Petroleum and Natural Gas Refining Plant Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Petroleum and Natural Gas Refining Plant Operators (ISCO-08 3134) score an average of 0.29 on a 0–1 exposure scale — more exposed than about 55% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Analysing sample products, performing tests, recording data and writing production logs.”

Scores 0.47 on the 2025 scale. The task "Analysing sample products, performing tests, recording data and writing production logs" involves several key components: data analysis, testing, and documentation. These elements indicate a moderate potential for partial automation using Generative AI tools. Comparing this task with similar tasks in the context, such as "Maintaining production process documentation" (score 0.455) and "Performing tests using testing support tools" (score 0.665), provides insight into the automation potential. AI can assist significantly with data-driven elements such as analyzing test results and maintaining records, but human oversight is critical for nuanced product assessment and ensuring accuracy in documentation, akin to the roles reflected in tasks like "Evaluating quality" (score 0.3525) and "Maintaining technological process documentation" (score 0.445). The physical inspection and subjective judgment involved in product testing are not fully automatable, which limits the overall score. Given the task's top-down alignment with structured data handling but need for human interpretation and decision-making, a score of 0.45 reflects the realistic potential for partial automation, acknowledging both AI's strengths in data analysis and the essential human role in testing and judgment. In a high-income country context, where digital infrastructure is robust, this balance assumes a prevalent integration of AI tools alongside human expertise.

Moving fastest, 2023 → 2025

“Analysing sample products, performing tests, recording data and writing production logs.”

Model capability on this task changed by +0.17 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3134, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Petroleum Pump System Operators, Refinery Operators, and Gaugers
• Gas Plant Operators
• Pump Operators, Except Wellhead Pumpers
• Gas Compressor and Gas Pumping Station Operators

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Petroleum and Natural Gas Refining Plant Operators sit at the 55th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Petroleum and Natural Gas Refining Plant Operators rank in the 55th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Analysing sample products, performing tests, recording data and writing production logs.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Petroleum and Natural Gas Refining Plant Operators". https://singulariki.com/gradient/3134-petroleum-and-natural-gas-refining-plant-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.