CN113109228B - Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling - Google Patents
Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling Download PDFInfo
- Publication number
- CN113109228B CN113109228B CN202010976801.7A CN202010976801A CN113109228B CN 113109228 B CN113109228 B CN 113109228B CN 202010976801 A CN202010976801 A CN 202010976801A CN 113109228 B CN113109228 B CN 113109228B
- Authority
- CN
- China
- Prior art keywords
- permeability coefficient
- coefficient
- geological drilling
- value
- coal mine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000035699 permeability Effects 0.000 title claims abstract description 82
- 239000003245 coal Substances 0.000 title claims abstract description 31
- 238000005553 drilling Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000012360 testing method Methods 0.000 claims abstract description 33
- 238000005086 pumping Methods 0.000 claims abstract description 12
- 238000012417 linear regression Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000010219 correlation analysis Methods 0.000 claims abstract description 7
- 238000012163 sequencing technique Methods 0.000 claims abstract description 6
- 239000004576 sand Substances 0.000 claims description 25
- 239000002245 particle Substances 0.000 claims description 11
- 230000003204 osmotic effect Effects 0.000 claims description 6
- 238000010220 Pearson correlation analysis Methods 0.000 claims description 5
- 239000006185 dispersion Substances 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 12
- 230000000694 effects Effects 0.000 abstract description 5
- 238000011160 research Methods 0.000 description 10
- 239000002689 soil Substances 0.000 description 10
- 230000008859 change Effects 0.000 description 8
- 239000004927 clay Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000007596 consolidation process Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 239000011148 porous material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012353 t test Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
- G01N15/082—Investigating permeability by forcing a fluid through a sample
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigation Of Foundation Soil And Reinforcement Of Foundation Soil By Compacting Or Drainage (AREA)
Abstract
The invention provides a loose confined aquifer permeability coefficient determination method based on geological drilling, which comprises the following steps: collecting geological drilling information and data; determining an influence factor of the permeability coefficient; carrying out correlation analysis and partial correlation analysis on each influence factor and the permeability coefficient obtained by the existing on-site pumping test, and selecting key influence factors; randomly combining the key influence factors, performing multiple linear regression analysis, performing fitting goodness test and equation significance test, and sequencing; determining a target regression equation according to the sequencing result; substituting the existing geological drilling information and data of the coal mine into a target regression equation, and determining the permeability coefficient of the loose confined aquifer. The invention applies the mathematical means to engineering practice, can fully utilize the existing coal mine geological drilling information and data, and provides technical support for researching anisotropy and regional effect of the loose layer permeability coefficient and preventing and controlling hydrogeological disasters such as mine water burst.
Description
Technical Field
The invention relates to the field of coal mine water control, in particular to a method for determining permeability coefficient of a loose confined aquifer based on coal mine geological drilling.
Background
The coal-based stratum of the North China hidden coal field is generally covered by a fourth-based thick loose layer, wherein a plurality of confined aquifers are developed. Mining of coal beds under near-loose confined aquifers often involves hydrogeological disasters such as mine water burst, surface subsidence, wellbore deformation damage and the like, and even generates huge economic losses and casualties. The permeability coefficient is an important parameter for reflecting the permeability of soil mass media of the loose confined aquifer, and in order to avoid the occurrence of the hydrogeological disaster, the accurate measurement of the permeability coefficient of the loose confined aquifer is necessary, thereby being beneficial to the scientific and reasonable expansion of the hydrogeological disaster prevention and control work.
At present, aiming at the research of the change rule of the permeability coefficient, a calculation method for determining the permeability coefficient of a common soil body is provided, and mainly an empirical relationship between the permeability coefficient and physical parameters such as the pore ratio, the liquid limit and the like is established. As indicated by the foreign scholars Taylor in monograph "Fundamentals of soil mechanics" in 1948, the logarithmic value of the permeability coefficient is linear with the change in the pore ratio; the domestic scholars have been led to the papers published by the et al that the law of change of the permeability coefficient of remolded clay in the compression process is analyzed by linear regression, the porosity ratio and the liquid limit are selected as main influencing factors, and a determination method suitable for remolded clay permeability coefficient is provided; liu Weizheng et al, in the paper "natural sedimentary saturated clay permeability coefficient test research and determination model", adopt a consolidation and permeation combined test to determine the permeability coefficient change rule of a Taihu lake marsh phase powdery clay undisturbed sample and a remolded sample with different early consolidation pressures, and establish a linear permeability coefficient determination method.
