Based on the proposed calculation method for determining the different dip angle fracture permeability coefficients derived from the well flow vibration equation and the self-developed slug test system, research on the slug test in a field in fractured rock mass was carried out based on the underground water sealed cavern project of the National Huangdao Petroleum Reserve. The formation lithology of the test site was granite gneiss, and the fractures were not developed, which is conducive to the research of slug tests on fracture permeability. On the basis of obtaining the geological information of boreholes by using the self-developed slug test system, comparative research on the segmented slug test and conventional water pressure test was carried out. The test results show that the proposed slug test method and self-developed test system has good accuracy and applicability for determining the fracture permeability coefficient, equivalent permeability coefficient and rock mass permeability coefficient tensor, which is more convenient and efficient than other test methods.

In large-scale water diversion projects, especially in the central and western regions of China, long-distance deep buried tunnels are generally involved. Therefore, it is essential to carry out field tests to obtain the permeability of the rock mass through which tunnels pass. However, the test holes of large-scale water diversion projects are basically located in mountain areas with complex hydrogeological conditions. Meanwhile, the test holes are far apart and large in depth. As a result, traditional pumping tests cannot meet the requirements. Therefore, the slug test was chosen as the main test method, and the calculation results of the water injection test, the water pressure test and the slug test are analyzed and compared. The calculation results of the three test methods are basically consistent. However, the water injection test and the water pressure test are difficult to implement at a large scale due to many environmental constraints, complex test equipment, long test periods and other factors. Furthermore, the Kipp model, the CBP model and the proposed HWS model, considering the effect of the finite thickness well-skin layer for the first time, were used to analyze and process the slug test data, respectively. The curve fitting effect of the Kipp model was the best, but the calculations were generally larger. The difference between the CBP model and the proposed HWS model is smaller in the calculation results; however, the curve fitting effect of the CBP model is the worst, and the CBP model needs to be further improved. The curve fitting effect of the proposed HWS model was between that of the Kipp model and the CBP model, and the proposed HWS model can be applied to the parameter calculations of the slug test with well-skin. In general, with reference to the criteria for the damping coefficient of the aquifer in the Kipp model, the Kipp model was applicable to the slug test for test holes without well-skin and an aquifer damping coefficient between 0.1 and 5.0. The CBP model was applicable to the slug test under the conditions of no well-skin and an aquifer damping coefficient greater than 2.0. The novel proposed HWS model was applicable to the slug test when the aquifer damping coefficient was greater than 1.0 under the conditions of no well-skin, positive well-skin and negative well-skin.

In the process of slug test in the field, mud and debris may infiltrate into the aquifer during the formation of the test well, and a poorly permeable annulus area appears around the test well wall. This is called the positive well-skin effect, which can have a significant impact on the results of slug tests due to the characteristics of a slug test. Therefore, to carry out the research on the determination of aquifer permeability coefficients by slug test under the influence of positive well-skin effect, a mathematical model of a slug test considering the positive well-skin effect was constructed. The Laplace transform method and the AWG algorithm were used to solve for a series of standard curves, and specific steps were given for calculating aquifer permeability coefficients by slug test under the influence of positive well-skin effect. Meanwhile, an indoor test model with positive well-skin effect was constructed, then several sets of slug tests and pumping tests with different excitation strengths were carried out. Based on this study, the presence of positive well-skin layer had a great impact on the aquifer permeability coefficient determined from both the pumping test and Kipp model of slug test, resulting in smaller results. The HWS model overcomes the influence of positive well-skin effect. This will further improve the theory of slug test, increase the accuracy of slug test in determining the permeability coefficient, and promote the application scope of slug test. It is therefore possible to discern the well-skin factor and determine the aquifer permeability coefficient by fitting the slug test data to a standard curve.

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m³ of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO₂ emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety.