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Petroleum engineers

Also called Reservoir engineer, Drilling engineer, Completions engineer, Well engineer, Subsurface engineer

In brief

In May 2025, BLS counted 18,060 US petroleum engineers, with median pay of $144,910 a year. Of the BLS counts since 2021, the highest was 22,100, in May 2021. The earliest figure here is an estimate of 15,000 from 1950, not measured the same way as today's count. The last full technology era for petroleum engineers: Cloud E&P platforms + data-driven completion optimization, 2014 to 2022. Capital efficiency gains from data-driven operations meant operators could extract more oil and gas with fewer engineers per well.

What the sources say

Official outlook

BLS projects employment to grow 2% from 2025 to 2035.

BLS Employment Projections 2025-35

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Petroleum engineers: jobs and pay since 1950 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/17-2171

Chicago

Future History. "Petroleum engineers: jobs and pay since 1950." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/17-2171.

BibTeX

@misc{futurehistory_17_2171, author = {{Future History}}, title = {Petroleum engineers: jobs and pay since 1950}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/17-2171}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1925195019752000now
Country
2026
Known then as Petroleum Engineer (AIME Petroleum Division era)
Known then as Petroleum Engineer (SPE era)
Known then as Petroleum Engineers (BLS SOC 17-2171)
Known today as Petroleum Engineers (BLS SOC 17-2171)
Projected to still be called Petroleum Engineers (BLS SOC 17-2171)
Data from the May 2025 survey
US Employment
18K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$144,910
Est.

≈ $141,195 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1901

January 10, 1901: Anthony Lucas strikes oil at Spindletop Hill near Beaumont, Texas, in a gusher reaching 150 feet high and producing close to 100,000 barrels per day, then the largest oil discovery in US history. Lucas used rotary drilling equipment on a salt dome formation, proving that the Pennsylvania cable-tool tradition was not the only path to oil. Spindletop triggered a land rush and the founding of Gulf Oil, Texaco, and what would become Exxon. More importantly, it demonstrated that large-scale oil production required engineers who understood geology, fluid mechanics, and drilling technology in combination. The demand for such engineers drove the formation of the petroleum engineering discipline within AIME thirteen years later.

Beat · 1957

October 6, 1957: the first Board of Directors meeting of the Society of Petroleum Engineers is held in Dallas, Texas, with President John H. Hammond presiding. The SPE had emerged from the AIME Petroleum Branch, whose membership had grown from approximately 3,700 in 1950 to 12,500 by 1957 as the postwar oil boom pulled engineers into the field. SPE becoming an independent organization signaled that petroleum engineering had fully professionalized: it had its own body of knowledge, its own journal (the Journal of Petroleum Technology), its own credential structure, and enough critical mass to sustain a society independent of mining engineering. SPE became a separately incorporated organization in 1985.

Beat · 1982

The oil boom of the late 1970s, driven by the 1973 OPEC embargo and the 1979 Iranian Revolution, pulled petroleum engineering enrollment to its highest levels in history: BS graduates peaked at 1,587 in 1984, and US oil and gas industry employment peaked at approximately 860,000 workers in 1982. When OPEC refused to cut production in November 1982 and oil prices began their decline toward $10/barrel in 1986, the crash was severe: 100,000 oil industry workers were laid off in the first five months of 1986 alone. About 200,000 total jobs vanished from Houston between 1982 and the late 1980s. Oil-related employment in Texas, Oklahoma, and Louisiana fell 38% from the 1981-82 peak. Petroleum engineering enrollment flat-lined from 1983-1988 and the phrase "job security" became a dark joke in E&P offices.

Beat · 1984

Petroleum engineering BS graduates peaked at 1,587 in 1984, the historical high for the profession. This graduating cohort entered the workforce at the worst possible moment: oil prices were already declining, and many of the class of 1984-1988 could not find engineering jobs and left the industry permanently. SPE historical analysis notes that the workforce experienced a "lost generation" gap: experienced engineers who had survived the bust were running lean teams through the 1990s, and the pipeline of new graduates from the late 1980s trough was thin. When the shale boom arrived in the 2000s, this demographic hole contributed to a rapid escalation in engineer compensation as operators competed for a scarce workforce.