The inventor finds that the permeability coefficient determination method for the common soil body in the prior art is provided based on an indoor test, and the research object is a shallow surface soil body. The North China hidden type coal field loose confined aquifer has complex composition and large burial depth, the soil body in the loose confined aquifer is in a high confining pressure environment, direct sampling is difficult, the disturbance of the taken soil sample is too large, and the actual geology and environment of the soil sample are difficult to reproduce in an indoor test. Therefore, a series of physical parameters, such as the porosity, the water content and the like, which are measured through the indoor test are difficult to reflect the real characteristics of the soil body. If the conclusion obtained by the indoor test is forcedly applied to the calculation of the soil permeability coefficient of the loose confined aquifer, the technical problem of larger deviation between the calculation result and the actual value exists.
The inventor also finds that the permeability coefficient of the loose confined aquifer of the North China hidden type coal field is often determined through an on-site water pumping test. Because of the limitation of manpower, material resources and financial resources, the drilling holes for pumping tests are limited, and the permeability coefficient obtained by the tests cannot reflect the change rule and the regional effect of the actual permeability coefficient of the loose confined aquifer site in the range of the research area. And the existing researches show that the permeability coefficient is an essential parameter for analyzing the anisotropy of the loose aquifer, if the permeability coefficient of the whole aquifer in a research area is treated as a constant value, a large error is necessarily caused, so that the permeability coefficient determination method suitable for the loose confined aquifer has important engineering application value based on the existing coal mine geological drilling data.
Disclosure of Invention
The invention mainly solves the technical problem of how to provide a permeability coefficient determination method suitable for a loose confined aquifer of a North China type coal field.
The invention solves the technical problems by the following technical means:
the invention provides a loose confined aquifer permeability coefficient determination method based on coal mine geological drilling, which comprises the following steps:
step A: geological borehole information and data are collected.
And (B) step (B): the influence factor of the permeability coefficient is determined.
Step C: and carrying out correlation analysis and partial correlation analysis on each influence factor and the permeability coefficient obtained by the existing on-site water pumping test, and selecting key influence factors according to the calculated correlation coefficient and significance.
Step D: and randomly combining the key influence factors, performing multiple linear regression analysis, performing fitting goodness test and equation significance test, and sequencing the fitting degree of each equation.
Step E: and determining a target regression equation according to the fitting degree sequencing result.
Step F: substituting the existing geological drilling information and data of the coal mine into a target regression equation, and determining the permeability coefficient of the loose confined aquifer.
Optionally, the influence factors of the permeability coefficient include: the confined aquifer thickness, the mud layer sand layer ratio, the thickest sand layer ratio, the effective stress and the effective grain size.
Optionally, the step C includes:
c1: the thickness of the confined aquifer, the sand layer ratio of the mud layer and the thickest sand layer ratio, the effective stress and the effective grain size are subjected to logarithmic treatment.
C2: and (3) performing pearson correlation analysis on the osmotic coefficient and each influence factor by using SPSS (Statistical Product and Service Solutions) software, and removing the influence factors with the osmotic coefficient correlation lower than a first preset threshold and the significance value larger than a second preset threshold from a regression equation.
And C3: calculating the partial correlation coefficient of the permeability coefficient and each influence factor by using SPSS software, and removing the influence factors with the net correlation with the permeability coefficient lower than a third preset threshold and the significance value larger than a fourth preset threshold from a regression equation;
and C4: the remaining influencing factors are taken as key influencing factors.
Optionally, the step D includes:
d1: and randomly combining the key influence factors, and performing multiple linear regression analysis on the combined key influence factors to obtain a plurality of regression equations.
