Hydrocarbon resource evaluation using combined petrophysical analysis and seismically derived reservoir characterization, offshore Niger Delta

Autor: Kehinde David Oyeyemi, Mary Taiwo Olowokere, Ahzegbobor Philips Aizebeokhai
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Journal of Petroleum Exploration and Production Technology, Vol 8, Iss 1, Pp 99-115 (2017)
Druh dokumentu: article
ISSN: 2190-0558
2190-0566
DOI: 10.1007/s13202-017-0391-6
Popis: Abstract Subsurface characterization and hydrocarbon resource evaluation were conducted using integrated well logs analysis and three-dimensional (3D) seismic-based reservoir characterization in an offshore field, western Niger Delta basin. Reservoir sands R1–R4 were delineated, mapped and quantitatively evaluated for petrophysical characteristics such as net-to-gross, volume of shale, water saturation, bulk water volume, porosity, permeability, fluid types and fluid contacts (GOC and OWC). The volume attributes aimed at extracting features associated with hydrocarbon presence detection, net pay evaluation and porosity estimation for optima reservoir characterization. Neural network (NN)-derived chimney properties prediction attribute was used to evaluate the integrity of the delineated structural traps. Common contour binning was employed for hydrocarbon prospect evaluation, while the seismic coloured inversion was also applied for net pay evaluation. The petrophysical properties estimations for the delineated reservoir sand units have the porosity range from 21.3 to 30.62%, hydrocarbon saturation 80.70–96.90 percentage. Estimated resistivity R t, porosity and permeability values for the delineated reservoirs favour the presence of considerable amount of hydrocarbon (oil and gas) within the reservoirs. Amplitude anomalies were equally used to delineate bright spots and flat spots; good quality reservoirs in term of their porosity models, and fluid content and contacts (GOC and OWC) were identified in the area through common contour binning, seismic colour inversion and supervised NN classification.
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