Oil seeps, shallow gas, and surface features such as seabed pockmarks and mud volcanoes are historically believed to be signs of deeper hydrocarbon accumulations. In the search for connections between shallow features and deeper hydrocarbon accumulations, gas chimneys and faults have been studied as possible routes for vertical migration of gas and fluids from source rocks and hydrocarbon-charged traps. Understanding these fluid migration pathways can help evaluate whether a trap is charged or has leaked. A method based on seismic attributes and use of neural networks has been developed to detect and display gas chimneys. This method makes it possible to detect and map gas chimneys in a consistent manner and to see the position of chimneys relative to faults and traps. The detection of gas chimneys in seismic data has therefore been used as a tool in an effort to distinguish between hydrocarbon-charged traps and dry traps with associated chimneys. Based on such case studies, a model of trap classification has been proposed and tested on more than 100 drilled traps in the Norwegian North Sea with good results.