Rotary Drill Operators, Oil and Gas
47-5012.00Median wage $67,890/yr12,600 employed (US)Rank #877 of 923 scored · top 95% by substitution
Set up or operate a variety of drills to remove underground oil and gas, or remove core samples for testing during oil and gas exploration.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution10
Exposure6
Augmentation33
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
20 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%6
panel mean rating 1.2/5 → substitution pressure 6/100
Technical feasibility todayw 20%6
panel mean rating 1.2/5 → substitution pressure 6/100
Cost vs. human wagew 15%8
panel mean rating 1.3/5 → substitution pressure 8/100
Adoption barriersw 20%inverted — strong barriers lower the score26
panel mean rating 4.0/5 (barrier strength) → substitution pressure 26/100
Sector adoption velocityw 10%7
panel mean rating 1.3/5 → substitution pressure 7/100
Task breakdown (20 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Maintain records of footage drilled, location and nature of strata penetrated, materials and tools used, services rendered, and time required.
46CI 25–67 · exposure 45 · augmentation 63 · importance 4.4/5 · click for rater detail
Maintain records of footage drilled, location and nature of strata penetrated, materials and tools used, services rendered, and time required.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas operators have invested in digital well-logging systems, but adoption of fully autonomous record-keeping remains limited; most drilling operations retain human record-keepers for regulatory compliance and liability reasons. Sector digitization is moderate and lagging relative to software-native industries. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Oil and gas is a capital-intensive, moderately digitized sector; digital rig instrumentation and logging software adoption is steady but not as fast as software/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted tools can help organize sensor data, flag anomalies in drilling parameters, and draft preliminary record summaries, improving speed and consistency. However, the human operator's judgment remains central to interpreting geology and certifying accuracy, so augmentation is meaningful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI and automated logging tools already substantially help operators capture, organize, and analyze drilling data, reducing manual transcription while operators retain oversight of strata interpretation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could extract and organize drilling data from logs and sensor feeds, the task requires judgment about what constitutes a discrete 'stratum' and interpretation of geological samples—decisions that currently demand human expertise. Partial automation of data entry and formatting is possible but falls short of the ≥50% time-saving threshold for end-to-end execution. |
| Task automatability | claude-sonnet-5 | 4/5 | This is a structured data-logging task involving text/numeric entry from sensor readings and observations, well suited to automated logging systems and AI-assisted data entry/transcription with drilling telemetry integration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Drilling records are subject to strict regulatory oversight (SEC, EPA, state oil and gas commissions) and are legally admissible evidence in environmental and liability disputes. A licensed human operator or geologist is typically required to sign off on drilling data, and liability for inaccurate strata identification creates high error-cost asymmetry. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some regulatory recordkeeping requirements exist for well logs but no licensing requirement mandates a human personally record this data, so barriers are moderate-low. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI-assisted record systems still requires substantial human validation and correction, plus ongoing maintenance of logging infrastructure. The all-in cost of deploying and overseeing automated systems remains comparable to or exceeds the cost of a driller or technician maintaining records manually. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated rig data systems and sensors are far cheaper per record than dedicating operator time to manual logging, especially at scale across many wells and shifts. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature production system currently performs comprehensive drilling record-keeping autonomously. Some SCADA and well-logging software assists with automated sensor capture, but human geologists and operators must validate strata identification and manually reconcile discrepancies—this is not reliably deployable end-to-end today. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Digital drilling data acquisition systems and mud-logging software already automate much of this recordkeeping in production rigs, though integration with all manual observations and legacy paper processes still varies by operator and rig age. |
Count sections of drill rod to determine depths of boreholes.
35CI 18–52 · exposure 33 · augmentation 38 · importance 4.4/5 · click for rater detail
Count sections of drill rod to determine depths of boreholes.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas rig operations are capital-intensive, safety-critical, and use long-lived equipment with high switching costs. Adoption of AI/automation in this subsector remains slow and cautious, especially for core drilling operations. Most rigs are still operated by human crews using established protocols. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas field operations are a physically-oriented, moderately slow-adopting sector for digitization compared to information/professional services, though automated tally systems are gradually spreading in more modern rigs. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | A digital display or heads-up system could assist by logging counts and detecting anomalies, but this task is already straightforward enough that augmentation yields limited productivity gain. The operator's primary cognitive load is managing overall drilling parameters, not the counting itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Automated counters and digital tally systems can assist operators by reducing manual counting errors and providing real-time depth tracking, improving accuracy and speed while humans remain in charge of rig operations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Counting drill rod sections is a straightforward numerical task that could be automated with sensors or image recognition, but the task occurs in a physically dynamic, wet, oily environment on active rig floors where camera angles, lighting, and material deformation create substantial challenges. Current vision systems struggle reliably with this in field conditions, and even if automated, this task represents only a small fraction of the operator's work. |
| Task automatability | claude-sonnet-5 | 3/5 | Counting drill rod sections to determine borehole depth is a simple, well-defined measurement task that could be automated via sensors, counters, or automated logging systems, but it's typically embedded in a physical rig-operation context requiring on-site presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas drilling is heavily regulated (API standards, MMS oversight, liability for borehole integrity). Any automation of depth tracking has direct safety and legal liability implications; operators and companies are reluctant to trust automated systems without extensive validation, and regulatory acceptance of automated depth measurement in critical wells is not yet standardized. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for counting drill rods, though safety and accuracy standards on drilling operations create some institutional caution around depth-recording reliability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A sensor or vision-based system would require significant capital investment (hardware, installation, integration with rig IT), plus ongoing maintenance and calibration costs. The labor cost of a single operator performing this task is modest, making the ROI questionable unless the system handles many adjacent tasks simultaneously. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Sensor-based tally systems can be cheaper long-term than manual tracking, but installation, calibration, and integration costs make the near-term cost comparable to having a rig worker perform tallying manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production system reliably counts drill rod sections autonomously on active rigs. Prototype computer vision solutions exist in research contexts, but they do not operate at rig-floor scale with the 99%+ accuracy required for drilling safety. Manual counting remains the industry standard. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated pipe tally and depth-tracking systems exist and are used in modern drilling rigs, but many rigs still rely on manual counting/tallying, especially smaller or older operations, so reliability varies by equipment generation. |
