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Optimizing BHA and fluid selection with a machine learning-based drilling system recommender

An innovative digital solution prepares an operational database and stores performance results of drill bits, motor power sections, RSS, BHA configurations and drilling fluids. After parameters of a planned drilling run are set, a multidimensional distance-based approach selects similar previous drilling runs for analysis. The process enables rational technology selection recommendations. ....

New York , United States , Fatma Mahfoudh , Sergey Makarychew Mikhailov , Greg Skoff , Cheolkyun Jeong , University Of Colorado , International Drilling Conference , International System Of Units , Query Language , International System , Dataiku Data Science Studio , Microsoft Azure , Azure Devops , K Th Nearest Neighboring , Light Gradient Boosting , Random Forest , Extreme Gradient Boosting , West Texas , Gulf Coast , Python Streamlit , Drilling Conference , Machine Learning ,