Real-Time Quantification of Offshore Oil Spills and Environmental Damage During Blow-Out Accident

In the event of offshore oilfield blow-out, real-time quantification of both spilled volume, recovered oil and environmental damage is essential. It is due to costly recovery and restoration process. In order to develop a robust and accurate quantification, we need to consider numerous parameters, which are sometimes tricky to be identified and captured. In this work, we present a new modeling technique under uncertainty, which accommodates numerous parameters and interaction among them. We begin the model by identifying possible parameters that contributes to the process: grouped into (1) subsurface, (2) surface and (3) operations. Subsurface e.g. well and reservoir characteristics. Surface e.g. ocean, wind, soil. (3) Operations e.g. oil spill treatment blow-out rate, oil characteristics, reservoir characteristics, ocean current speed, meteorological aspects, soil properties, and oil-spill treatment (oil booms and skimmers). We assign prior distribution for each parameter based on available data to capture the uncertainties. Before progressing to uncertainty propagation, we construct objective response (amount of recovered oil) through mass conservation equation in data-driven and non-intrusive way, using design of experiment and regression-based method. We propagate uncertainties using Monte Carlo simulation approach, where the result is presented in a distribution form, summarized by P10, P50, and P90 values. This work shows how to robustly calculate the amount of recovered oil under uncertainty in the event of offshore blow out. There are several notable challenges within the approach: 1) determining the uncertainty range in blow-out rate in case of rupture occurs in the well, 2) obtaining data for wind and ocean current speed since there is an interplay between local and global climate, and 3) accuracy of capturing the shoreline geometry. Despite the challenges, the results are in-line with the physics and several recorded blow-out cases. Define what is blow out rate (important as has highest sensitivity). Through sensitivity analysis with Sobol decomposition (define this …), we can define the heavy hitters. These heavy hitters give us knowledge on which parameters should be aware of. In real-time quantification, this analysis can provide an insight on what treatment method should be performed to efficiently recover the spill. We also highlight about the sufficiency of the model to obtain several parameters’ range, for example blow-out rate. The model should at least capture the physics in high details and incorporate multiple scenarios. In the case of blow-out rate, we extensively model the well completion and consider leaking due to unprecedented fractures or crater formation around the wellbore. We introduce a new framework of modeling to perform real-time quantification of offshore oil spills. This framework allows inferring the causality of the process and illustrating the risk level.