Weigh clay, and mix with water and chemicals to make drilling mud.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Weigh clay, and mix with water and chemicals to make drilling mud.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The oil and gas industry is digitizing, but mud mixing remains primarily a manual, on-site task at drill sites. Automation investment has been slower here than in upstream data systems or remote monitoring, reflecting the physical, variable nature of the work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physical, lower-digitization sector with slow adoption of AI-driven automation for manual rig-floor tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted mixing (via real-time density and viscosity monitoring with suggested adjustments) could meaningfully help operators achieve target properties faster, though the human operator remains the primary actor executing the mix. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with mud formulation calculations or chemical ratio recommendations via decision-support software, but it doesn't materially change the physical mixing labor itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While weighing clay can be automated with sensors and scales, the mixing process requires real-time adjustment based on viscosity, density, and chemical composition feedback that demands human judgment and intervention. Current AI systems lack the embodied dexterity and sensory integration to handle this end-to-end without significant human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task (weighing solids, mixing fluids with chemicals) requiring on-site handling of materials and equipment; no off-the-shelf AI system can perform this physical process end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some automation (weighing scales) is common, but mud quality directly impacts drilling safety and well integrity, creating liability concerns that encourage operator involvement; moreover, field conditions demand human judgment not easily substitutable by rule-based systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but safety regulations, rig environment hazards, and reliance on experienced crew judgment create meaningful organizational and safety friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automation of weighing is cheap, but building a fully integrated, reliable mud-mixing system with sensors and control systems would be capital-intensive and require skilled technicians for maintenance, making it comparable to or more expensive than a skilled operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require specialized robotic/industrial hardware investment far exceeding the marginal cost of a human operator performing this manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some components (weighing via automated scales) exist in production, but no deployed end-to-end system reliably performs mud mixing without human operators managing consistency, chemical reactions, and quality adjustments based on field conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously weighs, measures, and mixes drilling mud on a rig floor; automated mud mixing systems exist as industrial control/robotic equipment, not general AI products. |
Observe pressure gauge and move throttles and levers to control the speed of rotary tables, and to regulate pressure of tools at bottoms of boreholes.
14CI 5–23 · exposure 13 · augmentation 38 · importance 4.5/5 · click for rater detail
Observe pressure gauge and move throttles and levers to control the speed of rotary tables, and to regulate pressure of tools at bottoms of boreholes.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains a capital-intensive, physically remote, and highly regulated sector with mature human workforce and strong safety culture. Adoption of autonomous drilling is slow; most operators still rely on experienced human crews, and digitization of control systems has been incremental rather than transformative. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector where AI/agent adoption for direct equipment control is slow and mostly limited to specialized industrial automation vendors, not general AI tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Real-time pressure monitoring dashboards and predictive sensor analytics can assist operators by highlighting anomalies and recommending throttle adjustments, reducing cognitive load. However, augmentation is limited to decision support; the human remains the primary actor in a safety-critical role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensor analytics and predictive monitoring can provide some decision support (e.g., alerting on pressure anomalies), but it offers limited direct assistance for the moment-to-moment manual throttle/lever control described. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While pressure monitoring is suitable for AI-driven sensor systems, physically moving throttles and levers requires robotic actuation in a dynamic, safety-critical drilling environment with irregular sensor inputs and uncontrolled variables. Current systems can monitor and recommend, but reliable end-to-end automation with equal quality remains beyond today's deployed technology for this physically embodied, high-stakes task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical manipulation of drilling equipment based on continuous sensor feedback in a hazardous field environment; no off-the-shelf AI system can perform this hands-on control task end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas drilling is heavily regulated (OSHA, API standards, environmental rules) with strict liability for equipment failure and blowouts. Operators must be licensed, and legal/regulatory frameworks require human accountability for critical safety decisions, making autonomous substitution difficult to certify and deploy. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas drilling operations carry major safety, liability, and regulatory oversight requirements, and equipment control changes typically require certified engineering and safety sign-off, creating strong barriers to informal AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current sensor systems and pneumatic/hydraulic control systems add capital and integration costs that approach or exceed the loaded wage of a rotary drill operator. Full automation would require expensive robotics and safety-certified control systems, making the all-in cost uncompetitive with human operation today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Automating this requires expensive specialized rig automation hardware/software integration, not cheap AI inference, so costs are not clearly lower than a human operator's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system currently automates this task end-to-end in real oil and gas drilling operations. Automated pressure control systems exist in narrow laboratory or test contexts, but deployed drilling automation still relies on human operators for throttle and lever adjustment in response to real-time gauge readings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While some rigs have automated drilling control systems, these are specialized industrial control systems (not general AI products) and human operators remain the standard for this direct control task; deployed general AI does not perform this. |
Train crews, and introduce procedures to make drill work more safe and effective.
