RESUMEN
In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples.
RESUMEN
The wind energy sector has seen an increasing growth in the last decade and this is foreseen to continue in the next years. This has posed several challenges in terms of skilled and prepared professionals that have always to be up to date in an industry that is constantly changing. Thus, teaching tools have gained an increasing interest. The present research reviewed the state of the art in terms of digital interactive training tools pinpointing that the existing options do not feature the user involvement in the development of the training material. Hence, the main aim of this paper is to develop and test an innovative method based on gamification to increase wind energy sector industrial skills, providing a digital interactive environment in the form of a new user-friendly software that can allow its users to train and contribute to the teaching and learning contents. The first methodological step deals with the associated background studies that were required at strategy implementation and development stages, including market analysis and technology trade-offs, as well as the general structure and the implementation steps of the software design. Obtained results pinpointed that with minimal use of web-based database and network connectivity, a mobile phone application could work in the form of a time-scored quiz application that remotely located staff at wind energy farms could benefit from. The technological innovation brought by this research will substantially improve the service of training, allowing a more dynamic formative management contributing to an improvement in the competitiveness and a step towards excellence for the whole sector.