Machine Learning Predicts Well States, Enabling Proactive Maintenance

A leading player in the oil and gas industry faced frequent unplanned downtime and disruptions in their well operations, resulting in reduced productivity and profitability. They wanted to move from reactive to predictive maintenance by forecasting well states based on field parameters and climatic conditions. However, the complexity of the data made deriving actionable insights difficult. To address these challenges, Utthunga implemented an end-to-end machine learning solution tailored to the client’s needs.

After conducting in-depth analysis of the client’s vast data assets using technologies including TensorFlow, NumPy and Pandas, we developed customized LSTM, RNN, GRU and CNN models. By incorporating both predicted field parameters and forecasted weather data, the models achieved an impressive accuracy of over 90% in predicting well state in our lab tests. Our solution provided the client with accurate, real-time visibility into predicted well conditions, enabling them to optimize maintenance planning and maximize resource utilization.

Also, the predictive capabilities helped drive substantial cost savings, improve worker safety, and resulted in a 15% increase in well productivity. With this transformative solution, we empowered the client to unlock the full potential of their data. Our machine learning models delivered actionable insights previously hidden in the complexity of well data. This facilitated optimization of oil and gas operations through data-driven and predictive maintenance capabilities.