Oil & Gas

Oil & Gas


Predictive Maintenance Strategy

The client belongs to the oil and gas industry, which deploys large and expensive machinery to extract oils from the wells. The oil and gas industry faces a major problem of equipment failure that occurs due to the inefficiency of oil wells and their performance at a subpar level. In order to optimize the high value machinery for manufacturing and refining oil products, the client wants to build a machine learning model for predictive maintenance.

Technology Used

Python, SQL (for data extraction)


The problem of predictive maintenance was solved with the help of a survival analysis technique which is widely used to detect machine/product failures. With the telemetry data extracted through sensors, a steady stream of historical data was used to train our machine learning model.  The final model was based on the non-parametric approach called Kaplan-Meier estimator which performed better than some other parametric models. This survival analysis model predicts the failing of machine parts and will notify the operators of timely maintenance in order to avert oil losses, which in turn led to a boost in oil production and prevent further loss.

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