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Fault complexity in seismic data can be more accurately represented by integrating geomechanical simulation and forward seismic modelling. A multi-layer stratigraphic model subjected to progressive deformation was constructed using the Discrete Element Method (DEM). Acoustic impedance fields derived from mechanical evolution provided the basis for calculating vertical reflection coefficients, which were then convolved with zero-phase Ricker wavelets using a 1D approach to produce synthetic seismic sections. Compared to conventional planar-fault representations, the resulting images display intricate reflector terminations, and amplitude dimming associated with distributed fault damage and rotated blocks. These results highlight that even a single fault, when modelled with physics-based deformation, produces richer and more varied seismic responses than matrix-deformation/warping approaches used to create labelled training datasets, providing a more geologically reliable basis for AI fault-segmentation and interpretation.

The oil and gas industry continues to digitise subsurface data, yet much of the high value information remains trapped in unstructured formats such as scanned final well reports, daily drilling reports, and biostratigraphy analyses. While the OSDU® Data Platform standardises structured datasets, unlocking value from unstructured records requires semantic enrichment and rigorous security.

This article presents a scalable, entitlement first Retrieval Augmented Generation (RAG) architecture that transforms unstructured, OSDU referenced content into actionable intelligence. The approach combines document reconstruction, header aware chunking, and hybrid retrieval - Best Matching 25 (BM25) + vector search fused via Reciprocal Rank Fusion (RFF) – with preretrieval filtering that maps Entra ID identities to OSDU Access Control Lists (ACLs). On a curated 250 question pilot set representative of subsurface workflows, semantic reconstruction and hybrid retrieval improved recall and precision by up to 20% relative to a naïve baseline, with reported reductions in time-to-answer. The article clarifies how RAG grounds generation and how ReAct agents can orchestrate multistep decision support on top of a trusted foundation.

Overall, the study outlines a practical path from unstructured ‘text soup’ to compliant, auditable answers suitable for enterprise deployment at scale.

This article presents a practical framework for AI-assisted subsurface data access based on explicit data representations, agent-based workflows, and efficient information retrieval. We demonstrate large-scale conversion of SEG-Y archives into self-describing MDIO v1 datasets and present a case study on agent-driven reconstruction of seismic metadata from legacy text headers. A second case study evaluates embedding-based retrieval across acquisition and processing reports, showing that vector quantisation and graph-based indexing enable low-latency, relevance-driven search. These capabilities are integrated into an interactive, multi-agent system that supports natural-language analysis and coordinated access to structured and unstructured subsurface information.