Machine learning model helps ConocoPhillips optimize ROP in Eagle Ford operations

Last year, ConocoPhillips successfully developed a data-driven machine learning model for predicting ROP in the vertical section of a well. According to Eric Muller, Data Analytics Engineer at ConocoPhillips, the model has already improved drilling performance, with the operator seeing a significant increase in ROP in the vertical section for select Eagle Ford wells. Speaking to DC from the IADC Drilling Engineers Committee (DEC) Q4 Tech Forum in Houston on 16 November, Mr Muller discusses the key parameters that were considered in developing the model, as well as the ways in which the model fits the operator’s overall vision for closed-loop drilling and completions.