Module 20: Rule-Based Modeling of Stratigraphic Architectures and Reservoir Facies

This module is the core part of ReservoirStudioTM, our geologic rule-based modeling for stratigraphic architecture and reservoir facies distributions, removing the limitations of traditional static reservoir grid construction used in legacy software systems. Traditionally, stratigraphic features in models are represented through facies, simulated using either object-based or pixel-based methods. These traditional methods often struggle to adhere to geological rules, resulting in less accurate models.

Geologic Rule-Based Modeling (RBM)

In contrast, Module 20 implements a Geologic Rule-Based Modeling (RBM) methodology, developed as part of the SBED Joint Industrial Project (JIP). This innovative approach allows for the integration of geologically realistic features into static models, enhancing both flexibility and geological realism. The RBM approach marks a significant advancement over traditional modeling methods, enabling the incorporation of complex geological features that were previously challenging to model accurately.

Key Features of RBM

The RBM methodology introduced in Module 20 offers several key features that significantly enhance the modeling of stratigraphic architectures:

Fit-for-Purpose Modeling Templates:

Meandering Channels: Accurately models the winding courses of river channels, critical for understanding sediment deposition patterns.

Crevasse Splays and Point Bars: Offers templates for these dynamic features, crucial for detailed floodplain modeling.

Depositional Lobes in the Lower-Fan: Captures the nuances of sediment spread in submarine environments.

Stacked Multi-Story Channels: Allows for the modeling of complex channel stacking, essential for accurate reservoir characterization.

Hierarchy-Turbidite Channels in the Middle Fan: Models the hierarchical distribution of channels typical of turbidite systems in the mid-fan region.

Preservation of Flow-Sensitive Layers:

The RBM methodology is adept at preserving thin-bedded layers that are flow-sensitive, which are often overlooked in traditional models. This feature is critical for generating more geologically accurate models that can be directly used in reservoir production planning.

Synthetic Seismic Model Integration:

RBM can generate stratigraphic layer grids that serve as essential inputs for constructing synthetic seismic models. These models are invaluable for training AI algorithms, enhancing the predictive accuracy of seismic interpretations and reservoir behaviors.

By implementing RBM in Module 20, SubsurfaceAI significantly enhances the capability to model complex geological features with higher accuracy and geological fidelity. This advancement not only improves the static modeling process but also provides a robust framework for integrating geological data into AI-driven predictive models, paving the way for more informed and effective reservoir management strategies.