A systematic review of cognitive computing for reservoir characterisation and exploration optimisation

This study presents a systematic review of cognitive computing applications in reservoir characterization and exploration optimization. Using a PRISMA-guided literature review of more than 180 peer-reviewed studies published between 1980 and 2025, we evaluate how cognitive systems integrate machine learning, natural language processing, and reasoning frameworks to support subsurface decision-making. The review synthesizes applications including seismic facies classification, intelligent well-log correlation, petrophysical property prediction, and integrated geological modeling. Beyond summarizing existing work, the study proposes a conceptual cognitive workflow for petroleum data integration and highlights emerging research directions, including explainable AI, hybrid human-AI decision systems, and digital twin integration.