D2: for each regression equation, SPSS software is used to calculate the R-square value and the adjusted R-square value for that regression equation.
D3: and F checking each equation by using SPSS software, and sorting the regression equations with F check values larger than a fifth preset threshold according to the adjusted R square values.
Optionally, the step D2 includes:
by means of the formula (i),calculating an R square value, wherein ESS is regression square sum; RSS is the sum of squares of the residuals; TSS is the sum of the overall squares.
Using the formula:calculating an adjusted R-party, wherein n-k-1 is the degree of freedom of the sum of squares of the residual errors; n-1 is the degree of freedom of the sum of squares of the total dispersion.
Optionally, the D3 includes:
using the formula:and calculating an F test value, wherein k is the number of parameters.
Optionally, the step F includes:
using the formula, y=a 4 +b 4 ·x 1 +c 4 ·x 2 +d 4 ·x 3 Calculating the permeability coefficient of the loose confined aquifer based on geological drilling information and data, wherein y is the permeability coefficient logarithmic value; a, a 4 Is a constant; b 4 Is the effective particle size coefficient; x is x 1 Is the effective particle diameter logarithmic value; c 4 Is the effective stress coefficient; x is x 2 Is the effective stress logarithmic value; d, d 4 Is the sand layer ratio coefficient of the mud layer; x is x 3 Is the mud sand layer comparison value.
The invention has the advantages that:
the invention provides a method for determining the permeability coefficient of a loose confined aquifer of a coal mine based on key influence factors by collecting and analyzing the existing geological drilling data of the coal mine. The parameters used in the method are obtained based on the analysis of the geological drilling data of the existing coal mine site water pumping test, and compared with the method for determining the permeability coefficient of the loose confined aquifer by directly utilizing the indoor test, the data are closer to reality, so that the determined permeability coefficient is more accurate.
In addition, compared with the method for determining the permeability coefficient by the on-site water pumping test in the prior art, the method is more rapid and convenient, and the permeability coefficient of more points can be obtained, so that the change rule of the on-site actual permeability coefficient and the regional effect thereof can be reflected more.
Drawings
FIG. 1 is a flow chart of a loose confined aquifer permeability coefficient determination method based on coal mine geological drilling, which is provided by the embodiment of the invention;
FIG. 2 is a graph showing the comparison between the calculated and measured permeability coefficients of a loose confined aquifer according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic flow chart of a method for determining permeability coefficient of a loose confined aquifer region according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s101: and collecting geological drilling data of a certain research area.
S102: determining the influence factors of the permeability coefficient of the loose confined aquifer:
according to data analysis in advance, the influence factors influencing the permeability coefficient of the loose confined aquifer are summarized into 5: the confined aquifer thickness, the mud layer sand layer ratio, the thickest sand layer ratio, the effective stress and the effective grain size.
S103: and carrying out pearson correlation analysis and partial correlation analysis on each influence factor and the permeability coefficient obtained by the existing on-site pumping test, and selecting key influence factors according to the calculated correlation coefficient and significance.
C1: the thickness of the confined aquifer, the sand layer ratio of the mud layer and the thickest sand layer ratio, the effective stress and the effective grain size are subjected to logarithmic treatment. Table 1 shows the results of the logarithmic treatment of the drilling impact factors of the water pumping tests on each site of a certain coal mine, as shown in Table 1.
TABLE 1
C2: the SPSS software is used for carrying out pearson correlation analysis on the permeability coefficient and each influence factor respectively, and the influence factors with the correlation coefficient smaller than the first preset threshold value of 0.3 are considered to have no correlation with the permeability coefficient, so that a regression equation can be eliminated; and the influence factors with the significance level larger than the second preset threshold value of 0.05 are considered to be not significant in relation with the osmotic coefficient, and the regression equation can be eliminated. Table 2 shows the results of pearson correlation analysis of the permeation coefficients after the logarithmization of each influencing factor in the embodiment of the present invention, as shown in Table 2:
TABLE 2
The results show that in the embodiment of the invention, the correlation coefficient between the thickness logarithmic value and the permeability coefficient logarithmic value of the confined aquifer is smaller than 0.3, the significance level is larger than 0.05, the linear correlation is not significant, and the regression equation can be eliminated.
