Optimizing the dynamic behavior of wells and facilities with machine learning and agent negotiation techniques

The paper proposes an approach to deal with the day by day dynamic behaviour of Oil & Gas assets, providing support for optimized decisions on wells and facilities. The approach is based on: o A set of software agents, trained with a machine learning approach to understand the health status of the components of the reservoir/well/plant system and capable of proposing optimization actions for the corresponding subsystem; o An inter-agent negotiation approach, capable of evaluating the optimization actions of the single agents in the wider picture of the overall optimization of the producing asset. The paper will describe how this approach has been implemented, as well as an example application.