Ben Yuxing

Drilling State Classification with Machine Learning Yuxing Ben, Chris James, Dingzhou Cao Advanced Analytics and Emerging Technology, Anadarko Petroleum Corporation

The sensors on drilling rigs and production sites are leading oil and gas companies to mine so-called big data. Leveraging historical time series data and real-time drilling data can help drilling engineers improve rig and well delivery efficiencies; however, it can also help geoscientists understand the geophysical properties of the reservoir. In this case study, we describe how to use machine learning to classify drilling states. We investigated several machine learning methods and architectures including Random Forest tree models, Convolutional Neural Networks, and Recurrent Neural Networks which were then tested against 15 million rows of real, labeled drilling time-series data. We found that machine learning models were superior to rule based models. For wells drilled in two different onshore basins, the accuracies of our in-house rule based models were 70% and 90% respectively, while the accuracies of machine learning models were over 99%. The best identified machine learning model has been deployed in a drilling analytics platform and used to automatically detect the drilling state in realtime for use by Drilling Engineers to evaluate and analyze well performance.