Beat · 2018

By 2018, the United States had become the world's largest oil producer, overtaking Russia and Saudi Arabia, a reversal of decades of declining US production that almost no analyst had predicted before the shale revolution made it possible. US crude oil production exceeded 10 million barrels per day in February 2018 and continued climbing to 13 million bbl/day by 2023. This production record was achieved with approximately half the petroleum engineer headcount of the 2012-2015 shale peak. The productivity gain was driven by longer lateral wellbores, more fracture stages per well, and AI-assisted completion optimization: a direct demonstration of augmentation economics, where each engineer was accomplishing more without fewer engineers being needed.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace petroleum engineers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+2%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 17-2171 (Petroleum engineers): 18.5 thousand jobs in 2025, projected 18.8 thousand in 2035, a change of +0.4 thousand (+2.0%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.7% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
Today, in this role

What's changing day to day for petroleum engineers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereManage artificial-lift optimization across large well fleets (ESP, rod pump, plunger lift, gas lift) using Baker Hughes Leucipa or Ambyint AI: review AI-generated setpoint recommendations

Manage artificial-lift optimization across large well fleets (ESP, rod pump, plunger lift, gas lift) using Baker Hughes Leucipa or Ambyint AI: review AI-generated setpoint recommendations; approve changes to pump speed, injection gas rate, or chemical dosing; interpret failure-prediction alerts for ESPs flagged weeks before failure; analyze production uplift metrics across the fleet and escalate wells requiring intervention beyond AI-adjusted parameters.[8],[9]

Where your edge is

Ambyint manages nearly 250,000 BOE/d autonomously, and Baker Hughes Leucipa delivered double-digit production uplift in documented deployments. These tools automate the majority of daily setpoint adjustments. The production engineer's irreplaceable contribution is the decision to change lift method entirely (e.g., gas lift to ESP), diagnose production-chemistry problems (scale, corrosion, emulsion) that sensor-only AI misclassifies, and make the economic call on whether workovers are justified. Develop deep expertise in artificial-lift system physics (pump intake pressure curves, gas-interference signatures, and torque-load analysis) so you can override AI recommendations with defensible engineering rationale.

AI is sitting alongside you hereInterpret seismic reflection data and petrophysical logs using AI-assisted platforms (Geoteric AI Hub, SLB Petrel with ML add-ons): apply deep-learning fault detection and facies classification to 3D seismic volumes to identify reservoir geometry

Interpret seismic reflection data and petrophysical logs using AI-assisted platforms (Geoteric AI Hub, SLB Petrel with ML add-ons): apply deep-learning fault detection and facies classification to 3D seismic volumes to identify reservoir geometry; validate AI-predicted structural maps against well-penetration data and regional geology before committing to drilling locations.[10],[11]

Where your edge is

Geoteric AI Hub compresses seismic interpretation cycle times by up to 90% and reduces fault-mapping to single-click workflows (PETRONAS partnership, Sep 2025), but AI-interpreted seismic requires the engineer to validate structural picks against well-log calibration data, regional pressure gradients, and depositional analogs. Build a systematic review protocol: for every AI-generated structural interpretation, run a blind cross-validation against at least two wells before accepting it as drilling-location input.

AI is sitting alongside you hereGenerate probabilistic production and reserves forecasts for field development decisions and SEC/regulatory reporting: use Novi Labs Forecast Engine or SLB Petrel RE to run ML-accelerated decline analysis across hundreds of wells

Generate probabilistic production and reserves forecasts for field development decisions and SEC/regulatory reporting: use Novi Labs Forecast Engine or SLB Petrel RE to run ML-accelerated decline analysis across hundreds of wells; apply Petroleum Resources Management System (PRMS) classification rules to categorize reserves as Proved, Probable, or Possible; present uncertainty ranges to leadership and certify the reserves submission as a qualified reserves evaluator.[5],[12]

Where your edge is

Novi Labs ML forecasts outperform Arps decline by >30% accuracy and cut cycle time 80%; 75% of U.S. reservoir engineers now use AI for well forecasting (Novi Labs 2025 survey). The certified reserves evaluator's irreplaceable contribution is applying PRMS classification judgment, determining which technical uncertainties shift a well from Proved to Probable, whether offset performance supports the analogy underpinning a development drilling program, and signing the SEC or NI 51-101 certification that carries personal legal liability. Pursue qualified reserves evaluator (QRE) certification under SPE-PRMS guidelines and maintain rigorous documentation of the engineering assumptions underlying each classification so the certification withstands third-party audit.

