Untitled Ite<b>AI-Driven Real-Time Chemical Injection Optimization for Flow Assurance in Niger Delta Mature Field Pipelines</b>m

This study develops an AI-driven framework for real-time optimization of chemical inhibitor injection in multiphase pipelines of Niger Delta mature oil fields to mitigate hydrate formation, wax deposition, and corrosion risks. Building on a validated XGBoost surrogate model for slug flow prediction derived from prior CFD simulations, the framework integrates real-time sensor data with reinforcement learning to dynamically adjust dosing rates of thermodynamic and kinetic inhibitors. The model achieves 92% accuracy in recommending optimal injection rates, reducing chemical consumption by an average of 28% while maintaining flow assurance thresholds under simulated gas breakthrough and high water-cut conditions. The proposed system provides a low-cost, deployable digital tool for indigenous operators, supporting cost reduction, production stability, environmental compliance, and alignment with the Petroleum Industry Act (2021) and Decade of Gas initiative.