SubsurfaceAI Inc. Announces
Groundbreaking Workflow Development in Collaboration with ENI for Enhanced SBED Near Wellbore Heterogeneity Modelling
Calgary, Canada – SubsurfaceAI Inc. is thrilled to announce a significant breakthrough in near-wellbore heterogeneity modeling technology, developed in collaboration with Eni. This development was unveiled by Eni at the Fifth EAGE Borehole Geology Workshop, November 21, 2023 at Al Khobar, Saudi Arabia.
The new workflow, part of the SBED 2024.1 software release, represents the culmination of a collaboration between SubsurfaceAI and Eni. It leverages grain size data from laboratory analyses to refine and enhance near wellbore heterogeneity models at the lamina scale, facilitating more accurate property modeling. This innovative approach marks a significant advancement in the field of reservoir characterization and modeling, offering unprecedented precision and reliability to build static reservoir property models.
Innovative Approach to Grain Size and Core Plug Data Integration
This latest workflow improvement integrates quantitative grain size distribution data, enabling the creation of more consistent and robust near wellbore reservoir heterogeneity models at lamina scales. By simulating lithological realizations based on grain size data, alongside bedding templates, the workflow minimizes discrepancies between real and synthetic core plug properties through advanced inversion algorithms.
Bridging the Scale Gap in Reservoir Modeling
The cutting-edge workflow addresses the often-overlooked small-scale geological heterogeneities that significantly impact subsurface fluid flow in both hydrocarbon recovery and carbon dioxide or hydrogen storage systems. By seamlessly transitioning from sub-millimetric to decimetre scales, this technology provides effective property values essential for large-scale reservoir simulations, thus enhancing the accuracy of subsurface models.
Real-World Application and Benefits
The integration of grain size data into digital core modeling represents a leap forward in capturing fine-scale geological details necessary for high-resolution near-wellbore modeling. This advancement not only improves the fidelity of digital core models but also ensures that they more accurately reflect physical properties, thereby enabling better informed decision-making in reservoir management.
Acknowledgements and Future Directions
SubsurfaceAI Inc. extends their gratitude to the dedicated team of researchers and developers who made this breakthrough possible. The success of this project underscores the power of collaborative innovation in advancing subsurface exploration and production technologies.
As we look to the future, SubsurfaceAI Inc. remains committed to pushing the boundaries of digital core modeling and reservoir characterization. This development is a testament to our ongoing effort to provide the energy industry with the tools needed to meet the challenges of efficient resource extraction and management.
For further information and updates on our technologies and collaborations, please visit subsurfaceai.ca.
About ENI
Eni is a global energy company engaged in the exploration, development and extraction of natural gas and oil, power generation from traditional and renewable sources, refining and chemicals. The goal of sustainability incorporates business at every level.
About SubsurfaceAI Inc.
SubsurfaceAI Inc. is a leading provider of advanced artificial intelligence and digital modeling solutions for the oil and gas industry. Based in Calgary, Canada, we specialize in the development of cutting-edge technologies aimed at enhancing subsurface exploration and production efficiency.
About SBED
SBED (stands for Sedimentary BEDding) is small-scale geologic modeling & upscaling software developed by SubsurfaceAI Inc. (formerly Geomodeling Technology Corp.) under the support of SBED Join Industry Project (JIP) from 2000-2010. SBED JIP was initiated by Equinor (formerly Statoil) in 2000, and was later jointed by BHP, ConocoPhillips, ExxonMobil, Eni, NorskHydro (now part of Equinor), Shell, and TotalEnergies. More information about SBED software can be found at subsurfaceai.ca/sbed.