Where petroleum engineers go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Architectural and Engineering Managers

Senior petroleum engineers who develop strong program governance, AI tool evaluation, and cross-domain leadership skills are well-positioned to move into Engineering Manager roles overseeing digital transformation programs in E&P organizations. This is the highest-value pivot available in the current market: operators are urgently seeking engineering managers who can credibly evaluate and govern the rapidly expanding AI toolset, deciding which SLB Delfi, Halliburton DecisionSpace, Baker Hughes Leucipa, or Cognite Data Fusion deployments to invest in, setting authority boundaries for autonomous AI operations, and translating AI productivity gains into portfolio-level decisions. The Mordor Intelligence AI-in-oil-and-gas market is projected to grow between 2026 and 2031, and the companies riding that growth need engineering leaders who understand both the subsurface physics and the AI platform landscape.

What you'd add
  • · AI governance for E&P operations: defining authority boundaries for autonomous drilling and production AI, building validation frameworks for ML production forecasts submitted to management
  • · Engineering program management: portfolio budget ownership, multi-year asset development plan stewardship, drilling program schedule management
  • · Vendor management and contract strategy: EPC / EPCM contracting for major capital projects, software licensing negotiations with SLB, Halliburton, Baker Hughes digital divisions
  • · People leadership: hiring and developing reservoir, drilling, and completions engineers; performance management; cross-functional team coordination with geoscience, facilities, and HSE
  • · Executive communication: translating reservoir uncertainty, drilling risk, and AI-driven production upside into capital allocation decisions and board-level reporting
What it takesSome new skills to pick up
Tools of the era

How has the work of petroleum engineers changed since 1914?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Rotary drilling rig + cable-tool logs (field craft era)

    The early petroleum engineer worked with rotary drilling equipment that had evolved from water-well technology and with cable-tool methods inherited from the Pennsylvania oil fields. There were no computers, no seismic surveys, and no formal reservoir models: the engineer read cuttings from the drill bit, correlated them with nearby well records kept by hand, and made production decisions based on a combination of measured data and field intuition. The first petroleum engineering textbook (Lester Uren, 1924) codified these practices into teachable methods, but the underlying tools were mechanical and empirical. A key innovation of the 1920s was the systematic use of casing programs and packers to prevent formation damage and control water influx, tasks that required both engineering judgment and hands-on field supervision that could not be delegated to any machine.

    Effect on the work

    The profession grew rapidly in this era: AIME's Petroleum Branch membership rose from a handful in 1914 to approximately 3,700 by 1950, tracking the industrialization of US upstream oil. The discipline was still small enough that most petroleum engineers knew each other personally through SPE-predecessor meetings.

  • Hydraulic fracturing (Halliburton / Stanolind, commercial from 1949)

    On March 17, 1949, Halliburton and Stanolind personnel performed the first two commercial hydraulic fracturing treatments near Duncan, Oklahoma. The technique had been invented by Riley Floyd Farris at Stanolind in 1946-47: rather than using explosive torpedoes to crack rock as Col. Roberts had done since 1865, Farris pumped pressurized fluid into the formation. In the first year of operations, hundreds of oil wells were treated and average production increased by 75%. This was the first oil-field technology that fundamentally changed what a petroleum engineer could do with a wellbore: instead of drilling until you found what nature had put there, you could stimulate the reservoir and change its productivity. By the mid-1950s, hydraulic fracturing was standard practice in tight-rock formations across the mid-continent, and petroleum engineers needed to understand fluid mechanics, proppant transport, and fracture geometry to specify treatment designs.

    Effect on the work

    Hydraulic fracturing created a new sub-specialty within petroleum engineering: stimulation engineering. It also created the service-company technical interface that would define much of the profession's daily work: a petroleum engineer at the operator specifying and supervising a treatment performed by a Halliburton or BJ Services crew.

