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dc.contributor.advisorGHARIB, Toufik-
dc.contributor.authorMERAD, Mohammed-
dc.contributor.authorARIF, Lakhdar-
dc.contributor.authorSOLTANE, Mohammed Messaoud-
dc.date.accessioned2024-10-02T14:20:25Z-
dc.date.available2024-10-02T14:20:25Z-
dc.date.issued2024-
dc.identifier.urihttps://dspace.univ-ouargla.dz/jspui/handle/123456789/37042-
dc.descriptionFaculty of Hydrocarbons, Renewable Energies and Earth and Universe Sciences Department of drilling and Petroleum workshop mechanics Dissertation submitted in partial fulfilment of the requirement for the Master's Degree in field of Hydrocarbons Specialty: Drillingen_US
dc.description.abstractArtificial intelligence (AI) has revolutionized drilling operations and well integrity management in unconventional reservoirs, especially in shale gas exploration. By harnessing machine learning algorithms and real-time data analytics, AI enables informed decision making, predicts operational challenges, and optimizes drilling parameters. In the dynamic environment of unconventional reservoirs, AI adapts to changing conditions, enhancing safety and efficiency. It proactively identifies hazards, mitigates risks, and ensures reliable well structures. Post-drilling, AI facilitates continuous learning, driving cost savings and productivity gains. Overall, integrating AI-based monitoring and control systems holds immense promise for sustainable and efficient shale gas explorationen_US
dc.language.isoenen_US
dc.subjectLife Cycleen_US
dc.subjectArtificial intelligence-baseden_US
dc.titleArtificial intelligence-based monitoring and control of drilling operation and well integrity in unconventional reservoirs shale gas exploration ''A Comprehensive Case Study on Well Integrity Throughout the Full Life Cycle'en_US
dc.typeThesisen_US
Appears in Collections:Département de Forage et Mécanique des chantiers pétroliers - Master

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