3D Depositional and Diagenetic Facies Modeling: An Integrated Approach to Capture Heterogeneities in a Lower Cretaceous Carbonate Reservoir

Abstract

Carbonate reservoirs can develop complexities due to diagenetic overprint which cause alterations in the properties of the original depositional facies, consequently increasing reservoir heterogeneities. Therefore, it is very important and challenging to properly integrate depositional and diagenetic facies in 3D static models. 3D property modeling routinely relies heavily on geostatistical propagation of well data, without considering and honoring depositional or diagenetic geological conceptual models. Hence the resulting images can be unrealistic and unreliable in predictions. Nevertheless, even when used, the facies don’t tend to have any relationship with petrophysical properties if classified using the classical approaches which are based on depositional texture (e.g. Dunham Classification). The main objective is to build an integrated 3D static model that incorporates carbonate sequence stratigraphic zonation scheme, uses a suitable facies classification/clustering scheme other than "Dunham Textural Classification" to capture areal and vertical facies distribution as well as diagenetic overprints, and successfully and realistically constrains the petrophysical properties distribution within the 5th order carbonate cycles. 3D depositional and diagenetic facies modeling workflow has been established by considering an integrated approach to capture reservoir heterogeneities in the lower cretaceous carbonate reservoir. Consequently hierarchical 3D facies model was built in which large scale depositional facies form main framework per sequence by honoring conceptual depositional geological model. Within each depositional facies region the diagenetic facies are distributed stochastically under the guidance of established diagenetic trends to capture possible local variations. The petrophysical properties were then modeled, conditioned to the distributed diagenetic facies constraining the porosity and permeability ranges and trends to each particular diagenetic facies. In this study, in particular, diverting from the usual Dunham classification scheme has provided the advantage and freedom of being able to incorporate more information about the rock final state and texture into its nomenclature. Being sensitive to diagenetic overprint, it gives an insight into the reservoir quality and associated petrophysical properties variations. The established workflow enabled successful integration of the depositional and diagenetic patterns within the sequence stratigraphic framework and the ability to model it to establish a spatial link between geology and petrophysics. Blind tests revealed promising results in terms of properties predictability. Overall, the model is believed to be more representative of the geological understanding as it combines sedimentological framework and diagenetic overprint in carbonate reservoirs. This provides a robust tool for predicting reservoir architecture which can potentially lead to a better field development planning and improved decision making.