Abstract:
In this thesis, the study on the gas chimney delineation and prospect identification using
Artificial Neural Networks is described. The study was mainly concerned with the
identification of prospects by the use of Artificial Neural Networks as well as the proposal
of well locations. To interpret and analyze seismic and well data, version 6.2.0 of the
OpendTect software was used. The available data of the F3 block extends from the
Zechstein unit of the Paleozoic era to the Upper North Sea unit of the Cenozoic era. In the
interpreted horizons, three different horizons have been chosen for the identification of
hydrocarbon presence. Three differently derived outputs have been generated for the
interpretation, which is attribute attribute-derived, amplitude-derived, and spectral
decomposition output. Chimney cube have been generated by the use of Artificial Neural
Networks whereas the amplitude derived result was calculated by RMS amplitude and
similarity. Lastly, the RGBA blending was used to calculate and generate the spectral
decomposition result. Finally, all the outputs were evaluated and proposed three different
vertical wells to drill at the site.
