Sequential indicator simulation algorithm is one of the popular stochastic simulation algorithms for reservoir geomodelling. It has been used to model delineated lithofacies and depofacies units ...within the OPO field, western Niger Delta. This simulation algorithm was chosen because of its ability to honour the well logs as local conditioning data using the global histogram, areal and vertical geological trends of the data, as well as the patterns of correlation. Three lithofacies were identified and modelled, namely sand, shaly sand and shale units. Vertical succession of the depositional facies within the field reveals five major facies which are basal shelf shale facies, heterolithic (sand–shale) facies, lower shoreface sand facies, upper shoreface sand facies and shoreface channel systems. The general environment of deposition is interpreted to be shoreline–shelf systems where the shoreface channel units, upper shoreface sand, lower shaly sand and heterolithic units constitute the parallic reservoir sequences, while the shale units within the shoreface and coastal environments act as potential source rocks and caprocks for hydrocarbon accumulation.
The daunting challenge in the exploration and production of oil and gas in the face of continual rise in the world’s energy consumption has long been how to economically recover bypassed reserves ...within existing assets. This research is focused on the analysis of prospects and volumetric estimation of the hydrocarbon reservoirs delineated within an exploratory field using 3D seismic data and suites of wireline logs. The prospectivity of the delineated reservoir was carried out using seismo-structural interpretation and formation evaluation towards the assessment of the prolific hydrocarbon occurrence within the field. The reservoirs have porosity (0.29–0.32) for H1, (0.20–0.31) for H2 and (0.30–0.40) for H3 and the average computed hydrocarbon saturation of (0.31–0.62) for H1, (0.16–0.52) for H2 and (0.64–0.73) for H3, hydrocarbon pore volume (HCPV) of 28,706.95, 33,081.2 and 45,731.49 barrels for H1, H2 and H3, respectively, while the estimated stock tank oil initially-in-place (STOIIP) range (136.8–140.73) MMSTB for H1, (36.77–489.64) MMSTB for H2 and (166.62–308.14) MMSTB for H3. The observed porosity and hydrocarbon saturation for the delineated reservoirs as well as the estimated hydrocarbon pore volume and storage total oil in place indicate that the reservoirs are highly prolific. The study has therefore contributed to the understanding of hydrocarbon resource potential within the study area.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Sequence stratigraphic concepts for continental settings were assessed to analyze depositional systems of the formations penetrated by wells in the Otumara field. Identification and delineation of ...five sequences and their bounding surfaces was carried out using well logs. Reservoir sands A to D were mapped using conventional 3-D seismic interpretation techniques. Geostatistical simulation was carried out to provide equiprobable representations of the reservoirs, and the distribution of reservoir parameters and system tracts delineated from the stratigraphic framework. The modeled reservoir properties resulted in an improved description of reservoir distribution and connectivity. Reservoir sands A and B have the highest distribution of both highstand systems tract (HST) and lowstand systems tract (LST) deposits, while reservoir sands C and D have the lowest. Since reservoir sands C and D are from deeper depth, the results indicate that HST and LST decrease with depth while transgressive systems tract (TST) increases with depth. Correlating the 3-D geostatistical model with structures shows prospects with low and high hydrocarbon saturation. Crossplot of porosity and permeability for all reservoirs yielded good correlation. The crossplot of systems tract and hydrocarbon saturation with lithofacies as
Z
value shows a strong correlation of 0.89. The result also indicates that high hydrocarbon saturation is related to sandy facies of lowstand systems tract. Thus, the LST has the highest hydrocarbon potential. The models resulting from this study can be used to improve reservoir management and well placement, and to predict reservoir performance in Otumara field.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Deterministic reservoir modeling using geostatistical approach is inherently ambiguous because of the uncertainties contained in the generated reservoir models. Stochastic reservoir modelling using ...sequential gaussian simulation algorithm can resolve this problem by generating various realizations of petrophysical property models in order to map this uncertainties caused by subsurface heterogeneity. Suites of well logs for four wells with seismic data in SEG-Y format were used for this analysis. The wells were correlated and a reservoir was mapped across them in other to map their lateral extent, synthetic seismogram was generated in other to match the event on the seismic with that of the synthetic after carrying out a shift of -12ms. Seismic to well tie was done to ensure that the horizons were mapped accurately. The structural maps generated and the wells were input that goes into the stochastic modelling process. Five realizations each of facies(lithology), effective porosity, total porosity, net to gross, volume of shale and one realization for permeability and water saturation were generated. The facies models showed the distribution of sand and shale with sand at the existing well locations and the effective porosity, total porosity, net to gross, volume of shale models showed excellent values around the well location. Permeability and water saturation models showed that the existing wells were drilled at the flank of the anticlinal structure. Two drillable points (prospects) were proposed by considering all the initial petrophysical property models and the parameters of the two points named P1 and P2 showed that they contain hydrocarbon in commercial quantity. Stochastic reservoir modelling has proved effective in mapping uncertainties and detecting bypassed hydrocarbons. Â
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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