And C3: calculating the partial correlation coefficient of the permeability coefficient and each influence factor by using SPSS software, wherein the partial correlation coefficient is smaller than a third preset threshold value of 0.3, which indicates that the net correlation between the factor and the permeability coefficient is extremely low, and a regression equation can be eliminated; and (3) regarding the influence factors with significance level larger than the fourth preset threshold value of 0.05 as not significant in net correlation with the osmotic coefficient, and eliminating the regression equation. Table 3 shows the calculated results of the partial correlation coefficients of the logarithmic change of each influencing factor and permeability coefficient according to the embodiment of the present invention, as shown in Table 3:
TABLE 3 Table 3
The net correlation degree of the effective grain diameter logarithmic value and the permeability coefficient logarithmic value is highest, the net correlation degree of the confined aquifer thickness logarithmic value, the thickest sand layer accounting logarithmic value and the permeability coefficient logarithmic value is far lower than other influencing factors, the net correlation coefficient is smaller than 0.3, the correlation probability values obtained by the regression coefficient t test, namely the significance levels are 0.151 and 0.246 respectively and are all larger than 0.05, and the significance level of the linear relation is poor and the regression equation is eliminated.
In summary, key influencing factors are effective particle size, mud sand layer ratio and effective stress.
It should be emphasized that the pumping test in this step involves only a partial area of the investigation region.
S104: and randomly combining the key influence factors, performing multiple linear regression analysis, performing fitting goodness test and equation significance test, and sequencing the fitting degree of each equation.
D1: and randomly combining the key influence factors, and performing multiple linear regression analysis on the combined key influence factors to obtain a plurality of regression equations. It is understood that the random combination includes: two-by-two combinations and combinations of all three influencing factors.
D2: for each regression equation, SPSS software is used to calculate the R-square value and the adjusted R-square value for that regression equation. The closer the adjusted R-square value is to 1, the better the fit of the regression equation.
D3: f-checking each equation using SPSS software, a critical value F for significance level α=0.05 0.05 (k, n-k-1) is set to a fifth preset threshold value whichK is the number of parameters, and n-k-1 is the degree of freedom of the sum of squares of the residuals. The linear relation between independent variables and dependent variables of the regression equation with the F test value larger than the fifth preset threshold is remarkable, and the regression equation passing through the F test is ranked according to the R square value.
Table 4 shows regression equations obtained in the examples of the present invention and the fitting goodness test and regression equation saliency test results of each regression equation, as shown in table 4:
TABLE 4 Table 4
Wherein y is the osmotic coefficient logarithmic value; a, a i (i is 1,2,3, 4) is a constant; b i Is the effective particle size coefficient; x is x 1 Is the effective particle diameter logarithmic value; c i Is the effective stress coefficient; x is x 2 Is the effective stress logarithmic value; d, d i Is the sand layer ratio coefficient of the mud layer; x is x 3 Is the mud sand layer comparison value.
S105: and determining a target regression equation according to the fitting degree sequencing result.
Specifically, the calculated value and the measured value of the regression equation with the highest fitting degree can be compared, the error is controllable, and the equation is the target regression equation. In the embodiment of the invention, the regression equation 4 comprising the effective particle diameter, the mud sand layer ratio and the effective stress of all key factors is adopted, and the improved R square value is 0.871. In general, the closer the improved R-square value is to 1, the better the fit. Fig. 2 is a graph of the calculated value and the measured value of the permeability coefficient of the loose confined aquifer of the regression equation 4, wherein the upper and lower limits of the calculated value of the permeability coefficient are 3 times and 1/3 times that of the test value respectively, and are far smaller than an order of magnitude, and the error range is completely acceptable in the formula of the calculated value of the permeability coefficient, namely the error is controllable, which means that the error is within the set numerical range. The regression equation 4 has an effective particle diameter coefficient of 1.138, an effective stress coefficient of-0.361, a mud sand layer ratio coefficient of-0.461, and a constant value of-0.413, namely the target regression equation expression is:
y＝-0.413+1.138x 1 -0.361x 2 -0.461x 3
s106: substituting the existing geological drilling information and data of the coal mine research area into a target regression equation to determine the permeability coefficient of the unconsolidated confined aquifer of the research area.
