Dynamic Evolution Mechanism of Hydraulic Conductivity in Heterogeneous Salt Lake Brine Aquifers during Artificial Solution Mining
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摘要: 人工溶采技术将储卤层蒸发盐矿物转化为卤水，对盐湖资源开发具有重要意义.然而补水溶矿引发含水层渗透性演化机制尚未得到充分阐述.研究基于Python开发耦合MODFLOW6和PhreeqcRM的数值模拟工具MF6PQC，系统研究卤水反应运移对储卤层渗透系数及溶采过程的影响.结果表明，含水层初始非均质结构决定了渗透系数的时空演变规律.溶矿初期在高渗区优先发生地球化学反应，光卤石等高活性矿物溶解使孔隙度与渗透系数显著增大，并在对流-弥散的正反馈作用下形成优势渗流通道.均质或高渗连通结构有利于溶浸剂均匀波及，而强连通或含低渗隔挡的弱连通地层则使固体矿物无法被有效接触，从而降低整体溶采效果.研究深化对储卤层渗透性变化的认识，为优化卤水溶采提供理论依据.Abstract: Artificial solution mining technology, which converts evaporite minerals in brine aquifers into brine, is crucial for the sustainable development of salt lake resources. However, the dynamic evolution of aquifer hydraulic conductivity induced by mineral dissolution during water injection remains insufficiently understood, hindering accurate process prediction. In this study, a Python-based modeling tool, MF6PQC, coupling MODFLOW6 and PhreeqcRM, was developed to systematically investigate the effects of reactive transport on the hydraulic conductivity of brine aquifers and the overall solution mining process. Simulation results show that aquifer heterogeneity governs the spatiotemporal evolution of hydraulic conductivity. During the early stage of dissolution mining, hydrogeochemical reactions preferentially occur in high permeability zones. The dissolution of highly reactive minerals such as carnallite significantly enhances porosity and hydraulic conductivity, ultimately forming preferential flow paths driven by positive advection-dispersion feedback. Relatively homogeneous aquifers or those with extensive, well-connected high-permeability zones facilitate uniform lixiviant distribution and achieve higher solid-to-liquid conversion efficiency. In contrast, strongly preferential or poorly connected formations interrupted by low permeability barriers limit mineral contact and dissolution, thereby reducing overall solution mining efficiency. This study deepens the understanding of hydraulic conductivity evolution in brine aquifers during water injection and provides a theoretical basis for optimizing salt lake brine resource exploitation.
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图 1 盐湖补水溶矿过程中各物理场相互作用
Fig. 1. Interactions among various physical fields during artificial solution mining
图 2 基于SNIA的MF6PQC耦合模拟流程
Fig. 2. Flowchart of the MF6PQC coupled modeling based on the SNIA
图 3 模型几何结构与网格剖分
Fig. 3. Geometric configuration and mesh discretization of the numerical model
图 4 初始对数渗透系数随机场分布特征(场景A为均质场)
Fig. 4. Initial distributions of the heterogeneous lnK fields (scenario A is the homogeneous base case)
图 5 不同时刻矿物剩余含量空间分布
Fig. 5. Spatial distribution of residual mineral content at different time steps
图 6 场景B孔隙度和渗透系数时空演化
Fig. 6. Spatio-temporal evolution of porosity and K for scenario B
图 7 不同因素与渗透系数变化Pearson相关性系数(r)
Fig. 7. Pearson correlation coefficients relating changes in various factors to the change in ΔK
图 8 不同因素对渗透系数变化的RF贡献权重(λ)
