Submarine gravity flows constitute the last link in the source-to-sink sediment transport chain. They are the main mechanism for the transportation of sediment from the shallower to the deeper parts of the ocean. Due to their great volume, mobility, and power, they pose a formidable threat to the offshore infrastructures, and can generate tsunamis which can result in human mortality and cause great damage to onshore structures. In addition, deposits of ancient submarine gravity flows host many hydrocarbon reservoirs. The quality of these reservoirs is primarily controlled by the grain size and the clay con- centration of the flows that deposited the sediments. Due to the growing population and rise in the per capita energy consumption, connecting the dynamics of clay-laden density flows to their depositional characteristics has become important for oil and gas exploration purposes. The principle questions that were investigated in this study were: (1) How are the dynamics of subaqueous gravity flows related to their deposits?, and, (2) Why are these flows able to travel so far? ...

Geological reservoir models, created based on sparse core and seismic data, inform hydrocarbon production, geothermal applications and aquafer management. Important factors contributing to reservoir quality in these applications include the heterogeneities within and connectivity between the relevant geo‐bodies constituting the reservoir. The transport and preservation of sediment at the time of deposition impacts these factors. Therefore, a better understanding of sediment delivery, transport and deposition can be used to better quantify reservoir properties. This same computational methodology can also be applied test hypotheses concerning the depositional processes responsible for preservation of ancient deposits. Constraining such hypotheses improves our understanding of the paleo‐sediment dynamics and the accuracy of future geological models.

For reservoir characterization, the subsurface heterogeneity needs to be qualified in which the distribution of lithologies is an essential part since it determines the location and migration paths of hydrocarbons. Preliminary analysis of well-log data could help to identify various lithologies in a one-dimensional direction (depth), while the lateral information is missing because of the sparse locations. On the other hand, a larger areal coverage of the target reservoir could be provided by seismic data, and from the inversion thereof, inferences of lithologies could be made. However, just like other geophysical inversions, translation of seismic inversion results to these categorical variables (lithologies) is a non-unique problem, which means that different lithologies could produce the same, or similar, property responses. In order to mitigate this problem, geological prior information should be introduced in the sense of Bayes’ theorem. Thus, the main motivation for this thesis is to investigate the usage of geological prior information in the classification of reservoir lithologies from properties obtained from seismic inversion. Different methods have been tried in this process in order to fully understand their performances and to make comparisons.