IS WORK OVER?

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

Petroleum 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.

TaskShareAIPoints
Reservoir, rate and the well programme.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
Rig, mud and lost circulation.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Completion, tests and the ops report.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
Production, water cut and the workover.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
HSE, blowout and the drill that is not a game.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Not killing the well, and signing the programme.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%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

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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