This paper described the novel approach for stochastically modeling complex carbonate reservoir lithofacies and properties distribution within a High Resolution Sequence Stratigraphy (HRSS) framework. The carbonate lithofacies discussed in this paper contains heterogeneous pore types and properties. The reservoir displays an extensive range of geologic and petrophysical properties that make the efficient recovery of hydrocarbons is a challenging task. Hence one of the key steps in improving the recovery factor is by defining the three dimensional variability patterns in the reservoir in the form of fine geocellular static model. The key static geological elements that must be well defined are HRSS framework, lithofacies architecture, and field wide rock properties.
Subsurface analysis was done by examining 600 feet core footage from more than 15 wells, conventional logs from more than 50 wells, and more than 350 thin sections. The reservoir section averages 35 feet that can be subdivided into 6 high-frequency sequences. The reservoir consists of lagoonal packstone-rudstone, grain rich ooid-peloid shoal, and rudstone-boundstone mid-ramp. The shoal deposits exhibit the best permeability and oil saturation and it consists of discontinuous lithofacies body that depicts locally excellent porosity and permeability characteristics.
Lithofacies geometry and properties studies must form a fundamental basis for characterizing and modeling HRSS framework and lithofacies architecture variability through the reservoir. Combined with wireline-log data, they provide a basis for defining both reservoir framework and rock attribute distributions.
Complex lithofacies geometries and transitions, both vertically and laterally between the mound and discontinuous grain-rich ooid-peloid shoal lithofacies together with the continuous and sequential lagoonal and mid-ramp lithofacies does not allow to simulate these sorts of lithofacies assemblage using single lithofacies model algorithm. Hence a new holistic approach was implemented. A combination of Object Based (OB) algorithm and Truncated Gaussian Simulation (TGS) algorithm was employed to handle the complex lithofacies transition. This enables generating multiple realistic field wide lithofacies distribution and relationship which aligns with data trend, subsurface analog in the nearby fields, as well as that is from the outcrop exposure. The established lithofacies distribution within HRSS framework was then used to constrain field-wide properties and diagenetic trend and distribution in the reservoir.
This new holistic approach has recently been successfully implemented in the studied field. The resulted geostatistical model was able to explain pressure depletion and production rate as shown in historical production data of the field. The resulting dynamic model will hence provide reliable production forecast and reservoirs development plan which will eventually allow accomplishing the mandate recovery target.