Porosity has always been an important parameter for the definition of the reservoir quality and volumetric. Porosity determination poses many challenges with an acceptable range of certainty during subsurface reservoir characterization. The tight deep marine Plover reservoir in Poseidon area from North Western Australia exhibits a special depositional environment, unique interior structure and a complex porosity distribution that poses challenges in predicting reliable reservoir properties. This has large impacts on reserves and production forecast, and consequently on the economy of a prospect. The aim of this study is to enhance the characterization of the Poseidon reservoir using a combination of reservoir geophysics (seismic attributes and petrophysics) through machine-learning (ML) techniques. This aim is fulfilled by testing different porosity prediction approaches. These approaches include Multi-linear Regression (MLR), Probabilistic Neural Network (PNN) and Deep Feed Forward Neural Network (DFNN). The approaches have been tested at well location before application to whole data. The Probabilistic Neural Network showed promising results, far better than other comparative methods and was able to capture the variations in the porosity. Apart from PNN, DFNN gave limited success due to the scarcity of labeled data. Poseidon porosity maps from the PNN revealed geological information of interest that otherwise are not seen in the seismic inversions or seismic attribute analysis. PNN porosity results was found more reliable and can be used as an input to volume and reserve calculation. On average, the highest porosity zone predicted from the PNN around Plover reservoir is around 10 – 14 % which correlate with the log derived average (effective) porosity of 10.8 %.