Queensland’s mine rehabilitation and closure reforms ensure that almost all active and closed open cut mines require a Progressive Rehabilitation and Closure Plan (PRCP). Until recently, void modelling has been quite unsophisticated, with annual average stresses, such as rainfall, used for modelling. Traditionally a number of discrete methods are used to model surface water, groundwater and contaminant transport. These systems do not communicate or align closely. This compromises the reliability of recovery predictions.
Traditionally, these predictions were calculated in isolation, with no sophisticated feedback loops between climate, surface water interactions, the final landform, and surrounding groundwater systems. Data needed to be simplified to integrate system predictions, which limited our ability to predict how stresses interacted. Furthermore, the ability to view the true cumulative effects was inadequate.
Neil Manewell, Technical Modelling Lead at AGE Consultants, describes this method as ‘problematic’, especially when there are significant volumes of dry coal seams and spoils, with varied permeability, that surface water models often misrepresent. This can have the effect of overstating future water levels in the pit lake and surrounding strata and can speed up the expected recovery of the groundwater system and pit lake.
A 3D groundwater model is the best way to assess the complex nature of groundwater flows patterns and contaminant transport. At AGE, when simulating post-mining recovery, we have devised an alternative method to simulate pit lake recovery. Using the 3D groundwater model, so that groundwater can be accurately simulated, a ‘reservoir node’ is built into the pit void. The reservoir node is where information is integrated from the surface water model. The inflows and outflows prescribed to the reservoir node are calculated from other analytical models, such as AWBM or SWAT+. We can also make use of particle tracking (mp3du), basic contaminant transport (BCT), and reactive-active transport simulators (BCT + PHT-USG).
By integrating surface water, groundwater and possibly geochemical data onto one platform, the data and outputs can be managed consistently over time. Detailed daily representations of climate (rainfall, surface water, and evaporation) and the impacts of short, sharp incidents can be simulated as a system. Landform disturbances or a major climatic event, are automatically updated throughout the model. High-level chemical reaction modelling can be embedded to manage the risks of salinity and other contaminant interactions with the aquifer. These models could be used to calculate the probability of various risks to groundwater dependent assets, prompting proactive redesigns of mine plans to avoid future impacts and ensure compliance.
When the data is integrated, the risk of errors is reduced, saving time in meeting PRCP reporting requirements. AGE has used this modelling in feasibility studies of various landform configurations, and future applications could include comparative analysis of various closure and rehabilitation options.
We have applied this new modelling method to models of the Glendell, Mt Owen, Hunter Valley and Liddell operations. We successfully implemented a reservoir node into a MODFLOW USG model, and particle tracking for several different scenarios was simulated to confirm contaminants would not migrate to local groundwater dependent assets.
Future modelling will work on increasingly complex systems and simulate the interactions between systems with live monitoring data. We will continue to work at the forefront of this technology to improve the information and analysis required for optimised decisions and planning.
Ultimately, this new method of integrated water level predictions in mine voids, surrounding groundwater systems and connected streams helps our industry improve rehabilitation outcomes, leading to better financial assurance after the closure of a mine. The reservoir node approach maintains consistency with the surface water modelling, enabling reliable predictions of groundwater drawdown, thus improving governance and planning decisions.