This paper reports the development and test of an advanced workflow for prediction of hazardous events like flaring, providing the main prescriptions in order to monitor, mitigate and root-cause these issues. The tool is able to forecast in advance the insurgence of dangerous process upsets able to highly affect the normal field operations, thanks an innovative approach based on Big Data analytics and machine learning algorithms. The actions prescribed by the algorithm can significantly reduce and avoid flaring events, improving operation safety, production and the overall asset value.
The flaring prediction and prescription tool has been developed creating a strong pipeline that implement four modules: the model conceptual design, data processing, features selection and extraction and predictive model development and validation. During the first phase, the number of classes for flaring classification, the prediction horizon and the input time window have been set in order to achieve the best functionality of the tool considering the physical phenomena forecasted. The second module allow to prepare, manage and pre-process real-time raw data from field, discarding non-informative signals applying a linear interpolation and ad hoc developed filters. Feature selection has been performed in order to identify the best subset of weak and strong signals, which make the prediction algorithm robust and accurate. This diagnostic phase has been performed by the pre-application of an innovative classification algorithm. The last module is the final development of a tuned and cross-validated classification model, based on Artificial neural networks.
The framework pipeline developed has been implemented on real time data coming from an operating field in southern Europe. The effectiveness achieved by the robust architecture of the tool allow to overcome some main issues such as: lack/status of data, rapid dynamic of physical phenomena analysed and complexity of flaring network system. The tool has been able to identify and root cause in advance the insurgence of weak signals that cause consequently dangerous overpressures within the producing system, giving to field engineers the possibility to highlight the operating parameters that have to be modified or managed.
Flaring networks represent the main over-pressure relief system of an upstream treatment plant. Hence, the implementation of this big data analytics framework is able to maximize the operational safety of the plant, predicting the hazardous events with prescription of mitigation actions. Moreover, it allows to maximize the asset value, granting steady operations and consequently optimum production and the lowest environmental impact.
