Abstract

Offshore oil exploration and production in deep water are associated with environmental risks to marine ecosystems. An effective oil spill response critically relies on understanding the transport and fate of spilled petroleum in complex marine environmental compartments. Oil spill models have been used for decades to help responders make informed decisions by forecasting the movement and fate of released petroleum fluids. However, few existing operational tools could capture the sophisticated behaviors of deep-sea oil spills under extreme ranges of ambient conditions. A deepwater oil spill model (DWOSM) is developed in this study to predict the trajectory and weathering processes of subsea blowouts. This system incorporates droplet size distribution model, buoyant plume model, near- and far-field particle tracking algorithms, and various advanced fate algorithms into three modules: DWOSM-DSD aims to predict the quasi-stationary droplet size distribution resulting from a blowout; DWOSM-Nearfield is to simulate near-field plume dynamics; DWOSM-Farfield forecasts the trajectory and fate of dispersed oil and gas beyond the near-field. Unlike most other oil spill models, DWOSM introduces near-field particle tracking to enable a smooth transition between near-field and far-field. It also takes advantage of thermodynamic modeling to predict the evolution of oil and gas's physicochemical and thermodynamic properties in deep water. In addition to model development, a state-of-the-art stochastic simulation-based risk assessment framework is improved by embedding the DWOSM and a polycyclic aromatic hydrocarbons (PAH)-related risk evaluation index.
The application of DWOSM is demonstrated in three cases. The first study case is a hypothetical oil blowout in 800 m of offshore waters of East Newfoundland, Canada. The DWOSM and its each module are juxtaposed with some established oil spill models. The verification shows that predictions in DWOSM are primarily in line with other model outputs. Different choices in fate algorithms cause a particular discrepancy in simulation results. Multiple spill scenarios are implemented to investigate the impacts of weathering processes on oil fate numerically, which reveals the vital role of natural dispersion in surface oil mitigation under windy conditions. Second, DWOSM is applied to perform a hindcast of the largest offshore oil spill in US history, the Deepwater Horizon (DWH) blowout. Primary model outputs, such as surfaced gas composition and surface oil trajectory, are validated through field observations and relevant modeling efforts. A good performance is presented in most validation results, manifesting the reliable capability of DWOSM to simulate deep-sea spill behaviors. Last, the newly developed model is integrated into a risk assessment framework to evaluate the subsea blowout risk in the offshore area of East Newfoundland. Data mining techniques are used to extract representative met-ocean conditions from long-term hydrodynamic and atmospheric reanalysis products, making computationally demanding stochastic oil spill modeling practicable. A series of deterministic simulations corresponding to each met-ocean condition are conducted to yield regional oil spill hazard and risk mapping. The spill scenarios with and without applying subsea chemical dispersants are formulated to analyze their efficacy on spill mitigation. The results indicate a low-risk level around the nearshore waters of the study area, but PAH exposure can jeopardize the aquatic biota in the oil-infested region. Moreover, dispersant use can reduce the risk peak but facilitate the dissemination of oil spills.
In conclusion, this research contributes a novel modeling toolkit for predicting the complex behaviors of deep-sea blowouts and an improved stochastic simulation-based risk assessment methodology to quantitatively evaluate the regional subsea spill risk.