Detection of gas chimney and its linkage with deep-seated reservoir in Poseidon, NW shelf, Australia from 3D seismic data using multi-attribute analysis and artificial neural network approach

Abstract Accurate delineation of hydrocarbon seepage has significant implications in accentuating hydrocarbon migration pathways and assessing seal integrity thereby alleviating drilling hazards. Knowledge of migration pathways and understanding about the source/reservoir plays a vital role in successful evaluation of hydrocarbon seepage. Many researchers have reported events of gas leakage in Poseidon area; however, it has never been investigated in detail to confirm the origin of the gas leakage and their migration pathways. In this study, an attempt has been made to decipher the relationship between shallow gas migration expressions such as pockmarks, mud-volcanos, direct hydrocarbon indicators (DHIs), push-downs, amplitude blanking, with the reservoir present in the study area. Adopted approach includes development of a chimney probability cube (CPC), in which extracted seismic attributes are optimally combined using a non-linear multi-layer perceptron (MLP) network. During the training and testing phase of the 3-layer MLP network, 0.3-0.25 normalized RMS error and