# Prediction of the Mine Water Inflow of Coal-Bearing Rock Series Based on Well Group Pumping

^{1}

^{2}

^{*}

## Abstract

**:**

^{3}/d from the Taiyuan Formation aquifer in the bauxite ore roof, which was lower than the value predicted by the large well method (72,786.66 m

^{3}/d). The numerical method predicted an average water inflow of 59,000 m

^{3}/d and a maximum water inflow of 82,600 m

^{3}/d from the Majiagou Formation in the bauxite ore floor. A dependence has been established that the numerical method estimates water inflow with accuracy. Additionally, the model predicts future mining water inflow, and also provides a standard framework for estimating inflow in similar mining conditions.

## 1. Introduction

## 2. Overview of the Research Area

_{2}m) comprises thick-bedded and dolomitic limestone. This layer is the bauxite deposit direct water-bearing floor in the Yanlong mining area. The karst forms in the boreholes are diverse, with a cave visibility rate of 15%, indicating uneven karst development. The hydrostatic pressure of the Ordovician limestone is relatively high. It can supply water to the limestone aquifer of the Taiyuan Formation through water-conducting faults and fractures, making it one of the factors affecting the water influx in the bauxite roof. The Carboniferous Benxi Formation (C

_{2}b) is the host layer of the bauxite deposit, with an average thickness of 12.3 m. It has an overall poor development of fractures and low water conductivity, serving as a vital aquiclude within the area. The Permian Taiyuan Formation (P

_{1}t) comprises thick-bedded argillaceous limestone, sandstone, and interbedded coal. The karst fractures are poorly developed and mostly filled with calcite veins. The formation can be divided into three sections, with the lower section of limestone serving as the direct water-bearing roof of the bauxite deposit. The Permian Shanxi Formation (P

_{1}s) consists of fine-grained sandstone, carbonaceous shale, and siltstone interbedded with coal. It has a large thickness, compact structure, and poor water permeability. This layer is the primary coal-bearing stratum, with the bottom coal seam (E

_{2-1}) being the central exploitable coal seam. The goaf is mainly distributed in this layer. The Permian Shihezi Formation (P

_{1-2}s) includes feldspar quartz sandstone, thick-bedded shale, and mudstone. The Quaternary and Neogene (Q + N) is extensively spread with a significant thickness on the mining area surface. It unconformably covers various bedrock formations. It mainly consists of slope deposits, alluvial deposits, and gravel layers, with interbedded clay layers. The thickness gradually increases from the southeast to the northwest. Only in the southern part does it directly come into contact with the Taiyuan Formation limestone and the Ordovician limestone, which has a replenishing relationship with the water-bearing layers of the roof and bottom of the ore deposit but has little impact on the water influx into the bauxite deposit.

## 3. Research Methods

#### 3.1. Establishment of Hydrogeological Conceptual Model

^{2}, as shown in Figure 1d. The stratigraphy is generalized into six layers from youngest to oldest, namely the Quaternary and Neogene aquifer (Q + N), Permian Shihetanzi Formation low-permeability layer (P

_{1-2}s), Permian Shanxi Formation low-permeability layer (P

_{1}s), Permian Taiyuan Formation limestone confined aquifer (P

_{1}t), Carboniferous Benxi Formation aquiclude (C

_{2}b) (including the bauxite layer), and Ordovician Majiagou Formation limestone confined aquifer (O

_{2}m), as shown in Figure 2.

#### 3.2. Establishment of a Mathematical Model of Hydrogeology

#### 3.3. Water Inflow Prediction Methods

^{3}/d); K is the permeability coefficient of the aquifer (m/d); H is the water level reduction value (m); M is the thickness of aquifer (m); R is the influence radius (m); ${r}_{0}$ is the reference radius (m); F is the ore body area (m

^{2}); and ${R}_{0}$ is the reference influence radius of the large well (m).

#### 3.4. Establishing a Numerical Simulation Model of Yanlong Area

^{−4}and 0.29 × 10

^{−3}[23,32], respectively.

