Development of a novel method for estimating and planning automatic skimmer operation in response to offshore oil spills

Offshore oil production and transportation of oil by pipelines and tankers are associated with the risks of an oil spill, and accidents of various scales, from emissions of several liters to several thousand tons, occur regularly in different parts of the world. Currently, there are no automatic or automated systems for responding to such incidents, although special equipment exists that is able to collect oil from the surface of water. The oil collected by skimmers can be used for its intended purpose. The purpose of this research is to develop a novel method for estimating the number of skimmers required for automated oil recovery in the event of oil contamination in the open sea, taking into account errors in measured weather conditions and initial spill information. In this work, a program is developed to simulate the position and state of an oil slick on the water surface, based on realistic weather conditions, and the movement of a group of skimmers while performing the oil removal task. The results of the study demonstrate the robustness of the system with respect to errors in the initial data, weather condition, position and parameters of the oil spill. Two main emergencies are considered: an instantaneous release of oil from a tanker and continuous leakage from a damaged pipeline. The developed system detects and collects oil on the map in a limited time, even with a significant shift in the initial coordinates, and limits the spread of the oil slick where there is continuous leakage. In addition, the designed method has a short-term overestimation of the skimmer group size in case of time delay in the response to the spill. The developed method can be applied in real cases of oil spills to create and update the plan of movement and collection of oil for a group of skimmers.

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  • English

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  • Accession Number: 01855276
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 22 2022 4:14PM