In addition, the permeability coefficient of most of the loose confined aquifers in the coal mines in China is usually determined through an on-site water pumping test at present, and due to the limitation of manpower, material resources and financial resources, the drilling hole for the on-site water pumping test is limited, and the change rule and the regional effect of the on-site actual permeability coefficient of the loose confined aquifers in the range of a research area cannot be reflected. The embodiment of the invention can accurately determine the permeability coefficient of the loose confined aquifer based on the existing geological exploration drilling data of the coal mine, is simple, convenient and quick, saves manpower, material resources and financial resources, has obvious economic benefit, and provides a new method and thought for determining the permeability coefficient of the loose confined aquifer of the coal mine. The method can also be used for analyzing the anisotropy and regional effect of the permeability coefficient of the loose confined aquifer, and provides technical support for preventing and controlling hydrogeological disasters such as mine water burst.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The method for determining the permeability coefficient of the loose confined aquifer based on coal mine geological drilling is characterized by comprising the following steps of:
step A: collecting geological drilling information and data;
and (B) step (B): determining an influence factor of the permeability coefficient, wherein the influence factor of the permeability coefficient comprises: the thickness of the confined aquifer, the sand layer ratio of the mud layer, the proportion of the thickest sand layer, the effective stress and the effective grain diameter;
step C: carrying out correlation analysis and partial correlation analysis on each influence factor and the permeability coefficient obtained by the existing on-site pumping test, and selecting key influence factors according to the calculated correlation coefficient and significance;
step D: randomly combining the key influence factors, performing multiple linear regression analysis, performing fitting goodness test and equation significance test, and sequencing the fitting goodness of each equation, wherein the multiple linear regression equation used by the multiple linear regression analysis comprises: y=a 1 +b 1 ·x 2 +c 1 ·x 3 、y＝a 3 +b 3 ·x 1 +d 3 ·x 2 Or y=a 2 +c 2 ·x 1 +d 2 ·x 3 Or y=a 4 +b 4 ·x 1 +c 4 ·x 2 +d 4 ·x 3 Y is the osmotic coefficient logarithmic value; a, a 1 、a 2 、a 3 、a 4 Is a constant; b 1 、b 3 、b 4 For each effective particle size coefficient; x is x 1 Is the effective particle diameter logarithmic value; c 1 、c 2 、c 4 For each effective stress coefficient; x is x 2 Is the effective stress logarithmic value; d, d 2 、d 3 、d 4 The sand layer ratio coefficient of each mud layer; x is x 3 The sand layer ratio value is the mud layer sand layer ratio value;
step E: determining a target regression equation according to the fitting degree sequencing result;
step F: substituting the existing geological drilling information and data of the coal mine into a target regression equation, and determining the permeability coefficient of the loose confined aquifer.
2. The method for determining permeability coefficient of a loose confined aquifer based on geological drilling of coal mine as set forth in claim 1, wherein said step C comprises:
c1: the thickness of the confined aquifer, the sand layer ratio of the mud layer, the ratio of the thickest sand layer, the effective stress and the effective grain size are subjected to logarithmic treatment;
c2: the SPSS software is used for carrying out pearson correlation analysis on the permeability coefficient and each influence factor respectively, and the influence factors with the permeability coefficient correlation lower than a first preset threshold and the significance value larger than a second preset threshold are removed out of a regression equation;
and C3: calculating the partial correlation coefficient of the permeability coefficient and each influence factor by using SPSS software, and removing the influence factors with the net correlation with the permeability coefficient lower than a third preset threshold and the significance value larger than a fourth preset threshold from a regression equation;
and C4: the remaining influencing factors are taken as key influencing factors.
