This presentation will first introduce machine learning and its applications in oil and gas industry in the past few years, then share the experiences and learnings from three examples in real-time drilling and hydraulic fracturing.

• For real-time drilling, the operator developed a general machine learning model to classify rig states. Time series data was gathered from 40 wells with 30 million rows representing three US onshore basins. The model proved to have over 99% accuracy after being deployed on all the company's unconventional drilling rigs. The model predicts real-time rig states every second with tolerant latency. The results are used to generate drilling KPIs in real time for drilling engineers in the office, aid in directional analysis, and optimize drilling operations.
• Continuous learning was used to predict wellhead pressure to avoid screenout and optimize completion costs in real time. More than 100 hydraulic fracturing stages were selected from several wells completed in the Delaware Basin. The wellhead pressure can be predicted with an acceptable accuracy by a neural network model. The ML model was tested in the Cloud, where real-time streaming data such as slurry rate and proppant concentration are gathered. The computation is fast enough that real-time wellhead pressure can be predicted.
• System identification was combined with model predictive control to allow the engineers to adjust the pumping schedule and optimize hydraulic fracturing costs.

The presentation will conclude with several takeaway points including future research and development directions for machine learning applications in oil and gas industry.