  • Analog and digital reservoir simulation (IBM 7090, Craft and Hawkins 1959)

    By the late 1940s, researchers at major oil companies recognized that the behavior of a reservoir could be described mathematically using partial differential equations for fluid flow in porous media. The first crude but useful computer programs for reservoir simulation emerged in the late 1950s, running on 32K IBM 7090 computers: they solved sets of finite-difference equations describing two- and three-dimensional transient multiphase flow in heterogeneous rock. The landmark 1959 textbook by B.C. Craft and M.F. Hawkins formalized the reservoir engineering fundamentals that would anchor the discipline for decades. By the 1960s and 1970s, major operators (Shell, Exxon, Arco) had in-house reservoir simulation groups, and petroleum engineering graduate programs began requiring computer programming as a core skill. The engineer who had once read cores by hand and run material-balance calculations on a slide rule now needed to build and validate mathematical models of underground systems.

    Effect on the work

    Reservoir simulation elevated the technical floor for petroleum engineers: a reservoir engineer without computing skills became professionally obsolete. The transition created a generation gap in the workforce between engineers trained before and after the computing era, and raised the premium on PE graduate degrees with quantitative modeling competency.

  • Commercial horizontal drilling (Austin Chalk 1985, Barnett Shale 1981-1990s)

    Horizontal drilling became commercially viable in the mid-1980s, with the Austin Chalk formation in Texas as its first major proving ground: by 1998, 2,317 horizontal wells had been completed there, producing an additional hundreds of millions of BOE over the vertical baseline. The technology had roots in directional drilling that dated to the 1940s, but the combination of improved downhole motors, measurement-while-drilling (MWD) tools, and rotary steerable systems made it economically practical at scale. Independent producer George P. Mitchell drilled the C.W. Slay No. 1 well in the Barnett Shale in 1981 and spent two decades refining the combination of horizontal drilling and hydraulic fracturing that would define the shale revolution. For petroleum engineers, horizontal drilling created an entirely new set of technical challenges: planning wellbore trajectories through 3D geological space, managing torque-and-drag in long lateral wellbores, and optimizing completion designs across hundreds of fracture stages. The engineering content of a single well had multiplied by an order of magnitude.

    Effect on the work

    Horizontal drilling created demand for a new specialization: directional drilling engineers and geosteering specialists who could navigate a wellbore through a thin pay zone measured in tens of feet while interpreting real-time formation data. This specialization was not well-supplied by the existing PE workforce and helped drive the premium salaries of the 1990s recovery.

  • Shale revolution (Barnett, Eagle Ford, Permian, Marcellus: horizontal drilling + multi-stage fracking at industrial scale)

    The shale revolution began not as a single breakthrough but as the culmination of two decades of experimentation by George Mitchell and his team in the Barnett Shale. By 2003, the combination of horizontal drilling, multi-stage hydraulic fracturing, and slickwater fracturing fluids had unlocked economic production from formations previously dismissed as source rocks. What followed was the most dramatic transformation in US energy history since Spindletop: US natural gas production overtook Russia's by 2009, making the US the world's top gas producer; US tight-oil production transformed the country into the world's largest oil producer by 2018. For petroleum engineers, the shale revolution meant industrializing a process: drilling hundreds of nearly identical horizontal wells in a manufacturing cadence, optimizing completion designs statistically across large well populations, and managing production decline curves across portfolios of thousands of wells. The engineering challenge shifted from "find the oil" to "optimize the factory." Employment peaked: BLS estimated 38,500 petroleum engineers in 2012 with projected 26% growth to 2022 before the oil-price collapse ended that trajectory.

    Effect on the work

    Petroleum engineer employment rose from approximately 16,000 in 2000 to an estimated 38,500 in 2012-2015, more than doubling in several years. Enrollment in petroleum engineering programs surged, then crashed after the 2015-2016 price collapse.

