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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.

Evaluating, analyzing and monitoring drill bit performance is crucial to ongoing development of new bit designs, materials optimization and component selections, which increase ROP and bit life, while reducing NPT and well costs. Digitally enabled automated offset analysis helps to visualize and understand bit performance at scale and across geographies.

As ML applications become easier to access and use, they are setting the stage for rapid scaling across the E&P industry to assist the human decision-making process. A new ML-based drilling system recommender illustrates how this scaling is improving drilling performance and reducing energy extraction costs.

As ML applications become easier to access and use, they are setting the stage for rapid scaling across the E&P industry to assist the human decision-making process. A new ML-based drilling system recommender illustrates how this scaling is improving drilling performance and reducing energy extraction costs.