Large-scale dynamic hydrofracturing, healing and fracture network characterization

Permeability of a rock is a dynamic property that varies spatially and temporally. Fractures provide the most efficient channels for fluid flow and thus directly contribute to the permeability of the system. Fractures usually form as a result of a combination of tectonic stresses, gravity (i.e. lithostatic pressure) and fluid pressures. High pressure gradients alone can cause fracturing, the process which is termed as hydrofracturing that can determine caprock (seal) stability or reservoir integrity. Fluids also transport mass and heat, and are responsible for the formation of veins by precipitating minerals within open fractures. Veining (healing) thus directly influences the rock’s permeability. Upon deformation these closed factures (veins) can refracture and the cycle starts again. This fracturing-healing-refacturing cycle is a fundamental part in studying the deformation dynamics and permeability evolution of rock systems. This is generally accompanied by fracture network characterization focusing on network topology that determines network connectivity. Fracture characterization allows to acquire quantitative and qualitative data on fractures and forms an important part of reservoir modeling. This thesis highlights the importance of fracture-healing and veins’ mechanical properties on the deformation dynamics. It shows that permeability varies spatially and temporally, and that healed systems (veined rocks) should not be treated as fractured systems (rocks without veins). Field observations also demonstrate the influence of contrasting mechanical properties, in addition to the complexities of vein microstructures that can form in low-porosity and permeability layered sequences. The thesis also presents graph theory as a characterization method to obtain statistical measures on evolving network connectivity. It also proposes what measures a good reservoir should have to exhibit potentially large permeability and robustness against healing. The results presented in the thesis can have applications for hydrocarbon and geothermal reservoir exploration, mining industry, underground waste disposal, CO2 injection or groundwater modeling.

Physicochemical complexity in complex chemical systems

Self-replication and compartmentalization are two central properties thought to be essential for minimal life, and understanding how such processes interact in the emergence of complex reaction networks is crucial to exploring the development of complexity in chemistry and biology. Autocatalysis can emerge from multiple different mechanisms such as formation of an initiator, template self-replication and physical autocatalysis (where micelles formed from the reaction product solubilize the reactants, leading to higher local concentrations and therefore higher rates). Amphiphiles are also used in artificial life studies to create protocell models such as micelles, vesicles and oil-in-water droplets, and can increase reaction rates by encapsulation of reactants. So far, no template self-replicator exists which is capable of compartmentalization, or transferring this molecular scale phenomenon to micro or macro-scale assemblies. Here a system is demonstrated where an amphiphilic imine catalyses its own formation by joining a non-polar alkyl tail group with a polar carboxylic acid head group to form a template, which was shown to form reverse micelles by Dynamic Light Scattering (DLS). The kinetics of this system were investigated by 1H NMR spectroscopy, showing clearly that a template self-replication mechanism operates, though there was no evidence that the reverse micelles participated in physical autocatalysis. Active oil droplets, composed from a mixture of insoluble organic compounds in an aqueous sub-phase, can undergo processes such as division, self-propulsion and chemotaxis, and are studied as models for minimal cells, or protocells. Although in most cases the Marangoni effect is responsible for the forces on the droplet, the behaviour of the droplet depends heavily on the exact composition. Though theoretical models are able to calculate the forces on a droplet, to model a mixture of oils on an aqueous surface where compounds from the oil phase are dissolving and diffusing through the aqueous phase is beyond current computational capability. The behaviour of a droplet in an aqueous phase can only be discovered through experiment, though it is determined by the droplet's composition. By using an evolutionary algorithm and a liquid handling robot to conduct droplet experiments and decide which compositions to test next, entirely autonomously, the composition of the droplet becomes a chemical genome capable of evolution. The selection is carried out according to a fitness function, which ranks the formulation based on how well it conforms to the chosen fitness criteria (e.g. movement or division). Over successive generations, significant increases in fitness are achieved, and this increase is higher with more components (i.e. greater complexity). Other chemical processes such as chemiluminescence and gelation were investigated in active oil droplets, demonstrating the possibility of controlling chemical reactions by selective droplet fusion. Potential future applications for this might include combinatorial chemistry, or additional fitness goals for the genetic algorithm. Combining the self-replication and the droplet protocells research, it was demonstrated that the presence of the amphiphilic replicator lowers the interfacial tension between droplets of a reaction mixture in organic solution and the alkaline aqueous phase, causing them to divide. Periodic sampling by a liquid handling robot revealed that the extent of droplet fission increased as the reaction progressed, producing more individual protocells with increased self-replication. This demonstrates coupling of the molecular scale phenomenon of template self-replication to a macroscale physicochemical effect.