  • Cloud E&P platforms + data-driven completion optimization (SLB Delfi, Halliburton DecisionSpace 365)

    As oil prices collapsed through 2014 in January 2016, operators slashed headcount and demanded that surviving engineers do more with less. Cloud-based E&P software platforms emerged as the answer: SLB (then Schlumberger) launched the Delfi cognitive E&P environment in 2017, Halliburton launched DecisionSpace 365, and a wave of data-science startups applied machine learning to completion design, production forecasting, and artificial-lift optimization. The petroleum engineer's daily workflow shifted from spreadsheets and desktop reservoir simulators to cloud-native platforms integrating subsurface, drilling, and production data in a unified environment. Completion optimization moved from intuition to statistical analysis of hundreds of offset wells: engineers used ML models to select stage spacing, cluster count, and proppant loading based on patterns across their company's well inventory. This era separated engineers who mastered data tools from those who did not.

    Effect on the work

    Capital efficiency gains from data-driven operations meant operators could extract more oil and gas with fewer engineers per well. Fewer engineers, more wells per engineer, higher wages for those who remained.

  • Autonomous drilling and AI production optimization (SLB DrillOps, Baker Hughes Leucipa, Novi Labs)

    By 2025, AI-native E&P software had crossed from augmentation into partial autonomy. SLB DrillOps achieved fully autonomous drilling operations: an AI planner orchestrates weight-on-bit, rotary speed, hydraulics, and directional steering in real time without human intervention per-parameter. Novi Labs ML forecasting delivers well production forecasts in minutes across hundreds of wells, with greater than 30% accuracy improvement over traditional Arps decline curves and 80% reduction in cycle time; 75% of US reservoir engineers reported using AI for well forecasting in 2025. The petroleum engineer's role is shifting from hands-on parameter adjustment to exception management, AI governance, and the geomechanics and regulatory judgments that algorithms cannot make. The energy-transition dimension is also shaping the role: subsurface hydrogen storage, carbon capture and sequestration, and geothermal drilling all apply the same core skills petroleum engineers hold.

    Effect on the work

    AI adoption jumped from 43% to 56% of US reservoir engineers between 2024 and 2025 (Novi Labs 2025 survey). The primary barrier to faster adoption is data quality, cited by 84% of engineers as the main implementation challenge. Engineers who govern AI platforms and maintain authority over high-consequence decisions (regulatory sign-off, well integrity, multi-disciplinary well planning) are commanding the highest wages in the profession's history.

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Petroleum Engineers employment and pay by year

On record since1914
Latest US employment18,060 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$144,910 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 21332)

    Employment, 2020
    5,185, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 21332 Petroleum engineers, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$160,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21332 Petroleum engineers, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
195015,000n/aEst., SPE History Researched contextReport
198255,000n/aEst., BLS Researched contextReport
199020,000n/aEst., CS Monitor (1987) Researched contextReport
1991n/a$40,164Est., SPE / NACE Researched contextReport
200016,000n/aEst., O*NET 17-2171.00 Researched contextReport
200311,630$83,900BLS OEWS May 2003 Official statisticReport
200414,690$88,500BLS OEWS May 2004 Official statisticReport
200514,860$93,000BLS OEWS May 2005 Official statisticReport
200615,060$98,380BLS OEWS May 2006 Official statisticReport
200716,060$103,960BLS OEWS May 2007 Official statisticReport
200820,880$108,020BLS OEWS May 2008 Official statisticReport
200925,540$108,910BLS OEWS May 2009 Official statisticReport
201028,210$114,080BLS OEWS May 2010 Official statisticReport
201130,880$122,280BLS OEWS May 2011 Official statisticReport
201236,410$130,280BLS OEWS May 2012 Official statisticReport
201334,910$132,320BLS OEWS May 2013 Official statisticReport
201433,740$130,050BLS OEWS May 2014 Official statisticReport
201534,600$129,990BLS OEWS May 2015 Official statisticReport
201632,780$128,230BLS OEWS May 2016 Official statisticReport
201732,010$132,280BLS OEWS May 2017 Official statisticReport
201832,510$137,170BLS OEWS May 2018 Official statisticReport
201932,620$137,720BLS OEWS May 2019 Official statisticReport
202027,850$137,330BLS OEWS May 2020 Official statisticReport
202122,100$130,850BLS OEWS May 2021 Official statisticReport
202220,540$131,800BLS OEWS May 2022 Official statisticReport
202320,390$135,690BLS OEWS May 2023 Official statisticReport
202418,970$141,280BLS OEWS May 2024 Official statisticReport
202518,060$144,910BLS OEWS May 2025 Official statisticReport
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