Petroleum engineers

Analyze production data to improve oil and gas extraction and reservoir recovery

AI systems are highly capable at analyzing production data patterns, identifying inefficiencies, and recommending optimization strategies. Machine learning models can process vast datasets from sensors and historical records to suggest extraction improvements, with engineers primarily reviewing and approving recommendations.

BLS evidence: Petroleum engineers analyze data to improve oil and gas production and reservoir recovery, and research new ways to recover more oil and gas.

Monitor well production and troubleshoot underperforming operations

AI can continuously monitor production metrics from IoT sensors, detect anomalies, and diagnose common performance issues using pattern recognition. Troubleshooting recommendations can be generated automatically for standard problems, though complex or novel issues still require human expertise and on-site assessment.

BLS evidence: Production engineers monitor wells' oil and gas production, and if wells are not producing as much as expected, they figure out ways to increase extraction.

Estimate recoverable oil and gas volumes from reservoirs

AI can process geological surveys, seismic data, and reservoir characteristics to estimate recoverable volumes using established models and probabilistic methods. These calculations are data-intensive and rule-based, well-suited to AI, though engineers review estimates for reasonableness and adjust for uncertainties.

BLS evidence: Reservoir engineers estimate how much oil or gas can be recovered from reservoirs and monitor operations to ensure optimal recovery.

Develop plans to drill in oil and gas fields and recover resources

AI excels at analyzing geological data, optimizing drilling trajectories, and generating recovery plans based on reservoir models. Modern systems can process seismic data and production histories to recommend drilling strategies, though human engineers still validate plans against operational realities and risk factors.

BLS evidence: Petroleum engineers develop plans to drill in oil and gas fields, and then to recover the oil and gas, including selecting drilling methods and equipment.

Design facilities and wells to extract oil and gas from underground reserves

AI can generate facility and well designs from geological data and engineering constraints, optimizing for extraction efficiency. However, designs require human validation for site-specific complexities, safety considerations, and integration with existing infrastructure that AI cannot fully assess without physical presence.

BLS evidence: Petroleum engineers design facilities to extract and produce oil and gas from reserves deep underground, and determine the best methods of extraction through wells.

Ensure drilling processes are safe, efficient, and environmentally sound

AI can monitor drilling parameters for safety and environmental compliance, flagging deviations from protocols and optimizing for efficiency. However, ensuring processes are safe and environmentally sound requires human judgment on risk assessment, regulatory interpretation, and real-time decision-making during drilling operations.

BLS evidence: Drilling engineers ensure that the drilling process is safe, efficient, and minimally disruptive to the environment.

Oversee well completion processes including tubing and hydraulic fracturing

While AI can assist with planning completion processes and optimizing parameters like fracturing fluid composition, the actual oversight requires real-time physical presence at well sites, coordination with field crews, and judgment calls on equipment performance that cannot be delegated to remote AI systems.

BLS evidence: Completions engineers decide how to finish building wells so that oil or gas flows up from underground and oversee work to complete the building of wells.

Ensure oilfield equipment is installed, operated, and maintained properly

Ensuring proper installation, operation, and maintenance requires physical inspection of equipment in harsh field environments, hands-on troubleshooting, and coordination with crews. AI can monitor sensor data and flag issues, but the verification and corrective actions demand human presence and mechanical expertise.

BLS evidence: Petroleum engineers ensure that oilfield equipment is installed, operated, and maintained properly.

Visit drilling and well sites to meet with engineers, workers, and customers

Physical site visits require human presence in remote, often harsh environments to inspect operations, build relationships with field personnel and clients, and make observations that cannot be captured by sensors or video. This interpersonal and physical task is fundamentally unautomatable.

BLS evidence: Petroleum engineers often visit drilling sites to meet with other engineers, oilfield workers, and customers, sometimes traveling to remote or international locations.