Machine learning agents to support efficent production management: Application to the Goliat's asset

GOLIAT is an offshore production field that spans from the subsea wells up to a complete process plant installed on a FPSO. Due to the comprehensive instrumentation installed on the plant, it is the perfect case study to test an innovative agent based software architecture able to support production management. The modularity and the scalability provided by the agent based architecture guarantees the applicability of the method to any part of the plant. Each agent is in charge of supervising a specific or a group of equipment and is fed by the real-time data coming from the field. These data are then analysed through Machine Learning and Deep Learning algorithms which are incorporated within the agents. The machine learning algorithms estimate the current state of the equipment and provide a set of KPIs in order to understand both the production efficiency and the health status of the machines. Furthermore, learning from the observations of the state transition paths which happened in the past, the agents are capable of predicting the most likely future state. The latter capability is fundamental to prevent unplanned shutdowns and optimize the maintenance plans. On the basis of the estimated current state, each agent can also provide a list of actions targeted to maximize the efficiency from an "equipment" point of view. The actions coming from all the agents can then be collected and negotiated in order to maximize the production from a "plant" point of view. The negotiating algorithms are implemented in a super-agent that can support a human operator in the day-by-day management tasks of the plant. Even though the negotiating capabilities will be implemented in the future version of the application, the modular nature of the system guarantees an easy integration of the super-agent inside the agent's framework. The paper will present the results of the agent framework in terms of the robustness of state estimation and the correctness of the computed KPIs.