Data-Driven Optimization for Oilfield Operations

Abstract Field operations and logistics optimization in the oil and gas industry are complex by nature. They involve high-risk dynamic variables and significant resource limitations. This paper introduces two interrelated AI-driven optimization models designed to increase operational efficiency and decision-making in field operations and consumption tank trucking. The first model focuses on increasing worker efficiency using advanced prioritization techniques. It models behavioral study data and iterative reinforcement learning to improve task planning and execution. Key innovations include Fibonacci prioritization for task scoring, K-means clustering for spatial analysis, and creating a Prophet model to predict production trends and anomalies. Rigorous testing with simulations demonstrates the model's ability to maximize production output, reduce costs, and increase safety by overcoming challenges such as parameter tuning and managing large datasets through the consolidation of data sources. The second model addresses consumption tank trucking optimization using OptaPlanner (Geurts et al. 2020), an open-source framework that uses heuristic algorithms such as local search, simulated annealing, and Tabu search. This solution improves route planning, task prioritization, and resource allocation for specific domain-related constraints like tank rates and delivery schedules. The model aims to dispatch and deliver consumption-based liquids like methanol, which are key to ensuring smooth operations in the field, such as preventing pipelines from freezing. The model is also enhanced by reinforcement learning techniques for dynamic improvements over time. Deployment through the Java Spring Boot framework on AWS ensures scalability and integration with real-time data input. Together, these models demonstrate the potential of combining reinforcement learning methods and predictive analytics in oil and gas to optimize real-time inputs and resource utilization in this high-risk environment.