Understanding reservoir heterogeneity using variography and data analysis: an example from coastal swamp deposits, Niger Delta Basin (Nigeria)
| dc.contributor.author | Obi, Ifeanyichukwu S. | |
| dc.contributor.author | Onuoha, K. Mosto | |
| dc.contributor.author | Obilaja, Olusegun T. | |
| dc.contributor.author | Dim, C. I. Princeton | |
| dc.date.accessioned | 2021-01-08T08:33:39Z | |
| dc.date.available | 2021-01-08T08:33:39Z | |
| dc.date.issued | 2020-12 | |
| dc.description.abstract | For efficient reservoir management and long-term field development strategies, most geologists and asset managers pay special attention to reservoir chance of success. To minimise this uncertainty, a good understanding of reservoir presence and adequacy is required for better ranking of infill opportunities and optimal well placement. This can be quite challenging due to insufficient data and complexities that are typically associated with areas with compounded tectonostratigraphic framework. For the present paper, data analysis and variography were used firstly to examine possible geological factors that determine directions in which reservoirs show minimum heterogeneity for both discrete and continuous properties; secondly, to determine the maximum range and degree of variability of key reservoir petrophysical properties from the variogram, and thirdly, to highlight possible geological controls on reservoir distribution trends as well as areas with optimal reservoir quality. Discrete properties evaluated were lithology and genetic units, while continuous properties examined were porosity and net-to-gross (NtG). From the variogram analysis, the sandy lithology shows minimum heterogeneity in east-west (E–W) and north-south (N–S) directions, for Upper Shoreface Sands (USF) and Fluvial/Tidal Channel Sands (FCX/TCS), respectively. Porosity and NtG both show the least heterogeneity in the E–W axis for reservoirs belonging to both Upper Shoreface and Fluvial Channel environments with porosity showing a slightly higher range than NtG. The vertical ranges for both continuous properties did not show a clear trend. The Sequential Indicator Simulation (SIS) and Object modelling algorithm were used for modelling the discrete properties, while Sequential Gaussian Simulation (SGS) was used for modelling of the continuous properties. Results from this exercise show that depositional environment, sediment provenance, topographical slope, sub-regional structural trends, shoreline orientation and longshore currents, could have significant impacts on reservoir spatial distribution and property trends. This understanding could be applied in reservoir prediction and for generating stochastic estimates of petrophysical properties for nearby exploration assets of similar depositional environments. | pl |
| dc.identifier.citation | Geologos 26, 3 (2020): 207-218 | pl |
| dc.identifier.doi | https://doi.org/10.2478/logos-2020-0020 | |
| dc.identifier.uri | http://hdl.handle.net/10593/25991 | |
| dc.language.iso | eng | pl |
| dc.publisher | Instytut Geologii UAM | pl |
| dc.rights | info:eu-repo/semantics/openAccess | pl |
| dc.subject | porosity | pl |
| dc.subject | permeability | pl |
| dc.subject | flow zone index | pl |
| dc.subject | property modelling | pl |
| dc.subject | shoreface | pl |
| dc.subject | fluvial channels | pl |
| dc.title | Understanding reservoir heterogeneity using variography and data analysis: an example from coastal swamp deposits, Niger Delta Basin (Nigeria) | pl |
| dc.type | Artykuł | pl |