2019 AAPG Annual Convention and Exhibition:

Datapages, Inc.Print this page

Structural and Resource Assessment of XX Field, Onshore Niger Delta


The study identified, mapped and described the structural features defined on seismic reflection data from XX Field, Onshore Niger Delta with a view to having a better understanding of the fault distribution and their roles in hydrocarbon prospectivity within the study area. An integration of 3D seismic, checkshot and composite well log data were used in developing the structural and stratigraphic framework which served as inputs for structural framework development and maps generation. After carrying out well to seismic tie, seismic attributes were employed in validating conventional fault and horizon interpretations and as possible direct hydrocarbon indicator. Log interpretation revealed the presence of possible hydrocarbon bearing reservoirs with varying thicknesses intercalated with shale units at various depth intervals. Eighteen (18) major seismic-scale syn-depositional faults with a general NW-SE trend were mapped on the seismic sections occurring mostly between 0.6s - 1.8s and begins to die out at about 2.0s. The structural framework model coupled with log interpretation revealed vertically stacked and laterally continuous reservoir distribution that are controlled by faults which constitute the trapping geometry within the area. This complex structural deformation within the area enabled different trapping zones for hydrocarbon accumulation within these units. Rock physics analysis revealed high impedance sand for both hydrocarbon and wet cases which are above the tunning thickness. Amplitude extractions carried out for interval of interest revealed a fair to good conformance to structure giving high confidence for resource assessment. A STOIIP of 83 MBO was calculated to be trapped within a faulted dependent closure. The conclusion is that the faults which characterized the study area play significant roles in hydrocarbon entrapment and such a detailed structural understanding will significantly improve optimum hydrocarbon recovery within the study area.