Petrel 2024.11 is a continuous-delivery/maintenance release of the Petrel 2024 platform, focusing on deep integration of domain AI/ML models, Python-driven workflow automation, and enhancements across geophysics, structural modeling, and reservoir engineering.

1. AI-Driven Seismic Interpretation & Quantitative Interpretation (QI)

• 3D AI Fault Extraction: Advanced deep-learning inference engines provide automated, continuous 3D fault extraction, significantly reducing cycle time compared to manual stick-picking.
• Interactive Geobody & Facies Segmentation: Machine-learning-based classification tools enable interactive extraction of complex geological bodies and facies volumes directly from 3D multi-attribute volumes.
• ML-Based QI Reservoir Characterization: Predicts elastic and petrophysical properties (ϕ\phiϕ, VshV_{\text{sh}}Vsh​, acoustic/shear impedances) directly from pre-stack angle gathers and seismic angle stacks using machine-learning models.

2. Geology, RGT & Structural Framework

• Geologically Constrained Relative Geologic Time (RGT): Refined RGT workflows allow users to track horizons across structurally complex zones while honoring stratigraphic bounding surfaces, unconformities, and fault displacements.
• Intelligent Horizon Clean-Up: Automated filtering removes poor-quality data and artifacts near fault zones using a combination of proximity and algorithmic confidence filters.
• Rapid Geological Scenario Modeling: Enables fast multi-realization geological modeling for structural uncertainty evaluation and scenario testing.

3. Fracture & Reservoir Simulation Coupling (EDFM)

• Embedded Discrete Fracture Modeling (EDFM): Integrated capabilities allow hydraulic and natural fracture systems to be modeled as explicit 2D surfaces without requiring complex unstructured grid conformality.
• Seamless INTERSECT Coupling: Direct parameter export and coupling with SLB’s INTERSECT high-resolution reservoir simulator for dual-porosity and complex naturally fractured reservoir (NFR) simulations.

4. Automation, Python Integration & Performance

• Native Python Environment: Seamless execution of custom Python scripts inside the Petrel Workflow Editor, enabling custom ML pipelines, data transformation, and automated multi-well log processing.
• Performance & Window Loading: Optimized window caching and viewport rendering engines ensure significantly faster load times for large corporate projects.
• Delfi & Studio Data Interoperability: Continued integration with the Delfi platform and OSDU Data Platform, facilitating reference project transfers, multi-user concurrency, and cloud data synchronization.

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