Finally, the AI-assisted query layer was evaluated through structured operational scenarios in which users performed natural-language queries related to performance indicators, drilling cycles, and energy metrics. Response accuracy and retrieval consistency were verified against the centralized database records. This methodological approach enabled technical validation of interoperability, data integrity, resilience under field conditions, and analytical capacity, supporting the reliability of the proposed framework as a scalable digital solution for production drilling environments. 6. RESULTS AND COMPARATIVE ANALYSIS The implementation of the proposed digital drilling architecture enabled quantitative and qualitative evaluation across four principal dimensions: data availability, operational traceability, decision latency, and energy-based analytical capability. In terms of data availability, the transition from fragmented data capture to continuous centralized acquisition significantly improved historical completeness and temporal consistency. Under baseline conditions, drilling data was partially consolidated through shift-based manual reporting and isolated system extraction, resulting in limited granularity and delayed accessibility. Following implementation, operational variables were captured continuously at high temporal resolution and consolidated under a unified data model. Edge-level buffering mechanisms ensured preservation of data during intermittent communication events, eliminating historical data gaps previously observed in mobile equipment operations. Regarding operational traceability, the architecture enabled hole-level tracking across equipment, shifts, operators, and geological domains. This granular traceability was not previously available in a structured format. The automated registration of drilling cycles reduced inconsistencies associated with manual reporting, improving dataset reliability for longitudinal performance analysis and cross-equipment benchmarking. A significant impact was observed on decision latency reduction. Under the prior reporting model, performance deviations were typically identified retrospectively at the end of operational shifts. Real-time monitoring and centralized visualization allowed supervisors and technical personnel to identify deviations in penetration rate, thrust force, or torque during active drilling operations. This capability enabled earlier operational adjustments and improved coordination between drilling and downstream planning functions. From an analytical perspective, the integration of specific energy calculation at the individual hole level provided new insight into the interaction between operational parameters and geological variability. Correlation analysis between computed specific energy values and predefined geological domains showed coherent patterns, with higher energy intensity observed in zones characterized by increased rock strength. While this metric does not replace formal geomechanical testing, it demonstrated practical value as a real-time proxy indicator supporting adaptive operational decisions.
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