13CI 9–16 · exposure 5 · augmentation 50 · importance 4.5/5 · click for rater detail
Train crews, and introduce procedures to make drill work more safe and effective.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas training remains conservative and human-centered. While some firms pilot digital training supplements, live crew instruction by qualified personnel is the norm. Adoption of AI-led training is nascent and sector-wide shift remains slow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically-oriented sector with slow AI adoption for hands-on crew training and procedure implementation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by drafting safety materials, generating procedure videos, and analyzing incident reports to inform training improvements. These tools can improve trainer productivity and content quality, but human judgment and crew interaction remain central to effective training delivery. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft training materials, safety checklists, and procedure documentation, and simulate scenarios, providing moderate assistance to human trainers who still lead crew instruction. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Training crews and introducing procedures requires real-time interaction, judgment about crew readiness, cultural change management, and on-site adaptive instruction. Current AI lacks the embodied presence, interpersonal responsiveness, and authority needed for safety-critical crew training in hazardous environments. |
| Task automatability | claude-sonnet-5 | 1/5 | Training crews and instituting safety procedures on a physical drill site requires hands-on demonstration, judgment about specific rig conditions, and interpersonal leadership that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations in oil and gas typically require licensed, accountable human trainers and supervisors to conduct and certify crew training. Liability for training effectiveness and accident prevention creates strong regulatory and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety training on oil and gas rigs is subject to regulatory and liability requirements (e.g., OSHA, well-control certification) that typically mandate qualified human trainers and sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted content creation reduces some preparation costs, but the core task—live instruction, authority, crew buy-in, and safety verification—remains labor-intensive. Total cost per trained crew remains dominated by human instructor time and on-site presence. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could cheaply generate training materials or checklists, but the actual training delivery, supervision, and enforcement on-site still requires paid human labor, keeping overall cost comparable to human-led training. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can generate training materials, safety documentation, and procedure drafts, but no deployed system reliably delivers live crew training or manages the interpersonal and organizational dynamics of procedure adoption in oilfield contexts. Human trainers remain essential for this role. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product trains oilfield crews or authors site-specific safety procedures autonomously; this remains a human supervisory and mentorship function. |
Bolt together pump and engine parts, and connect tanks and flow lines.
10CI 10–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Bolt together pump and engine parts, and connect tanks and flow lines.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains a physical, field-based sector with limited automation of hands-on assembly tasks. Adoption of automation in this space is slow, focused on larger infrastructure (remote monitoring), not field-level mechanical tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with minimal AI/robotics adoption for hands-on mechanical assembly tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with documentation, procedure recall, or safety checklists via wearable interfaces, but the core assembly task itself offers minimal augmentation opportunity. The work is inherently manual and requires real-time physical judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with checklists, torque specifications, or diagnostic guidance via AR/tablets, but offers minimal direct augmentation to the physical bolting and connecting work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical assembly and connection of heavy mechanical components in outdoor/field conditions—manipulating bolts, tanks, and flow lines. Current AI and robotics cannot reliably perform unstructured physical assembly at the scale and dexterity needed, especially in variable field environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical assembly task requiring manual dexterity, strength, and fine motor control to bolt heavy equipment and connect flow lines in a field environment; no current AI system can perform this physical labor.aracter |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no formal license is required to perform mechanical assembly, workplace safety regulations and on-site oversight of equipment integrity create moderate organizational friction. Customer expectation for human supervision and inspection adds some protection against pure automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed work per se, oilfield equipment assembly involves safety-critical connections (pressure vessels, flow lines) where errors can cause serious accidents, creating strong organizational and safety-driven barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of field assembly would require substantial capital investment, custom engineering, and site-specific programming—far exceeding the cost of skilled labor for this routine task. The ROI does not exist at current technology levels. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this physical task at scale, so cost comparison favors the human worker who can already do this reliably and affordably relative to any hypothetical automation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs field assembly of drill pump and engine components end-to-end. Specialized industrial robots exist for controlled manufacturing but cannot handle the variability, spatial constraints, and decision-making of field drilling operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical bolting and connecting of oilfield equipment; this remains firmly in the domain of human manual labor with robotics still research-stage for this specific unstructured task. |
Start and examine operation of slush pumps to ensure circulation and consistency of drilling fluid or mud in well.
