Application of Reverse Time Migration to Faults Imaging in Rakhine Basin, Myanmar

Abstract

The Rakhine Basin in the northeastern Bay of Bengal is an active field in hydrocarbon exploration and development. It contains fault structures and steeply sloping stratigraphic reservoirs, both primary features of interest for hydrocarbon exploration that needs to be accurately imaged to improve the interpretation of seismic data and facilitate the accurate identification of features of interest. Although faults are an indicator of possible hydrocarbon traps, they are difficult to identify in seismic images using traditional stack or prestack time migration due to the rather complex behaviors of wave propagation. On the other hand, prestack depth migration (PSDM) can significantly improve the accuracy of seismic images, especially of complex subsurface structures such as faults, folds, overthrusts, and salt domes. Among the various PSDM approaches, reverse time migration (RTM) has been shown to be the most powerful. Here, we show how PSDM-RTM can significantly improve the representation of fault structures and steeply dipping structures in seismic images from field data collected in Rakhine Basin, which is characterized by complex geology including stratigraphic and strati-structural traps as well as complex channel systems. Typically, these structures appear heavily blurred and are difficult to identify using normal stack and prestack time migration. We demonstrate that they become clearer and easier to detect with the PSDM–RTM approach, making this approach particularly suitable for seismic interpretations of geologically complex areas within the context of hydrocarbon prospecting.

Advanced Processing of 2D Marine Reflection Seismic Data Using the Common Reflection Surface (CRS) Stack Method with K-L Filter Application

“…The conventional stack method is performed by averaging the NMOcorrected data or migrated data sets (Jang & Lee, 2022). This method is only optimal when the noise components in all traces are unrelated, normally distributed, stationary, and with the same magnitude (Mandal et al, 2014;Pussak et al, 2014).…”