Investigating the Preconditioning to Manage Cave Back Propagation

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dc.contributor.advisorKarakus, Murat
dc.contributor.advisorAmrouch, Khalid
dc.contributor.advisorChester, Chris (BHP)
dc.contributor.authorPowlay, Benjamin James
dc.contributor.schoolSchool of Chemical Engineering : Mining and Petroleum Engineering
dc.date.issued2024
dc.description.abstractHydraulic fracturing, a hydro-mechanical process that induces fractures in rock masses to alter subsurface properties, has long been established in various resource industries and is now gaining traction in mining as the demand for resources rises and viable ore bodies are found at greater depths. While extensive research exists in the oil and gas sector, the specific needs and challenges of hydraulic fracturing in mining environments remain underexplored. This thesis addresses this gap by investigating the optimisation of hydraulic fracturing campaigns in the cave mining industry through a combination of laboratory experiments, numerical modelling, and real field data analysis. The study begins with an examination of how controllable operational variables, such as notches, injection pressures, and rates, affect fracture growth in different mining stress environments. Laboratory-scale experiments provide foundational data, which are then used to validate numerical models. These experiments and models explore the influence of notches on fracture pressures and geometries within different stress regimes, fracture densities, and distances to cave boundaries. Field data from a hard rock mine are analysed using correlation analysis and machine learning techniques to identify key relationships between variables like injection rate, field stresses, and injected volume. These variables are found to influence critical fracture characteristics such as geometry, seismic response, and fracture pressures. The combined insights from baseline experiments, numerical models, and field data lead to the development of empirical design charts for fracture growth, offering practical guidelines for hydraulic fracturing in hard rock cave mining. Key findings also highlight the effectiveness of incorporating factored fracture toughness into predictive models for hydraulic fracturing pressures. AUSBIT test results and Linear Elastic Fracture Mechanics (LEFM) theories are utilised to show that notches lower injection pressures and guide initial fracture growth, which can be accurately replicated in numerical models. In-situ rock conditions, far-field stress regimes, fracture densities, and tensile strength are identified as dominant factors in fracture behaviour, overshadowing operational parameters like sequencing and injection angle. However, operational variables such as flow rate, volume, and notch placement significantly influence fracture characteristics in the near-well field. As fracturing progresses, these operational variables affect fracture volume, growth rate, seismic response, cave connection, and the creation of new fracture planes. This proven correlation enhances the predictability and controllability of fractures in mining environments, enabling the production of design guideline charts. Challenges remain in predicting seismic events, with operational influences showing slight correlations. Further research is needed to understand and mitigate seismic risks associated with hydraulic fracturing. While injection pressure magnitude is a factor in seismic prediction, its influence is less significant than principal stress and injected volume, indicating a complex relationship between operational parameters and seismic activity. The research underscores the potential of real-time monitoring, data analysis, and adjustment systems to optimise hydraulic fracturing designs. The proposed empirical design charts, supported by numerical modelling, field data, and precise experimental work, offer a robust framework for advancing hydraulic fracturing practices in the mining industry.
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Chemical Engineering : Mining and Petroleum Engineering, 2024en
dc.identifier.urihttps://hdl.handle.net/2440/144740
dc.language.isoen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjectHydraulic Fracturing
dc.subjectMining
dc.subjectPreconditioning
dc.subjectCaving
dc.subjectParameters
dc.subjectHard Rock
dc.subjectNumerical Modelling
dc.subjectMachine Learning
dc.titleInvestigating the Preconditioning to Manage Cave Back Propagation
dc.typeThesisen

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