Seismic Avo Attributes and Machine Learning Techniques Characterise a Distributed Carbonate Build-Up Deposit System in the Salawati Basin, Eastern Indonesia

Abstract

There are several undeveloped discoveries and drill-ready prospects in the Salawati Basin, Eastern Indonesia, especially offshore. These offer significant growth potential, particularly the challenging Miocene hydrocarbon-producing reservoirs. The seismic data suggests that this play extends to the north of the producing area, but this has not been confirmed by a successful well. The combination of standard seismic attributes with seismic Amplitude Variation with Offset (AVO) attributes is key to revealing the reservoir in the exploration phase. In this project, poststack attributes from an AV O inversion were used as input for an unsupervised clustering technique based on a Growing Neural Gas algorithm, to generate the most probable facies distribution as well as the probability per facies, in order to better characterise a complex regional channel deposition system. The classification of AVO-related seismic attributes as direct hydrocarbon indicators is used to extrapolate reservoir information from the seismic data that correlates with well data from surrounding fields. The study demonstrates that seismic volume-based unsuper-vised facies classification associated with advanced visualisation and detection helps delineate the prospect’s potential. In this example, the workflow identified a reservoir within the Kais interval of the Miocene Carbonate. The model also shows lateral variations in other reservoir intervals and contributes to the exploration hydrocarbon strategy.