United StatesEnergy and utilities2020 – ongoing

ConocoPhillips

Eagle Ford machine-learning drilling optimization

ConocoPhillips reported 65% fewer premature drilling-motor failures in Eagle Ford tests

WorkingCompany's own claimOfficial ↗

Problem

Harsh drilling conditions caused motors to fail prematurely. The Eagle Ford team also wanted to drill vertical well sections faster.

Approach

Engineers and analytics specialists built machine-learning models using historical well performance, drilling-recorder data and geological information. Crews used the predictions to adjust operating parameters in a drilling optimization initiative that began in 2020.

Results

65%reduction in premature drilling-motor failures during tests, per ConocoPhillipsCompany's own claim[1]

20%increase in vertical drilling penetration rate during tests, per ConocoPhillipsCompany's own claim[1]

more than $3 millionEagle Ford drilling costs saved since implementation, per ConocoPhillipsCompany's own claim[1]

Takeaway

The reported gains came from applying predictions to drilling decisions. The motor-failure reduction was measured during tests.

Sources

How ConocoPhillips is leveraging data to optimize its Lower 48 operations
ConocoPhillips9 Aug 2023Company publication

Source summaries

[1]ConocoPhillips9 Aug 2023

ConocoPhillips described its drilling models and field tests. It reported fewer premature motor failures, faster vertical drilling and cumulative drilling-cost savings.

Full details are in the original sources.

Added 1 Nov 2026 · Updated 1 Nov 2026