Geological storage of CO₂ in saline aquifers and depleted oil and gas reservoirs can help mitigate CO₂ emissions. However, CO₂ leakage over a long storage period represents a potential concern. Therefore, it is critical to establish a good understanding of the interactions between CO₂–brine and cement–caprock/reservoir rock to ascertain the potential for CO₂ leakage. Accordingly, in this work, we prepared a unique set of composite samples to resemble the cement–reservoir rock interface. A series of experiments simulating deep wellbore environments were performed to investigate changes in chemical, physical, mechanical, and petrophysical properties of the composite samples. Here, we present the characterisation of composite core samples, including porosity, permeability, and mechanical properties, determined before and after long-term exposure to CO₂-rich brine. Some of the composite samples were further analysed by X-ray microcomputed tomography (X-ray µ-CT), X-ray diffraction (XRD), and scanning electron microscopy–energy-dispersive X-ray (SEM–EDX). Moreover, the variation of ions concentration in brine at different timescales was studied by performing inductively coupled plasma (ICP) analysis. Although no significant changes were observed in the porosity, permeability of the treated composite samples increased by an order of magnitude, due mainly to an increase in the permeability of the sandstone component of the composite samples, rather than the cement or the cement/sandstone interface. Mechanical properties, including Young’s modulus and Poisson’s ratio, were also reduced.

A probabilistic risk assessment framework was developed to mathematically represent the complex engineering phenomena of rock bursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity present in the coal seam to respect their geological origin. The changing mining conditions during longwall top coal caving mining (LTCC) were extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among abutment stress, pore pressure, the volume of total gas emission and incremental energy release rate, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rock bursts and gas outbursts. The results obtained from the analysis incorporate the variability in mechanical, elastic and reservoir properties of coal due to lithological heterogeneity and result in the probability of the occurrence of rock bursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used.

Excessive strain energy accumulation in the coal seam is one of the essential conditions for rockbursts to occur. It is imperative to understand the parameters that affect the strain energy accumulation in retreating longwall mining to optimise the design and minimise rockbursts occurrences. In this paper, new analytical models were developed to calculate the strain energy accumulation considering the current state-of-the-art longwall machinery being used in the industry. Seven parameters, i.e., mining depth, length of the cantilever roof, coal seam thickness, thickness of the immediate roof, Young’s modulus of coal and the roof, and Poisson’s ratio of the coal seam, were identified as the parameters affecting the strain energy accumulation. A detailed statistical analysis of the parameters was conducted using the orthogonal testing method, which revealed that mining depth, Young's modulus of coal and the coal seam thickness significantly influence the strain energy accumulation within a 99% confidence interval.