_{2-1}of the Shanxi Formation is the mineable coal seam in the entire area, and mining activities have resulted in goaf formation. The distribution range of goaf and old kiln water within the key study area was identified through field surveys, mining data analysis, the mutual verification of various geophysical methods, and geological drilling verification. The goaf and old kiln water have caused corresponding changes in the original structure of the aquifer, leading to changes in hydrogeological parameters. Based on hydrogeological and pumping test data, parameter zoning was conducted for the Shanxi and Taiyuan Formation. The Shanxi Formation was divided into goaf and non-goaf areas (Figure 3a). The Taiyuan Formation was divided into four zones (Figure 3b), from left to right: Zhuge zone, Changcun and Jiaocun zone, Fudian zone, and Songshan zone. Different parameter values were assigned to the model based on the average values of hydrogeological parameters obtained from hydrological boreholes in each zone layer.

^{3}/y), diving evaporation (0.0336 mm/d), and surface drainage points (26,440 m

^{3}/d). The water level value from 10 January 2018 is selected as the initial water level. The MODFLOW module simulates the underground flow system in the mining area. The model identification and validation are based on water level data from hydrological boreholes from January 2018 to May 2019, with data monitored every 5–10 days.

^{2}); $n$ is the number of parameter horizontal levels (set as 7 in this case); ${h}_{i}$ is the average simulated water level of the wells during the identification phase (m); ${h}_{i}^{\prime}$ is the average simulated water level at wells for different parameter levels (m); and $T$ is the number of water level change points in parameter analysis, where the value 7 is taken.

#### 3.4.1. Calibration and Verification of Model Parameters

#### 3.4.2. Hydrogeological Parameters and Flow Map

## 4. Results and Analysis

#### 4.1. Predicting Water Inflow of Deposit XII Using the Analytical Method

^{2}, and the reference radius (r₀) is calculated as 853 m using Formula (7). The influence radius (R) is calculated as 3154 m using Formula (8), resulting in a reference influence radius of 4007 m. The calculated average inflow volume of the Taiyuan Formation aquifer in the No. XII orebody is 72,786.66 m

^{3}/d, as shown in Table 5. The maximum inflow volume of a mine is generally 1.4 times the average inflow volume. Therefore, the maximum inflow volume of the Taiyuan Formation aquifer in No. XII orebody is estimated to be 101,901.32 m

^{3}/d.

#### 4.2. Numerical Method for Predicting Water Inflow of Ⅻ Orebody

_{s}is the inrush coefficient (MPa/m); and M is the adequate aquitard thickness (m). The critical water inrush coefficient at −50 to 200 m mining elevation is usually set to 0.06 MPa/m [33].

#### 4.2.1. Inflow Water Volume of Bauxite Roof

^{3}/d, with an average hourly pumping rate of −2980 m

^{3}/h. The total inflow water volume from the Taiyuan Formation in the No. XII orebody is estimated to be 71,500 m

^{3}/d. Well T12 has the highest pumping rate among the pumping wells at 10,000 m

^{3}/d, while well T3 has the lowest at 1100 m

^{3}/d. The equipotential contour map is shown in Figure 7.

#### 4.2.2. Inflow Water Volume of Bauxite Floor

^{3}/d, while well T10 has the lowest at 200 m

^{3}/d. The total pumping volume is −59,000 m

^{3}/d, and the average hourly pumping rate is −2458 m

^{3}/h. The normal inflow water volume from the Majiagou Formation in the Ordovician system is estimated to be 59,000 m

^{3}/d, with a maximum inflow water volume of 82,600 m

^{3}/d.

#### 4.3. Comparison of the Two Methods

^{3}/d and 71,500 m

^{3}/d, respectively. The predicted value from the large well method is higher than that from the numerical method, consistent with previous research [17,18]. This is because the analytical method (large well method) is a lumped parameter method that generalizes the actual conditions, while the method of numerical simulation is a distributed parameter method that considers more influencing factors and discretizes the study area in time and space, providing a more accurate reflection of the actual conditions in the study area. Therefore, the numerical method is more exact than the large well method. However, in previous research [21], the predicted value from the numerical method is more significant than that from the large well method, which may be attributed to the generalization in the large well method.

## 5. Discussion

^{2}) being the significant metric for assessing the model’s performance. The R

^{2}values for the drilling locations ZK9606, ZK2304, SJ02, ZK13408, ZK7100, and ZK4108 are documented as 0.91, 0.90, 0.89, 0.85, 0.82, and 0.78, respectively. The average R

^{2}value across these locations is calculated to be 0.86. It is worth mentioning that borehole ZK9606 has the maximum degree of precision, as indicated by its R

^{2}value of 0.91, whilst borehole ZK4108 exhibits the lowest level of precision, with an R

^{2}value of 0.78. The researchers utilized the MODFLOW-CFP model to simulate the dynamic flow of spring water. The simulation yielded correlation coefficients of 0.891 and 0.866 between the simulated flow results of Spring Creeks Springs and Wakulla Springs, respectively, and the observed observations [40]. The steady flow simulation of the groundwater level of Kabodarahang aquifer in Hamadan province, Iran, was carried out with MODFLOW, and the correlation coefficient was 0.917 [41]. When examining the simulated correlation coefficients given in this study, it is observed that they exhibit a modest decrease compared to the correlation coefficients obtained in the steady flow simulations. Nevertheless, it is essential to highlight that the simulated correlation coefficients for most boreholes examined in this work exceed those obtained from unsteady flow simulation. Simulating steady flow is easier than turbulent flow, which may explain the correlation coefficient variation. The quantity of hydrological boreholes has limited this simulation’s accuracy. Expanding the hydrological borehole network and pumping experiments may improve the model’s accuracy for Yanlong mining activities. This proactive technique can improve groundwater flow estimates and decision making in mining.

^{3}/d) is greater than that of the numerical method (71,500 m

^{3}/d) on bauxite roof. The average and maximum water inflow of bauxite floor is predicted to be 5900 m

^{3}/d and 82,600 m

^{3}/d, respectively. For security, the more significant value of the water inflow is generally taken, that is, the minimum extraction of the bauxite roof and floor is 72,786.66 m

^{3}/d and 82,600 m

^{3}/d in the Songshan mining area. Where the bauxite is thin, the upper aquifer cannot be excavated to the bottom, and the lower aquifer must be further lowered to prevent water inrush.

## 6. Conclusions

- (1)
- The model is identified and verified by the measured water level, and the average values of ${R}^{2}$, ${E}_{ns}$ and $PBIAS$ are 0.86, 0.81 and 2.71, respectively. The correlation or interdependence of the 3D simulation model is built in order to forecast water inflow in the bauxite layer.
- (2)
- The large well method and numerical method predict the water inflow in the Taiyuan Formation of the No. XII orebody in the Songshan mining area to be 72,786.66 m
^{3}/d and 71,500 m^{3}/d, respectively. The numerical method predicts the average inflow and maximum inflow of the Majiagou Formation in the No. XII orebody to be 59,000 m^{3}/d and 82,600 m^{3}/d, respectively. Thus, it is established that the bauxite in the Songshan mining area will be exploited; the displacement of the roof and floor is greater than 72,786.66 m^{3}/d and 82,600 m^{3}/d. - (3)
- When mining bauxite ore, it is recommended to construct curtain walls in the southern mining area to reduce the infiltration of precipitation into the bauxite ore roof and floor. Alternatively, the grouting and inclined borehole pumping methods can be used, which are beneficial for draining the bauxite ore roof and dewatering the floor below the critical safe water level.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**The Yanlong mining area. (

**a**) Research area; (

**b**) the Yanlong karst water system and subsystem in Henan Province; (

**c**) the Yanlong karst water system and subsystem; (

**d**) a plane view of the simulated area.

**Figure 3.**(

**a**) Parameter partition map of Shanxi formation; (

**b**) parameter partition map of Taiyuan group.

**Figure 4.**(

**a**) Fitting the graph of simulated values and observed values of SJ02; (

**b**) fitting the graph of simulated values and observed values of ZK2304.

**Figure 5.**(

**a**) Iso-water level map of Taiyuan Formation; (

**b**) iso-water level map of Majiagou Formation.

**Figure 7.**Distribution map of virtual pumping holes and iso-water lines in Taiyuan Formation, No. XII orebody roof, Songshan Mining area.

