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Large scale development of coal seam gas (CSG) is occurring in many sedimentary basins around the world including Australia, where commercial production of CSG has started in the Surat and Bowen basins. CSG development often involves extraction of large volumes of water that results in depressurising aquifers that overlie and/or underlie the coal seams thus perturbing their flow regimes. This can potentially impact regional aquifer systems that are used for many purposes such as irrigation, and stock and domestic water. In this study, we adopt a probabilistic approach to quantify the depressurisation of the Gunnedah coal seams and how this impacts fluxes to, and from the overlying Great Artesian Basin (GAB) Pilliga Sandstone aquifer. The proposed method is suitable when effects of a new resource development activity on the regional groundwater balance needs to be assessed and account for large scale uncertainties in the groundwater flow system and proposed activity. The results indicated that the extraction of water and gas from the coal seam could potentially induce additional fluxes from the Pilliga Sandstone to the deeper formations due to lowering pressure heads in the coal seams. The median value of the rise in the maximum flux from the Pilliga Sandstone to the deeper formations is estimated to be 85 ML/year, which is considered insignificant as it forms only about 0.29% of the Long Term Annual Average Extraction Limit of 30 GL/year from the groundwater management area. The probabilistic simulation of the water balance components indicates only small changes being induced by CSG development that influence interactions of the Pilliga Sandstone with the overlying and underlying formations and with the surface water courses. The current analyses that quantified the potential maximum impacts of resource developments and how they influences the regional water balance, would greatly underpin future management decisions. [ABSTRACT FROM AUTHOR]

This study focuses on the analyses of influx and efflux of groundwater at the aquifer−sea interface in response to the total groundwater extraction from a regional coastal aquifer. The groundwater planning and management goal is formulated as a multi-objective optimization problem to optimize the total pumping, groundwater influx and efflux through the coastal boundary. A four-stage optimization strategy is implemented for solving this optimization problem, whereby the first three stages are the iterative optimizations using the proposed Multi-Objective Particle Swarm Optimization algorithm and the analysis of Pareto-optimal solutions, and the last stage is the selection of one bargaining solution using the Kalai-Smorodinsky approach considering compromises among multiple objectives. In order to improve the efficiency of the simulation-optimization model, Deep Neural Networks emulators are fitted to approximate individual optimization objective, and a novel dynamic sampling strategy is applied to adaptively improve the accuracy of the emulators. This study demonstrates that accurate and efficient emulators can be achieved for the regional coastal aquifer with zone-based pumping rate multipliers from eight bands (Band 1 to Band 8) of increasing distance from the coastal boundary as the decision variables. The results from the Pareto-front suggest that the abstractions of Band 2 (close to the sea) can be reduced to the lower boundary of the rate multiplier (0.5) whereas the abstraction of farther bands can be significantly enhanced. The water influx through the coastal boundary was decreased by 15.69% under the slight compromise of the total pumping and water efflux by the selected compromising pumping pattern in the regional model. The final Pareto-optimal solution set and the compromised solution provide valuable information for the groundwater manager to plan sustainable groundwater use. The proposed simulation-optimization approach used in this study can be applied for a wide range of groundwater management problems. ga1 • Deep-learning assisted Multi-Objective Particle Swarm Optimization for coastal aquifer management. • Multi-stage adaptive training of DNN-based emulators to achieve improved solutions for management. • Kalai−Smorodinsky approach for selecting an ideal solution from Pareto-front. • Adjusting abstraction to the farther zones from the coast benefits this over-extraction system. [ABSTRACT FROM AUTHOR]