Abstract
This study focuses on the use of some geostatistical tools to spatially distribute reservoir parameters in order to identify the bypassed prospects from the earlier seismic interpretation that was carried out in the field using 3D seismic data. Four wells and seismic data were used to generate the interpreted input horizon grids, fault polygons and to carry out detailed petrophysical analysis. Structural and property modeling which include; facies, net to gross, porosity and water saturation were distributed stochastically within the constructed 3D grid using Sequential Gaussian Simulation (SGS) algorithm. The reservoir structural model show system of different oriented growth faults F1 to F9. Faults F1, F2, F3 and F4 were the major growth faults, dipping towards south-west and are quite extensive almost across all the seismic section. A rollover anticline formed as a result of deformation of the sediments deposited on the downthrown block of fault F1. The other faults were minor fault (synthetic and antithetic). The trapping mechanism is a fault assisted anticlinal closure. Results from well log analysis and petrophysical models shows Godwin reservoir to be a moderate to good reservoir in terms of facies, with good net to gross, porosity, permeability and low water saturation. This study has also demonstrated the effectiveness of 3D geostatical modeling technique as a tool for better understanding the distribution with respect to space of continuous reservoir properties. It will also provide a framework for the future prediction of reservoir qualities and yield rate of the reservoirs.
Highlights
The key point is that 3-D models are three dimensional; they are built so that the interpreter can use spatial data in their correct relation to the data around them for visualization and for calculations, whereas statistical spreadsheet simulations deal with averaged input values and spatially detached data (Liz, 2009)
There are some publications in different aspects of the reservoir modeling such as dynamic reservoir simulations (Labourdette et al, 2006; Jackson et al, 2005), fracture intensity (Wong, 2003; Masaferro et al, 2003), 3D stratigraphy, 3D structural model (Mitra and Leslie, 2003; Mitra et al, 2006; Hennings et al, 2000)
The thickness of the two reservoirs looks appreciably uniform from the North-Western to the South-Eastern direction and the reservoirs thins along the same direction
Summary
The key point is that 3-D models are three dimensional; they are built so that the interpreter can use spatial data in their correct relation to the data around them for visualization and for calculations, whereas statistical spreadsheet simulations deal with averaged input values and spatially detached data (Liz, 2009).In reservoir modeling subject there are different methods for 3D reservoir modeling. This research '3D Spatial Distribution of Reservoir Parameters For Prospect Identification In ''Bizzy'' Field, Niger Delta'made use of petrophysical well log data within reservoirs using geostatical method across the reservoirs to have a better knowledge of the reservoir quality for the recommendation of possible location for drilling of developmental well(s) that will help to effectively extract the hydrocarbon in place for a long period of time.
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