We have developed a workflow based on neural network to compute new attributes from a set of known seismic attributes that can discriminate geologic features from gas clouds/chimneys. Application to time migrated 3D seismic data in Maari field of highly structured and deformed Taranaki basin of New Zealand has brought out clear gas clouds that have originated from the Late Cretaceous source rocks (Pakawau Group) and migrated into the Eocene (Kapuni Group) and Miocene (Mahakatini Group) formations (Fig.1). The study also reveals that gas has seeped through the overlying Pliocene to recent formations, the imprints of which are observed as pockmarks on the seabed. The findings correlate reasonably with the results from Moki-1 well available in the study region. This workflow can be used for interpreting plausible geological features such as faults, mud diapirs, mud volcanoes, salt bodies, slum deposits, debris flows etc. from seismic data. Several fault intersection zones (weak zones) within the reservoirs exhibit high probability of gas chimneys. This study acts as an add-on-tool for understanding the petroleum system and provides preventive clues for mitigating hazards in future exploitation program. The technique can be extended in characterizing reservoir properties such as the porosity, permeability, saturation etc.
Fig.1. 3D visualization of gas clouds rising from thermally matured source rock and propagating through Eocene and Miocene sandstone reservoirs to the seabed.
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D. Singh, Priyadarshi Chinmoy Kumar & K. Sain, 2016. Interpretation of gas chimney from seismic data using artificial neural network: A study from the Maari 3D prospect of Taranaki basin, New Zealand, Jour. of Natural Gas Science & Engineering, 36, 339-357.