Transforming OEE Optimization in Oil and Gas: Integrating AI and Sensitivity Analysis for Improved Performance Analysis

This paper presents an AI-driven approach aimed at improving Overall Equipment Effectiveness (OEE) in oil and gas production facilities. The study focuses on controllable factors affecting OEE, specifically internal unplanned shutdowns and slowdowns. For the analysis, four plants with varying capacities were selected. Data was then used to engineer monthly-derived features to analyze temporal dynamics. Feature selection was refined through Pearson’s correlation and input from Subject Matter Experts (SMEs) to ensure impactful features. Three models—Polynomial Chaos Expansion, Sparse Approximation, and Gaussian Process Regression—were developed and validated for each plant using Leave-One-Out Cross-Validation (LOO CV) and evaluated with Mean Absolute Percentage Error (MAPE). Sobol sensitivity analysis highlighted primary features influencing production loss variance. The model exhibiting the strongest feature-target relationship and lowest MAPE was selected. Scenario planning was employed to simulate the effects of hypothetical adjustments on future production loss, providing insights for strategic interventions. The findings revealed more predictable outcomes in worse performing plants and challenges in stable performance plants. The study highlights limitations such as monthly data reliance and suggests that daily data and early indicators could improve accuracy. The study underscores the necessity for customized optimization strategies tailored to the unique operational characteristics of each plant.