**Figure 8.**Distribution map of virtual pumping holes and iso-water lines in Majiagou Formation, No. XII orebody floor, Songshan Mining area.

**Table 1.**The permeability coefficient of Taiyuan and Majiagou formations in the Yanlong mining area.

Drill Hole | Water Inflow (L/S.M) | Permeability Coefficient (m/d) | Formation | Water Inflow (L/S.M) | Permeability Coefficient (m/d) | Formation |
---|---|---|---|---|---|---|

ZK13408 | 0.307 | 0.4893 | P_{1}t | 0.1643 | 1.4033 | O_{2}m |

ZK14606 | 0.047 | 0.2124 | 0.0331 | 0.1763 | ||

ZK12608 | 0.171 | 0.2838 | 0.0282 | 0.1227 | ||

ZK15812 | 0.101 | 0.1435 | 0.0606 | 0.2493 | ||

ZK15802 | 0.547 | 0.8125 | 0.0161 | 0.0588 | ||

ZK9606 | 0.0393 | 0.0648 | 0.00668 | 0.0174 | ||

ZK2304 | 0.635 | 0.9538 | 0.0295 | 0.1283 | ||

ZK4108 | 0.0392 | 0.0489 | 0.0411 | 0.1682 |

Parameter | Average Water Level Change in Observation Well (m) | M | ||||||
---|---|---|---|---|---|---|---|---|

−20% | −10% | −5% | 0% | 5% | 10% | 20% | ||

$K$ | 1.3586 | 0.7014 | 0.2471 | 0 | −0.1128 | −0.2386 | −0.6714 | 5.7 |

$\mu $ | −0.4114 | −0.2986 | −0.1471 | 0 | 0.1718 | 0.3357 | 0.6796 | 2.4 |

Rainfall | −0.0443 | −0.0186 | −0.0014 | 0 | 0.0157 | 0.0329 | 0.0757 | 0.2 |

$Ss$ | −0.0691 | −0.0457 | −0.0157 | 0 | 0.0114 | 0.0071 | 0.0114 | 0.1 |

Borehole | Evaluation Index | ||
---|---|---|---|

${\mathit{R}}^{2}$ | ${\mathit{E}}_{\mathit{n}\mathit{s}}$ | $\mathit{P}\mathit{B}\mathit{I}\mathit{A}\mathit{S}$ | |

SJ02 | 0.89 | 0.88 | 1.72 |

ZK2304 | 0.90 | 0.88 | −0.62 |

ZK9606 | 0.91 | 0.73 | −1.70 |

ZK13408 | 0.85 | 0.83 | 3.39 |

ZK4108 | 0.78 | 0.72 | −4.92 |

ZK7100 | 0.82 | 0.80 | 3.92 |

Number | Formation | Parameter Partition | Horizontal Permeability Coefficient (m/d) | Vertical Permeability Coefficient (m/d) | Storage Coefficient (1/m) | Gravity- Specific Yield (1/m) |
---|---|---|---|---|---|---|

1 | Q + N | 12 | 1.2 | — | 0.2 | |

2 | P_{1-2}s | 0.0012 | 0.00012 | 0.000011 | — | |

3 | P_{1}s | 1–7 | 1.2 | 0.12 | 0.00001 | — |

8 | 0.4 | 0.04 | 0.000012 | — | ||

4 | P_{1}t | 1 | 0.19 | 0.019 | 0.000015 | — |

2 | 0.53 | 0.053 | 0.000015 | — | ||

3 | 0.28 | 0.028 | 0.000015 | — | ||

4 | 0.4 | 0.04 | 0.000015 | — | ||

5 | C_{2}b | 0.00015 | 0.000015 | 0.000011 | — | |

6 | O_{2}m | 2.6 | 0.26 | 0.000030 | — |

XII Ore Body Parameters | K (m/d) | H (m) | M (m) | F (km^{2}) | R (m) | r_{0} (m) | R_{0} (m) | Q (m^{3}/d) |
---|---|---|---|---|---|---|---|---|

