Naranjo-Zolotov, Mijail JuanovichOnyeri, Ugochukwu Obinna2025-11-182025-11-04http://hdl.handle.net/10362/190950Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis study examines the role of Artificial Intelligence (AI) in optimising production within the upstream oil and gas industry. Using a sociotechnical lens, it draws insights from in-depth interviews with nine professionals across engineering, reservoir management, and digital transformation. Findings show that AI enhances workflow efficiency, predictive maintenance, and decision-making, but its success also hinges on human factors like trust, digital skills, and organizational readiness. Despite its potential, the research reveals gaps in workforce competency and cultural acceptance, challenging assumptions of industry-wide AI readiness. Integration with digital twins and edge computing offers further gains but requires better alignment of people, processes, and technology. This study bridges technical and organizational views on AI adoption and offers practical guidance for achieving responsible, sustainable productionengArtificial IntelligenceAIUpstream Oil and GasProduction OptimisationOperational EfficiencyHuman-AI CollaborationTrustDigital AdoptionSustainabilitySDG 7 - Affordable and clean energySDG 12 - Responsible production and consumptionSDG 13 - Climate actionmaster thesis204073111