Grid-less subsurface modeling technology
talk to an expertA unique grid-less and dynamically scalable approach to earth modeling which provides multi-scale, multi-resolution subsurface property modeling on demand. Big data processing jobs are rapidly run to generate multiple geological scenarios to help create an accurate, integrated, and reliable shared earth model without the need to upscale data from their original resolution.
A single, shared, subsurface model is generated, visualized, and analyzed at multiple scales and resolutions to help accelerate the transition from exploration to prospect evaluation and reservoir development, and assist in addressing multi-scale modeling needs of CCS projects.
Common subsurface understanding: Knowledge transfer across disciplines and asset teams helps ensure interpretation and modeling consistency to aid in developing a common and comprehensive understanding of the subsurface from basin to borehole.
High-resolution subsurface characterization: Integrating all available data at their original resolution, without simplification, enhances subsurface rock characterization.
Multi-purpose subsurface modeling: Properties from a single grid-less earth model can be quickly transferred to multiple grids of any scale and resolution, for multiple purposes, including flow and geomechanical simulations, and plume migration.
Grid-less modeling enables local and global model updates without having to rebuild from scratch, helping to maintain an accurate shared earth model in the long-term.
Dynamic updates: Newly interpreted horizons and faults along with log data acquired from newly drilled wells are seamlessly integrated.
Scalable updating: Only areas of interest specified by the user need to be re-simulated to account for structural edits and property changes.
Real-time integration: Deep-resistivity data acquired during drilling are rapidly consumed to update well plans and help optimize geosteering.
The lack of grids and the full flexibility regarding the resolution of the simulations facilitates the testing of alternative geological scenarios for prospect evaluation and field development.
Sensitivity to first-order uncertainties: Grid-less modeling helps the impact of alternative structural and depositional interpretations on volumetric estimates and resulting project decisions to be easily assessed.
Incremental learning: Users can start with coarse resolution simulations to quickly evaluate a variety of structural and property modeling parameters, and progressively refine the resolution of the simulations as required.
Cloud technology helps enable the rapid creation of large-scale models with complex structural frameworks, and the integration of exceptionally large datasets, to arrive at informed decisions faster.
More realizations faster: Run more realizations and build models in less time with unmatched speed thanks to the parallelized implementation of the modeling algorithms.
More data: Incorporate all available data regardless of their scale, resolution, and location. This is ideal for handling 1000’s of wells producing from 100’s of reservoirs typically associated with unconventional fields.
Cell resolution optimization: The cell resolution of the grids to which the grid-less property model is transferred are optimized based on the geological heterogeneity observed in the grid-less model. This helps provide the foundation upon which subsequent flow simulations can be run with minimized computation time.