Taiyuan Formation | 0.40 | 498.72 | 99.70 | 2.28 | 3154.00 | 853.00 | 4007.00 | 72,786.66 |

Virtual Pumping Well | X Coordinate | Y Coordinate | Floor Elevation of Taiyuan Formation (m) | Water Inflow (m^{3}/d) | Formation |
---|---|---|---|---|---|

T11 | 397,966 | 3,824,532 | 97 | −1900 | P_{1}t |

T9 | 397,105 | 3,824,550 | 66 | −1800 | |

T6 | 397,153 | 3,825,038 | −68 | −5000 | |

T8 | 397,942 | 3,824,942 | −11 | −5000 | |

T7 | 397,551 | 3,824,960 | −42 | −5000 | |

T10 | 397,515 | 3,824,550 | 66 | −1300 | |

T3 | 396,304 | 3,825,523 | −207 | −1100 | |

T1 | 395,337 | 3,825,162 | −158 | −8500 | |

T2 | 396,115 | 3,825,217 | −144 | −9000 | |

T5 | 396,846 | 3,825,177 | −110 | −6000 | |

T4 | 396,838 | 3,825,429 | −160 | −7000 | |

T12 | 395,661 | 3,825,424 | −189 | −10,000 | |

The total water inflow | −71,500 m^{3}/d | P_{1}t |

Virtual Pumping Well | X Coordinate | Y Coordinate | Benxi Formation Floor Elevation (m) | Minimum Inflow (m^{3}/d) | Critical Safe Water Level (m) | Normal Inflow (m ^{3}/d) | Maximum Inflow (m^{3}/d) |
---|---|---|---|---|---|---|---|

T11 | 397,966 | 3,824,532 | 84 | −500 | 60 | −300 | −420 |

T9 | 397,105 | 3,824,550 | 60 | −500 | 36 | −300 | −420 |

T6 | 397,153 | 3,825,038 | −75 | −4000 | −99 | −3000 | −4200 |

T8 | 397,942 | 3,824,942 | −22 | −4500 | −46 | −4800 | −6720 |

T7 | 397,551 | 3,824,960 | −60 | −4000 | −84 | −3500 | −4900 |

T10 | 397,515 | 3,824,550 | 37 | −500 | 13 | −200 | −280 |

T3 | 396,304 | 3,825,523 | −216 | −12,000 | −240 | −12,500 | −17,500 |

T1 | 395,337 | 3,825,162 | −165 | −8000 | −189 | −9850 | −13,790 |

T2 | 396,115 | 3,825,217 | −151 | −6000 | −175 | −6650 | −9310 |

T5 | 396,846 | 3,825,177 | −117 | −5000 | −141 | −4800 | −6720 |

T4 | 396,838 | 3,825,429 | −167 | −5000 | −191 | −5500 | −7700 |

T12 | 395,661 | 3,825,424 | −196 | −7600 | −220 | −7600 | −10,640 |

The total water inflow | −57,600 m^{3}/d | −59,000 m^{3}/d | −82,600 m^{3}/d |

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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhai, H.; Wang, J.; Lu, Y.; Rao, Z.; He, K.; Hao, S.; Huo, A.; Adnan, A.
Prediction of the Mine Water Inflow of Coal-Bearing Rock Series Based on Well Group Pumping. *Water* **2023**, *15*, 3680.
https://doi.org/10.3390/w15203680

**AMA Style**

Zhai H, Wang J, Lu Y, Rao Z, He K, Hao S, Huo A, Adnan A.
Prediction of the Mine Water Inflow of Coal-Bearing Rock Series Based on Well Group Pumping. *Water*. 2023; 15(20):3680.
https://doi.org/10.3390/w15203680

**Chicago/Turabian Style**

Zhai, Hongtao, Jucui Wang, Yangchun Lu, Zhenxing Rao, Kai He, Shunyi Hao, Aidi Huo, and Ahmed Adnan.
2023. "Prediction of the Mine Water Inflow of Coal-Bearing Rock Series Based on Well Group Pumping" *Water* 15, no. 20: 3680.
https://doi.org/10.3390/w15203680