Drilling Automation in the Oil & Gas Industry: Strategic, Operational, and Organizational Dimensions of Transitioning from Advisory Systems to Autonomous Drilling Operations.

The world has experienced the greatest disruption in oil and gas since the invention of rotary drilling. Turbulent economic conditions, heightened environmental consciousness, and increased health and safety regulation, combined with artificial intelligence, digital twins, and edge computing, have created both a "perfect storm" and an unheralded opportunity for best practices and a common target for the industry. Transitioning from decision support systems to constrained autonomous systems, contemporary drilling automation is executing discrete work flows with precision and consistency beyond human capabilities. Drilling automation technology's strategic, operational, and organisational factors are examined in this thesis by synthesising 32 technical papers, white papers, case studies, and press releases published between 2013 and 2025. Organizational barriers appear to be the primary inhibitors of autonomous drilling technology, which has demonstrated and decreased non-productive time by up to 30% [F27 p.2-5] and control trajectories within meters of the intended well path with zero incidents [F15 p.1] . This study on remote operations centers, digital twins, edge computing, and closed-loop automation found that technology may assist in drilling automation, but not exclusively. The Psychological Technology Adoption Framework (F17, pp. 3-8) lists risk aversion, low trust, and fear of failure as the primary drivers of technology adoption resistance. Human capital, rather than technology, is what separates 12% of "AI Achiever" organisations, according to Accenture's AI Maturity research (F18, pp. 4-10). The eight-point managerial framework presented in this thesis may help operators migrate from decision support to bounded autonomy. Phased implementation, use of taxonomic precision, Remote Operations Centres, constraining autonomy by tasks and workflows, model governance and trust, human-systems integration, orchestration of multiple AI advisors, and environmental and economic performance metrics are all encouraged. Drilling automation is needed for the energy transition, according to case studies on geothermal drilling, due to uncertainties, high costs, and environmental sensitivities. Frameworks for drilling and well construction managers to manage this shift are presented by researchers, increasing environmental performance, efficiency, and safety.