AI-ENABLED REAL-TIME DRILLING OPTIMISATION FOR SMART OIL AND GAS FIELDS IN SAUDI ARABIA: INTEGRATING MWD/LWD, AUTOMATED WELL PLANNING AND PREDICTIVE WELLBORE RISK MANAGEMENT

Artificial intelligence promises to improve drilling decisions, but field deployment remains constrained by delayed measurements, uncertain geology, inconsistent data quality and safety-critical operating limits. This critical review examines how measurement-while-drilling and logging while drilling information can support drilling rate optimisation adaptive well planning and predictive wellbore risk management within Saudi oil and gas operations. Thirty peer-reviewed publications issued between 2020 and 2025 were assessed through a structured, question led synthesis.The evidence was organized around measurement integrity, formation interpretation, optimisation architecture, trajectory revision, hazard prediction and deployment assurance. Published studies demonstrate substantial progress in rate - of - penetration forecasting, activity-aware influx detection and integrated geological-engineering planning. Nevertheless, model performance is generally assessed within particular fields or datasets; prospective validation, uncertainty calibration and evidence of avoided incidents remain less developed. The review proposes a bounded, human-supervised decision architecture in which physics-based constraints, data-quality controls and explicit operational authorisation separate algorithmic recommendations from drilling actions.