This session presents a data-driven approach to enhancing oilfield performance using deep learning techniques. The aim of the research is to develop a predictive model capable of accurately forecasting daily oil, gas, and water production rates from petroleum wells, using historical field data. By leveraging a neural network trained on the Volve Field dataset, the study demonstrates how artificial intelligence can uncover complex production trends and inform operational decisions. The goal is to showcase the potential of machine learning in optimizing hydrocarbon recovery, managing associated fluid production, and supporting more efficient, sustainable petroleum operations. The session will also explore how such approaches can be adapted to revitalize production strategies in Nigerian fields, contributing to energy security and sustainable development.