Seismic imaging in the northern Malay Basin frequently suffers due to the limitations imposed on Kirchoff Time Migration by the complexities of structure, lithology, stratigraphy and fluid effects that occur in the area (Reilly et al., 2008 and Gosh et al., 2010). A recent Pre-Stack Depth Migration (PSDM) project not only enhanced the reservoir imaging, it enabled the production of amplitude preserved volumes that were used to improve reservoir characterisation studies for gas pay prediction. Reservoir characterisation studies within 4 producing gas fields of the northern Malay Basin have successfully used offset stacks and seismic inversion products Acoustic Impedance (AI) and Poisson's Ratio (PR) to target gas pay sands for development drilling. These sands display a weak type III AVO response. One field contains less of the type III AVO sand facies and displays lower seismic contrast; in an effort to improve reservoir prediction, the multi linear analysis EMERGE process was applied to generate target logs Volume of Sand, Porosity, Gas Saturation and Pay Flag. Although more successful in identifying pay sands, data quality issues hampered results. Shallow gas effects such as amplitude absorption and time distortion of underlying reflectors, in addition to fault shadow effects negatively impacted the pre stack and post stack data and the derivative inversion and EMERGE data. Although not a complex structural or velocity environment, PSDM processing was applied to 2 of the gas fields with the greatest data quality issues and generated improved pre and post stack data. The EMERGE process was rerun and delivered improved target attribute cubes. Simultaneously an inversion trial was run to test potential uplift in PSDM derived AI and PR data. The PSDM derived AI was of slightly better quality than the original while the PR was clearly superior; the data quality uplift is attributed predominantly to the improved PSDM pre stack data input. Acoustic Impedance and to a lesser extent PR form significant inputs to the EMERGE multi-attribute process and full re-inversion of the PSDM data is under consideration to use in future characterisation studies.

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