3. The method for determining permeability coefficient of a loose confined aquifer based on geological drilling of coal mine as set forth in claim 1, wherein said step D comprises:
d1: randomly combining the key influence factors, and performing multiple linear regression analysis on the combined key influence factors to obtain a plurality of regression equations;
d2: for each regression equation, calculating an R-square value and an adjusted R-square value of the regression equation by using SPSS software;
d3: and F checking each equation by using SPSS software, and sorting the regression equations with F check values larger than a fifth preset threshold according to the adjusted R square values.
4. A loose confined aquifer permeability coefficient determination method based on coal mine geological drilling as claimed in claim 3, wherein said step D2 comprises:
by means of the formula (i),calculating an R-square value, wherein ESS is the regression square sum; RSS is the sum of squares of the residuals; TSS is the sum of the overall squares;
using the formula:calculating an adjusted R-party, wherein n-k-1 is the degree of freedom of the sum of squares of residuals; n-1 is the degree of freedom of the sum of squares of the total dispersion; n is the sample size。
5. A loose confined aquifer permeability coefficient determination method based on coal mine geological drilling as claimed in claim 3, wherein said D3 comprises:
using the formula:calculating an F test value, wherein k is the number of parameters; n is the sample size; n-k-1 is the degree of freedom of the sum of squares of the residuals; k is the number of interpretation variables in the model that include constant terms.
6. The method for determining permeability coefficient of a loose confined aquifer based on geological drilling of coal mine as set forth in claim 1, wherein said step F comprises:
using the formula, y=a 4 +b 4 ·x 1 +c 4 ·x 2 +d 4 ·x 3 Calculating permeability coefficient of loose confined aquifers based on coal mine geological drilling information and data, wherein y is permeability coefficient logarithmic value, a 4 Is constant, b 4 As effective particle size coefficient, x 1 Is the effective particle diameter logarithmic value, c 4 To be effective stress coefficient, x 2 As effective stress logarithmic value d 4 Is the sand layer ratio coefficient of the mud layer, x 3 Is the mud sand layer comparison value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010976801.7A CN113109228B (en) | 2020-09-17 | 2020-09-17 | Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010976801.7A CN113109228B (en) | 2020-09-17 | 2020-09-17 | Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113109228A CN113109228A (en) | 2021-07-13 |
| CN113109228B true CN113109228B (en) | 2024-03-29 |
Family
ID=76708934
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010976801.7A Active CN113109228B (en) | 2020-09-17 | 2020-09-17 | Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113109228B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115508263B (en) * | 2022-08-17 | 2023-07-21 | 中国地质大学（北京） | Method, device, equipment and medium for measuring permeability coefficient of aquifer in polluted site |
| CN115311822B (en) * | 2022-10-11 | 2023-01-24 | 西南交通大学 | Underground construction Cheng Ouge geological disaster early warning method and related equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1104327A (en) * | 1993-09-07 | 1995-06-28 | 铁道部第三勘测设计院第二分院 | Quick detecting method for weak water-contained layer permeable coefficient |
| JP2006161340A (en) * | 2004-12-03 | 2006-06-22 | Tone Geo Tech Co Ltd | Method and storage medium for analyzing pumping test |
| KR20120004113A (en) * | 2010-07-06 | 2012-01-12 | 한국지질자원연구원 | A development of prediction model for groundwater productivity potential based on probability and statistic |
| CN106485018A (en) * | 2016-10-21 | 2017-03-08 | 安徽理工大学 | One kind asks for infiltration coefficient method for coal mine roof plate outflow test |
-
2020
- 2020-09-17 CN CN202010976801.7A patent/CN113109228B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1104327A (en) * | 1993-09-07 | 1995-06-28 | 铁道部第三勘测设计院第二分院 | Quick detecting method for weak water-contained layer permeable coefficient |