8CI 0–16 · exposure 5 · augmentation 38 · importance 4.3/5 · click for rater detail
Start and examine operation of slush pumps to ensure circulation and consistency of drilling fluid or mud in well.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Despite digitization trends in energy, physical well-site operations remain heavily reliant on human operators due to safety, liability, and technical complexity; automation adoption is slow and limited to monitoring rather than control. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas drilling operations are a physically intensive, lower-digitization sector where automation of rig floor tasks proceeds slowly compared to office-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Remote monitoring dashboards and IoT sensors can alert operators to fluid parameters, but AI offers limited assistance in the core task of starting equipment and making real-time adjustments to pump operation based on tactile and visual cues. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital dashboards, sensor telemetry, and predictive maintenance analytics can help operators monitor pump performance and detect irregularities faster, aiding but not replacing hands-on judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical control of equipment, real-time sensory monitoring (visual and tactile assessment of fluid consistency), and on-site troubleshooting in a dynamic well environment. Current AI cannot perform these embodied actions or replace the hands-on operator role end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical operation of heavy drilling equipment and hands-on sensory examination (sound, vibration, visual inspection) of slush pumps on a rig, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations have strict regulatory requirements, safety certifications, and licensing mandates that require a qualified, physically present or remotely licensed human operator to assume responsibility for equipment operation and fluid management. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Well control safety is heavily regulated, and equipment failure risk (blowouts, fluid loss) creates strong liability and safety incentives to keep trained human operators directly responsible for this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of physical automation (robotics, remote operation infrastructure) plus integration and safety oversight would far exceed the loaded wage of a rotary drill operator in the field. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor-based monitoring systems add cost on top of the human operator who must still be present to start equipment and respond to conditions, so there is no clear cost advantage yet. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously start and physically operate a slush pump or assess drilling fluid properties in real time on an active well site. Remote monitoring systems exist but do not replace the operator's direct control and judgment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some rigs have automated monitoring/sensor systems that flag pump anomalies, but starting and physically examining pump operation is still performed by human operators in production environments. |
Clean and oil pulleys, blocks, and cables.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Clean and oil pulleys, blocks, and cables.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas operations are capital-intensive but operationally conservative; field maintenance tasks remain largely manual labor with minimal AI adoption in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, low-digitization sector with slow uptake of AI-driven robotics for manual maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with maintenance scheduling or diagnostic monitoring of equipment condition, but the core hands-on cleaning and oiling work offers limited room for meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical acts of cleaning and oiling equipment, though sensor-based monitoring could indirectly inform maintenance schedules, which is tangential to this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of mechanical components in outdoor/field conditions with sensory feedback and dexterity. Current AI systems cannot perform this hands-on maintenance work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical maintenance task requiring hands-on cleaning and lubrication of heavy drilling equipment in a rugged field environment; no current AI system can perform this physical labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, equipment-specific authorization, liability for failures in critical drilling infrastructure, and the need for human judgment on equipment condition create strong adoption barriers even if automation were technically feasible. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this maintenance task, but it occurs on hazardous oilfield equipment where safety protocols and hands-on physical presence create practical barriers to any remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a robot system capable of safely navigating an oil rig and performing detailed mechanical maintenance would be far more expensive than the loaded wage of a skilled drill operator performing this routine task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute for this physical task, so AI cost is essentially infinite relative to human labor performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs physical cleaning and oiling of industrial drill equipment autonomously. Robots exist for some industrial maintenance, but not at the scale or reliability needed for this specific field task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical cleaning and oiling of drilling rig components; this remains entirely manual work done by rig crews. |
Remove core samples during drilling to determine the nature of the strata being drilled.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Remove core samples during drilling to determine the nature of the strata being drilled.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas remains a capital-intensive, geographically dispersed, and regulation-heavy sector with slow digital transformation. Physical automation of drilling-floor tasks lags far behind information-sector adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, low-digitization sector with slow adoption of AI/robotics for manual rig-floor tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with post-sample analysis (e.g., automated core imaging and mineral classification) but offers minimal productivity boost for the physical removal task itself, which is already tightly proceduralized. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with logging, analyzing, and interpreting core sample data once extracted, but offers minimal help with the physical extraction task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Removing core samples is a hands-on physical task requiring precise handling of drilling equipment and fragile geological specimens in a dynamic, safety-critical environment. Current AI lacks the embodied manipulation, situational awareness, and real-time adaptation needed to perform this field task reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring hands-on removal of core samples from drilling equipment in a hazardous field environment, which current AI systems cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, equipment certification, and operator licensing create strong legal and organizational barriers. Oil and gas operations also impose strict liability and insurance requirements for any automated system handling drilling equipment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for sample removal, but safety protocols, equipment certification, and the hazardous drilling environment create operational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A trained rotary drill operator on an active rig commands high wages ($80k–$120k+ loaded). Building and maintaining specialized robotics for core removal would be orders of magnitude more expensive than human labor on a per-task basis. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical task, so AI cost is effectively infinite relative to the human performing it; any robotics solution would require costly specialized hardware exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs physical core sampling removal on actual drilling operations. This is a fundamentally embodied task requiring robotic hardware integrated into hazardous drilling rigs—not a software-solvable problem at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product performs core sample extraction from active drilling rigs; this remains a manual, physical operation performed by rig crew. |
Maintain and adjust machinery to ensure proper performance.
