AI helps design the long-laterals holding up US shale

Artificial intelligence is expanding beyond traditional equipment diagnostics to influence critical well design decisions in U.S. shale, with service companies deploying machine learning to optimize increasingly complex drilling and completion operations. The innovation comes at a crucial time, as 25% of Midland Basin wells and 8% of Delaware Basin wells now reach three miles or longer.

While these extended laterals show some per-foot production degradation compared to shorter wells, the impact is minimal in high-quality rock, with Tier 1/2 reservoirs showing only 4-10% degradation over 24 months. ExxonMobil leads the charge with 135 long laterals drilled in 2024.

The industry's ability to execute these complex wells efficiently has become increasingly critical as Occidental CEO Vicki Hollub warns that U.S. energy independence could be at risk within five years as shale production plateaus. Current Permian production of 6 million barrels per day is expected to peak at just over 7 million barrels per day by decade's end, according to Jefferies' Pete Bowden.

The remaining drilling inventory in the Permian varies significantly by region, with the Delaware sub-basin holding 10-25 years of inventory and the more mature Midland sub-basin containing 8-15 years, according to ConocoPhillips and Enverus data. This finite inventory is driving operators to maximize recovery from each well, with AI helping optimize spacing, completion designs, and production management.

Industry leaders from Halliburton, NexTier, and ChampionX report that AI applications now range from "push-button" fracs to production optimization, enabling operators to push the boundaries of lateral length and well spacing.

However, the rush to implement AI faces a fundamental challenge in the form of fragmented and poorly organized land data – a legacy of the Permian's complex ownership history. Success in extended lateral drilling requires precise understanding of mineral rights, surface access, and legacy wellbore locations, with incomplete or inaccurate land records potentially creating legal and operational risks that could undermine even the most sophisticated AI applications.

The outperformance of companies like Texas Pacific Land also highlights how strategic land position and well-organized property data have become critical assets in enabling technical innovation.