Sustaining hydrocarbon production to meet growing energy demand is an industry-wide challenge, especially for Middle East projects. Consequently the need for sound, robust models is increasingly important to accurately represent the subsurface ahead of field development/production. A common hurdle to overcome involves scaling core-based geologic characterization to models, such as representing thin high perm streaks or populating facies mosaics. This paper summarizes the approach used to characterize and model a thick carbonate reservoir from a giant offshore Abu Dhabi oil field.
Core descriptions from 36 key wells across the field establish the setting as a structurally modified ramp characterized by patchy facies distributions that was frequently reworked by storms. Core:log calibration was investigated but deemed not applicable for this reservoir, thus core was the primary control on the rock type model. High permeability grainstones and algal/rudist floatstone interpreted as storm deposits are thin (<1 foot) and not correlative between cored wells. The wells were rock typed and mapped to reveal trends between rock types, thickness and reservoir quality.
These trends were integrated with previous learnings and modern analogs to help bridge between the core and model scales, such as for geobody dimensions and spatial variability within the model. Deterministically-guided stochastic distribution of rock types using Truncated Gaussian Simulation was the preferred method to build the rock type model based on data coverage and the vertical stack of facies observed in core. Fine-scale layering in certain zones of the geomodel was necessary to capture the thin high perm streaks. This necessity led to coarser model layers, where applicable, to maintain a manageable geomodel size. Distribution and connectivity of high perm geobodies were guided by multiple feedback iterations from history matched results. These iterations were key to achieve a sound, robust model.