6CI 5–7 · exposure 0 · augmentation 50 · importance 4.3/5 · click for rater detail
Maintain and adjust machinery to ensure proper performance.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The oil and gas sector is moderately digitized with remote monitoring systems, but physical maintenance work remains largely manual and geographically distributed across remote sites with limited AI infrastructure adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, lower-digitization sector where AI adoption for hands-on equipment maintenance remains nascent and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered predictive maintenance and remote diagnostics can assist technicians in identifying problems and planning repairs, raising their efficiency without replacing the hands-on adjustment and maintenance work itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven predictive maintenance and sensor analytics can help flag issues and optimize adjustment schedules, meaningfully aiding but not replacing the operator's physical work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Maintaining and adjusting rotary drill machinery requires physical intervention on complex equipment under dynamic subsurface conditions, hands-on troubleshooting, and real-time judgment that current AI cannot perform end-to-end. No meaningful automation threshold is met for this inherently physical, context-dependent task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation, sensory inspection, and hands-on adjustment of heavy drilling machinery in a hazardous field environment, which current AI systems cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Oil and gas operations face heavy regulatory oversight, safety-critical equipment requirements, and strict liability standards that mandate human technician sign-off on machinery maintenance and adjustments for compliance and safety. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, equipment certification requirements, and liability for rig operations create strong barriers to full automation of hands-on machinery maintenance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, technician expertise, and on-site physical requirements mean AI cannot reduce costs compared to skilled human operators; remote diagnostics and monitoring systems add cost without replacing human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so cost comparison favors the human by default since AI cannot deliver the output alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously maintain or adjust rotary drill machinery in production environments. While remote monitoring and diagnostics exist, they require human technicians to execute the actual maintenance and adjustment work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously maintains or physically adjusts rotary drilling equipment; remote monitoring/predictive maintenance software exists but does not perform the physical task itself. |
Locate and recover lost or broken bits, casings, and drill pipes from wells, using special tools.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Locate and recover lost or broken bits, casings, and drill pipes from wells, using special tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains heavily reliant on physical human presence and specialized craft skills in field operations; adoption of autonomous systems for subsurface intervention is nascent and limited to simple monitoring, not complex recovery tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically intensive sector with minimal AI/robotic adoption for hands-on drilling recovery tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with predictive modeling of tool locations or historical trend analysis of bit failures, but current systems offer minimal real-time support for the actual physical recovery operation, which depends on tactile feedback and adaptive human expertise. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, sensor data interpretation, or planning fishing strategy, but offers little direct help with the physical recovery process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of specialized downhole tools in a dynamic, often unpredictable subsurface environment. Current AI systems cannot physically operate drilling equipment, deploy specialized recovery tools, or make real-time decisions about subsurface conditions without direct human control. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a highly physical, hands-on fishing operation requiring manual manipulation of specialized downhole tools and real-time tactile/sensory feedback; no current AI system can perform this physical recovery task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory frameworks (API standards, OSHA, state mining regulations) mandate licensed, competent personnel for well intervention work, and liability for unrecovered tools or formation damage creates strong legal and financial incentives to use qualified human operators rather than automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical oilfield operations require certified, experienced personnel due to high liability, equipment cost, and risk of well damage or blowout, creating strong organizational and regulatory barriers to any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Downhole recovery tools and the expertise required to deploy them cost tens of thousands of dollars per operation, far exceeding typical AI inference costs; however, the human operator's judgment and skill are essential to avoid catastrophic well damage, making human labor irreplaceable on a cost-per-safe-outcome basis. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so AI cost is not comparable; human crews with specialized tools remain the only means of performing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs downhole bit/casing recovery autonomously. This remains a specialized manual task requiring experienced human operators who interpret real-time sensor data and adjust tool positioning in response to wellbore conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs fishing operations for lost drill components; this remains entirely a human-operated mechanical task with specialized rig equipment. |
Repair or replace defective parts of machinery, such as rotary drill rigs, water trucks, air compressors, and pumps, using hand tools.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Repair or replace defective parts of machinery, such as rotary drill rigs, water trucks, air compressors, and pumps, using hand tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The oil and gas sector remains operationally conservative and relies on skilled human technicians for hands-on maintenance. No meaningful adoption of autonomous repair systems exists in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field maintenance is a physically intensive, low-digitization sector where AI adoption for manual repair tasks is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through diagnostic guidance or procedure documentation, but current systems cannot meaningfully augment the core physical repair work that dominates this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, parts lookup, or repair manuals/troubleshooting guidance, but offers minimal help with the actual hands-on repair or replacement work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Physical repair work requiring dexterity, spatial reasoning, and hands-on manipulation of heavy machinery cannot be performed by current AI systems. The task demands real-time sensory feedback and mechanical problem-solving in unstructured field environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical diagnostic and repair task requiring manual dexterity, hand tool use, and situational judgment on heavy machinery; no AI system can perform the physical repair work itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated; maintenance and repairs must be performed by licensed, trained technicians who bear liability for safe operation. Regulatory requirements and equipment safety standards create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate strictly requires a human, but the physical environment, safety requirements, and need for hands-on manipulation of heavy equipment create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no capability to perform this task, making cost comparison moot. Robotic solutions for field maintenance are expensive, specialized, and far exceed the loaded wage of a skilled technician. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven substitute performing this physical repair work, so the human mechanic remains the only cost-effective and available option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product exists that can physically repair drill rigs, water trucks, or pumps autonomously. This remains purely in the domain of human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical mechanical repair of drill rigs, compressors, or pumps; this remains purely a human manual labor task with no robotic substitute in production. |
