Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

Abstract

This research aims at characterizing and modeling delineated reservoirs in ‘Falad’ Field, Niger Delta, Nigeria, to mitigate the challenge caused by the heterogeneous nature of the reservoirs. Seismic and well log data were integrated, and geostatistics was applied to describe the reservoir properties of the interwell spaces within the study area. Four reservoirs, namely RES 1, RES 2, RES 3 and RES 4, were delineated and correlated across four wells. The reservoir properties {lithology, net to gross, porosity, permeability, water saturation} of all the delineated reservoirs mapped were determined, and two reservoirs with the best quality were picked for further analysis (surface generation and modeling) after ranking the reservoirs based on their quality. Structural interpretation of the field was carried, nine faults were mapped (F1—F9), and the fault polygon was generated. The structural model showed the area is structurally controlled with two of the major faults mapped (F1 and F3) oriented in the SW–NE direction while the other one (F4) is oriented in the NW–SE direction. A 3D grid was constructed using the surfaces of the delineated reservoirs and the reservoir properties were distributed stochastically using simple krigging method with sequential Gaussian simulation, sequential indicator simulation and Gaussian random function simulation algorithms. Geostatistical modeling used in this study has been able to give subsurface information in the areas deficient of well data as the estimated reservoir properties gotten from existing wells have been spatially distributed in the study area and will thus aid future field development while also they are used in identifying new prospect by combining property models with structural maps of the area.

SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM

“…The results obtained with the SGS method (Figure 6) represent the sample data spatially, meaning that regions with low sample values had lower simulated values and vice versa. This behavior has been observed in other works that have employed SGS [45][46][47][48][49][50], where the simulated values follow the spatial variability of the sample values.…”