TY  - JOUR
N2  - 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
Rt, 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.
TI  - Hydrocarbon resource evaluation using combined petrophysical
analysis and seismically derived reservoir characterization,
offshore Niger Delta
UR  - https://doi.org/10.1007/s13202-017-0391-6
ID  - eprints10738
Y1  - 2018///
VL  - 8
A1  - Oyeyemi , Kehinde D.
A1  - Olowokere, Mary Taiwo
A1  - Aizebeokhai, A. P.
AV  - public
SP  - 99
EP  - 115
KW  - Petrophysics Hydrocarbon prediction Seismic attributes  Amplitude anomaly  Neural network
JF  - J Petrol Explor Prod Technol
ER  -