Plug observation wells, and restore sites.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Plug observation wells, and restore sites.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains a physical, field-based industry with limited digitization of core operations. Adoption of autonomous systems in well-plugging and site restoration is minimal; pilots are rare and deployment is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically demanding sector with minimal AI/robotic adoption for well decommissioning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Operators may benefit from diagnostic AI (sensor analysis, real-time well-state modeling) or planning tools, but such assistance is narrow and peripheral to the hands-on, safety-critical work of plugging and restoration. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, documentation, regulatory compliance tracking, and site data analysis, but offers little direct help with the physical plugging and restoration work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation in hazardous field environments (drilling sites), specialized equipment operation, and judgment about well integrity—capabilities far beyond current AI agents. No end-to-end automation is feasible today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring heavy equipment operation, cementing, and site remediation work in remote/rugged locations that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Well-plugging and site restoration are subject to strict environmental and safety regulations, require licensed operators in most jurisdictions, and demand direct accountability for environmental compliance and worker safety—creating legal and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Well plugging is typically regulated (environmental and safety regulations, permitting, and often requires certified operators/inspectors to sign off), creating strong legal and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current autonomous drilling and well-plugging equipment (where it exists) requires significant human oversight, specialized training, and integration costs that exceed the loaded wage of experienced rotary drill operators. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical labor, so AI cost is not comparable or cheaper than human/equipment costs for well plugging and site restoration. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems perform well-plugging or site restoration in production environments. This work requires embodied robotics in complex, safety-critical conditions where no commercial solutions operate reliably at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI products plug wells or restore drill sites; this remains manual field work requiring specialized crews and equipment. |
Push levers and brake pedals to control gasoline, diesel, electric, or steam draw works that lower and raise drill pipes and casings in and out of wells.
4CI 0–7 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Push levers and brake pedals to control gasoline, diesel, electric, or steam draw works that lower and raise drill pipes and casings in and out of wells.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains a heavily human-supervised, conservative sector with slow adoption of autonomous equipment; remote operation pilots exist but full automation of draw-work control is not standard practice in the industry. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Oil and gas drilling is a physical, heavy-industry sector with lower digitization/AI agent adoption compared to information or professional services; automated drilling equipment adoption is real but gradual and capital-intensive. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While teleoperation and sensor displays can assist operators in monitoring equipment, the primary task—physical lever and pedal control—is difficult to augment meaningfully; AI could provide alerts but does not substantially transform operator productivity on this core mechanical task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Modern rigs increasingly use sensor-based automated control systems and monitoring software to assist drillers, but this is more traditional automation/control engineering than generative AI assistance improving this specific lever/pedal operation task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical control of heavy equipment in a dynamic, safety-critical environment with continuous sensory feedback and adaptive decision-making. Current AI cannot remotely operate drill equipment with the precision and responsiveness needed, and no end-to-end automation exists for this on-site mechanical control task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires direct physical manipulation of heavy equipment controls in a hazardous field environment; no off-the-shelf AI system can perform this physical operation end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Drilling operations are heavily regulated by federal and state authorities; licensed operators are legally required to oversee well drilling, and liability for equipment damage, worker safety, and environmental incidents creates hard barriers to unattended or fully autonomous control. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical, heavy machinery operation in hazardous environments typically requires certified, trained personnel on-site, with regulatory oversight (OSHA, well-control certifications) that create substantial barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous drilling equipment for draw-work control would require custom robotics, sensors, and safety systems costing millions, far exceeding the loaded wage of a skilled drill operator earning tens of thousands annually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any substitute would require expensive specialized robotics/automated rig systems (top drives, automated pipe handling) with high capital and integration costs, not a cheap AI inference-based solution comparable to a driller's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product autonomously operates rotary drill draw works in production; this remains a human-operated task requiring on-site presence, situational awareness, and immediate corrective action for safety and equipment protection. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI products operating draw works levers/brakes on rigs in production; automation in drilling exists mainly as advanced hydraulic/electronic control systems designed and monitored by engineers, not autonomous AI agents replacing this task. |
Monitor progress of drilling operations, and select and change drill bits according to the nature of strata, using hand tools.
