Statistical Machine Learning for Improving Failures Detection

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The present project seeks to innovate the oil field sector by integrating statistical machine learning and data science into its day to day operations. This project consists of embedding artificial intelligence technology in real-time sensors of running drilling and fracturing equipment. These sensors will be capable of detecting microscopic metallic particles and contaminants in any fluid. As such, the abundant amount of testing data will include: mechanical data, metallurgical data, geological data, fluid property data, environmental data, operational data, CAN Bus data (subsystems that span engine, electrical, transmission) as well as electrochemical impedance data. It is our vision that these sensors will not only detect trends indicating imminent failure of components, but will also learn from the data. This problem won't be without any challenges. The biggest struggle will be to organize and select the best features amongst this Big Data set to adequately predict machine failures. Nonetheless, to surmount this issue, we plan to implement the state of art unsupervised learning algorithms for feature selection. Once the features have been chosen optimally, we will combine different supervised learning algorithms to create an adaptable and efficient learner capable of improving itself with incoming data.