Automation index
How much of a Petroleum engineer’s job can AI already do?
A reservoir model comes earlier. Drilling without killing the well do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Petroleum engineer
25%
AI can already do 25% of this job.
#232 / 623
View rankingPetroleum engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Reservoir, rate and the well programme.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Rig, mud and lost circulation.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Completion, tests and the ops report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Production, water cut and the workover.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
HSE, blowout and the drill that is not a game.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not killing the well, and signing the programme.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model reservoirs
- well programmes
- drilling mud
- completion
- workover
- water cut
- well HSE
- production tests
Sources for this job· O*NET 17-2171.00· ESCO/ISCO 2146
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511