4CI 0–7 · exposure 0 · augmentation 50 · importance 4.2/5 · click for rater detail
Monitor progress of drilling operations, and select and change drill bits according to the nature of strata, using hand tools.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Oil and gas sector adoption of automation in drilling is cautious and limited to remote monitoring and decision support; physical rig automation remains in pilot stages due to safety, regulatory, and operational complexity in harsh, variable subsurface conditions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physical, hazardous, low-digitization environment where AI adoption for hands-on drilling tasks is minimal and slow compared to office-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted sensor analysis and real-time geological modeling could help operators choose drill bits and adjust parameters, but the human operator remains central to judgment calls and manual execution in current practice. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven sensor analytics and predictive maintenance tools can help monitor drilling parameters and suggest bit wear patterns, assisting operators in decision-making even though physical execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical manipulation of drill bits and decision-making based on tactile and visual feedback from geological strata. Current AI cannot perform the manual tool-handling, bit selection based on subsurface conditions, and physical substitution required on an active drilling rig. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy equipment and hand tools on a drilling rig, plus real-time judgment about subsurface strata; no off-the-shelf AI system can perform the physical bit change or reliably interpret drilling conditions end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Drilling operations are heavily regulated by safety and environmental authorities, require licensed personnel, and involve extreme liability if equipment failure or bit selection causes wellbore damage, uncontrolled release, or injury—hard legal and insurance barriers prevent full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas drilling operations are heavily regulated for safety, require certified personnel, and involve high liability for errors, creating strong barriers against non-human execution of physical rig tasks. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment cost, integration complexity, and safety-critical nature of drilling operations make autonomous systems far more expensive than a trained rotary drill operator's loaded wage, with no current cost-competitive alternative available. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical task, so any 'AI cost' comparison is moot—human labor with specialized equipment remains the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously monitor drilling progress and physically change drill bits on an active rig. While sensor monitoring exists, the physical manipulation and real-time geological judgment remain human-dependent in all production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously monitor and physically execute drill bit changes; this remains a manual, safety-critical field task performed by skilled crews. |
Connect sections of drill pipe, using hand tools and powered wrenches and tongs.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Connect sections of drill pipe, using hand tools and powered wrenches and tongs.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains largely manual and on-site. While some remote operation and automation exists in niche contexts, the broader industry has not adopted robotic drill operators at scale, and digitization barriers in remote offshore work are high. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a low-digitization, physically demanding sector with slow adoption of AI/robotics for hands-on rig floor tasks compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation exists—powered tools and computer-aided monitoring assist operators, but AI does not meaningfully enhance human productivity on the core physical task of pipe connection itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with monitoring drilling parameters, torque data, and predictive maintenance, but offers minimal direct assistance to the physical act of connecting pipe sections. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Connecting drill pipe sections requires precise physical manipulation in hazardous, variable conditions at depth. Current AI systems lack the embodied dexterity, real-time sensorimotor feedback, and adaptive strength control needed to perform this safety-critical task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task on a drill floor requiring dexterity, force application, and real-time judgment under hazardous conditions; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Offshore drilling and well operations are heavily regulated (API, OSHA, MMS standards); human operators must be licensed and responsible for safety-critical tasks. Liability for pipe failures, blowouts, or personnel injury creates a hard legal and operational barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas drilling operations are heavily regulated for safety, require certified personnel, and involve high liability for equipment failure or blowouts, creating strong barriers to unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Purpose-built robotics for subsea or drilling operations are extremely expensive to develop, deploy, and maintain. The cost per connection would far exceed the loaded wage of a skilled rotary drill operator for many years. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI itself is irrelevant here; any automation would require expensive specialized robotics/machinery investment far exceeding a driller's wage cost for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform drill pipe connection in production oil and gas operations. This task requires custom robotics integration, environmental adaptation, and safety certification—none of which exist as mature off-the-shelf solutions today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product connects drill pipe sections; some rigs have automated iron roughnecks/pipe handling equipment, but these are hardware automation, not AI-driven task completion, and still require human oversight. |
Direct rig crews in drilling and other activities, such as setting up rigs and completing or servicing wells.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Direct rig crews in drilling and other activities, such as setting up rigs and completing or servicing wells.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The oil and gas extraction sector has shown minimal adoption of autonomous crew direction systems; regulatory, safety, and operational risk prevent rapid automation of supervisory roles even in digitized operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, low-digitization sector with minimal AI agent deployment for on-site crew supervision. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide data dashboards and alerts on equipment status to assist a human operator, but current systems offer limited augmentation for the core task of directing crews in real-time, physical, safety-critical environments. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with drilling data analytics, predictive maintenance alerts, or scheduling, providing some support, but it does not meaningfully augment the real-time crew direction and physical oversight central to this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing rig crews requires real-time decision-making, physical coordination of personnel on a dynamic job site, and dynamic problem-solving in hazardous environments. Current AI cannot substitute for human crew leadership and safety oversight on oil rigs. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing a rig crew involves real-time physical coordination, safety judgment, and hands-on supervision in a hazardous field environment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated by federal and state authorities; crew safety and site direction fall under licensed professional responsibility and legal liability for accidents. Human operators must sign off on critical safety and operational decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Oil and gas drilling operations are heavily regulated for safety, require certified personnel on-site, and carry major liability for well-control incidents, creating strong barriers to full automation of crew direction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of automated systems that could supervise complex rig operations, combined with required redundancy and safety oversight infrastructure, far exceeds the cost of deploying experienced drilling supervisors. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this supervisory/physical leadership role, so any AI system would be additive cost on top of the human, not a replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs human crew direction and on-site supervisory leadership in oil and gas drilling. This remains fundamentally a human leadership and safety function in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously directs drilling crews or manages rig setup and well servicing; this remains a research-stage aspiration at best for physical robotics/AI integration. |
