AI Driven Approach for Drillstring Survival Modeling

Abstract Well construction has always been a challenging endeavor, with operators seeking the most efficient and cost-effective ways to optimize their activities. A critical factor in achieving this is balancing tool costs with reliability by selecting the best combination of drill-bit and bottom hole drive types. Manually selecting the drill-bit and BHA involves intensive analysis of vast amounts of data that must be processed, reviewed, quality-checked, and evaluated. This manual process does not guarantee the most optimal setup regarding operational efficiency and costs. This paper describes an AI statistical-base recommendation driven from the historical data for the most cost-efficient drill-bit and BHA selection in just a few seconds. This ensures achieving high performance from shoe to shoe, breaking new records more often than before.