Abstract

Delineation of subsurface reservoirs need detailed analysis of geophysical data. Inversion studies gives scope of de-risking the envisaged prospects in an identified area of work. Integration of seismic attributes with inversion results makes an interpreter more assured about the keen understanding of the subsurface. For any inversion, proper spatial distribution of wells is necessary to understand the nature of different litho-layers. Moreover, characterization of carbonate reservoir warrants much more focus on well data as reservoir properties laterally changes frequently. The current study area falls in the back-arc region of Andaman basin with good seismic coverage but no well has been drilled in the carbonate section. One well which has penetrated clastic section but has no favorable hydrocarbon shows is away from carbonate area. An innovative approach was adopted by drawing analogy from the far away well which had penetrated carbonate section in the same geological setup and carried out an inversion. Integration of inversion results with seismic attributes generated using AASPI consortium tool followed by unsupervised machine learning based facies classification helped in identification of porous zones in the carbonate build up.