulation of the mineralization process in a certain area of interest meets the convergence criterion;
FIG. 3 is a graph illustrating the statistical relationship between known nuggets and pp values provided by the present invention;
FIG. 4 is a graph of the distribution characteristics of quantitatively extracted mineralization favoured pp and vsr for a certain area of study provided by the present invention;
fig. 5 is a diagram of the favorable lump of a certain hydrothermal deposit predicted to be mineralized based on numerical simulation.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a hydrothermal deposit three-dimensional quantitative prediction evaluation method based on numerical simulation, which specifically comprises the following steps:
step S1, collecting the existing basic data of exploration data in the research area
Collecting the existing basic data of exploration data in the research area, wherein the basic data comprises: the three-dimensional geological model of the research area can be established by the detailed degree of basic data such as a topographic map, an exploration line profile, a middle section plane, drilling data (inclinometry data, lithology analysis, sample analysis and the like), geophysical data, geochemical data and the like.
Step S2, three-dimensional modeling and grid cell division
Step S21: constructing three-dimensional geological models of a research area
The collected geological data of the research area are utilized to construct a three-dimensional geological model of the research area, the existing three-dimensional modeling methods and software are many, and the specific operation and thinking of constructing the three-dimensional model by each software are different, but the three-dimensional model can be roughly divided into two types: display modeling and implicit modeling. The method for display modeling and implicit modeling is completely different from the idea, and no matter which modeling method and software are selected, the only one point to be met is to ensure that the built three-dimensional geological model can be converted into the FLAC3D software applicable FLAC3D file format after gridding.
Step S22: grid cell partitioning for three-dimensional geological models
Taking the currently mainstream three-dimensional modeling software Surpac as an example, the three-dimensional geological model of the research area can be established by using the data of the research area through the operation functions of ' entity modeling ' — ' entity tool ' — ' cutting and retaining entities higher than DTM or ' cutting and retaining entities lower than DTM ' of the Surpac software. Then, a block model of a research area is established through a block model function of Surpac software, and attributes of each geological body are sequentially established through a block model function, an attribute function and a new building function (basic geological element attributes need to be guaranteed to exist in the FLAC3D software by means of simulation, and the geological elements comprise stratums with different lithologies, rock masses with different lithologies and fractures). Finally, the block model is exported to be a table file in a csv format, a table file in the csv format and a FLAC3D file in a FLAC3D format through a function of 'block model' -export '-block centroid or plane-to-line file', wherein all blocks in the respective software are stored in the form of columns of X, Y, Z and attributes, and the number of each unit block is row.
Thus, the interconversion between the csv format table file and the FLAC3D file of the FLAC3D format can be achieved by studying the respective storage rules of the two pieces of software.
Because there are many three-dimensional modeling software on the market at present, if a three-dimensional model constructed by using other three-dimensional modeling software wants to adopt the technical route described by the method to develop three-dimensional mineralization prediction, the interconversion problem between the grid unit of the respective software and the flac3D format file also needs to be researched, and the idea of carrying out transition through the csv format file provided by the method can be used for reference.
Step S3, ore-forming process numerical simulation
Step S31, geologic body parameter assignment
Since the simulation of the mineralization process involves coupling between mechanics, thermodynamics and fluids, it is necessary to set the mechanics, thermodynamics parameters and the like of different geological unit bodies, and the specific parameters are shown in table 1.
TABLE 1 hydrothermal deposit numerical simulation parameters to be set
In table 1, the specific meanings of the parameters are as follows: density refers to an inherent property of rockRepresenting the mass of the rock in relation to unit volume; the bulk modulus is a measure of the degree of deformation of a material to the pressure around the surface, is defined as the pressure required to produce unit volume shrinkage, describes the elasticity of a homogeneous isotropic solid, and represents incompressibility; the shear modulus is that under the action of shear stress, in the limit range of elastic deformation proportion, the elastic material can generate shear strain when bearing the shear stress, and is defined as the ratio of the shear stress to the shear strain; cohesion is the mutual attraction between adjacent parts within the same substance; tensile strength is the ability of a material to resist failure when subjected to a tensile force, and the maximum tensile stress that an object can resist before breaking (breaking) is an indicator of the mechanical properties of the material, and has a unit of newton per square centimeter (N/cm)2) Or pascal (Pa); the internal friction angle is an important concept in rock-soil mechanics, is one of important parameters of rock mass, and is an index of shear strength; the expansion angle is relative to the internal friction angle of a critical state, namely the internal friction angle of a Moire Coulomb model, and is used for expressing the volume expansion amount under plastic shear stress deformation; the porosity is the ratio of the sum of all pore space volumes in the rock sample to the volume of the rock sample; the permeability refers to the capacity of the rock to allow fluid to pass through under a certain pressure difference, and represents the parameter of the liquid conducting capacity of the rock; thermal conductivity is a parameter that measures the rate of heat transfer from an object.
