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TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractFollowing the reengineering of ENI's Development Management System, which occurred in the early 2004, ENI has developed an IT integrated Project Management System to support Field Development Projects. ENI's Development Management System promotes a stage&gate approach to the development of a field, starting from the assessment of the value of the field, the identification of the viable development scenarios, the definition of the selected development strategy and the execution of the project (including the construction of the facilities and the hand-over to operations). At the end of each stage, a predefined set of data and documents have to be prepared by the project team to be submitted to a Decision Gate in order to support an informed decision by ENI's executives and authorize the start of the following phase.The IT system has been developed with a Web Portal technology and it assists: o the multidisciplinary team involved in the project (geologists, reservoir and drilling engineers, facility & construction specialists, HSE consultants) in the daily work. In fact, it provides web based tools to share and verify project documents; develop and control the schedule baseline of the project; automatically track cost expenditures against budget values; document and review risks and lessons learned; manage milestones, issues and project changes; o the executives involved in decision making. In fact, it provides a high level dashboard with an integrated visualization of the last updated schedule and cost figures for the project, as well as direct access to the main project documents relevant for the Decision Gate of the current project phase. The system integrates the document management capabilities of Domino; extends the planning and control functions of MS Project; automatically imports the budget/actual expenditures from SAP R3; authorizes and profiles users according to their role. It has been used to support 50 project phases across 34 projects.

Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is relevant to develop algorithms that are useful for actual industrial processes. In this work, we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni’s Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.

Objective/Scope Despite tremendous efforts, AI-based predictive maintenance is still not fully exploited in Oil&Gas plants worldwide. The reason mainly relies on the fact that predictive maintenance algorithms need many examples of failures to be trained on, and this is not always the case. For this reason, we developed an efficient unsupervised approach for predictive maintenance, based on deep learning algorithms and applied successfully to predict and anticipate the failures of a coalescer of an Eni's offshore plant. Methods/Procedures/Process Our method is based on a Recurrent Neural Network (RNN) autoencoder architecture, coupled with clustering algorithms. The RNN is based on a combination of two algorithmic steps, respectively called encoder and decoder. The encoder reads multivariate chunks of data and summarizes them in a vector of fixed length, named context vector. Then, the decoder brings this context vector and reconstructs the input signals. Once the reconstruction error is minimized, we cluster context vector by choosing an optimal number of clusters and associating them to the operating conditions of the equipment, in particular by distinguishing ‘healthy’ from ‘faulty’ states. Results/Observations/Conclusions We applied the aforementioned workflow to distinguish the operating conditions of a small equipment in an Eni's offshore plant. This equipment, an electroastic coalescer, suffered repeated troubles during the first phases of plant start-up. We picked up all the sensor measurements available for the coalescer (pressures, levels, temperatures) with very tight sampling (10 seconds resolution) and trained the RNN architecture on 9 months of data. After the application of a suitable clustering method on the context vector minimizing reconstruction error, we were then able to detect up to 5 different operating conditions of the coalescer, associating them to healthy and faulty states of it. In particular, the method was able to authomatically cluster the failures periods of the coalescer, with an advance of around 4 hours before the failures occurred. Novel/Additive Information ‘Effective unsupervised learning – learning without labelled data – remains a holy grail of AI’ (Andrew Ng, WIPO Technology Trends 2019, Artificial Intelligence). We tried to do a step forward in the application of unsupervised approaches to predictive maintenance of industrial equipments by developing an innovative deep learning based method and applying it to a coalescer of an Oil&Gas plant, getting results that are very promising for massive, large scale application in real production settings.