DEPARTMENT OF PETROLEUM ENGINERING

Worldwide efforts to reduce carbon emissions require technologies that can substantially decrease human-caused CO₂ releases while preserving energy reliability. Carbon Capture and Storage (CCS) presents an interim solution by capturing carbon dioxide from industrial facilities and storing it permanently underground in geological structures. This study examines the viability of storing CO₂ in a typical depleted oil field in Nigeria's Niger Delta Basin using sophisticated compositional modeling through Computer Modelling Group (CMG) software. A detailed three-dimensional reservoir model was constructed using geological, rock property, and production data to simulate extended-term CO₂ injection, plume movement, and entrapment patterns. The research assesses actual storage volumes, injection limitations, pressure changes, and the roles of various trapping methods—including structural containment, residual entrapment, dissolution, and mineralization—across a century-long timeframe. Findings show storage capacity ranging from 5.5 to 11.2 million tonnes of CO₂ with pressure remaining safely under fracture thresholds, confirming reliable containment with negligible escape potential. Parameter studies demonstrate that variations in rock permeability, remaining oil content, and injection speeds significantly affect storage performance and CO₂ distribution patterns. The results validate that Nigeria's depleted petroleum reservoirs offer suitable geological and technical conditions for secure, effective carbon sequestration. This research provides a simulation-driven methodology for nationwide CCS implementation, advancing Nigeria's progression toward reduced-carbon energy systems and adherence to global climate commitments.

This research presents a comprehensive systematic review of artificial intelligence (AI) techniques and algorithms employed in predictive analytics for drilling hazard management, specifically focusing on stuck pipe incidents, lost circulation events, and wellbore instability. Drilling hazards collectively account for 30-40% of non productive time (NPT) in global drilling operations, costing the oil and gas industry approximately $8-12 billion annually. Traditional monitoring systems rely on reactive, empirical approaches that fail to provide early warnings, while modern drilling operations generate 1-2 terabytes of data per well, creating opportunities for AI-based predictive solutions. Through systematic analysis of 78 peer-viewed research papers published between 2010-2024, this study evaluates the performance characteristics, implementation challenges, and economic viability of various AI algorithms including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, ensemble methods, and deep learning approaches. The research reveals a clear performance hierarchy among AI methods, with deep learning achieving the highest accuracy rates (90-97%) but requiring substantial computational resources and datasets exceeding 50,000 examples. Traditional neural networks demonstrate optimal balance between performance (88-94% accuracy) and practicality, making them the most widely adopted approach in commercial implementations.