The setting of the geologic body parameters in table 1 can be accomplished by the built-in commands of FLAC3D software according to the measured rock sample parameter values.
Step S32, initial condition and boundary condition setting
The method mainly comprises the initial condition setting of a stress field, a temperature field and a pressure field, wherein the geological background of a research area needs to be carefully researched, the stress state and the temperature field during mineralization are determined, and ideal simulation initial conditions are specifically set.
The simulation of the initial conditions should include the following:
the initial temperature of each geological unit;
initial pressure of each geological unit;
the initial stress magnitude and direction of each boundary of the model;
the setting of the initial conditions can be realized by the built-in command of the FLAC3D software, and as shown in FIG. 1, the initial condition effect is set for a certain research area.
Step S33, solving simulation result and adjusting parameters
The solving of the simulation result can use the default convergence standard of FLAC3D software, namely when the ratio R of the maximum unbalanced force to the typical internal force of the system is less than a fixed value of 10-5When this is the case, the calculation is terminated. Of course, the convergence criterion can be reset according to the needs during the simulation, for example: the simulation process may be controlled by specifying the number of steps of the numerical simulation or specifying the time for which the numerical simulation is calculated.
Whether the simulation parameter setting is reasonable or not is verified through comparative analysis of the simulation result and the actual geological condition of the research area, and the specific judgment method comprises the following steps:
(1) whether the numerically-simulated ore formation trend is similar to the actual exploration condition or not;
(2) numerically simulating whether the spatial position (plane position, mineralization depth, spatial position with other geologic bodies and the like) of the formed ore body is the same as the actual situation of the ore deposit;
(3) whether the variation trend of the temperature and the pressure in the numerical simulation process conforms to the existing ore-forming theory or not.
If the parameter setting is not reasonable, the steps S31 and S32 are repeated to readjust the parameters and the initial conditions until the simulated result meets the actual geological conditions.
Step S34, deriving simulation result
Geological anomaly parameters that may be obtained by the mineralization process simulation include: pp, temp and vsr (i.e. void pressure, temperature and volume strain). As shown in fig. 2, the distribution characteristics of pp and vsr after the convergence criterion is met by numerical simulation for the mineralization process in a certain research area show that the mineralization fluid is converged at the contact part of the rock body and the surrounding rock along with the change of temperature and pressure, which indicates that the contact part of the rock body and the surrounding rock is the beneficial part of the mineralization. The FLAC3D software may export the above three variables as a csv formatted file.
Step S4, deep mineral resource prediction and evaluation
Step S41, importing simulation result
Three variables obtained by simulation are imported into the established block model through the functions of 'block model' — 'import' — 'text file'.
Step S42, extracting abnormal variable information
Determining the mineralization favorable intervals of pp, temp and vsr by counting the relations between the known ore body and the value ranges of pp, temp and vsr variables, wherein the specific standards are as follows: "count the number of known ore blocks in different value intervals respectively, select the value interval containing a larger number of known ore blocks as the favorable value range of pp and vsr for mineralization", as shown in fig. 3, for the statistical relationship between the known ore blocks and the pp value, determine that the favorable pp value for mineralization is 2e9-3e9。
Step S43, Ore-forming favorable target zone delineation
The pp, temp and vsr variable blocks of a specific value range can be quantitatively extracted by using a block model, constraint and newly-built constraint functions of Surpac software. As shown in fig. 4, the blocks are quantitatively extracted according to the value range of the mineralization favorable value extracted in step S42. Combining the favorable pp and vsr values in the mineralization process, the mass with deep favorable mineralization can be extracted substantially, as shown in fig. 5, to realize deep mineral prediction based on the mineralization process simulation.
As can be seen from fig. 5, the block range containing both pp and vsr favorable value ranges is significantly contracted, which indicates that the method proposed herein effectively reduces the predicted target area and improves the reliability of the predicted result.
The present invention has been described in detail with reference to the drawings and examples, but the present invention is not limited to the examples, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention. The prior art can be adopted in the content which is not described in detail in the invention.
Claims (16)
1. A hydrothermal deposit three-dimensional quantitative prediction evaluation method based on numerical simulation is characterized by comprising the following steps:
s1, collecting the existing basic data of exploration data in the research area;
step S2, three-dimensional modeling and grid unit division;
step S3, simulating the numerical value of the ore forming process;
and step S4, predicting and evaluating deep mineral resources.
2. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 1, wherein the basic data in step S1 includes: topographic maps, survey line profiles, mid-section plans, borehole data, geophysical data, geochemical data.
3. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation according to claim 2, wherein the step S2 includes:
step S21: constructing a three-dimensional geological model of a research area;
step S22: and carrying out grid unit division on the three-dimensional geological model.
4. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 3, wherein the step S21 is specifically as follows: and constructing a three-dimensional geological model of the research area by a display modeling or implicit modeling method, wherein the constructed three-dimensional geological model can be converted into a FLAC3D file format applied to FLAC3D software after gridding.
5. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 4, wherein the step S22 is specifically as follows: building block models of a research area, building attributes of each geologic body, and then exporting the block models into a table file in a csv format, a table file in the csv format and a FLAC3D file in a FLAC3D format, wherein all blocks in the respective files are stored in the form of columns of X, Y, Z and attributes, and the serial number of each unit block is a row.
6. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 5, wherein the step S3 includes:
step S31, assigning geologic body parameters;
step S32, initial condition and boundary condition setting;
step S33, solving a simulation result and adjusting parameters;
and step S34, deriving a simulation result.
7. The method as claimed in claim 6, wherein the geologic body parameters in step S31 include: density, bulk modulus, shear modulus, cohesion, tensile strength, internal friction angle, expansion angle, porosity, permeability, thermal conductivity.
8. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 7, wherein the step S32 is specifically as follows: and researching the geological background of a research area, determining the stress state and the temperature field during mineralization and setting simulation initial conditions.
9. The method of claim 8, wherein the simulating initial conditions in step S32 includes: initial temperature of each geological unit, initial pressure of each geological unit, initial stress magnitude and direction of each boundary of the model.
10. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation according to claim 9, wherein the step S33 specifically comprises: and solving a simulation result, comparing and analyzing the simulation result and the actual geological condition of the research area, verifying whether the simulation parameter setting is reasonable, if the parameter setting is unreasonable, repeating the steps S31 and S32, and readjusting the parameters and the initial conditions until the simulation result meets the actual geological condition.
11. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation of claim 10, wherein the method for determining whether the simulation parameter setting is reasonable in step S33 comprises:
(1) whether the numerically-simulated ore formation trend is similar to the actual exploration condition or not;
(2) whether the spatial position of the ore body formed by numerical simulation is the same as the actual condition of the ore deposit or not is judged;
(3) whether the variation trend of the temperature and the pressure in the numerical simulation process conforms to the existing ore-forming theory or not.
12. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 11, wherein the step S34 is specifically as follows: and (4) obtaining geological abnormal parameters through the simulation of an ore forming process, and exporting the geological abnormal parameters in a csv format file.
13. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 12, wherein the step S4 includes:
step S41, importing a simulation result;
step S42, extracting abnormal variable information;
step S43: the ore formation is beneficial to target area delineation.
14. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 13, wherein the step S41 is specifically as follows: and importing the geological abnormal parameters into the established block model.
15. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 14, wherein the step S42 is specifically as follows: and (4) counting the relation between the known ore body and the value range of the geological abnormal parameter, and determining the mineralization favorable interval of the geological abnormal parameter.
16. The method for three-dimensional quantitative prediction and evaluation of hydrothermal deposit based on numerical simulation as claimed in claim 15, wherein the step S43 is specifically as follows: and quantitatively extracting geological abnormal parameter variable blocks in a specific value range, combining favorable geological abnormal parameter values in the process of mineral formation, extracting blocks with deep parts favorable for mineral formation, and delineating favorable target areas of the mineral formation.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114970361A (en) * | 2022-06-08 | 2022-08-30 | 山东省地质矿产勘查开发局第五地质大队（山东省第五地质矿产勘查院） | Three-dimensional fluid field modeling and ore resource amount prediction method and system |
| CN117456118A (en) * | 2023-10-20 | 2024-01-26 | 山东省地质矿产勘查开发局第六地质大队(山东省第六地质矿产勘查院) | Ore finding method based on k-meas method and three-dimensional modeling |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114970361A (en) * | 2022-06-08 | 2022-08-30 | 山东省地质矿产勘查开发局第五地质大队（山东省第五地质矿产勘查院） | Three-dimensional fluid field modeling and ore resource amount prediction method and system |
| CN117456118A (en) * | 2023-10-20 | 2024-01-26 | 山东省地质矿产勘查开发局第六地质大队(山东省第六地质矿产勘查院) | Ore finding method based on k-meas method and three-dimensional modeling |
| CN117456118B (en) * | 2023-10-20 | 2024-05-10 | 山东省地质矿产勘查开发局第六地质大队(山东省第六地质矿产勘查院) | Ore finding method based on k-meas method and three-dimensional modeling |
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