| JP2006161340A (en) * | 2004-12-03 | 2006-06-22 | Tone Geo Tech Co Ltd | Method and storage medium for analyzing pumping test |
| KR20120004113A (en) * | 2010-07-06 | 2012-01-12 | 한국지질자원연구원 | A development of prediction model for groundwater productivity potential based on probability and statistic |
| CN106485018A (en) * | 2016-10-21 | 2017-03-08 | 安徽理工大学 | One kind asks for infiltration coefficient method for coal mine roof plate outflow test |
Non-Patent Citations (3)
| Title |
|---|
| Prediction of Water Inrush Areas Under an Unconsolidated, Confied Aquifer: The Application of Multi-information Superposition Based on GIS and AHP in the Qidong Coal Mine, China;Luwang Chen等;《Mine Water and the Environment》;第786-795页 * |
| 基于室内抽水试验的多种潜水含水层渗透系数 测定方法比较;董小松等;《甘肃科技纵横》;第15-18页 * |
| 天然沉积饱和黏土渗透系数试验研究与预测模型;刘维正等;《岩土力学》;第2501-2508页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113109228A (en) | 2021-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Experimental study of permeability change of organic-rich gas shales under high effective stress | |
| Bray et al. | Assessment of the liquefaction susceptibility of fine-grained soils | |
| Robertson | The James K. Mitchell Lecture: Interpretation of in-situ tests–some insights | |
| Niu et al. | Anisotropic adsorption swelling and permeability characteristics with injecting CO2 in coal | |
| Quinn et al. | Using constant head step tests to determine hydraulic apertures in fractured rock | |
| CN108009705A (en) | A kind of shale reservoir compressibility evaluation method based on support vector machines technology | |
| Quinn et al. | Validation of non-Darcian flow effects in slug tests conducted in fractured rock boreholes | |
| CN113109228B (en) | Loose confined aquifer permeability coefficient determination method based on coal mine geological drilling | |
| Croisé et al. | Hydrogeological investigations in a low permeability claystone formation: the Mont Terri Rock Laboratory | |
| Ran et al. | The permeability of fault zones: a case study of the Dead Sea rift (Middle East) | |
| Nooraiepour et al. | Effect of brine-CO2 fracture flow on velocity and electrical resistivity of naturally fractured tight sandstones | |
| Liu et al. | Numerical simulation to determine the fracture aperture in a typical basin of China | |
| Rahman et al. | Probabilistic analysis of Vette fault stability in potential CO2 storage site Smeaheia, offshore Norway | |
| Zhou et al. | Sensitivity analysis in permeability estimation using logging and injection-falloff test data for an anthracite coalbed methane reservoir in Southeast Qinshui Basin, China | |
| Kurikami et al. | Scale effect and heterogeneity of hydraulic conductivity of sedimentary rocks at Horonobe URL site | |
| Pijnenburg et al. | Intergranular clay films control inelastic deformation in the Groningen gas reservoir: Evidence from split‐cylinder deformation tests | |
| Mandal et al. | Triaxial deformation of the goldwyer gas shale at in situ stress conditions—Part I: Anisotropy of elastic and mechanical properties | |
| He et al. | Novel approach to predicting the spatial distribution of the hydraulic conductivity of a rock mass using convolutional neural networks | |
| Sun et al. | Impact of effective stress on permeability for carbonate fractured-vuggy rocks | |
| Song et al. | Hydrocarbon retention and leakage in traps bounded by active faults: A case study from traps along the NDG fault in the Qinan area, Bohai Bay Basin, China | |
| Dobbs et al. | Yield envelope assessment as a preliminary screening tool to determine carbon capture and storage viability in depleted southern north-sea hydrocarbon reservoirs | |
| Akbarzadeh Kasani et al. | Geotechnical properties of Rasht clay and silt in Northern Iran | |
| Liu et al. | Non-uniform Distributions of Gas Pressure and Coal Permeability in Coalbed Methane Reservoirs Induced by the Loess Plateau Geomorphology: A Case Study in Ordos Basin, China | |
| Zhang et al. | Experimental study on deterioration of bedrock strength and P-wave velocity by pressurized seawater | |
| Zeng et al. | Permeability prediction method for dipping coal seams at varying depths and production stages in northeastern Ordos basin |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |