Environmental Monitoring

Natural gas production from gas hydrates releases the most potent greenhouse gas methane, which can rise along disturbance zones and enter the marine environment. The gas hydrate deposits are not focused on small-scale structures, but occur in sediment horizons with a large area. They are overlaid with a layer of only 0.1-0.5 km, which can be disrupted during exploitation. This situation calls for a specially adapted monitoring strategy, which aims at detecting possible methane leaks in natural gas production from gas hydrates at an early stage and in a comprehensive manner, and to quantify the leakage rates. The work done so far in the SUGAR project has shown that equipment for the hydroacoustic and chemical detection of methane leakages at the seabed is best suited for this purpose.

However, environmental monitoring at seafloor level faces great difficulties: high pressure (up to 300 bar), energy-limitation and a corrosive environment challenge the production of reliable results. Also, changes in environmental conditions (in particular the water currents) complicate reliable detection, localization and quantification of gas leaks over a long period of time. The companies CONTROS, L-3 ELAC Nautik and KM Embient want to develop such an environmental monitoring system in cooperation with GEOMAR within the framework of SUGAR-III.



  • Development of a measurement strategy and a measuring system for environmental monitoring in natural gas production from submarine gas hydrate deposits
  • Detection of gas bubble discharges at the seabed by the use of ship-supported fan echolot systems,
  • Optimizing the evaluation of the ship-based hydroacoustic data for the safe identification, location, and detection of gas bubbles and estimation of leakage quota at the seabed,
  • Improvement of chemical sensors for the detection of dissolved methane in bottom water and the water column,
  • Development of stationary systems for continuous monitoring of leakages (lander systems),
  • Development of numerical models for the quantification of leakage data