Fig. 8. Contribution weights λ of various factors to the change in ΔK
图 9 不同非均质场景下渗透系数时空演化特征
Fig. 9. Spatio-temporal evolution patterns of K under different heterogeneity scenarios
图 10 高/低渗区渗透系数均值及变化率
Fig. 10. Mean K and its relative change over time within high- and low-K zones
表 1 蒸发盐矿物及物化参数
Table 1. Evaporite minerals and their physicochemical parameters
矿物名 化学式 分子量
(g/mol)摩尔体积
(cm3/mol)参考含量
(%)密度
(g/cm3)石盐 NaCl 58.43 27.1 45~80 2.17 光卤石 KMgCl3·6H2O 277.85 173.7 0.9~3.4 1.60 钾石盐 KCl 74.55 37.5 2~13 1.99 杂卤石 K2MgCa2(SO4)4·2H2O 602.91 218 2~4 2.78 石膏 CaSO4·2H2O 172.16 73.9 2~11 2.32 
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表 2 水文地球化学反应式及平衡常数
Table 2. Hydrogeochemical reactions and their equilibrium constants
矿物 反应式 平衡常数 石盐 NaCl = Cl- + Na+ 1.57 光卤石 KMgCl3·6H2O = K+ + Mg2+ + 3Cl- + 6H2O 4.35 钾石盐 KCl = K+ + Cl- 0.9 杂卤石 K2MgCa2(SO4)4·2H2O = 2K+ + Mg2+ + 2 Ca2+ + 4 SO42- + 2H2O -13.744 石膏 CaSO4·2H2O = Ca2+ + SO42- + 2H2O -4.58 离子交换 Na++X- = NaX 0.00 K++X- = KX 0.70 Ca2++2X- = CaX2 0.80 Mg2++2X- = MgX2 0.60
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表 3 溶浸剂与原卤溶剂离子浓度(mol/L)
Table 3. Ion concentrations of the injected lixiviant and the native brine
密度(g/L) pH K+ Na+ Ca2+ Mg2+ Cl- SO42- CO32- 初始溶剂 1.277 8 6.82 0.444 1 0.387 0 0.011 0 3.638 0 8.023 5 0.051 9 0.000 09 溶浸剂 1.198 8 7.31 0.000 4 5.328 9 0.000 3 0.000 5 5.189 4 0.070 3 0.000 08
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表 4 单位网格初始矿物组分信息(mol/cell)
Table 4. Initial mineral composition per grid cell
矿物名 石盐 光卤石 杂卤石 钾石盐 石膏 X- 初始含量 77.00 1.78 2.70 0.66 4.39 1.00
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表 5 5种模拟场景的定义与参数
Table 5. Definitions and parameters of the five scenarios
场景 分布转换定义 方差 lx lz 场景A $ f\left(\gamma \right)=10.0 $ 0.00 \ \ 场景B $ f\left(\gamma \right)=\gamma $ 2.12 15 5 场景C $ f\left(\gamma \right)=\sqrt[]{2}\mathrm{e}\mathrm{r}{\mathrm{f}}^{-1}\left(2\mathrm{e}\mathrm{r}\mathrm{f}\left(\frac{\left|\gamma \right|}{\sqrt[]{2}}\right)-1\right)\times (-1) $ 1.23 15 5 场景D $ f\left(\gamma \right)=\sqrt[]{2}\mathrm{e}\mathrm{r}{\mathrm{f}}^{-1}\left(2\mathrm{e}\mathrm{r}\mathrm{f}\left(\frac{\left|\gamma \right|}{\sqrt[]{2}}\right)-1\right) $ 2.12 15 5 场景E $ f\left(\gamma \right)=\frac{2}{\mathrm{\pi }}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{s}\mathrm{i}\mathrm{n}\left(\sqrt[]{\gamma }\right)=\frac{\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{s}\mathrm{i}\mathrm{n}\left(2\gamma -1\right)}{\mathrm{\pi }}+\frac{1}{2} $ 10 15 5 注：γ为场景B生成的高斯分布；erf(·)为高斯误差函数.
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表 6 非均质场景下光卤石固液转换效率和波及效率(%)
Table 6. Carnallite solid-liquid conversion efficiency and affected volume fraction under different heterogeneity scenarios
光卤石 场景A 场景B 场景C 场景D 场景E 5年固液转换效率R 44.08 35.27 30.89 30.99 41.91 5年波及效率S (阈值=5%) 47.65 38.73 33.12 33.49 44.41 5年波及效率S (阈值=10%) 46.80 37.96 32.62 32.90 43.82 5年波及效率S (阈值=15%) 46.22 37.5 32.23 32.57 43.42
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