Cap wells with packers, or turn valves, to regulate outflow of oil from wells.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Cap wells with packers, or turn valves, to regulate outflow of oil from wells.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas is a capital-intensive, conservative sector with long equipment lifecycles. Adoption of autonomous well intervention remains in early research stages with minimal production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, historically low-digitization sector with minimal AI-driven automation of hands-on wellhead tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide remote monitoring or decision support (e.g., pressure alerts, procedural checklists), but the core physical task of capping and valve adjustment offers limited augmentation potential given the need for direct manual control and tactile feedback. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor monitoring and predictive analytics can inform when and how to adjust valves, but the physical act of capping and regulating flow itself receives little direct AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of equipment (capping wells, turning valves) in a hazardous, dynamic subsurface environment. Current AI systems lack embodied robotics capable of reliable subsurface intervention, making end-to-end automation infeasible today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy oilfield equipment (packers, valves) at a wellhead, a manual/mechanical task current AI systems cannot perform end-to-end without robotic hardware that doesn't exist for this application. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated; well intervention typically requires licensed personnel and regulatory compliance. Safety-critical well control operations have strict legal and liability requirements that mandate human expertise and sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical well control operations are subject to strict regulatory and operational protocols requiring trained, certified personnel on-site, creating strong barriers beyond mere technical feasibility. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Developing and deploying specialized subsurface robotics for well intervention would be orders of magnitude more expensive than the labor cost of a single drill operator performing these tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of substituting for the human physical action, so the AI cost is effectively infinite relative to a human performing the task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously cap wells or regulate well valves in production. This requires specialized robotics integrated with subsurface sensing and decision-making in real-time—well beyond current commercial offerings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical wellhead operation autonomously; this remains a manual task performed by skilled human drill operators on-site. |
Line drilled holes with pipes, and install all necessary hardware, to prepare new wells.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Line drilled holes with pipes, and install all necessary hardware, to prepare new wells.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains a capital-intensive, physically demanding industry with limited automation adoption for core operational tasks like pipe installation; workforce and equipment remain largely traditional, with automation limited to monitoring systems rather than manual operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, lower-digitization sector where AI adoption for hands-on drilling tasks is minimal compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through real-time monitoring, predictive maintenance alerts, or drilling parameter optimization, but offers minimal productivity enhancement for the core manual tasks of lining holes and installing hardware. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI/software can assist with drilling parameter monitoring, planning, and predictive maintenance around this task, but offers little direct assistance to the physical act of lining holes and installing hardware. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy pipes and hardware in a dynamic drilling environment with real-time safety constraints; current AI systems lack the embodied robotics, precision, and environmental adaptability needed for end-to-end automation of pipe lining and hardware installation. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, heavy-industrial task involving casing pipe installation and hardware rigging on drilling rigs, requiring manual and mechanical manipulation of heavy equipment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Oil and gas operations are heavily regulated; well-drilling tasks are subject to strict safety standards, licensing requirements, and liability frameworks that mandate trained, certified human operators responsible for equipment operation and site safety. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Well construction is heavily regulated for safety and environmental integrity, requiring certified operators and inspections, and physical on-site presence is inherently required to install hardware. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotic systems capable of performing this task, including hardware, integration, and safety certification for hazardous environments, vastly exceeds the loaded wage of skilled drill operators. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system substituting for this physical task, so any AI cost would be additive rather than a cheaper substitute for the human/crew labor and equipment already required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this complex mechanical task reliably in production oil and gas environments; the task demands physical interaction, spatial reasoning, and site-specific troubleshooting beyond current system capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product autonomously lines wellbores with casing pipe and installs wellhead hardware; this remains a manual/crew-operated process with some mechanized rig assistance. |
Position and prepare truck-mounted derricks at drilling areas specified on field maps.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Position and prepare truck-mounted derricks at drilling areas specified on field maps.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Oil and gas drilling remains heavily dependent on skilled manual labor and in-person expertise; adoption of automation in this sector is slow and conservative, with equipment designed around human operator control rather than autonomy. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Oil and gas field operations are a physically intensive, low-digitization sector with minimal AI/robotic adoption for equipment positioning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While GPS and digital field maps provide minor navigational aids, AI offers limited augmentation for the core task of physically positioning and preparing derricks, which relies on tactile feedback, real-time judgment, and equipment-specific manual skills. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based mapping and GPS/field-map digitization tools can help identify and communicate drilling locations, but they offer limited assistance for the physical positioning and setup itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical manipulation of heavy equipment in variable field conditions, precise positioning based on site-specific terrain, and constant environmental assessment—capabilities far beyond current autonomous systems. No meaningful automation of the end-to-end task exists in production. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical positioning and rigging task requiring driving heavy equipment, site assessment, and manual setup on uneven terrain, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strict OSHA regulations, insurance liability requirements, and industry safety standards mandate human operators certified and physically present to supervise and control heavy drilling equipment; legal liability for autonomous failure is prohibitively high. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy equipment operation in oil and gas drilling involves safety regulations, certification requirements, and liability concerns that require a trained human operator on-site. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A skilled rotary drill operator commands significant loaded wages ($70–100k+), while the capital and integration cost of any autonomous derrick positioning system would exceed years of human labor cost, making AI economically unviable today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system performing this physical task, so AI cost per task-equivalent is effectively infinite/not applicable compared to a human operator's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products autonomously position and prepare truck-mounted derricks; this remains dependent on skilled human operators reading maps, assessing ground conditions, and manually maneuvering multi-ton equipment. Research prototypes in robotics are nowhere near production readiness for this safety-critical operation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product positions or prepares truck-mounted derricks; this remains a manual, on-site operator task with no robotic substitute in production. |
Related occupations — Construction & Extraction
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.